30 items tagged "strategy"

  • 3 important considerations when strategizing digital transformation

    3 important considerations when strategizing digital transformation

    While business and IT leaders are under pressure to boost their digital transformation plans, they should stop, rethink, and assess before fully diving into a roadmap.

    The question all companies face today isn’t whether to embrace digital transformation, but how to best take advantage of digitalization advancements, especially considering change driven by COVID-19.

    This mean that businesses must rethink their roadmap and long-term planning. Besides taking advantage of lessons learned from previous digital efforts, there are three considerations needed to support these changes: prioritizing the right technology before investing, developing holistic plans for data, and considering new organizational roles.

    1. Prioritize the right tech when digitizing

    IDC reported that businesses will spend $2.3 trillion a year on digital transformation within the next four years, with worldwide digital transformation technology investments set to hit at least $7.4 trillion in the same time period. But before discussing the technology, it's important to note that many digitalization projects fail due to lack of competencies or a misunderstanding of complexity.

    With many technology options available, it can be difficult to gauge what will benefit your organization. Prior to investing in any solution, businesses should conduct thorough quantitative and qualitative research. A qualitative investigation, featuring employee focus groups or interviews, can identify areas where workers need assistance. Quantitative research, leveraging analytics tools, collects data tracking performance, experience or engagement, which can then support the qualitative observations.

    The average digital transformation project has a 45% chance of delivering less profit than expected. Poor planning is to blame for many transformation efforts falling short. A thorough roadmap is needed to keep up with the pace of innovation. One way to do this is to devise both long- and short-term digital plans to meet immediate and future needs.

    Start by creating a cohesive timeline with monthly goals (ROI sales, website visits, etc). A short-term plan should last roughly three to six months, while a long-term plan should last about six to 12 months. To get an accurate picture of performance, organizations should regularly run performance tests on their systems. They can also survey employees to gauge how they are reacting to the technology.

    In order to meet the demand for disruption, companies need to focus on data-driven innovation. This means businesses must consider the aspects of scope complexity and adoption. One element of this is assessing the learning curve for employees. When introducing new technology solutions, whether it be IoT or machine learning programs, you will need to train employees to use it.

    To get a sense of both the learning curve the technology presents, and the scalability of it, consider undergoing a small-scale trial program before investing. This will provide visibility into how the technology performs and how employees react to it. From there, businesses can fully assess whether it is a fit for the company.

    2. Create a data-driven strategy for the long (and short) haul

    It is critical for businesses to devise a holistic plan for data, taking into account whether there are any privacy concerns or how to manage a potential data increase. Often, businesses do not understand how to gain the most value from the data they have. In order to do this, they must conduct a thorough data audit. This consists of identifying the current technology you use and interviewing your teams on how they are leveraging it. This can allow your business to learn what data you have, how to use it and what is still needed.

    3. Prepare your people

    Prioritizing the needs of your workforce is often an afterthought. Businesses must look to hire new talent or create new roles within the organization to help oversee transformation efforts.

    COVID-19 has accelerated the convergence of information and operational technology networks. According to Claroty, sixty-five percent of IT/OT professionals say their networks have become more interconnected since the pandemic began, and 73% expect them to become even more interconnected as a result of it. IDC research also found that 70% of G2000 organizations will have invested in a common IoT platform layer by 2024. With this situation, having a “connected worker” to oversee these changes can extend the life of your transformation. Organizations can drive projects forward and educate their workers by holding workshops on new technologies, providing employees with the opportunity to learn or potentially enter a specialized role within the organization.

    Eight in 10 organizations fast-tracked some part of their transformation this year, and nearly 80% reinvented their business model as a result of the pandemic. To ensure lasting success, organizations need to conduct thorough investigations, trial their technology, and regularly audit results. Investing in new roles and in workforce education will help organizations make the most of digital technology. While COVID-19 has put the pressure on businesses to boost their digital transformation plans, there remains a need to stop, rethink, and properly assess before fully diving into a roadmap.

    Author: Dave Goddard

    Source: InformationWeek

  • 5 Important elements to build the right CRM strategy

    5 Important elements to build the right CRM strategy

    The growth of customer relationship management (CRM) has become inevitable, compelling software development companies around the world to make the most out of it by putting customers first and driving a more customer-centric approach. The benefits of Customer Relationship Management (CRM) include storing relevant information about your prospects and customers to maintain effective communication, featuring a wide range of assorted tools for sales and marketing in order to increase profitability, to name a few. And that’s the reason why it is crucial to build your CRM strategy absolutely right.

    Reaching any destination becomes easy when it’s done with the help of a map, right? All you require knowing is where to start from and where to end things. Creating a CRM strategy is quite similar to this. Here you need to identify your baseline performance (the starting point) your SMART destination (Specific, Measurable, Actionable, and Time bound) user, customer and business outcomes, and designs. The only challenge is that you require achieving these measurable objectives in the shortest way. Strategizing things in prior is very important in order to try and win the CRM journey. Great execution is out of the question if your strategy is wrong! Also, the right CRM strategy is nothing but something tackled with a clarity of focus, reduced guesswork, and measurable execution.

    Below are 5 important elements to keep in mind when building a winning CRM strategy for your business.

    1.  Set business objectives

    One of the basic rules of building a CRM strategy is that it is always built keeping core business goals in mind. So before doing anything else, it is essential to analyze and identify the objectives you want your CRM to achieve. Whether you wish to improve your workforce efficiency, customer satisfaction, communication or you are trying to understand your customers better in general, with a better CRM strategy everything will start to fall in place. One of the best ways is to start is by breaking down your goals into smaller objectives, the ones which are easier to achieve. And then go with your plan of action. Know all the why, what and hows in prior. After this, you can begin your journey but be sure that the map you are using is flexible so that changes can be made easily whenever and wherever required.

    2.  Know your customers

    Many of us assume that businesses always end up treating their customers at an equal pace. However, in reality, this is not the case! To succeed as a business, one needs to delve into the details, categorize the customers based on values each one brings. Of course, each organization is of a kind and understands a different version of what makes a customer valuable. Whether you identify the main characteristics for a typical buyer or not that’s totally upon you.

    Once the vision is clear and objectives are set, it’s time to head further to initiate a strategy. By using the customer data, you can easily understand how they interact with your business and maximize value. Make sure to keep the down below mentioned pointers into account:

    • The type of data required on our customer
    • Level of detail required to track
    • Plan on characterizing customers
    • The type of communication will work best for our customer profile

    3.  Leverage your process wisely

    Clarity is a must-have factor to take into consideration. You can even think of breaking the objectives into small achievable chunks and connect that to how CRM fits to achieve the composite goal. Let’s dig in a bit deeper here!

    1. Evaluate: Whether you are planning to implement a strategy for sales, marketing or even customer service make sure you understand your current procedures and review how CRM and automation both can be used to enhance the overall efficiency of your business.
    2. Identify: As I said before, the keyword is “efficiency”; so make sure you focus well on several aspects of your businesses. In comparison to evaluation, identifying is a bit granular, and it requires defining both where and how your venture needs to be enhanced.
    3. Align: In most cases, businesses are found struggling in some department or the other. Initially, you may feel pressured as in an effort to produce better results you may require implementing new procedures and technologies as quickly as possible which is definitely not an easy task.

    However, this isn’t always the case. Making too many changes at once can also have a negative impact on your employee’s productivity and usage. This is the reason why you should considering a simple approach and implementing a CRM strategy gradually.

    4.  Break down organizational silos

    Once you begin collecting customer data, it becomes vital to come up with a culture of collaboration to enhance customer service. By breaking these organizational silos down, you can solve several issues. Now, do you really think marketing, sales and customer service departments can automatically be on the same page? Not necessarily. And organizations as a whole require to be working at cross-purposes. As soon as you start promoting cross-department collaboration, you will find yourself capable enough to deliver better than the best. Data collected by one department can also be fed to another department to improve overall business operations.

    5.  Kickstart communication channels

    One of the main objectives of having an effective CRM strategy is to increase the effectiveness of all your support channels. Doing so makes it easy for your end customers to reach out to support in the case if they encounter any problem. A good idea is to implement a live chat option on your website so that customers can get instant solutions for their queries. Live chat not only enhances the customer support experience but also helps boost conversions. Similarly, you can also consider using social media channels to solve customers’ problems.

    Author: Kibo Hutchinson

    Source: SAP

  • 5 Strategies that lead to a better customer understanding

    5 Strategies that lead to a better customer understanding

    Understanding your customers is a crucial component of building a successful customer experience (CX) program. Collecting and analyzing data is the key to unlocking deeper customer understanding, which (in turn) drives better customer experience efforts. Let’s take a look at five foundational customer experience information sources you can use to better understand your customers.

    5 ways to listen to your customers

    There are many ways to listen to your customers, but the most important thing is always that you’re listening at all.

    Let’s walk through five different strategies for building or enhancing your current customer listening program. You don’t need to adopt all five. Rather, implement a few of these strategies and make adjustments until you find the right combination for your business.

    1. Collect effectiveness data

    There are several ways to measure the current effectiveness of your CX efforts. Net Promoter Score (NPS), Customer Satisfaction Score (CSAT), and Customer Effort Score (CES) are among the most popular. By incorporating these measures into customer surveys, you can better understand your customers’ feelings of loyalty, satisfaction, and effort.

    NPS measures how likely your customer is to recommend your brand to someone else, which gives insight into satisfaction and loyalty. The CSAT is a direct measure of customer satisfaction, usually in relation to a specific interaction or experience. And your CES measures the amount of effort customers had to expend to achieve a particular goal.

    2. Create customer personas

    A customer persona is a finely honed profile of your best or target customer. Your company may want or need more than one persona, but you should focus on your most valuable customer types. A persona is more than a list of common characteristics. It should be as specific as possible and help you visualize the wants, needs, behaviors, and motivations of your customer.

    Think beyond demographic information like age, gender, income, or geography type. Psychographic (e.g., values, opinions, aspirations), transactional (e.g., purchase histories, service records), and behavioral (e.g., engagement on your website or social media profiles) information are key components of a richly built persona.

    3. Complete a customer journey map

    A customer journey map is an externally focused map of your customer’s experience throughout the full cycle of a particular journey. For example, the journey could start at the customer’s own awareness of a need and end with a product purchase, with steps for every interaction and impression in between.

    The process of building a customer journey map is an act of empathy. You should put yourself in your customer’s shoes and imagine their actions and feelings along the way. That doesn’t mean the map simply springs from your imagination, it should still be grounded in data. It means that by the end of the journey mapping process, you should have a deeper understanding of gaps or flaws in the customer experience and your customer’s motivations, desires, and feelings throughout.

    4. Supplement with outside data

    There are also ways to learn more about your customer using data from outside of your company. Contextual data in business draw on facts from the broader environment, including social media, news, events, weather, market changes, demographic changes, or geography.

    You can source contextual data from third-party businesses or organizations, such as market research firms or vendors. There is also a wealth of publicly available data from government sources, such as the Dutch CBS (Centraal Bureau voor de Statistiek), the U.S. Census Bureau or the Bureau of Labor Statistics. A variety of customer experience-related software can also aid your gathering of outside data.

    5. Use a voice-of-the-customer (VoC) program

    According to Gartner, VoC programs 'collect, aggregate, and provide the means to analyze direct feedback from surveys and interviews, indirect feedback from social media and customer care interactions, and inferred data such as web analytics and behavioral data'.

    According to Gartner, data sources for a VoC program can include customer complaints, customer surveys, employee feedback, company reviews, interviews, and social media, among others. Through rich, diversified sources of customer feedback, VoC programs help companies better understand customer experience and sentiment.

    Pull it all together with customer experience software

    Many customer experience software solutions can address any or all of the above listening approaches, from managing surveys and multichannel listening to visualizing the customer journey.

    By consolidating customer experience information into a single CX dashboard or hub, your organization can conduct more efficient analyses and see a fuller picture of your customers.

    Author: Kristen Bialik

    Source: Capterra

  • 6 myths about market research to stay away from

    6 myths about market research to stay away from


    In order for a company's products and services to be not just well-accepted but welcomed by clients and customers, a thorough understanding of the company’s markets, competitors and customers needs to be reached. Evaluating the feasibility of a new product or service is central to an organization's operations, which is why almost every successful business in present times conducts market research. Market research helps to identify problems, spot growth opportunities, formulate marketing strategies, determine consumer needs, forecast sales, and improve sales activities, and introduce new products and services. Without proper market research to drive your marketing strategies, especially regarding new products or services, you lack direction, which is a recipe for disaster. Information on market research and market research techniques is widely available nowadays, which has made the process easier to understand and execute. Then why do some organizations still fail to embrace market research? The larger problem with organizations these days is that they fall prey to the numerous myths that are unfortunately associated with market research. In this article, let us examine these common myths regarding market research, and then untangle them to help you conduct better market research.

    Why do market research myths exist at all?

    Well, the short answer is that in today's fast-paced world, we are exposed to too much information too fast. As a result, we suffer from something known as information overload, which causes confusion, and the inability to distinguish between information and misinformation. A larger problem arising from information overload is that different stakeholders in an organization have different versions of truth due to their exposure to divergent information, which makes it difficult to move strategically in one direction as an organization. Add to the fact that a number of organizations never take the time to actually study market research in detail, which results in the formation and propagation of market research myths. Consequently, they either write-off market research completely without even conducting it, or conduct it in a flawed manner which obviously doesn't bring accurate results. Millions of dollars are spent on an incomplete or imperfect idea, plus the extra time spent fixing what’s broken later on. Thus, to truly dispel market research myths, you need to conduct proper research on market research itself! Meanwhile, this article will raise your awareness about these common myths, so that you don't succumb to these many myths, and successfully predict which of your new ideas will thrive in the marketplace.

    6 common market research myths that you need to avoid

    1. Market research isn't necessary

    Most successful organizations in the world would disagree with you here. Market research is an integral step in a successful business development effort, whether you are part of a startup, launching a new product or rebranding your business. Even if you've been in the business long, and think that you know your customers pretty well, you never know when a new factor might emerge that changes customer behavior, or how the speed of societal change is continually altering the dynamics of the provider/customer relationship. Not conducting timely market research means that you will fail to identify new, emerging trends in the market, which will lead to lost opportunities that your competitors are not going to miss, which in turn will lead to a loss of market position. In a market as competitive and dynamic as today's, you need to keep a close watch on your target audience.

    2. We can do our own market research

    Of course you can. But unless you're a marketing professional with relevant experience in market research, have the necessary tools for it, have a dedicated internal research team with expertise in market research or have hired a research vendor to do it for you, it's unlikely that your venture is going to be fruitful. Don't confuse market research with online research. Market research is a systematic discipline that requires careful planning, time, expertise, and an ability to marry a variety of data sets or sources of information to clearly articulate business questions and then answer them. Unless you've studied it, or done it before successfully, with proven results, you're not eligible to conduct your own research, especially when it's the future of your organization at stake. If you still do it, be ready for inaccuracies and impaired decisions.

    3. Market research takes too long

    Not necessarily. It depends on multiple factors such as your research objectives, key questions, the primary techniques used to collect data, the ideal respondents, the ease or difficulty in reaching them, the number of respondents needed, how the collected data will be analyzed, how it will be shared, etc. One thing is for certain however, long gone are the days of the lengthy survey timeline, when a market research project meant at least 6 months of data collection and analysis. Nowadays, on average, most research projects take about one to two months max, thanks to new methods, and new data sources available now, as well as sophisticated technologies such as market intelligence software.

    4. Market research is too expensive

    With affordable and scalable research solutions and tools available on the internet today, this is simply not true. Market research can be expensive, but it doesn't have to be. Online research using survey tools, email marketing platforms and social media polls can be leveraged to provide you a valuable insight into your target audience's minds, at virtually no cost. Then there's always primary methods and techniques like focus groups and expert interviews, which come at moderate costs, but are still relatively expensive. Or, you could just use a market intelligence tool that allows you to identify shifting trends as they emerge and make adjustments accordingly, enabling you to see the complete picture by bringing data from external sources into the mix. Anyway, insights gathered from market research are going to help you bring in much more than you invest in it. They'll bring competitive advantage and growth, building a future for your company, which is something you can't put a price tag on.

    5. Doing quantitative research is enough

    Granted that market research has a lot to do with numbers and statistics, but the impression that quantitative research should be enough is mistaken. Surveys and polls will get you quantitative data, but knowing the reason behind a particular phenomenon, the "why" behind is crucial in order to customize your product or service to your customers' needs and demands, and only qualitative research can get you that. This is why market research should always be a balance between quantitative and qualitative research with open-ended questions, customer feedback, and interviews included. It will be really helpful for your stakeholders if you complement each statistic or data with a "why" when presenting your findings, i.e. the reason a certain demographic chooses to behave a certain way.

    6. Surveys are all you need to conduct market research

    While surveys are often a primary technique to conduct market research, they're certainly not the only market research technique used to conduct market research. In addition to surveys, organizations employ a number of techniques such as focus groups, interviews, field trials, social media listening, observation, research reports, research partners, competitive analysis, sales data, and market intelligence tools to gather and analyze data on the market and their customers. Surveys are, no doubt, a valuable research tool, but these techniques provide a window into actual social behavior and the adoption of technologies, services, or other offerings among customers. Out of these, market intelligence tools are a recent favorite, as they combine the benefits of a number of market research techniques.

    How market intelligence tools can help organizations with market research?

    According to Forbes, there are two problems with traditional market research. One, a number of times the data gathered using surveys and polls isn't actionable. For example, just because you have a good idea of who you're targeting doesn't mean you'll be able to formulate a winning marketing strategy around that insight. And two, people lie. Your respondents may lie simply to impress the person administering the survey. People want to make a good impression, whether the results are anonymous or not. Market intelligence allows you to look beyond the individual pieces of the marketing puzzle (like polls, surveys and focus groups) and focus on the big picture. When you utilize market intelligence, along with supplemental market research, you can merge strengths, eliminate weaknesses and holistically identify growth opportunities.

    Market intelligence tools can be very capable form of gathering market research, and one which is being used extensively by organizations. Such tools can gather and analyze data from the internet, including news, blogs, company websites, social media, regulatory and government portals, industry trade association reports, press releases, job boards, review websites, etc., in an automated manner, saving precious time, effort and resources. In addition, market intelligence tools are not only useful for gathering market research data, but to inform strategic decision-making across the enterprise, as mentioned above. Market research is, in fact, nothing but a subset of the larger field of market intelligence.

    Market intelligence allows organizations to make informed decisions by supplying them with insights, trends, patterns, etc., propagating a data-driven business approach. Organizations that receive a steady stream of market intelligence have a bird’s-eye-view of their market, and understand their current position in the market, which in turn helps formulate strategies for success and growth. Isn't that the idea behind conducting market research in the first place?


    Successful organizations know their markets, understand their competitors’ and focus on their customers’ wants and needs. They do this by gathering all the information necessary for their businesses to be competitive. They know why people buy their products and services, not just when or where. Not conducting timely market research can result in indecision and inaction, fear of risk or the truth, and/or too many options, which can lead to paralysis. Hopefully, this article was able to dispel myths about market research, and clear any ambiguities about why market research is essential. I also hope that this article was able to shed some light on how market intelligence is a step forward from market research, and a useful strategy for organizations to invest in.

    Author: Malay Mehrotra 

    Source: Contify

  • 7 tips to become a better CIO

    7 tips to become a better CIO

    CIOs have long been viewed by many colleagues as second-string executives. Now is the time for IT leaders to expand their role, and to assert their unique vision and value.

    Being a CIO has never been more important. Major IT trends, including security and privacy protection, cloud computing, machine learning, and remote workforces, as well as complying with an avalanche of regulatory mandates, have elevated the CIO post to a level of importance equal to, or even exceeding, that of fellow C-level executives. Unfortunately, within many enterprises, however, management’s perception of CIOs remains firmly embedded in the past.

    It’s up to CIOs themselves to expand their role in the enterprise by moving into business areas once considered off-limits or, until recently, didn’t even exist. “Gone are the days of the CIO being a back-office IT cost manager,” declares Chris Scheefer, vice president of intelligent industry at technology services and consulting firm Capgemini Americas. “Today’s CIO must adapt and become a business strategist, a digital innovator, and an orchestrator for business.”

    Are you up to the task? If so, here are seven steps that will help you build a bigger, and more rewarding, role for yourself.

    1. Position yourself as a change agent

    From front office to back office, the CIO needs to operationalize strategy and be a change agent, driving new skills and talent into the business. “The role of the CIO, through applied digital technologies, is to build resiliency, organizational agility, and become the engine to scale new technologies and innovations into a sustainable competitive advantage,” Scheefer says.

    The key to success, Scheefer believes, is to become the C-suite’s vehicle for driving business strategy and transformation at pace and scale. “This means becoming more than a mechanism for technology project delivery and management,” he notes. It’s about bringing an outcomes- and value-oriented perspective to the job, along with a solid plan to activate the strategy holistically.”

    Too often, CIOs work reactively, waiting for the business to come to them. Proactive engagement and building an IT organization that’s integrated into front-office operations is critical to success, Scheefer says. This means embedding and integrating teams on key strategic priorities and supplying shared metrics to their business stakeholders to ensure a successful partnership. 

    2. Focus on outcomes, not technologies

    Many, perhaps even most, executive team members aren’t particularly interested in technology, yet all are interested in learning how innovation can benefit the enterprise. “Don’t hide in the basement,” advises John Abel, CIO at network equipment company Extreme Networks. “The worst thing to do as CIO is to not engage with executive stakeholders.”

    Abel suggests creating and leading monthly meetings with the executive team to bring transparency to IT planning and operations. “To play a bigger role, a good first step is to ensure your discussions are relevant to the people you’re talking to,” he says. Know what the hot-button items are and bring them to the table for dialogue and input. “This will allow the CIO to be better positioned in the company and more likely to be included in the decision-making process,” Abel explains.

    3. Build business value

    When IT begins delivering value in terms of profits earned rather than simple cost reductions, colleagues will begin viewing the CIO in an entirely new and positive light.

    “It will change the perception of the role of the CIO,” says Brian Jackson, a research director in the CIO practice at technology research and advisory company Info-Tech Research Group. “The more the CIO can support the business with key technology capabilities, the more peers will link their success to forming a strong relationship with the CIO.”

    The CIO gets a seat at the main decision-making table when IT is mature enough to deliver on initiatives that directly improve the organization’s business model, Jackson says. “If the CIO can bring in new revenue, provide customer-facing touchpoints, and drive data-driven decision making, then they are going to become integral to enterprise strategy.”

    CIOs should also consider participating in professional associations that will allow them to meet with their peers. “It can be especially insightful to discuss challenges and opportunities with other CIOs in the same industry,” Jackson notes. “It’s one way to discover some shortcuts to solving problems.” 

    4. Be open, upfront, and honest

    To build a bigger role, CIOs should clearly articulate precisely what they can and can’t do with the resources at their disposal. “Learning to present cyber needs through a risk management lens, with a clear financial outlook, is a great place to start,” suggests Tommy Gardner, CTO of government technology and services provider HP Federal. “The combination of a strong expert opinion backed by data is a powerful one and will elevate the CIO role.”

    Gardner says that CIOs also need to educate the C-suite on the fact that IT and security are continuously evolving and need to be constantly evaluated and supported from the top. “By making this an open conversation, and showing the ROI over time, CIOs can better position themselves and their teams as integral to the organization’s operations.”

    5. Seek external advice

    Relationships are built on listening, says Rebecca Fox, group CIO at security consulting firm NCC Group. Therefore, never overlook seeking external, unbiased advice when facing a particularly difficult challenge. “Getting help to create that roadmap is really helpful,” Fox notes. “It’s also more difficult for senior business leaders to dismiss external advice — with it, they are more likely to take action.”

    Building a network of fellow IT leaders to frequently interact with is key, says Prasad Ramakrishnan, CIO of SaaS provider Freshworks. “In IT, it’s not only about what you know, but also who you know,” he observes. “For example, if you need to bring in someone to solve a very niche problem, who do you know to get the job done right?”

    Ramakrishnan suggests participating in industry forums and encouraging team members to participate as well. “The collective learnings from these industry interactions can bring you together and provide you with the tools needed to solve business problems.”

    6. Aim for agility

    Emerging technologies and evolving customer and employee demands have forever changed the CIO’s role. “Today, the CIO is truly embedded,” says Florian Roth, chief digital and information officer at business application and technology provider SAP SE. “The mandate is to be agile; to actively design and deploy intelligent and sustainable technologies.”

    Roth notes that his mandate has changed dramatically in recent years. “From a classic IT manager to a strategic partner who actively shapes the intelligent and sustainable company and digital transformation,” he explains. “The role of the CIO has shifted from efficiency-driver to growth-driver, from system provider focused on managing traditional IT operations, to strategic decision-maker shaping and creating digital transformation.”

    Roth advises CIOs to take stock of gaps that need to be filled, including talent and technology, and to prioritize technologies that will drive the enterprise’s strategic vision forward. “Businesses are being pulled in many different directions and there’s an endless number of opportunities for technology to support broader company goals,” he says.

    7. Sharpen your strategy

    “What we tend to do with strategy is to make it long and cumbersome,” says Christine Ashton, CIO of SUSE, an open-source solutions provider. Yet it doesn’t have to be that way. “If you look at some of the biggest tech companies today, what they’re good at is re-running their strategy process at shorter intervals,” she notes.

    The pandemic has driven home the point that strategies can be constraining and shouldn’t limit the need to be agile. “It’s not about focusing on everything all at once, but rather focusing on the value levers that matter most to the business,” Ashton says.

    Instead of waiting a year or more to see whether a particular strategy is working, Ashton recommends running frequent and sharp strategy exercises. “By taking an end-to-end, value-based approach, you can rank projected results on how they contribute to closing your strategy gap overall,” she says. “In this way you are in a much better position to control scope and deliver real benefits faster and in phases.”

    Author: John Edwards

    Source: CIO

  • Best practices for cloud consulting: from overall strategy to attention for detail

    Best practices for cloud consulting: from overall strategy to attention for detail

    Do you know that the average person uses 36 cloud-based services every single day? Many businesses have naturally moved or are considering cloud computing for benefits like efficiency, knowledge, enhanced security, and reduced costs. So, there’s no denying that cloud computing has completely changed our lives and the way we conduct businesses.

    Still, many organizations get overwhelmed by the challenges lying ahead. Those challenges mainly include incompatibility issues, security risks, lack of expertise, and governance & compliance issues. And to tackle those challenges, the demand for cloud consulting services has grown dramatically; the global cloud infrastructure services spending increased to $55 billion in Q1 2022.

    The cloud consulting market is projected to reach $22 billion by 2022. However, as important as technical capabilities are skills necessary to work effectively with your clients. Here are seven must-know best practices for cloud consultants:

    1. Start with a Strategy

    This is where you and your team begin to outline a plan. You need to understand what the goals of your business are, who your target audience is and what you intend on selling them. Once these things are defined, you can move on to developing your brand. Branding is an important part of any business’s foundation because it helps define who you are and how others see you. In addition to this, it helps guide decision-making by giving employees something they can refer back to when making decisions that may affect how customers view their company as a whole (i.e. if something goes wrong).

    • Define Your Target Audience: Who is going to be buying from me? What do they look like? How old are they? What gender do I want my brand to appeal to? These questions will help shape where we go from here with our marketing strategy for targeting this particular demographic group.
    • Define Your Product Or Service: This one seems pretty obvious but there’s more to defining your product than just saying “I’m selling fruit.” You need specific details about what kinds of fruits or vegetables; whether its organic vs conventional; how much does each type cost per pound; etc…The better defined this information becomes now than later down the road when someone starts asking questions about why things aren’t working out quite right in terms of sales success rates etc…You’ll have answers ready at hand instead of scrambling around trying to figure out why certain tactics aren’t working anymore!

    2. Be Patient

    Cloud consulting is a long-term game. It takes time to build a successful cloud consulting business, team and practice. It also takes time to create a client base that attracts the right clients and projects for your business. Because of this, any cloud consultant who tells you they’ve hit their stride in six months or less is most likely lying through their teeth (or at least not telling you the whole truth).

    3. Be Prepared for Growing Pains

    Cloud consulting is an exciting and growing industry with a lot of potential. As the cloud continues to evolve and grow, it will create new problems that you need to be prepared for. The cloud is still a relatively new technology, so it’s best not to expect everything will go smoothly from day one.

    4. Testing, testing and more testing

    Testing is the most important aspect of the whole process. You can have a great team, but without proper testing, you will not be able to deliver quality products. Testing your applications on different platforms is just as important as testing them in different environments and browsers. Mobile devices are becoming more and more popular, so we might want to test our applications on various mobile devices as well. There are also many operating systems that need to be taken into consideration when you do your development work. For instance, if you are developing an application for Windows and Mac OS X users only (or vice versa), then make sure that your product works smoothly under both operating systems and does not crash at any point during its usage.

    5. Focus on automation

    The most important thing a cloud consultant can do to improve their business is to automate processes. Automation will help you scale your business, reduce errors, cut costs and improve customer experience. Automation helps you scale by enabling your employees to focus on higher-value tasks that require more specialized knowledge or skill. It also allows you to provide better service at a lower cost because it reduces the need for manual work that takes up time and resources. Improving customer service is one of the keys to being successful as a consulting firm; however, this can be difficult when customer satisfaction depends on the availability of human resources with expertise in certain areas such as compliance or security. With automation tools like Zapier automating repetitive tasks like sending out invoices after each project ends automatically so there’s no need for manual labor which frees up staff members who are better suited at providing value through other means (such as analyzing data or developing new products/services).

    6. Cloud consulting is in high demand

    Cloud consulting is the perfect career if you’re looking to learn new skills, earn money and build respect. Cloud consulting is in high demand right now. In fact, according to Glassdoor’s Best Jobs 2019 report, Cloud Solutions Architect was one of the top jobs with a median base salary of $100K per year.If you want to become a cloud consultant, there are two main types: technical and business-focused. Technical consultants will work on projects related specifically to cloud technologies like AWS or Azure while business consultants may help clients understand how their organization can benefit from moving some workloads off-premises into the public cloud or private cloud.

    7. Have eye for detail

    People who do well in cloud consulting have an eye for detail, love to solve problems and enjoy building new relationships.

    • You need to be detail-oriented. If you can’t handle the small stuff, it’s not going to work.
    • You need to be able to solve problems and troubleshoot problems quickly. You will get asked a lot of questions, so being able to come up with an answer right away is key.
    • You need good communication skills that allow for building relationships with new people and vendors/partners in your industry as well as existing customers who may have been with your company for years, if not decades!
    • Learning new things shouldn’t scare you-it should excite you! The more you know about different technologies and tools available today, the better equipped you will be when someone comes along with an idea they want to be implemented but aren’t sure how yet.


    Cloud consulting is a great niche to be in right now as more companies are looking for ways to reduce costs and increase efficiency. This can be done through the use of technologies like virtualization or containerization, which allow you to run multiple applications on one server instead of just one application per server. The best part about cloud consulting services is that they provide flexibility so that you can tailor your services based on what works best for your client’s needs without losing any functionality.

  • Big Data nog weinig ingezet voor real-time of voorspellingen

    Big DataDatagedreven opereren? Bij de meeste bedrijven zijn de datatoepassingen nog relatief simpel en vooral gericht op analyse in plaats van real-time en voorspellingen. Een gemiste kans én risico voor de lange-termijnkoers van een organisatie.

    Nu al zegt 22 procent van de bedrijven achter te lopen op de concurrentie terwijl ruim 81 procent van de respondenten aangeeft dat de mogelijkheden van Big Data voor de eigen organisatie groot zijn.

    Dat blijkt uit de Big Data Survey 2015 van data-consultancybureau GoDataDriven en vakbeurs Big Data Expo. Bijna 200 bedrijven werden onderzocht om inzicht te geven in de actuele rol van big data, de mate van adoptie, intenties en mogelijke valkuilen.

    Data uit voor de hand liggende bronnen
    Wat blijkt? De data die gebruikt wordt is over het algemeen numeriek en komt vaak uit voor de hand liggende bronnen, zoals CRM en klantendatabase (18 procent), websitestatistieken (18 procent), externe bronnen (14 procent) en marketingdata vanuit e-mailstatistieken (14 procent) en transactionele data (13 procent). Toepassingen met data uit rijkere bronnen zoals tekst, beeld en geluid zijn er nog zeer weinig, terwijl hier grote winst te behalen is.


    Meer budget voor datagedreven toepassingen
    De meeste bedrijven maken komend jaar meer budget vrij voor datagedreven toepassingen en zijn van plan te investeren in de kennisontwikkeling binnen het team. Een klein deel van de bedrijven is momenteel al bezig met het toepassen van kunstmatige intelligentie, machine learning, voorspellende modellen en deep learning.

    Maar dat verandert in hoog tempo. Binnen drie jaar verwacht 50 procent van de respondenten de eerste toepassingen met geavanceerde technologie ontwikkeld te hebben.

    Visie het belangrijkst voor succesvolle implementatie
    Wat de belangrijkste factoren zijn voor een succesvolle implementatie van een Big Data-strategie? Visie, aldus 28 procent van de ondervraagden, en ondersteuning vanuit de directie (19 procent). Maar ook ondersteunende systemen en processen (18 procent), budget (14 procent), talent (11 procent) en training (10 procent) spelen een belangrijke rol.


    Data als strategische pijler
    Tegelijkertijd geeft een opvallend groot deel van de ondervraagden aan dat het binnen het eigen bedrijf wel goed zit met de strategische rol van data. 37 procent vult in dat de bedrijfsdirectie data als een strategische pijler ziet, terwijl 27 procent het hier gedeeltelijk mee eens is. Bij bijna een kwart van de bedrijven (23 procent) is er binnen de bedrijfsdirectie op dit vlak juist een grote winst te halen.

    Ruim 67 procent van de bedrijven zegt dan ook dat de mogelijkheden van big data voor de eigen organisatie groot zijn. Nog eens 14,5 procent is het hier gedeeltelijk mee eens. Slechts 9 procent is het in meer of mindere mate oneens met deze stelling.

    Meer highlights:

    • Hadoop is het meest populaire dataplatform: 21 procent heeft een of andere Hadoop-implementatie (Hadoop, Horton, Cloudera).
    • Terwijl bij de licensed software SAP (8 procent), SPSS (7 procent) en SAS (6 procent) het beste scoren.
    • Datatoepassingen worden het vaakst gebruikt binnen marketing (19 procent).
    • Informatietechnologie is bij 13 procent een toepassing, terwijl fraudedetectie (6 procent) en riskmanagement (6 procent) ook regelmatig met behulp van data wordt uitgevoerd.
  • Building a base for successful AI with your data strategy  

    Building a base for successful AI with your data strategy

    For many years, the underlying complexities of AI, paired with a dramatic portrayal in the media as an inevitable replacement for human jobs, created a daunting narrative that made AI difficult for most people to understand, let alone to widely adopt. Now, we’re at an exciting turning point with AI. We're beginning to understand and articulate where AI is useful for elevating people's creativity and augmenting decision making. As the pandemic has accelerated digital transformation, organizations are successfully deploying and scaling AI projects across more sophisticated, critical scenarios.

    “Eighteen months back, 85% of the enterprises that we surveyed were actually experimenting,” said Nitin Mittal, a principal at Deloitte, specializing in AI and strategic growth. “They had data science groups, they had an AI center of excellence, they had investments, they were developing proof of concepts—trying to figure out the art of the possible. After just 18 months, more than 40% of those organizations are starting to adopt AI at scale.”

    So what’s changed? Organizations are not just focused on the technology elements of AI—they’re taking a strategic, human-centric approach that balances machine intelligence with human expertise. They're creating environments of trust, where people not only have the right data for building useful models, but understand the capabilities and limitations of AI and its outputs. After all, we can't forget that the purpose of these technologies is to improve people’s data-driven decision making

    Let’s look at a few focus areas of a people-centric strategy to help you achieve trusted data and successful AI projects: your data architecture, the processes for managing governed data, and balancing the roles of people and machines.

    Lay a strong foundation with your data architecture

    “I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. This starts with internal alignment—both organizationally and in terms of use cases and common goals.

    Your analysts, data scientists, data engineers, and machine learning engineers will offer unique viewpoints and preferences, and should all be brought into the conversation as experts in their areas of the business. A chief data officer and center of excellence will help in establishing ownership of the data and AI strategy, so as not to throw funding at uncoordinated or siloed efforts.

    Ali adds, “There’s a lot of stuff you have to do under the hood that you’ve got to get right—that your models are stored in the correct way, that you can reproduce your machine learning models, that you’re handling privacy in the right way—so it’s really important that you have an architecture that's built for AI from the ground up.” 

    The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse. This modern, cloud-based data stack enables you to have all your data in one place while unlocking both backward-looking, historical analysis as well as forward-looking scenario planning and predictive analysis. This kind of technology investment enables a broader set of data users to get value from your data within one platform—from business users doing traditional reporting and real-time analysis to data scientists working with algorithms for personalization, automation, and forecasting demand.

    Invest in strong data management and governance up front—it pays off downstream

    “Preparing data, classifying data, and tagging data so that it is machine consumable—as opposed to human consumable—is another artifact of organizations that have cracked the code,” Nitin shared of Deloitte’s findings. As data is the foundation of an AI system, the quality and reliability of AI-enabled prescriptive recommendations or automations are directly correlated to the quality and reliability of the data used to train the system. Trustworthy data can also remove a potential layer of complexity when validating that algorithms and their recommendations are transparent, ethical, and accurate.

    Organizations that lack sound data management practices or that have struggled to build traction and confidence in their data and analytics deployment stand little chance of successfully embracing AI. The same principles that have made self-service analytics possible at scale—a people-first approach to your data strategy, including a governance framework that ensures trust in data—are critical in the success of AI projects.

    “Our data strategy assures convergence on what we call the ‘golden rules’ in our decision supply chain,” said Una Shortt, VP of Data Platform and Engineering at Schneider Electric. “These rules are globally shared and they're published for everybody to access through a defined data catalog.” Setting this standard ensures that every team's systems use data from company-governed authoritative sources, including harmonized data and data objects built for reuse across analytics projects. 

    With these golden rules, data is everyone's business at Schneider Electric—not just an IT process. The upfront work to prep, clean, and govern the data means there are fewer downstream issues with quality and trust. “A shared data governance framework assures our users that if they're reusing a published, certified data object in our company, their colleagues are adopting this exact same view of the data,” shared Una. “We understand that when data is reliable, well structured, reusable, and trustworthy, it becomes a key accelerator for our business.”

    Harmonize human and machine capabilities to build trust in AI

    While machines are great at analyzing huge amounts of data (and getting better at finding hidden correlations in limited data sets), we still rely on people for long-term planning, abstract and creative thinking, and discerning causality from correlation. For this reason, AI projects need the domain experience of people who know your business and who can frame the data and AI results in the right context. By design, tools should explain the provenance of their recommendations to people, so they can make informed decisions—no one should passively accept AI recommendations.

    As Deloitte’s Nitin puts it, “Humans are working with and interacting with intelligent machines, so this notion of having human-centered AI experiences is absolutely critical.” AI offers incredible promise when we can balance its strengths with the creativity of human expertise. In short, people should always be in control of their relationship with AI.

    At Tableau and Salesforce, we believe that a people-first approach to your data strategy—combining the right architecture, tools, and processes—will help everyone trust the data and the AI applications it powers. Then, you’ll have a solid foundation for everyone to better understand and embrace AI in their decision making.

    Author: Vidya Setlur

    Source: Tableau

  • Competitive Intelligence: an overview of competitive pricing strategies

    Competitive intelligence: an overview of competitive pricing strategies

    At all business levels—and especially at the enterprise level—product pricing can be a challenging yet vital activity. How much should your company’s products cost? Should you go with tiered packages, or one-size-fits-all solutions? Do you price for volume, or for higher revenue per order?

    The simple answer to these questions—if such a thing exists—is this: You should price each product according to its worth. But determining a product’s worth isn’t as simple as assessing the combined value of its features and benefits; it’s a complex endeavor that needs to be informed by signals from the market.

    Of these signals, one of the most crucial is how your competitors price their products.

    Whether your company is B2B or B2C, leveraging competitive intelligence when pricing your product is a surefire way to put your brand in the driver’s seat and claim your share of the market. And if your target audiences are prone to comparison shopping—in the enterprise space, that’s almost a given—then adopting a competitive pricing strategy can help ensure you stand out from the rest of the pack.

    What is competitive pricing?

    Competitive pricing refers to any pricing strategy that’s used to give your product an advantageous market position over its alternatives. It’s not as simple as merely undercutting your competitors, though. There are several strategies your company can employ to encourage consumers to choose your product over other options in the market.

    Let’s run through five prime competitive pricing strategies and talk about how you can leverage knowledge of your competitors to attack your pricing problem with vigor.

    1: Penetration pricing

    Penetration pricing is designed specifically to help new products succeed in the market right away. The goal is to initially charge customers a lower rate in order to drive sales volume. Over time—as sales scale and the product gains traction—prices increase, with a twofold goal:

    1. Retain customers who already have affinity for the product, and
    2. Generate new customers based upon credibility the product has built in the market.

    Penetration pricing is particularly handy in hyper-competitive industries. So, if you’re Head of Product at a mid-to-enterprise level business, and you’re trying to bring a new product to market, you can set that product up for relatively quick success by (1) seeing how other products in the space are priced and (2) recommending a slightly lower initial price point. 

    Doing so can help do the valuable work of building your customer base, growing your marketing lists, arming your acquisition team with high-intent remarketing audiences, and kick-starting growth that you may not have otherwise experienced if you had initially priced aggressively.

    Keep in mind, though, that penetration pricing is a slippery slope. If left unchecked, it can influence your market into joining a “race to the bottom,” where your competitors adjust their rate cards down to compensate for the new entrant’s lower price floor, and the products themselves end up becoming commoditized. So, if you’re embarking on a penetration pricing strategy, have a data-backed plan to back yourself out of it. Don’t price your product so low that you water down its value, or risk alienating a segment of your customer base when you raise prices along your pre-determined timeline.

    2: Loss leader pricing

    Loss leader pricing is similar to penetration pricing, but differs meaningfully in that it's not used to bring a product to market (at least not always). In a sense, you’d price your product lower without having a goal to increase it later, and you’d make up for lost margin on the “back end” of the customer journey.

    The initial goal of loss leader pricing, like penetration pricing, is to get customers in the door with prices that are lower than the competition’s. The ultimate goal, however, is to cross-sell or upsell other products once you close the initial sale. The classic example is Gillette, which sold razors at extraordinarily low prices—only to sell replacement cartridges at a higher price point. They got customers in the door, and they drove a profit. Win-win.

    In some instances, loss leader pricing can be considered aggressive and predatory. Think, for instance, about WeWork, which kept prices so low for so long that their competitors couldn’t possibly keep up—and many of them folded. This, of course, presented WeWork with a golden opportunity to charge more.

    A more traditional, non-predatory example would be a marketing agency that sells an affordable site audit to get clients in the door, with the intention of selling them a complete redesign once they’ve signed on. Or consider Costco, which charges extremely low prices on some items—even taking big losses in some cases—in order to build customer affinity and increase volume on products for which they have better margins. 

    If you’re considering loss leader pricing, keep in mind that it’s very difficult to run a loss leader strategy if (1) you're a smaller company, (2) you don’t have other products with their own revenue streams, or (3) you don't have complementary products that would be a natural fit for an upsell or cross-sell. This is a strategy best reserved for mid-to-enterprise-level businesses with high budgets and high margins for error.

    3: Prestige pricing

    Prestige pricing, also referred to as premium pricing, is a strategy in which a brand purposely prices a product at a higher level in order to ascribe quality. 

    In theory, prestige pricing is similar to the above two pricing strategies, in that you’re deliberately pricing your product differently than its alternatives in order to position it favorably. 

    In practice, though, it’s the antithesis. Rather than undercutting your competitors, premium pricing gives your product a cache that it may not otherwise have. It says, this product is more expensive because it is hands down better than the rest of the field.

    Think about Apple products, which are consistently priced at higher levels than competitor products. The Apple example is instructive because it denotes one very important thing about prestige pricing: Your product has to actually have some level of functional or aesthetic superiority in order to sell in volume at a higher level. That is to say, it would be easy to disrespect potential customers with a prestige pricing strategy if their out-of-pocket cost far exceeds your product’s value. 

    If you know, on the other hand, that your product is superior to the field and you have anecdotal data to back that up, a higher price point than the competition would serve to communicate that superiority to your potential customers. This is where it’s especially handy to have competitive pricing data that allows you to track and react to changes in the market over time.

    4: Sticky pricing

    Price stickiness refers to a product’s ability to remain at the same price point while market conditions change. An example is the story of the Ritz-Carlton in the mid-1990s, a time when Southeast Asia was racked by forest fires. While their competitors slashed prices, the Ritz kept theirs as they were and stepped up their game in terms of services and amenities. And the stickiness of their pricing paid off.

    We’ve seen, in some of the above examples, how advantageous it can be to have a fluid pricing structure that allows you to adapt to market conditions. While it’s certainly an advantage to be flexible, there is also a ton of value in maintaining consistent pricing despite fluctuations in the market, especially when you’re trying to preserve the relationship equity you’ve built with your current customers. It’s the job of product stakeholders to determine when it’s in the company’s best interest to adapt, and when consistency should take precedence. 

    Sticky pricing does not preclude the need for competitive analysis. In fact, it makes having an informed understanding of competitor prices all the more important. An effective sticky pricing strategy leverages competitive intelligence to set prices favorably against competitors for the long run. Unlike some of the other pricing strategies, sticky pricing encourages you to be more predictive or anticipatory about how your market may evolve, since you’re trying to establish a price point that will remain constant for an extended period of time.

    In other words, advantageous pricing is not mutually exclusive with consistency. If you leverage competitive intelligence to set prices correctly from the get-go, you’ll equip your product with natural resistance to fluctuations in the market.

    5: Value-based pricing

    Let’s revisit a concept we introduced at the beginning of this post: Your company should price each product according to its worth. 

    That is value-based pricing in a nutshell. Value-based pricing looks at key differentiators between your product and its alternatives and puts a dollar amount on those differentiators. 

    As an example, let’s say your company sells email marketing software. You’re trying to bring to market a new “premium” offering, and you want to know how to price that package in a way that’s going to generate sales. After studying the key benefits and features of your competitors’ products (hopefully you’ve created battlecards) you determine that your premium offering gives customers access to automation that your competitors can’t match. 

    Ask yourself: If you were a customer, how much would that automation be worth? Can you quantify it? If so, that’s how much more wiggle room you have to increase prices based upon your product’s value.

    Value-based pricing has the advantage over some of these other strategies of being highly customer-centric. By putting yourself in the shoes of the customer and measuring demand in the context of your competitors, you can create pricing models that are rooted in data. If you’re selling B2B, that gives your sales team ammunition to back up pricing with raw numbers in conversations with prospects.

    A quick note on price sensitivity

    Price sensitivity refers to the tendency of prices in a given niche to change due to shifts in the market. With regards to the sticky pricing strategy mentioned above,  an understanding of price sensitivity in your market informs just how sticky you can afford to be.

    Price sensitivity is less a strategy than a feel for how changes in buyer behavior, competitor behavior, and other factors impact pricing in your vertical. And one of the best ways to cultivate that feel Is to keep track of changes in how your competitors price their products both diligently and regularly.

    Author: Laura Taylor

    Source: Crayon

  • Four Drivers of Successful Business Intelligence

    BICompanies across industries face some very common scenarios when it comes to getting the most value out of data. The life science industry is no exception. Sometimes a company sets out to improve business intelligence (BI) for a brand, division or functional area. It spends many months or years and millions of dollars to aggregate all of the data it thinks it needs to better measure performance and make smart business decisions only to yield more data. In another familiar scenario, a team identifies critical questions the BI system can't answer. Again, months and millions go into development. But by the time the system goes live, market and/or company conditions have changed so much that the questions are no longer relevant.

    Building Better Business Intelligence Systems
    Today's challenges cannot be met by throwing more dollars into the marketing budget or by building more, or bigger, data warehouses. Ultimately, navigating today's complexities and generating greater value from data isn't about more, it's about better. The good news is that other industries have demonstrated the power and practicality of analytics at scale. Technology has evolved to overcome fragmented data and systems. We are now observing a real push in life sciences for a BI capability that's smarter and simpler.

    So how do we build better business intelligence platforms? In working with life sciences companies around the globe, IMS Health has observed a recurring journey with three horizons of business intelligence maturity: alignment of existing KPIs, generation of superior insights and customer-centric execution (see Figure 1).

    What does it take to advance in business intelligence maturity?
    No matter where a company currently stands, there are four fundamental steps that drive BI success: the ability to align business and information management strategy, improving information management systems integration and workflow, engineering BI systems to derive more value and insights from data, and making the most of new cloud computing technologies and Software-as-a-Service (SaaS) models for delivery.

    Step 1: Align Business and Information Management Strategy
    Many IT and business leaders recognize that the traditional "build it and they will come" mentality can no longer sustain future growth in agile and cost-efficient ways. To be successful, companies need to focus upfront on developing an information management strategy that begins with the business in mind. Through a top-down and upfront focus on critical business goals, drivers and pain points, companies can ensure that key insights are captured to drive development of commercial information management strategies that align with prioritized business needs. Leading organizations have achieved success via pilot-and-prove approaches that focus on business value at each step of the journey. To be successful, the approach must be considered in the context of the business and operational strategies.

    Step 2: Improving Information Management Systems Integration and Workflow
    Although technology systems and applications have proliferated within many organizations, they often remained siloed and sub-optimized. Interoperability is now a key priority and a vehicle for optimizing commercial organizations-improving workflow speed, eliminating conflicting views of the truth across departments and paring down vendor teams managing manual data handoffs. Information and master data management systems must be integrated to deliver an integrated view of the customer. When optimized, these systems can enable advanced BI capabilities ranging from improved account management and evolved customer interactions (i.e. account-based selling and management, insights on healthcare networks and relationships with influencers and KOLs) to harnessing the power of big data and demonstrating value to all healthcare stakeholders.

    Step 3: Engineering BI Systems to Derive More Value and Insights from Data
    Life sciences companies compete on the quality of their BI systems and their ability to take action in the marketplace. Yet existing analytics systems often fail to deliver value to end users. Confusing visualizations, poorly designed data queries and gaps in underlying data are major contributors in a BI solution's inability to deliver needed insights.

    By effectively redesigning BI applications, organizations can gain new insights and build deeper relationships with customers while maximizing performance. Effective BI tools can also help to optimize interventions and the use of healthcare resources. They can drive post-marketing research by unearthing early signals of value for investigation, help companies better engage and deliver value to their customers and contribute to improve patient outcomes. This information can advance the understanding of how medicine is practiced in the real world-from disease prevention through diagnosis, treatment and monitoring.

    Step 4: Making the Most of New Cloud Computing Technologies and Software-as-a-Service (SaaS) Models for Delivery
    Chief information officers (CIOs) are increasingly looking to adopt cloud technologies in order to bring the promise of technology to commercialization and business intelligence activities. They see the potential value of storing large, complex data sets, including electronic medical records and other real-world data, in the cloud. What's more, cloud companies have taken greater responsibility for maintaining government-compliant environments for health information.

    New cloud-based BI applications are fueling opportunities for life sciences companies to improve delivery of commercial applications, including performance management, advanced analytics, sales force automation, master data management and the handling of large unstructured data streams. As companies continue their journey toward BI maturity, getting the most from new technologies will remain a high priority. Leveraging cloud-based information management and business intelligence platforms will bring tremendous benefits to companies as approaches are revised amidst changing customer demands and an urgent need for efficiency.

    The Way Forward
    While each organization's journey will be unique, advancing in business intelligence maturity-and getting more value from data - can be achieved by all with these four steps. It's time for BI that's smarter and simpler and that realizes greater value from data. With focus and precision-and the support of business and technology experts-companies can hone in on the key indicators and critical questions that measure, predict and enhance performance.

    Source: ExecutiveInsight

  • Hoe ziet Business Intelligence eruit in 2020?


    Hoe ziet Business Intelligence er over twee jaar uit?

    Business Intelligence (BI) is sterk veranderd. Waar het voorheen veelal hulp bij rapportering inhield, richt BI zich nu meer op self-service platforms en dataverkenning voor analyse. Met de recente zevenmijlssprongen in technologie groeit ook de informatiekennis en –kunde van zakelijke gebruikers mee. In onderstaande whitepaper wordt u geadviseerd over hoe u het beste uw BI-strategie kunt inrichten.

    BI is big business

    Universiteiten gebruiken BI en visual analytics om aanmeldingen, inkomsten en succesfactoren te meten voor hun langetermijnplanning. Hetzelfde geldt voor regionale gezondheidsdiensten die hun dekking in hoge risicogebieden willen verbeteren. Een nutsvoorziening gebruikt megadata voor het bouwen van een smart grid en een sportteam om de kaartverkoop beter te kunnen voorspellen. Een goede BI-strategie is dus essentieel om mee te komen in de vaart der volkeren.

    In de whitepaper kunt u lezen wat daarvoor nodig is, hieronder volgt een tipje van de sluier:

    • Hoewel met self-service de druk bij IT minder wordt, blijft de behoefte aan gecentraliseerde rapportering groot, bijvoorbeeld om te voldoen aan de nieuwe privacywet GDPR.
    • Ook voor self-service moeten er simpele en duidelijke regels van governance en data lineage komen om verwarring, data chaos en ‘verschillende waarheden’ te voorkomen.
    • De uitdagingen en kansen voor BI zullen nooit stoppen. Daarom moet iedere BI strategie worden gezien als een levend document.
    • Organisaties moeten blijven investeren in de scholing van hun werknemers, en wel in het gehele continuüm van eenvoudige rapportage tot aan voorspellende analyses.
    • Datamanagement zou geen onderdeel van self-service moeten uitmaken, dat wordt te ingewikkeld en schiet zijn doel voorbij.

    Serious business

    Een goede strategie ontwikkelen is essentieel voor een succesvol BI-programma, maar wel wanneer deze aan de hand wordt genomen door een stuur- of bestuursgroep. Idealiter zitten daar ook de directeur en CIO aan tafel, evenals de leidinggevenden van de relevante bedrijfstakken en IT. De groep houdt zich bezig met de grote lijnen van de BI-strategie, de doelstellingen, prioriteiten en investeringen. En het adviseert: welke rapportagemogelijkheden zijn wettelijk vereist of anderzijds noodzakelijk? Welke bedrijfsonderdelen of functies profiteren het meeste van nieuwe informatie en inzichten?

    Download hier de whitepaper en lees over hoe Business Intelligence er over twee jaar uitziet en wat de drie belangrijkste punten zijn die leidinggevenden moeten weten om het meeste uit BI te halen.

    Bron: www.analyticstoday.nl


  • How Big Data is changing the business landscape

    jpgBig Data is increasingly being used by prominent companies to outpace the competition. Be it established companies or start-ups, they are embracing data-focussed strategies to outpace the competition.

    In healthcare, clinical data can be reviewed treatment decisions based on big data algorithms that work on aggregate individual data sets to detect nuances in subpopulations that are so rare that they are not readily apparent in small samples.

    Banking and retail have been early adopters of Big Data-based strategies. Increasingly, other industries are utilizing Big Data like that from sensors embedded in their products to determine how they are actually used in the real world.

    Big Data is useful not just for its scale but also for its real-time and high-frequency nature that enables real-time testing of business strategies. While creating new growth opportunities for existing companies, it is also creating entirely new categories of companies that capture and analyse industry data about products and services, buyers and suppliers, consumer preferences and intent.


    What can Big Data analytics do for you?

    *Optimise Operations

    The advent of advanced analytics, coupled with high-end computing hardware, has made it possible for organizations to analyse data more comprehensively and frequently.

    Analytics can help organisations answer new questions about business operations and advance decision-making, mitigate risks and uncover insights that may prove to be valuable to the organisation. Most organisations are sitting upon heaps of transactional data. Increasingly, they are discovering and developing the capability to collect and utilise this mass of data to conduct controlled experiments to make better management decisions.

    * React faster

    Big Data analytics allows organisations to make and execute better business decisions in very little time. Big Data and analytics tools allow users to work with data without going through complicated technical steps. This kind of abstraction allows data to be mined for specific purposes.

    * Improve the quality of services

    Big Data analytics leads to generation of real business value by combining analysis, data and processing. The ability to include more data, run deeper analysis on it and deliver faster answers has the potential to improve services. Big Data allows ever-narrower segmentation of customers and, therefore, much more precisely tailored products or services. Big Data analytics helps organizations capitalize on a wider array of new data sources, capture data in flight, analyse all the data instead of sample subsets, apply more sophisticated analytics to it and get answers in minutes that formerly took hours or days.

    * Deliver relevant, focussed customer communications

    Mobile technologies tracks can now track where customers are at any point of time, if they're surfing mobile websites and what they're looking at or buying. Marketers can now serve customised messaging to their customers. They can also inform just a sample of people who responded to an ad in the past or run test strategies on a small sample.

    Where is the gap?

    Data is more than merely figures in a database. Data in the form of text, audio and video files can deliver valuable insights when analysed with the right tools. Much of this happens using natural language processing tools, which are vital to text mining, sentiment analysis, clinical language and name entity recognition efforts. As Big Data analytics tools continue to mature, more and more organisations are realizing the competitive advantage of being a data-driven enterprise.

    Social media sites have identified opportunities to generate revenue from the data they collect by selling ads based on an individual user's interests. This lets companies target specific sets of individuals that fit an ideal client or prospect profile. The breakthrough technology of our time is undeniably Big Data and building a data science and analytics capability is imperative for every enterprise.

    A successful Big Data initiative, then, can require a significant cultural transformation in an organisation. In addition to building the right infrastructure, recruiting the right talent ranks among the most important investments an organization can make in its Big Data initiative. Having the right people in place will ensure that the right questions are asked - and that the right insights are extracted from the data that's available. Data professionals are in short supply and are being quickly snapped up by top firms.

    Source: The Economic Times

  • How business analytics can benefit your business strategically

    How business analytics can benefit your business strategically

    Business analytics can provide companies with an accurate and holistic view of their business. Executives and managers now have the ability to use data for real-time, actionable insights into everything from customer buying patterns to inventory management without having to rely on IT for outdated, static reports. In this blog, we discuss five strategic benefits of business analytics .

    Strategic benefit of business analytics 1: staff will have faster access to data

    Comparison: A conservative wait time for an IT generated report is two days. In today’s fast-paced world, a lot can change in two days and usually by the time reports are received, the data is out-of-date. Your executives and managers need to be able to access up-to-date data in order to make quick decisions that will maintain your competitive advantage.

    How would your business look: With access to up-to-date data, your sales team is empowered when interacting with prospects. Over time, this will lead to increased revenue opportunities as sales staff become aware of what customers are buying and, more importantly, what they are not buying. With this data at their fingertips, your sales managers are able to monitor their teams’ performance on a daily basis to identify and implement strategies to improve performance overall. With an easy-to-learn and intuitive BI tool like Phocas, the typical ROI timeframe is between 2-4 months after implementation, but can sometimes be even faster. 

    Strategic benefit of business analytics 2: increase customer acquisition and retention

    Comparison: Sales reps rely on the right information in the right moment. Providing your reps with potentially outdated data may result in your reps wasting time as they hunt for current facts or figures. This could result in lost sales opportunities.

    How your business would look: Armed with current, relevant access to data, your reps are able to engage in more meaningful conversations that are of real value to your customers. By having  data on customer behavior patterns, previous customer feedback, customer preferences, and buying habits, your reps will know what your customers truly want and have the ability to demonstrate the value of your product or service to them. When prospects feel heard, they are more inclined to become loyal and satisfied customers. A quality BI tool will be accessible from mobile devices ensuring your reps have access to your data even when they are out of the office.

    Strategic benefit of business analytics 3: measure the effectiveness of campaigns  

    Comparison: Traditional marketing efforts are a game of trial and error. Businesses implement a strategy and wait to see if their efforts pay off. If sales increase, it’s assumed the strategy is successful. If not, the strategy is tweaked or scrapped for a new plan-of-action.

    How your business would look: BI empowers you to design, monitor, and evaluate the success of your promotional and marketing campaigns by offering real-time insight into how customers are reacting to them. By identifying which campaigns receive the best responses, you can streamline your marketing budget and allocate funds for the best ROI. If a campaign is not generating a positive response, you are able to quickly reorganize the promotion or customize the campaign message accordingly.

    Strategic benefit of business analytics 4: New sales opportunities will regularly present themselves

    Comparison: An Excel spreadsheet can inform your team that sales for a specific product are up, but it can’t clarify whether a specific color or other characteristic is performing better than others. Nor can spreadsheets indicate why certain products are underperforming. BI provides businesses with the ability to quickly evaluate data to identify sales issues and opportunities more effectively than ever before. 

    How your business would look: BI allows your team to quickly detect emerging sales trends by analyzing company data on customers as well as various market conditions. Your team will have the ability to swiftly visualize detailed changes in customer behaviors to reveal emerging opportunities.  By leveraging these insights, sales teams can improve the accuracy of their sales predictions and respond accordingly.

    Strategic benefit of business analytics 5: More stock moving off the shelves

    Comparison: Static reports identify the quantity of a product a company has on hand when the report is generated, and which products are slow moving or have become dead stock sitting in your warehouse graveyard. However, these reports cannot identify the cause of slow moving or dead stock, nor prevent future dead stock. It’s difficult for a company to avoid this situation without a tool in place to accurately monitor the purchasing process.

    How your business would look: BI can help you to isolate poor purchasing decisions because you are no longer relying on outdated static reports. With BI you are able to monitor inventory-to-purchase ratio, stock turns, and slow-moving stock by product, territory, or manufacturer. With BI, you are able to refine your inventory management processes. By identifying product selling patterns, you are able to reduce excess inventory and the cost to maintain it. Visualizations provide a clear picture of how much to order, when, and at what price. In addition to ensuring your stock moves, your managers are able to utilize the information to effectively adjust pricing tiers to increase your profit margins.

    Having your customer, sales, and inventory data at your fingertips gives you leverage to rapidly adapt to an ever-changing sales climate. With the right Business Intelligence tool in place companies are able to increase profit margins, reduce spending, and achieve competitive excellence.

    Source: Phocas Software

  • How to Communicate Challenging Insights from your Market Research  

    How to Communicate Challenging Insights from your Market Research

    When the truth resulting from our study is positive, it can be a joy to communicate those insights. However, research efforts sometimes reveal different results than were expected or hoped for. Although being the bad news bearer is never fun, ultimately it’s these types of scenarios that best make the case for the cruciality of market research. Accurate results, even if disappointing, can inform strategic pivots that can mark detours from disastrous marketing mistakes and place well-informed companies on the path to success.

    Treading Carefully

    Especially if bad news is anticipated, stakeholders may be eager for early access to research data. While it's important to be responsive, it is also wise to proceed with caution. Sharing preliminary previews and toplines may be necessary but ensure you only do so when you have sufficient substantiation to avoid skewed data. The first insights you communicate can often be perceived as “the answer,” so if final data analysis ultimately points to a different conclusion than data previews, this can create confusion and erode trust in the results. Strive to strike a balance between satisfying stakeholder curiosity and protecting data integrity.

    Crystal Clear Seas

    Clarity becomes paramount when presenting research results with negative implications. Ensure your report findings are concise, straightforward, and supported by substantial evidence. A great way to buoy the story is to incorporate verbatim quotes or video clips from interviews or open-ended responses from surveys. These authentic narratives in respondents' own words help illustrate the deeper insights behind the data and lend credibility to your findings.

    Surfacing Amidst The Waves

    While it's essential not to shy away from delivering bad news, it's equally important to highlight any positive aspects present in the data. These glimmers of good allow research stakeholders to come up for air amidst the waves of bad news and can help them better absorb the findings. Identify elements that demonstrate areas of strength, potential, or opportunities for improvement. Presenting positive findings as examples to continue or build upon can help balance the overall perception and encourage a more constructive discussion around the challenging aspects.

    A Lifeboat

    When possible, include a separate "respondent-generated" recommendations for improvement section in your report in addition to your recommendation summary. Spotlighting potential solutions directly from the perspective of the target audience can serve as a lifeboat, creating a hopeful way out amidst a sea of bad news. By incorporating respondents' suggestions and insights, you demonstrate that negative findings can serve as a catalyst for growth and transformation. These recommendations provide a clear way forward for stakeholders to take actionable steps to address the issues raised in the research.

    In The Same Boat

    Delivering research results that convey bad news is a delicate task that can feel like navigating shark-infested waters. By adhering to a strategic approach, market research professionals can effectively share challenging insights while maintaining trust in the findings. Treading carefully as you communicate early data, ensuring clear and well-substantiated findings, highlighting any positives, and buoying your findings with respondent-generated verbatims and recommendations help remove the researcher from the results. Our ultimate goal as researchers is to sail alongside our companies through even the most turbulent waters, providing honest insights that guide them towards improvement, growth, and success.

    Date: July 6, 2023

    Author: Heidi Loften

    Source: Decision Analyst

  • How to Optimize Analytics for Growing Data Stores

    Every minute of every day, mind-blowing amounts of data are generated. Twitter users send 347,222 tweets, YouTube users upload 300 hours of video, and Google receives more than four million search queries. And in a single hour, Walmart processes more than a million customer transactions. With the Internet of Things accelerating at lightning speed – to the tune of 6.4 billion connected devices in 2016 (up 30 percent from 2015) – this already staggering amount of data is about to explode. By 2020, IDC estimates there will be 40 zettabytes of data. That’s 5,200 GB for every person on the planet.

    This data is a gold mine for businesses. Or, at least, it can be. On its own, data has zero value. To turn it into a valuable asset, one that delivers the actionable intelligence needed to transform business, you need to know how to apply analytics to that treasure trove. To set yourself up for success, start out by answering these questions:

    What Is the Size, Volume, Type and Velocity of your Data?

    The answers to this will help you determine the best kind of database to store your data and fuel your analysis. For instance, some databases handle structured data, and others are focused on semi-structured or unstructured data. Some are better with high-velocity and high-volume data.

      RDMS Adaptive NoSQL Specialty In-Memory NewSQL Distributed
    Example DB2, Oracle, MySQL Deep Information Sciences Cloudera, MonoDB, Cassandra Graphing, Column Store, time-series MemSQL, VoltDB NuoDB Hadoop
    Data Type Structured Structured Un/semi-structured Multiple Structured Structured Structured
    Qualities Rich features, ACID compliant, scale issues Fast read/ write, strong scale, ACID, flexible Fast ingest, not ACID compliant Good reading, no writing, ETL delays Fast speed, less scale, ETL delays for analytics Good scale and replication, high overhead Distributed, document-based database, slow batch-based queries

     Which Analytics Use Cases will You Be Supporting?

    The type of use cases will drive the business intelligence capabilities you’ll require (Figure 1).

    • Analyst-driven BI. Operator seeking insights across a range of business data to find cross-group efficiencies, profit leakage, cost challenges, etc.
    • Workgroup-driven BI. Small teams focused on a sub-section of the overall strategy and reporting on KPIs for specific tasks.
    • Strategy-driven BI. Insights mapped against a particular strategy with the dashboard becoming the “single source of truth” for business performance.
    • Process-driven BI. Business automation and workflow built as an autonomic process based on outside events.


    Where Do You Want your Data and Analytics to Live?

    The main choices are on-premises or in the cloud. Until recently, for many companies – particularly those concerned about security – on-prem won out. However, that’s changing significantly as cloud-based solutions have proven to be solidly secure. In fact, a recent survey found that 40 percent of big data practitioners use cloud services for analytics and that number is growing.

    The cloud is attractive for many reasons. The biggest is fast time-to-impact. With cloud-based services you can get up and running immediately. This means you can accelerate insights, actions, and business outcomes. There’s no waiting three to four months for deployment and no risk of development issues.

    There’s also no need to purchase and install infrastructure. This is particularly critical for companies that don’t have the financial resources or skills to set up and maintain database and analytics environments on-premises. Without cloud, these companies would be unable to do the kind of analyses required to thrive in our on-demand economy. However, even companies that do have the resources benefit by freeing up people and budget for more strategic projects.

    With data and analytics in the cloud, collaboration also becomes much easier. Your employees, partners, and customers can instantly access business intelligence and performance management.

    Cloud Options

    There are a number of cloud options you can employ. Here’s a quick look at them:

    Infrastructure as a Service (IaaS) for generalized compute, network, and storage clusters. IaaS is great for flexibility and scale, and will support any software. You will be required to install and manage the software.

    Database as a Service (DBaaS), where multi-tenant or dedicated database instances are hosted by the service provider. DBaaS also is great for flexibility and scale, and it offloads backups and data management to the provider. Your data is locked into the provider’s database solution.

    Analytics as a Service (AaaS) provides complex analytics engines that are ready for use and scale as needed, with pre-canned reports.

    Platform as a Service (PaaS) is similar to DBaaS in that it scales easily and that application backups and data management are handled by the provider. Data solutions themselves are often add-ons.

    Software as a Service (SaaS) is when back office software is abstracted through a hosted application with data made available through APIs. Remote analytics are performed “over the wire” and can be limiting.

    How you leverage data can make or break your business. If you decide to go the cloud route, make sure your service provider’s database and analytics applications fit your current and evolving needs. Make sure the provider has the expertise, infrastructure, and proven ability to handle data ebbs and flows in a way that’s cost-effective for you and, equally important, ensures that your performance won’t be compromised when the data tsunami hits. Your business depends on it.

     Source: DataInformed

  • How tracking the right KPIs and using the right triage strategy lead to success

    How tracking the right KPIs and using the right triage strategy lead to success

    Let’s start with a hard truth: If you try to do everything, you won’t excel at anything. In a growing business, there’s no shortage of things that need attention, but you can’t do everything at once. Instead, you have to decide where to focus your resources to get the greatest impact. In a word, you must become a master of triage.

    Triage means making the tough calls. It means cutting program budgets to free up resources to run down existing leads. It means postponing the development of new features to shore up core functionality — or it could mean running the risk of alienating your existing customer base so you can develop a potentially industry-shaking new feature. In triage, there are going to be losers. But there will also be winners, and that is how companies survive, thrive, and grow.

    To start, decide on your triage philosophy. Are you playing offense or defense?

    Offensive triage strategy

    Defensive triage strategy

    Who plays it: younger startups and companies fresh off a new round of funding.

    Who plays it: companies on the verge of an acquisition or exit.

    Why play it: to take an aggressive stance for customer acquisition and growth.

    Why play it: to patch weak links in financial infrastructure.

    Example in action: Identify a strength and to turn it into a key industry differentiator. If you have earned a good reputation for customer service, then make that a cornerstone of your offering. Hire more customer service reps, build a marketing campaign around them, and arm your sales staff with battlecards detailing how you soundly beat the competition in service and support.

    Example in action: Identify where you are underperforming so you know where to invest your resources. Running short on leads? Give marketing more budget for lead-gen campaigns. Having trouble closing business? Maybe you need more sales reps to follow up existing leads. Is churn affecting customer lifetime value? See if there are opportunities to improve experience and stickiness.

    Which KPIs should I track?

    When you’re a fast-growing business, there are a million metrics that you could track. So many possibilities can make it challenging to isolate the handful that say something meaningful about the health of your company. That’s why it’s crucial to start by identifying your key objectives — the goals that will make the most significant impact. Your KPIs (key performance indicators) are the metrics that tell you how well you’re performing against the targets that matter most to your business.

    Key objectives will — and should — vary from company to company. They depend on where the company is in its growth, what challenges it’s facing internally or in the marketplace, what’s happening in the macroeconomic climate, and more. In the offensive triage strategy example above, a company establishing their position on customer service will want to measure things such as CSAT and NPS scores. An early-stage technology startup fresh off its Series A funding round may set aggressive product targets and will keep a close eye on its product metrics. Meanwhile, a company evaluating an exit either by acquisition or IPO, such as the defensive example above, will want to subject financial metrics such as ARR, CAC, burn rate, and the sales funnel to intense scrutiny.

    Once you know what you want to track, look for ways to automate KPI reporting. Automation will minimize the person-hours you invest in your reporting, freeing those assets to do the creative thinking of solving problems instead of measuring them. An automated reporting system will also let you set up background tracking for KPIs that aren’t part of your active strategy, so when you do have bandwidth to address them, you have that history at your fingertips without additional investment.

    It can be very easy to let KPI reporting slide — especially in high-growth companies where bandwidth is at a premium. Often the relevant metrics are still being tracked by someone somewhere, but the executive leaders who need the information most may not even have access to the tools or dashboards where those metrics live. As part of your KPI planning, think about how you are going to get the data from the systems where it originates and into the hands of senior leadership.

    Finding the right approach to executive reporting

    Early-stage companies frequently leave reporting up to the individual department heads — in fact, the company’s main data leader may be the head of an entirely different department, such as operations or finance. If that is your situation, you should provide clear direction on who is responsible for reporting, which metrics should be included in those reports, and how the reports should be formatted. After all, it can be difficult to have a meaningful conversation about KPIs when the marketing metrics are in a high-level slide presentation while the financial figures are shared through a complicated spreadsheet. Establish a protocol for reporting that ensures that metrics are readable, sharable, and comprehensible.

    On the other hand, you may be a more mature or established company that already has a BI tool that you use for building aggregate dashboards to report on cross-functional data. It cannot be emphasized enough that you must resist the temptation to use your existing dashboards for executive reporting. What seems like an appealing shortcut at first never works out that way — in the executive leadership meetings where they discuss the data, flipping between different dashboards will become a frustrating obstacle to valuable conversations, and the presence of irrelevant data points could spin the team off on futile tangents. Invest the time to build a new, clean dashboard exclusively for executive KPI reporting.

    Whether you build a single executive KPI dashboard or rely on individual owners to provide regular reports, you’ll want to establish a reliable method to deliver consistent KPI updates to senior leadership. While the report should highlight the most current data, it should also provide an easy way to pull up historical data when needed. At every meeting of the executive leadership team, they should refer to those KPIs and use them as a framework for discussions about the larger direction of the business.

    Always remember that no matter what your strategy, communication is key. The entire organization — from the executive leadership down to every individual contributor — should understand what you are tracking, why those numbers matter, and how they can contribute to your overall success.

    Source: Talend

  • How your organization can establish a results-based data strategy

    How your organization can establish a results-based data strategy

    Money never sleeps and neither does your data. In
    this article, we look at digital transformation: the ways of turning data into new revenue streams and apps that boost income, increase stickiness, and help your company thrive in the world of Big Data. 

    The first waves of digital transformation started decades ago and the ripples of this trend continue to be felt to this day. However, what exactly a digital transformation looks like varies widely from company to company. One common theme among many transformations, however, is trying to make better use of data, whether to build analytic apps to unlock new revenue streams or to make smarter decisions internally (or both).  

    While these are worthwhile applications, one blind spot that many teams charged with these projects share is that they look at the data they have on-hand before figuring out what kind of problems they wish to solve with it. 

    “I recommend starting your data strategy with a right-to-left approach, focusing on the desired business outcomes first, instead of the data, to support those outcomes,” says Charles Holive, Sisense Managing Director of Data Monetization and Strategy Consulting. “And there are primarily three areas that industries across the world look to improve: the top line, the bottom line, and customer satisfaction.”

    Define your desired outcome before you start building

    Every company knows they need to digitally transform in order to survive and excel in the modern era. However, many organizations fail to define their goals for this process before they start, and predictably encounter obstacles or outright failures instead of paving a path for future success.

    Business goals should be defined at the very beginning of the digital transformation in the “right-to-left strategy” that starts by answering this question: What is the organization specifically looking to solve or improve? Understanding the details is key, otherwise “digital transformation” will be merely a corporate buzzword that causes headaches, heartbreaks, and lost money instead of producing measurable improvements.

    From there, rather than trying to accumulate and house the company’s entire dataset, the digital transformation team should identify the specific actionable insights and associated data needed to solve for (and measure) agreed-upon outcomes.

    “Not every dataset is made equal; some are more valuable than the others. So being outcome-focused is a way that can you stack-rank the data sets that are most important. Your team can then begin moving that most-important data into your warehouse.”

    Experiment to guide a winning data strategy

    Just as the waterfall method of software development, the strategy of gathering all the requirements upfront and then building and releasing a complete application, has fallen out of favor for agile methods, the same thing should happen when creating an outcome-first data strategy: Rather than trying to build a complete data warehouse right from the outset, approach data strategy as an “innovation factory.”

    “Identifying the exact data you need to solve a singular problem results in a perfect candidate to go into your warehouse on the first cycle. This is because you know exactly what the business is going to do with that data set,” Charles explains. “It’s powerful because it’s already informing or measuring a specific business outcome.”

    And when this data is warehoused and accessible to business partners to make key decisions, you already have a chance to quickly prove this outcome-first data strategy. You’ve immediately created an experiment to win.

    Another piece of advice that Charles talks about in his “Hacking the Analytic Apps Economy” video series is where the innovation factory should live. Namely, not in a mature business unit, but in an agile, fast-reacting department that reports to a Chief Innovation Officer or similar highly-placed exec. This team can deliver on new ideas quickly and won’t get bogged down in pre-existing frameworks or goals that don’t work for what the new data experiments are trying to achieve.

    Create an innovation factory at your company

    “Creating an innovation factory for your company results in faster innovation. You can do these smaller experiments more cost-efficiently, saving money over the traditional data strategy. This also should help your team prioritize projects for the data warehouse that deliver the greatest value, as opposed to the department that screams the loudest.” 

    And while any experiment can fail, but here are some solid tips to help improve your likelihood of success and to maximize the impact of triumphant experiments: 

    • Start by listening to the frontline employees who use the data to make decisions, this will improve the odds of success for your experiment out of the gate.
    • If your experiment works, find other departments that can benefit from that same data, this is where it is key to have a good semantic layer on top of your data warehouse (courtesy of your data analytics software) so you can repurpose the same dataset for different ends.
    • If your experiment fails, see if you can tweak the dataset or use case to apply elsewhere in the company.

    Regardless, approaching data strategy with a focus on business outcomes will put you on the right course.

    “Everything else in the company is business-centered. It just seems counterintuitive not to approach data strategy in the same way.”

    Author: Jack Cieslak

    Source: Sisense


  • Implementing BI in your company with 6 helpful steps

    Implementing BI in your company with 6 helpful steps

    Your organization needs to know how to handle data and integrate it for optimal analytics and data-driven decision-making.

    Business Intelligence is the practice of collecting and analyzing data and transforming it into useful, actionable information. In order to make good business decisions, leaders need accurate insights into both the market and day-to-day operations. Business Intelligence uses methods and tools like machine learning to take massive, unstructured swaths of data and turn them into easy-to-use reports.

    But how exactly to implement BI into a company? What kinds of BI tools are available? This article aims to outline the process.

    1. Pitch to Key Players

    Because employees of many different departments will be involved in managing the data in business intelligence, everyone needs to be on the same page before moving forward. Because it is so important to have a shared understanding, have a look at BI software solutions together to make sure all of the key people that will be involved have a full understanding.

    2. Choose Tools and Create a Team

    The next step is preparation. In order to understand what sort of tool will be needed, defining the requirements for the new BI system is necessary. Smaller businesses are usually able to utilize a BI system as-is, while larger companies may need to look at custom solutions. Another important task at this stage is to gather a team to work on a Business intelligence strategy.

    It is important to have a representative from each department who will be involved in its implementation so that they can simplify communication and provide their own department-specific insights from the beginning. You need to make sure that all departments are data-friendly and in sync with each other.

    3. Develop a Strategy

    Depending on the industry, a BI strategy will require a variety of aspects. Most will include documentation of data sources, the KPIs of the specific industry, the kind of reporting necessary, and whether or not the data flow will require automation. Laying out these components will be helpful down the line.

    4. Set Up Data Integration

    This step is one of the most involved and will require quite a bit of time, as well as a lot of work from the IT department. Data warehouses, a database that keeps the information in a processed and defined format, cannot connect directly to information sources, so data integration tools must process the raw data ahead of time to allow it to be usable. Businesses with large volumes of data may also need additional technology to keep the processes from running too slow.

    5. Choose an End-User Interface

    This stage is where the data gets turned into usable information for the end-users. Previously, BI systems only presented statistical reports, but nowadays, interactive dashboards are available with customizable information. These interfaces allow for real-time reporting to find specific information easily. Real-time, customizable reports are called “ad-hoc reporting” and can prove invaluable.

    6. End-User Training

    The last step in integrating a system into a company is to make sure all employees understand how to use it. It is highly recommended to implement training sessions for all potential users, whether it is an automated process or in-person training sessions with a manager or other team member.

    Final Thoughts

    Although business intelligence has been around for a couple of decades, the landscape of available technologies is ever-changing. Rather than just supplying statistical reports, customizable, interactive interfaces allow for much simpler and more effective collaboration between analysts. The technology continues to evolve, including cloud-based and mobile platforms. What was once only a privilege for the highest executives is now a tool at the disposal of the entire company.

    Author: Sean Parker

    Source: Smart Data Collective

  • Information Is Now The Core Of Your Business

    DataData is at the very core of the business models of the future – and this means wrenching change for some organizations.

    We tend to think of our information systems as a foundation layer that support the “real” business of the organization – for example, by providing the information executives need to steer the business and make the right decisions.

    But information is rapidly becoming much more than that: it’s turning into an essential component of the products and services we sell.

    Information-augmented products

    In an age of social media transparency, products “speak for themselves”– if you have a great product, your customers will tell their friends. If you have a terrible product, they’ll tell the world. Your marketing and sales teams have less room for maneuver, because prospects can easily ask existing customers if your product lives up to the promises.

    And customer expectations have risen. We all now expect to be treated as VIPs, with a “luxury” experience. When we make a purchase, we expect to be recognized. We expect our suppliers to know what we’ve bought in the past. And we expect personalized product recommendations, based on our profile, the purchases of other people like us, and the overall context of what’s happening right now.

    This type of customer experience doesn’t just require information systems; the information is an element of the experience itself, part of what we’re purchasing, and what differentiates products and services in the market.

    New ways of selling

    New technologies like 3D printing and the internet of things are allowing companies to rethink existing products.

    Products can be more easily customized and personalized for every customer. Pricing can be more variable to address new customer niches. And products can be turned into services, with customers paying on a per-usage basis.

    Again, information isn’t just supporting the manufacturing and sale of the product – it’s part of what makes it a “product” in the first place.

    Information as a product

    In many industries, the information collected by business is now more valuable than the products being sold – indeed, it’s the foundation for most of the free consumer internet. Traditional industries are now realizing that the data stored in their systems, once suitably augmented or anonymized, can be sold directly. See this article on the Digitalist magazine, The Hidden Treasure Inside Your Business, for more information about the four main information business models.

    A culture change for “traditional IT”

    Traditional IT systems were about efficiency, effectiveness, and integrity. These new context-based experiences and more sophisticated products use information to generate growth, innovation, and market differentiation. But these changes lead to a difficult cultural challenge inside the organization.

    Today’s customer-facing business and product teams don’t just need reliable information infrastructures. They need to be able to experiment, using information to test new product options and ways of selling. This requires not only much more flexibility and agility than in the past, but also new ways of working, new forms of IT organization, and new sharing of responsibilities.

    The majority of today’s CIOs grew up in an era of “IT industrialization,” with the implementation of company-wide ERP systems. But what made them successful in the past won’t necessarily help them win in the new digital era.

    Gartner believes that the role of the “CIO” has already split into two distinct functions: Chief Infrastructure Officers whose job is to “keep the lights on”; and Chief Innovation Offers, who collaborate closely with the business to build the business models of the future.

    IT has to help lead

    Today’s business leaders know that digital is the future, but typically only have a hazy idea of the possibilities. They know technology is important, but often don’t have a concrete plan for moving forward: 90% of CEOs believe the digital economy will have a major impact on their industry. But only 25% have a plan in place, and less than 15% are funding and executing a digital transformation plan.

    Business people want help from IT to explain what’s possible. Today, only 7% of executives say that IT leads their organization’s attempts to identify opportunities to innovate, 35% believe that it should. After decades of complaints from CIOs that businesses aren’t being strategic enough about technology, this is a fantastic new opportunity.

    Design Thinking and prototyping

    Today’s CIOs have to step up to digital innovation. The problem is that it can be very hard to understand — history is packed with examples of business leaders that just didn’t “get” the new big thing.  Instead of vague notions of “disruption,” IT can help by explaining to business people how to add information into a company’s future product experiences.

    The best way to do this is through methodologies such as Design Thinking, and agile prototyping using technologies should as Build.me, a cloud platform that allows pioneers to create and test the viability of new applications with staff and customers long before any actual coding.


    The bottom line is that digital innovation is less about the technology, and more about the transformation — but IT has an essential role to play in demonstrating what’s possible, and needs to step up to new leadership roles.


    Source: timoelliot.com, November 14, 2016

  • Key competitive intelligence actions to take in 2021 according to experts

    Key competitive intelligence actions to take in 2021 according to experts

    As we head into the New Year, many of us are deep in planning, strategy brainstorm sessions, and goal-setting. For competitive intelligence (CI) practitioners, there are many elements to building your annual plan. CI pros have their own roles and KPIs to think about, but the impact of their work felt across the entire organization. 

    The Crayon team connected with many CI practitioners to gain insight into plans for the New Year. Some of these experts are sales leaders, some are product marketers, and some are competitive intelligence analysts. While all of these priorities and goals center around competitive intelligence, every expert we spoke to had a different action plan and goal in mind for how they wanted to focus their priorities in 2021. Some want to help specific teams do their jobs better with the help of competitive intel, while others want to hone in on specific skills. Let’s look at what these competitive intelligence practitioners are going to be focused on in 2021. 

    Incorporate competitive intelligence early on in the sales cycle

    Competitive intelligence data can make or break a competitive deal. When your sales team has the right information in front of them at the right stage of the sales process, they have a better chance of winning a competitive deal. Ultimately, you want to increase competitive win rate, which can be achieved by improving how CI is leveraged throughout the sales cycle. 

    “I’d say for our team, it’s to improve our ability to track competition throughout the sales cycle and leverage our data to be more strategic in how we focus our CI efforts (versus creating positioning or collateral as sales reps request it). This will ultimately allow us to improve win rates and increase market share.” - Christine Friscic, Senior Product Marketing Manager, Gainsight 

    Build a stronger partnership with customer success

    Competitive Intelligence can’t be done in a vacuum. Your action items, as well as your desired outcomes, are all related to the overall success of your business. That means you often need to work very closely with other teams to work toward one common goal. 

    Take a look at how Alex McDonnell at InVision is tackling customer churn:

    “It's all about partnering with our Customer Success team to improve our understanding of customer health. That will allow us to prioritize accounts where we face risk and intervene earlier."Alex McDonnell, Senior Product Marketing Manager, Market Intelligence, InVision 

    Become a better storyteller

    When you work at a very technical company, it might fall under your responsibility to turn the technical language into language that can be used to attract your customers. Working on this translation can be great for your team to better position your product and better tell your product story to bring in more business. 

    “Translate technical differentiators into value propositions / use cases and drive the behavior to be better storytellers.” - Kimberly Bauer, Senior Competitive Intelligence Analyst, VMware Carbon Black

    Apply competitive intelligence to more initiatives 

    You can apply competitive intelligence to many activities and initiatives across your organization. You can support sales with sales enablement materials, apply CI to your go-to-market strategy, and more. As the CI expert at your company, you might find yourself doing a little bit of everything in the New Year. 

    “I’m focused on creating more enablement content; establish a more regular communications cadence around CI and about applying CI to our GTM activities.” - Matt Powell, Product Marketing Manager, Docebo 

    Elevate competitive intelligence across the whole organization 

    If you’ve been conducting competitive intelligence as a manual and fractured process over the years, the New Year might signal it’s time to elevate your strategy. Combining existing methods of tracking your competition and centralizing everything, ideally within a competitive intelligence platform, can give your organization a fresh start for CI. 

    “In the next two quarters, our primary focus is taking our competitive strategy to the next level. We have too many competitors, and too few people to manage the workload. We have many eyes on us, and the pressure to meet our internal stakeholder expectations have grown large as we continue to grow. That requires us mining our existing ecosystem of disparate sources (eg Confluence Wiki, Google Drive, Email, Slack) for competitive insights, centralizing our competitive content into Crayon (eg battlecards, playbooks, talk tracks), and sunsetting the archaic means of disseminating competitive intel to the field (it's fractured). Phase 1... stabilizing our broken processes and tooling, and building it around Crayon.” - Adam Crown, Customer Identity and Access Management (CIAM) Researcher, Okta, Inc

    Build a competitive strategy to drive actionable outcomes in the market 

    "Given the market dynamics in the region, I would aim to focus on two sectors, 1) Exhibitions & 2) Start-Ups. Utilizing my MI/CI skills and experience to navigate the gaps in terms of building competitive strategy and enabling teams with actionable insights. I believe these two sectors will drive business & tourism impact within the region in the next couple of years.  From  a personal agenda, I also have plans to enhance my skills & get certified further in CI, including Product Marketing space." - Sunanda Thumati, Strategist at Think Curate Intelligence

    Centralize and communicate competitive intelligence data 

    Ensuring that everyone in your organization has access to your competitive intelligence information is critical to success. As we’ve mentioned, CI touches the entire organization, so you want to make sure your findings are in one centralized location and benefiting everyone across your organization. 

    “As we look towards 2021, Amplicare wants to ensure it remains a viable option for community pharmacies. One of our main goals for next year will be to organize our internal resources by implementing standard documentation of all the intelligence gathered. These documents are living, of course, constantly changing with new information. From there, it’s important to package the intelligence for the appropriate audience: The C-suite for making key strategic decisions, the product team’s roadmap, the marketing team for content creation and targeting, and the sales team’s need to infuse timely, byte size information into their conversations.” - Marvin Guardado, Strategic Partnerships, Amplicare 

    Setting goals for any initiative is the key to success, especially when it comes to competitive intelligence. No matter what your goal is for 2021, with the right competitive intelligence strategy in place you’ll be better prepared to beat the competition in the New Year.

    Author: Emily Dumas

    Source: Crayon

  • Making your Organization more intelligent with a Cloud Data Strategy

    Making your Organization more intelligent with a Cloud Data Strategy

    At a time when most major companies are showing a long-range commitment to “data-driven culture,” data is considered the most prized asset. An Enterprise Data Strategy, along with aligned technology and business goals, can significantly contribute to the core performance metrics of a business. The underlying principles of an Enterprise Data Strategy comprise a multi-step framework, a well-designed strategy process, and a definitive plan of action. However, in reality, very few businesses today have their Data Strategy aligned with overall business and technology goals.

    Data Management Mistakes Are Costly

    Unless the overall business and technology goals of a business are aligned with a Data Strategy, the business may suffer expensive Data Management failure incidents from time to time. If the Data Strategy is implemented in line with a well-laid out action plan that seeks to transform the current state of affairs into “strategic Data Management initiatives” leading to the fulfillment of desirable business needs and objectives in the long term, then there is a higher chance of that Data Strategy achieving the desired outcomes. 

    Data provides “insights” that businesses use for competitive advantage. When overall business goals and technology goals are left out of the loop of an Enterprise Data Strategy, the data activities are likely to deliver wrong results, and cause huge losses to the business.

    What Can Businesses Do to Remain Data-Driven?

    Businesses that have adopted a data-driven culture and those expecting to do so, can invest some initial time and effort to explore the underlying relationships between the overall business goals, technology goals, and Data Strategy goals. The best part is they can use their existing advanced analytics infrastructure to make this assessment before drafting a policy document for developing the Data Strategy.

    This initial investment in time and effort will go a long way toward ensuring that the business’s core functions (technology, business, and Data Science) are aligned and have the same objectives. Without this effort, the Data Strategy can easily become fragmented and resource-heavy—and ineffective.

    According to Anthony Algmin, Principal at Algmin Data Leadership, “Thinking of a Data Strategy as something independent of Business Strategy is a recipe for disaster.”

    Data Governance has recently become a central concern for data-centric organizations, and all future Data Strategies will include Data Governance as a core component. The future Data Strategy initiatives will have to take regulatory compliances seriously to ensure long-term success of such strategies. The hope is that this year, businesses will employ advanced technologies like big data, graph, and machine learning (ML) to design and implement a strong Data Strategy.

    In today’s digital ecosystem, the Data Strategy means the difference between survival and extinction of a business. Any business that is thinking of using data as a strategic asset for predetermined business outcomes must invest in planning and developing a Data Strategy. The Data Strategy will not only aid the business in achieving the desired objectives, but will also keep the overall Data Management activities on track.

    A Parallel Trend: Rapid Cloud Adoption

    As Data Strategy and Data Governance continue to gain momentum among global businesses, another parallel trend that has surfaced is the rapid shift to cloud infrastructures for business processing.

    With on-premise Data Management practices, Cloud Data Management practices also revolve around MDM, Metadata Management, and Data Quality. As the organizations continue their journey to the cloud, they will need to ensure their Data Management practices conform to all Data Quality and Data Governance standards.

    A nagging concern among business owners and operators who have either shifted to the cloud or are planning a shift is data security and privacy. In fact, many medium or smaller operations have resisted the cloud as they are unsure or uninformed about the data protection technologies available on the cloud. Current businesses owners expect cloud service providers to offer premium data protection services.

    The issues around Cloud Data Management are many: the ability of cloud resources to handle high-volume data, the security leaks in data transmission pipelines, data storage and replication policies of individual service providers, and the possibilities of data loss from cloud hosts. Cloud customers want uninterrupted data availability, low latency, and instant recovery—all the privileges they have enjoyed so far in an on-premise data center.

    One technology solution often discussed in the context of cloud data protection is JetStream. Through a live webinar, Arun Murthy, co-founder and Chief Product Officer of Horton Works, demonstrated how the cloud needs to be a part of the overall Data Strategy to fulfill business needs like data security, Data Governance, and holistic user experience. The webinar proceedings are discussed in Cloud Computing—an Extension of Your Data Strategy.

    Cloud Now Viewed as Integral Part of Enterprise Data Strategy

    One of the most talked about claims made by industry experts at the beginning of 2017 was that it “would be a tipping point for the cloud.” These experts and cloud researchers also suggested that the cloud would bring transformational value to business models through 2022, and would become an inevitable component of business models. According to market-watcher Forrester, “cloud is no longer about cheap servers or storage, (but), the best platform to turn innovative ideas into great software quickly.

    As cloud enables big data analytics at scale, it is a popular computing platform for larger businesses who want the benefits without having to make huge in-house investments. Cloud holds promises for medium and small businesses, too, with tailor-made solutions for custom computing needs at affordable cost.

    The following points should be kept in mind while developing a strategy plan for the cloud transformation:

    • Consensus Building for Cloud Data Strategy: The core requirement behind building a successful Data Strategy for the cloud is consensus building between the central IT Team, the cloud architect, and the C-Suite executives. This problem is compounded in cases where businesses may be mix-matching their cloud implementations.
    • Data Architectures on Native Cloud: The news feature titled Six Key Data Strategy Considerations for Your Cloud-Native Transformation throws light on cloud-native infrastructure, which is often ignored during a business transformation. According to this article, though enterprises are busy making investments in a cloud-native environment, they rarely take the time to plan the transformation, thus leaving Data Architecture issues like data access and data movement unattended. 
    • Creating Data Replicas: Data replication on the cloud must avoid legacy approaches, which typically enabled data updating after long durations.
    • Data Stores across Multiple Clouds: HIT Think: How to Assess Weak Links in a Cloud Data Strategy specifically refers to storage of healthcare data, where data protection and quick data recovery are achieved through the provisioning of multiple cloud vendors. These solutions are not only cost-friendly, but also efficient and secure. 

    Author: Paramita (Guha) Ghosh

    Source: Dataversity

  • Market development: how to grow your business internationally?  

    Market development: how to grow your business internationally?

    An essential and often recurring topic for many businesses is growth and the ways to successfully achieve this. It is a fundamental part of a company’s corporate strategy, which defines the destination towards where the company aims to move. Typically, companies start doing business in a single home market with a limited product/service portfolio. Over time, companies can follow different paths for growth, depending on their corporate strategy.

    When companies have made the most out of opportunities in their current market(s), for instance when ceilings are reached and performance is stable, it can be logical for them to think about expanding business into new ones. These new opportunities could bring the company growth in different ways; increasing sales and share, cost reduction, an extended client base, a broader geo presence, diversification, new talent opportunities, etc.

    Corporate growth strategies

    Although growth can lead to many benefits for businesses, there is no single strategy that leads to the most effective growth. Moreover, there is a wide variety of growth strategies companies can follow to expand business. One of the most cited and applied theories on this matter is the work by Igor Ansoff (1957). The Ansoff matrix, also referred to as Product/Market Expansion Grid, is a tool that can be used to analyze and plan growth strategies. Based on two axes, regarding existing versus new products and markets, growth strategies are divided into four types:

    1. Market penetration

    2. Market development

    3. Product development

    4. Diversification

    Ansoff Matrix

    ‍Each of these types has its own advantages, challenges, and risk profiles. The strategy with the least adjustments to the current situation is market penetration, where the main goal is to increase market share in the current Product Market Combination (for instance by intensifying marketing and promotions). With a product development strategy, companies develop new products/services for an existing market. A market development strategy focuses on entering new markets, can be either geo markets or customer groups, with existing products. The strategy with the most radical changes compared to a company’s current situation is diversification. Diversification can be related or unrelated to the current business. Integration moves are also part of diversification. This strategy generally has the highest risk profile.

    ‍Market development: identification and selection of target markets

    An often chosen strategy is market development. New markets can be new customer groups or new geo markets. Many companies expand their business internationally at some point in time and with increasing globalization these geo expansion efforts are only multiplying.

    Entering foreign countries can be tricky though. You have to adapt to a new culture, regulations, entry barriers, competition. Misfits with market needs can occur, compliance with the regulatory and taxes framework must be met but often differs from the home market situation, supply chain complexities might occur, and so on. Different foreign countries have different characteristics regarding these subjects, implying that a company’s ability to compete (and win!) in different countries varies. Next to knowing the characteristics and possible barriers of new markets, companies should know the market size, growth and potential to be able to choose the most attractive markets to enter. In order for a geo expansion strategy to be successful, market intelligence is crucial to identify and select the most attractive markets.

    ‍Market development: market entry options

    Next to deciding which foreign market(s) to enter, companies must choose how to enter the new market. Entry mode decisions are inherently linked to the pursued growth strategy. There are multiple ways to enter a market, differing in extent of risk and degree of ownership / control. Generally, entry modes can be categorized into equity-based modes and non-equity-based modes. The former refers to foreign direct investment (FDI) types, while the latter distinguishes between contractual agreements. FDI types typically provide a higher degree of control but come with higher risks compared to non-equity-based types. The most commonly used modes of international market entry are; exporting, licensing, franchising, partnering, strategic alliance, acquisition and greenfield venture (launching a new, wholly owned subsidiary). These are ranked from low to high on level of investment risk and degree of ownership and control.

    The choice for a specific entry mode with preferred risk and control profile strongly depends on a company’s proposition, its strengths, the desired entry/setup time and the way the new market is organized. The latter specifically referring to the value chain, distribution channels and purchase processes. Furthermore, culture, production and labor costs, sourcing possibilities, and the regulatory conditions in the new market are key subjects to consider when choosing between entry modes. Depending on a country’s characteristics regarding these subjects, some entry modes fit better with the company’s proposition and strategy than others. Market intelligence is needed to get a clear view on the market characteristics and how the new market is organized, enabling businesses to assess the fit of their proposition and strategy with different entry modes.

    ‍Key takeaways

    Companies pursuing growth through a market development strategy should ask themselves two key questions: 

    1. Which market to enter?

    2. How to enter?

    Market intelligence plays a crucial role in answering both questions. Firstly, MI is used to identify and select the most attractive markets based on market size, growth, potential, and the company’s ability to compete. Secondly, MI provides an overview of the target market’s value chain, distribution channels, sourcing and production opportunities and costs, regulations, consumer preferences and culture. Market insights in these topics support appropriate market entry strategies that fit with a company’s proposition and strategy. Successful geo expansion and market development strategies are therefore based on data driven market research.

    Author: Mark Diesveld

    Source: Hammer, Market Intelligence

  • Six Common Challenges when Adopting a Data-Driven Approach

    Six Common Challenges when Adopting a Data-Driven Approach

    Companies that embrace data-driven approaches stand to perform much better than those that don’t, yet they’re still in the minority. What’s standing in the way?

    It’s no surprise that becoming a data-driven company is at the top of the corporate agenda. A recent IDC whitepaper found that data-savvy companies reported a threefold increase in revenue improvement, almost tripling the likelihood of reduced time to market for new products and services, and more than doubling the probability of enhanced customer satisfaction, profits, and operational efficiency.

    But according to a January survey of data and information executives from NewVantage Partners, merely a quarter of companies describe themselves as data-driven, and only 21% say they have a data culture in their organizations.

    Several key factors help explain this disconnect, but cultural issues were cited by 80% of respondents as the biggest factor keeping them from getting value from their data investments, while only 20% pointed to technology limitations. Based on the experience of experts who have surmounted these roadblocks firsthand, others remain as well.

    Recognizing bad data

    Even the best of analytics strategies can be derailed if the underlying data is bad. But solving data quality problems requires a deep understanding of what the data means and how it’s collected. Resolving duplicate data is one issue, but when the data is just wrong, that’s much harder to fix, says Uyi Stewart, chief data and technology officer at Data.org, a nonprofit backed by the Mastercard Center for Inclusive Growth and the Rockefeller Foundation.

    “The challenge of veracity is much more difficult and takes more time,” he says. “This is where you require domain expertise to allow you to separate fact from fiction.”

    Simple technical skills are not enough. That’s what Lenno Maris found out when he joined FrieslandCampina, a multinational dairy cooperative, in 2017, when the company was embarking on a strategic plan to become a data-driven company.It was a big challenge. The company has over 21,000 employees in 31 countries, and has customers in over 100 countries. It quickly became clear that data quality was going to be a big hurdle.

    For example, inventory was reported based on the number of pallets, but orders were based on unit numbers, says Maris, the company’s senior global director for enterprise data and authorizations. This meant that people had to do manual conversions to ensure the right quantities were delivered at the right price. 

    Or take commodity codes. Each plant put in the commodity code that best fit the product, with different plants using different codes that were then used to reclaim import and export taxes. “But tax reporting is performed at the corporate level, so consistency is needed,” says Maris.

    To fix the data issues, FrieslandCampina had to evolve its data organization. At the start of the project, the team focused mostly on the technical details of data entry. But that changed quickly. “We’ve been able to retrain our team to become process experts, data quality experts, and domain experts,” Maris says. “That allows us to transition to proactive data support and become advisors to our business peers.”

    Similarly, the technology platform chosen to help the company improve its data quality, Syniti, had to adapt as well. “The platform is good but highly technical,” Maris says. “So we had some challenges with our business user adoption. We’ve challenged Syniti to provide a business-relevant user interface.”

    In 2018, the tier-one master data objects were in place: vendors, materials, customers, and finance. The following year, this expanded to tier-two data objects, including contracts, bills of materials, rebates, and pricing. By the end of 2022, the company had finished orchestrating the logical business flows and the project was fully deployed. The result was a 95% improvement in data quality and a 108% improvement in productivity.

    “Prior to implementation of the foundational data platform, we had over 10,000 hours of rework on our master data on an annual basis,” he says. “Today, this has been reduced to almost zero.”

    Data quality was also an issue at Aflac, says Aflac CIO Shelia Anderson. When Aflac began its journey toward becoming a data-driven company, there were different business operations across Aflac’s various books of business, she says.

    “There were multiple systems of data intake, which presented inconsistencies in data quality,” she says. That made it difficult to get useful insights from the data. To solve the problem, Aflac moved to a digital-first, customer-centric approach. This required data consolidation across various ecosystems, and as a result, the customer experience has improved and the company has been able to increase automation in its business processes and reduce error rates. “A significant benefit is that it frees bandwidth for customer service agents, enabling them to focus on higher complexity claims that require a more personal touch,” she says.

    Seeing data consolidation as a technology problem

    One of Randy Sykes’ previous employers spent eight years building a data warehouse without success. “That’s because we tried to apply standard system development techniques without making sure that the business was with you in lockstep,” he says. Today, Sykes is IT director of data services at Hastings Mutual Insurance Co. This time, he took a different approach to consolidating the organization’s data.

    Ten years ago, the company decided to bring everything together into a data warehouse. At the time, reports took 45 days to produce and business users didn’t have the information they needed to make business decisions.

    First, data would be collected in a landing area via nightly batch imports from legacy systems. It would then move into a staging area, where business rules would be applied to consolidate and reconcile data from different systems. This required a deep understanding of how the company operates and what the data means. But this time, the project was successful because there were subject matter experts on the team. “We had a couple of business folks who’d been with the company a long time and had a lot of knowledge of the organization,” he says. “You actually have a cross-functional team to be successful.”

    For example, different insurance policy systems might have different terms, and different coverage areas and risks. In order to consolidate all this information, the data team needs to have a good understanding of the business language and the rules needed to transform the raw data into a universal format. “That’s the biggest challenge that companies run into,” he says. “They try to get the data and technically put it together and forget the business story behind the information. A lot of times, these types of projects fail.”

    Today, a report that used to take 45 days can be turned around in 24 hours, he says. Then, as databases continue to get modernized and become event-driven, the information will become available in real time.

    No short-term business benefits

    Once Hastings started getting data together, the data project began producing value for the company, within a year, even though the data warehouse project, which began in 2014, wasn’t delivered until 2017. That’s because the landing and staging areas were already providing value in terms of gathering and processing the data. Data projects have to deliver business value all throughout the process, Sykes says. “Nobody is going to wait forever.”

    A similar “quick win” helped lead to the success of a major data project for Denise Allec, principal consultant at NTT Americas, back when she was the director of corporate IT at a major corporation. A six-week proof-of-concept project showed that the project had value, she says, and helped overcome challenges such as business units’ unwillingness to give up their silos of data. “Giving up ownership of data represents a loss of control to many,” she says. “Information is power.”

    This kind of data hoarding isn’t limited to senior executives, though. “Employees tend not to trust others’ data,” she says. They want to validate and scrub their own sources, and massage and create their own reporting tools that work for their unique needs. “We’ve all seen the numerous duplicative databases that exist throughout a company and the challenges that arise from such a situation,” she says.

    Choosing data projects that don’t have immediate benefits is a major roadblock to successful data initiatives, confirms Sanjay Srivastava, chief digital strategist at Genpact.  “Until you do this, it’s all a theoretical discussion.”

    The flip side is choosing projects that don’t have any ability to scale—another major barrier. Without the ability to scale, a data project won’t have meaningful long-term impact, instead using up resources for a small or idiosyncratic use case.

    “The key is how you deliver business value in chunks, in a time frame that keeps people’s attention, and that is scalable,” he says.

    Not giving end users the self-service tools they need

    Putting the business users first means giving people the data they need in the form they need it. Sometimes, that means Excel spreadsheets. At Hastings, for example, staff would historically copy-and-paste data into Excel in order to work with it. “Everybody uses Excel,” says Hastings’ Sykes. “Now we say, ‘Why don’t we just give you the data so you don’t have to copy-and-paste it anymore.’”

    But the company has also been creating dashboards. Today, about a quarter of the company’s 420 employees are using the dashboards as well as outside agencies. “They can now help agents cross-sell our products,” he says. “We didn’t have that before.”

    But providing people with the serf-serve analytics tools they need is a challenge. “We’re still behind the eight ball a little bit,” he says. But with 200 business-focused dashboards already in place, the process is well under way.

    Another organization that recently began the process of democratizing access to data is the Dayton Children’s Hospital in Dayton, Ohio. “We weren’t doing that well five years ago,” says CIO J.D. Whitlock. “There were still a lot of spreadsheets. Now we’re using the Microsoft data stack, like a lot of people are doing. So as long as someone knows a little bit about how to use PowerBI, we’re serving up the appropriate data, in the appropriate format, with appropriate security.”

    In addition, data analysts have also been decentralized, so people don’t have to go to a single team with their data questions. “Say you want to know how many of procedures X doctor Y did last year,” says Whitlock. “It’s a relatively simple query. But if you don’t give people the tools to do that themselves, then you’ve got a thousand requests.” Putting self-serve data tools in place has helped the company move toward being a data-driven organization, he says. “With the caveat that it’s always a journey and you never declare victory.”

    Not including end users in your development process

    Ignoring user needs is nearly always a recipe for disaster. For example, Nick Kramer recently worked with a national restaurant services company. Kramer is the leader of applied solutions at SSA & Company, a global consulting firm. The restaurant services company was growing rapidly but service levels were dropping. “Everybody was pointing fingers at each other,” he says. “But the CIO had no dashboards or reports—just anecdotes and opinions.”

    One of the problems was that the central installation system was widely ignored. Employees updated records, but after the fact. The system had been imposed on them and was hard to use. “People in the order department, in sales, legal, and on the installation side—every office had their own spreadsheets they ran their schedules on,” Kramer says. “None of the communication was happening and the data wasn’t flowing. So you had to go office by office to find out who was doing what and how well, and which delays were unsolvable and which ones could be addressed.”

    The solution was to get close to the business users, to understand how the data was used. Joshua Swartz, partner at Kearney, had a similar experience recently when he was working on a consulting project with a US food company with several billion in annual revenues.

    The company wanted to enable production managers to make better decisions about what to produce based on real data. “For example, there’s a production line in a certain production site and it can make either tortilla chips or pita bread,” says Swartz. “If there’s a switchover, you have to stop and clean and change the ingredients.”

    But, say, the old way was to do four hours on tortillas and four hours on pita bread, and the data showed that you should do two hours on tortilla chips—and then tomorrow it may be the opposite. And since food products are perishable, getting production wrong means that some product would have to be thrown away. But when the company first designed its solution, the production workers weren’t involved, says Swartz. “They were too busy producing food and didn’t have time to stop and attend meetings.”

    This wasn’t expected to be a problem because the company’s culture was hierarchical. “When the CEO says something and pounds their fist on the table, everyone has to follow suit,” he says. But the new system was used for only a couple of weeks in the pilot site and then the employees found that the system didn’t really work for them and went back to doing things the old way. Also, it didn’t help that the company’s data czar was located a couple of layers down in the company’s technology organization, rather than closer to top management or to the business units.

    Fixing the problem required bringing the actual employees to the design suite, even though it required adding capacity to the production lines to free up workers. “Food companies with very thin margins weren’t comfortable making that investment,” Swartz says. But when they became part of the process, they were able to contribute to the solution, and today a third to a half of the facilities are using the new technology.

    Swartz also recommends that the chief data officer be located closer to the company’s most valuable data. “If data is a strategic asset of the business, I would place the CDO closer to the part of the business that has ownership of the data,” he says. “If the organization is focused on using data for operational efficiency, then under the COO might be the right place.”

    A sales-driven company might want to put the CDO under the sales officer, however, and a product company, under the marketing officer, he says. One consumer packaged goods company he worked with actually had the CDO report directly to the CEO.

    “If you think of data as a technology problem, you’re going to keep running into challenges of how much value you are actually getting from data and analytics,” says Swartz.

    A lack of trust

    The responsible use of data is important for the success of data initiatives, and nowhere more so than in finance. “Trust is of utmost importance in the banking sector,” says Sameer Gupta, chief analytics officer at DBS Bank. “It’s crucial to use data and models responsibly, and ethical considerations must be upheld while using data.” Data use should be purposeful, he says, respectful, and explainable, and should never come as a surprise. “Data use should be expected by individuals and corporates,” he says.

    By focusing on trust, he adds, the bank has been able to deploy AI and data use cases across the enterprise—260 at the last count—ranging from customer-facing businesses like consumer and small and medium enterprise banking, to support functions like compliance, marketing, and HR.“In 2022, the revenue uplift from our AI and machine learning initiatives was about SGD 150 million [US $112 million], more than double that from the previous year,” he says. “We aspire to achieve SGD 1 billion in the next five years.”

    Earning trust takes time and commitment. Becoming a data-driven company is all but impossible without it. But once trust is gained, it begins a virtuous cycle. According to a CapGemini change management study released in January, in organizations with strong data analytics, employees are 18% more likely to trust the company. And when those companies need to evolve further, the probability of successful change is 23 to 27% higher than at other organizations.

    “Many people, including data experts, think most issues while transitioning toward becoming a data-driven company are technology-related,” says Eugenio Zuccarelli, a data scientist at a global retailer and former AI research scientist at MIT. But the real barriers are personal, he says, as people have to learn to understand the value of making data-based decisions.

    “While doing research at MIT, I often saw experts and leaders of organizations struggle with their transition toward becoming a more data-driven organization,” he says. “The main issues were usually cultural, such as a belief that technology would have overtaken their decision-making, rather than empowering them, and a general tendency to take decisions based on experience and gut feelings.” People need to understand that their expertise is still vital, he adds, and that the data is there to provide additional input. 

    Companies need to stop thinking about becoming a data-driven company as a technology problem. “All our clients are talking about becoming more data driven, and none of them know what it means,” says Donncha Carroll, partner in the revenue growth practice and head of the data science team at Lotis Blue Consulting. They focus on their technology capabilities, he says, not what people will be able to do with the data they get.

    “They don’t put the user of the solution in the frame,” he says. “Lots of data analytics teams provide data dashboards that provide information that is neither useful nor actionable. And it dies on the vine.”

    Author: Maria Korolov

    Source: CIO

    Date: May 25, 2023

  • The different levels of a CX strategy and how to level up

    The different levels of a CX strategy and how to level up

    Customer experience (CX) needs to be an essential part of your business plan to stay competitive.

    About seven in 10 customer experience management professionals (67%) say their organizations are already competing mostly or entirely on CX, according to a recent Gartner survey. By two years from now, nearly nine in 10 CX managers (86%) expect to mostly or entirely compete on the basis of CX.

    If you want your business to beat the competition, you need to create a robust CX strategy.

    In this article, we’ll cover the current state of CX marketing strategies and popular CX initiatives at other organizations. Use this information to identify gaps in your organization’s initiatives and to propose investing in improvements.

    Where organizations are now in their CX strategy

    Most organizations are still in the early stages of customer experience maturity, according to Gartner’s CX maturity model. This model is a tool to help organizations assess where they are and where they want to be in their CX strategy and initiatives. The model consists of five levels, ranging from an ad hoc approach to a fully embedded, organization-wide approach.

    About two-thirds of B2C organizations are in the earliest two stages of CX maturity, representing an initial ad hoc approach (32%) or an early established CX road map (33%). In comparison, just 5% of organizations are in the upper two levels, which are characterized by optimizing and fully embedding CX considerations across all levels of the organization.

    What a CX strategy looks like at different levels of maturity

    To get a stronger sense of what CX maturity looks like, take a look at the following table, which lays out key characteristics of what an organization’s customer experience program looks like across the five maturity levels.

    Ask yourself where your organization is now and where you want the organization to be.

    From there, you can begin building a strategy to close the gap between your current level and goal level.

    Customer experience maturity levels

    1. Ad hoc

    • Purpose and strategy:Reacting, fighting fires
    • Customer insight: No research team or budget
    • Personas and journeys: None exist
    • Voice of the Customer: Irregular surveys

    2. Establishing

    • Purpose and strategy: Reducing complaints, developing strategy
    • Customer insight: Dedicated researcher
    • Personas and journeys: Developed
    • Voice of the Customer: Standardized surveys

    3. Performing

    • Purpose and strategy: Implementing a unified CX strategy
    • Customer insight: Dedicated research team
    • Personas and journeys: Used to identify and prioritize efforts
    • Voice of the Customer: Limited, closed-loop feedback process

    4. Optimizing

    • Purpose and strategy: Optimizing to meet CX goals
    • Customer insight: Continuous
    • Personas and journeys: Detailed, represent full journey
    • Voice of the Customer: Fully operationalized across organization

    5. Embedded

    • Purpose and strategy: Pursuing innovation, whole organization buy-in
    • Customer insight: Insights widely distributed, used daily
    • Personas and journeys: Used throughout the organization
    • Voice of the Customer: Continuous monitoring

    The maturity model is not prescriptive. It’s important to note that not all organizations will even want to reach level 5, which involves continuously monitoring customer feedback to make real-time decisions. The technological and financial requirements for this approach are likely prohibitive for small and midsize businesses, not to mention the time and staffing it would require.

    Take your customer experience strategy to the next level with these 3 popular CX initiatives

    As you can see from the maturity model table, personas, journey maps, and a Voice of the Customer program are key characteristics that can help define where you are in the development of your CX program. Here are some tips for implementing or optimizing these initiatives.

    1. Develop customer personas to better identify CX needs

    A customer persona is a finely honed profile of your best or target customer and should be as specific as possible to help you visualize their wants, needs, behaviors, and motivations.

    Think beyond demographic information such as age, gender, income, or geography type. Psychographic (e.g., values, opinions, aspirations), transactional (e.g., purchase histories, service records), and behavioral (e.g., engagement on your website or social media profiles) information are key components of a richly-built persona.

    Where to start:

    If you don’t already have a customer persona, start by working on a persona for your most valuable customer type.

    Level up:

    If you already have a customer persona, consider creating additional personas to acknowledge other valuable customer types. Validate your existing persona by checking back in on the data you used when you created it and updating it as needed. Use your customer personas to identify CX needs.

    2. Build customer journey maps to better prioritize CX efforts

    A customer journey map is an externally focused map of your customer’s experience through the full cycle of a particular journey. For example, the journey could start at the customer’s own awareness of a need and end with a product purchase, with steps for every interaction and impression in between.

    The process of building a customer journey map is an act of empathy; you should put yourself in your customer’s shoes and imagine their actions and feelings along the way. By the end of the process, you should have a deeper understanding of gaps or flaws in the customer experience and your customer’s motivations, desires, and feelings throughout.

    Where to start:

    If you don’t already have a journey map, have a workshop with key stakeholders involved in any customer-facing touchpoint.

    Level up:

    If you have a journey map already, validate that it’s still accurate every year or so. Use your journey maps to identify pain points within your customer’s journey and brainstorm solutions.

    3. Create a Voice of the Customer program to improve CX efforts

    A VoC program helps measure customer experience (CX) by capturing and analyzing multiple types of customer feedback to identify customer experience areas that need improvement. As one of the core ways to better understand your customers, VoC programs enable organizations to follow one of the foundational pillars of strong CX.

    Data sources for a VoC program can include customer complaints, customer surveys, employee feedback, company reviews, interviews, and social media. Through rich, diversified sources of customer feedback, VoC programs help companies better understand customer experience and sentiment.

    Where to start:

    If you don’t have a VoC program in place, start by improving your customer survey program: Standardize surveys and make timing regular and consistent.

    Level up:

    If you already have a VoC program in place, consider adding other forms of feedback to enrich your VoC. Use this data to track progress on your CX efforts.

    Envision your long-term CX strategy goals

    Using the customer experience maturity table above, ask yourself where your organization’s CX program is now, where you want it to be, and how you can get there.

    Starting or improving your efforts in one or all of the popular CX initiatives laid out here (personas, journey maps, and VoC programs) is a great place to start in leveling up your CX maturity.

    Auhtor: Kristen Bialik

    Source: Capterra

  • The essence of using an organization-wide data analytics strategy

    The essence of using an organization-wide data analytics strategy

    Does your organization spend loads of time and money collecting and analyzing data without ever seeing the expected return?

    Some 60% of data and analytics projects fail to meet their objectives. Part of the problem is that you can now a just about anything, which has caused our appetite for data to grow exponentially, often beyond what enterprise organization’s data and analytics teams can handle. Too often, talented people with the right tools can’t create meaningful outcomes because of cultural or organizational challenges.

    Here are some telltale signs that your data resources are being wasted.

    • Road to nowhere: When data and analytics teams are seen as order-takers, it can lead to a one-way stream of requests that overload resources and don’t reflect strategic needs.
    • Garbage in: A lack of standards around how data requests are made leads to disorder and inefficiency.
    • Static data in a dynamic world: Data is treated as a retrospective recording of historical measurements with little ability to draw insights or solve problems.
    • Data distrust: Data silos lead to a lack of transparency around who is producing data, what data is actually being used and how they’re doing it. Over time, this can make business leaders start to doubt the accuracy of their own organization’s information.

    In this environment, employees often try to satisfy their own data needs outside the company’s defined channels, which worsens the problem by creating more internal customers for the centralized data analytics team.

    With growing demand for data, you need to organize your data and analytics teams to reflect big-picture goals. Data resources should be assigned based on your organization’s strategic and operational needs rather than the frequently narrow requests of individuals. The goal is to become an organization where data and analytics partner with the business to create value over the long term.

    Your business objectives should drive any and all decisions you make toward organizing data and analytics teams. Data is not the end but rather the means to support the broader strategy.

    The long road toward organizing your data and analytics strategy can be simplified as a three-step process.

    • Organize your analytics resources around business processes.
    • Put money behind products that will help the whole enterprise.
    • Build a product-centric workflow that is transparent, manages the demand of data resources, and delivers on outcomes.

    Mapping your data resources to business processes will help your organization get the most out of its people. It’s also an eye-opening experience for many, revealing the shared needs across departments. Arranging your organization in this way also reduces waste in the form of redundant data reporting. Your people will also have more time to generate insights and spend less time and effort curating their own data marts.

    These newly formed 'analytics centers' subsequently govern the demand and prioritization of analytic products and can help to assess what the major data needs of the organization are. A side benefit is that your data and analytics teams will be empowered. Rather than fielding requests, they’ll start working on products that help the company succeed.

    Developing a long-term product roadmap for your data needs also requires someone to build consensus. The analytics product manager serves a critical role here, understanding the business objectives and translating them for technical teams.

    When analytics centers are enabled, a company will see better return on their investment, as well as more manageable demand on their data and IT resources without the overflow of one-off and redundant requests. The point isn’t to create a totally centralized data and analytics process. Rather, these analytics centers serve as spokes to the company’s enterprise data managementand IT hubs.

    The centers are also a resource to individual departments and teams, relaying their needs to EDM. This arrangement enables the data and analytics centers to filter through mountains of requests to find out what truly matters to the organization.

    Spending more isn’t the answer. Start by identifying the strategic aim of data, organizing analytics resources around them and building products that add lasting value.

    Author: BJ Fineman & Kurt Knaub

    Source: Information-management

  • Using competitive intelligence to determine strategy

    Using competitive intelligence to determine strategy


    Gathering competitive intelligence (CI) usually has one foundational goal, and that is to enable an organization to make better business strategies. The importance of competitive intelligence can be determined by the fact that 90% of Fortune 500 companies collect competitive intelligence, and 55% of these companies say that they regularly use competitive information in formulating their business strategies. In fact, according to McKinsey, a company with regular competitive intelligence insights could reverse-engineer the moves of competitors and easily predict what they were likely to do next. However, not all organizations are familiar with how a competitive intelligence process is supposed to work. Obviously, different organizations can have different approaches or ways of competitive intelligence gathering. The reason for this is that the nature of competitive intelligence varies for different companies, depending on the industry, circumstance, and a host of other factors. Still, it's been observed that competitive intelligence professionals that follow a particular set of guidelines or best practices, are more successful in their CI efforts. 

    The reason organizations with a proper competitive intelligence process succeed in enhancing their organizational performance and growth is simple. Competitive intelligence forms the core of business strategy in these organizations, and when businesses make informed, data-driven strategies, they increase the likelihood of their success manyfold. Secondly, these organizations know the art of competitive intelligence gathering, one which allows them to formulate business strategies with ease. In this article, we'll discuss how to use competitive intelligence to find market opportunities, which competitive intelligence tools you can use to your competitive advantage, as well as some useful metrics you can utilize while strategizing. Let's begin.

    How competitive intelligence can help businesses discover market opportunities

    Competitive intelligence is often used to discover new market opportunities, as ignoring market dynamics is a guaranteed path to diminishing growth. There is no dearth of market opportunities in the modern business world, all you need to do is conduct competitive intelligence research in the right places and begin thinking analytically about what you uncover. A competitive intelligence tool can help unearth valuable competitive insights about market opportunities. Let's have a look at some places where you should direct your competitive intelligence efforts to discover new market opportunities.

    Identifying unmet customer needs

    Competitive intelligence tools can help you collect information from review sites and discussion forums, which in turn can tell you a lot about needs that are not being met. Most customers take to review sites to complain about poor service they've received, or a feature that a particular service or product is missing. The same goes for discussion forums. When you identify these unmet needs, you can work towards meeting them through your own product or service. Focus on keeping track of your direct competitors, but don't just look for unmet customer needs in your own market, because you might find something outside your traditional market that your organization may be able to help customers with.

    Discovering non-traditional customers or uses

    Once again, review sites, discussion forums, news and social media can help you collect information on consumer behavior, which will, in turn, help you find customers for your existing customers, albeit a different segment that you haven't been focusing on. For example, a male fashion brand may begin to offer clothing for women, or a soft-drink brand may begin selling fruit juice for those inclined towards health. Sometimes, customers may even find a new use for a product themselves, e.g. coasters were meant to cover a drink so that any dirt or insect may not fall in, but today, they're almost exclusively used to protect surfaces such as tables from getting wet, or stained.

    Finding new markets

    Finding new markets to enter is a bit more complex than identifying unmet customer needs or discovering non-traditional customers, as it involves strategic planning across functions, and will likely require designing a new product or service. Competitive intelligence can still be a valuable tool for assessing the potential upside and downside of entering a new market. The intelligence you collect can help you gauge the demand for your product or service and size up potential competitors. More importantly, it can help determine whether anyone (particularly your competitors) has already tried entering this particular market and whether they succeeded or failed, in addition to the reason behind their success or failure.

    Tools, methods, and metrics used by organizations to gather competitive intelligence

    Gathering competitive intelligence is something that almost every organization does these days, either unknowingly, or in a planned, strategic manner. No prizes for guessing which approach is better. These competitive insights let organizations bolster a robust competitive business strategy. Let us now look at some of the tools, methods and metrics used by successful organizations to gather competitive intelligence.

    Tracking competitor's marketing campaigns

    Your competitor's marketing campaigns provide you with valuable insights and data that you can use in your own marketing efforts. For example, tracking the duration of your competitors' marketing campaigns can tell you what's working for them, and what's not. If the campaign has been running for a long period of time, it's likely that the campaign is working, and they're getting conversions. Competitor websites and social media are the sources to track to keep an eye on their marketing campaigns.

    Tracking where competitors publish their marketing content

    It's always a good idea to track the publishers or platforms used by your competitors to publish and distribute their marketing content, particularly if it's working for them. It helps you find new avenues where you yourself can publish your own campaigns. It is also a great predictive metric to understand if there's been any change in their marketing strategy, for example, if they've begun publishing blogs at a new platform or website, when earlier it used to be just on their own website. In addition, it can also help in understanding which white spaces you can create content on, those that your competitors have been ignoring. 

    Tracking competitor's websites and pages

    There are a number of tools these days that let organizations track their competitors' websites and its pages, and can even notify you when there's been a change. Once again, this is a predictive metric that can be used to forecast changes in your competitor’s strategy. It is important to track company websites and pages (particularly landing pages) as this is where most conversions happen.

    Utilizing your sales team

    Your own sales people talk to your clients, current customers and prospects on a daily basis. Thus, they are the best suited to gathering competitive intelligence directly from your target audience, in the form of primary research. Customers, clients and potential customers/prospects can at times offer free tips and even tactical advice to sales reps which you can use in your future business strategies, and at the very least, offer key insights and understanding into the mind of your target audience. Make it a point to give them some key questions to ask of customers and prospects.

    There’s a common problem with this method, however. In the absence of a centralized repository to store such primary intelligence, they often get lost or remain in silos which are hard to find in a timely manner.

    Performing a competitive analysis

    It is a must for organizations to analyze their competitive landscape in order to gain a comprehensive understanding of their competitors' products, services, value proposition, capabilities, and weaknesses. A competitive analysis is a commonly used, albeit powerful way to do that, and thus formulate competitive strategies. Organizations usually have predefined quantitative and qualitative metrics on the basis of which they benchmark their organization against their competitors. These metrics may be slightly different for each organization, but usually include key areas like:

    1. Overall revenue

    2. Win rate

    3. Product metrics like:

    - trials started and/or demos requested

    - content views including product page views and video views

    - press coverage for the announcement of a new product

    - new customer or feature(s) upgrade revenue

    - product usage and/or adoption of a new feature

    4. Customer happiness/retention

    5. Qualitative feedback, both internal and external


    For an organization to be able to compete in this highly-dynamic and competitive business environment, data collection, and more importantly the analysis of this data to identify trends, patterns and glean insights is now critical. Times are rapidly changing, and leveraging technologies such as competitive intelligence tools have become necessary in order to regularly stay ahead of your competitors' moves and marketplace shifts. The information present on the internet can provide a wealth of competitive information, which is yours for the taking, but the same is true for your competitors. Wisdom for modern businesses lies in using competitive intelligence as the baseline to inform your decision making, so you don't make the same mistakes over and over. Hopefully, this article provides you with the impetus needed to start your competitive intelligence journey.

    Author: Shilpa Tandon

    Source: Contify

  • Wat maakt een company profile een krachtige tool?

    Company profileWat is een company profile of bedrijfsprofiel nu eigenlijk en waar wordt het door bedrijven voor gebruikt? 

    Wat is een company profile?

    In een company profile wordt op een systematische wijze een analyse gemaakt van een bedrijf. Onderwerpen die hierbij aan de orde komen zijn:

    • Algemene feiten van het bedrijf
    • Historie van het bedrijf
    • Strategie
    • Identiteit
    • Doelen
    • Competenties
    • Product portfolio
    • Organisatiestructuur
    • Financials

    Maar waar wordt het nu door bedrijven voor gebruikt?

    In het bedrijfsprofiel worden zaken in kaart gebracht die u als organisatie zelf kunt beïnvloeden zoals bijv: bedrijfsstrategie, concurrentiestratie, R&D strategie, product portfolio of imago. Verschillende afdelingen binnen organisaties maken gebruik van company profiles voor verschillende doelen:

    Afdelingen Doel

    Ontwikkelen organisatiestrategie, concurrentiestratie of product portfolio; Beoodelen bedrijfsovername

    Marketing Bepalen imago, positionering
    Sales Voeren van acquisitiegesprekken; voorbereiden proposaltrajecten; realiseren cross-selling bij klanten
    R&D Leveranciersanalyse, ontwkkelen R&D strategie door inzicht in technologie van concurrentie
    Inkoop Leveranciersanalyse

    Beoordelen financiele betrouwbaarheid

    Ervaring heeft uitgewezen dat het gebruik van company profiles de effectiviteit van bijv een acquisitiegesprek, cross-selling, bedrijfsovername, leveranciersselectie vergroot en verkleint de kans op verkeerde keuze voor leveranciers. Wilt u meer weten of een voorbeeld te downloaden klik dan hier.

    Bron: RK-Intelligence.nl, Ruud Koopmans, 4 November 2016

  • What do you want to achieve with CI? Summarizing 5 specific goals

    What do you want to achieve with CI? Summarizing 5 specific goals

    Businesses have never been more agile than they are right now. Bringing a product to market, releasing a new feature, launching an ad campaign — these initiatives, though they will never be easy by any means, are not as daunting as they once were.

    As markets become more crowded and pivots become more frequent, investments in competitive intelligence (CI) are growing like never before.

    This makes sense; as the intensity of competition climbs, businesses increase their CI investments in order to … remain competitive. That, of course, is the overarching goal.

    But what are the specific goals of competitive intelligence? In what specific ways does an investment in CI enable you to succeed?

    If these questions are on your mind, you’ve come to the right place.

    5 major goals of competitive intelligence

    The following non-exhaustive list was written in alignment with the five major competitive intelligence stakeholders: sales, marketing, product management, customer success, and executive leadership. Today, we’ll discuss one goal per stakeholder.

    With that being said, please note that there is plenty of overlap between stakeholders. Improved market positioning can translate into more deals, and an improved product roadmap can translate into higher rates of customer retention. We’ve aligned each goal with a unique stakeholder in order to emphasize the breadth of the impact of CI, but this is not to suggest that each stakeholder cares about one thing and one thing only.

    1. Win deals

    Getting a prospective buyer to choose your product or service over a laundry list of alternatives is no easy task. In order to make it happen — and make it happen consistently — sales reps need to be well-versed in the art of communicating differentiated value — i.e., they need to routinely convince prospects that your solution brings something truly unique to the table (assuming that is, in fact, the case).

    That’s impossible in the absence of competitive intelligence. If you know next to nothing about your competitors’ products or services, how are you supposed to convincingly argue that yours is the best option?

    By empowering your reps with the insights they need to consistently win deals, competitive intelligence can help your business succeed.

    2. Improve market positioning

    Let’s take a step back for a moment. Before one of your reps can have the opportunity to seriously engage with a prospective buyer, that prospect needs to feel as if there’s a legitimate reason to evaluate your solution. Unless they’re extraordinarily bored, no prospect is going to give a salesperson the time of day until they see something in your company.

    That something is, in large part, the product of your marketing team. Much like sales reps, marketers are responsible (among other things) for convincing prospects that your solution brings something truly unique to the table. A key difference, of course, is that your marketing team tends to touch a wider range of funnel stages than your sales team does.

    It’s through positioning that marketers — specifically product marketers — establish the differentiated value of your solution. Again, competitive intelligence is essential; when you’re unfamiliar with your competitors, you’re ill-equipped to earn the attention of your prospects.

    By empowering your marketers with the insights they need to improve market positioning, competitive intelligence can help your business succeed.

    3. Optimize product roadmaps

    Up until this point, we’ve been focused on the communication of value — a business function handled primarily by your sales and marketing teams.

    But what about the creation of value? Who’s in charge of making sure that your reps and marketers are telling the truth when they advertise your solution as the best in the industry?

    That would be your product management team — the folks responsible for (1) figuring out what to build and (2) building it. More precisely, product managers are tasked with creating and executing a product roadmap that differentiates your solution from its alternatives — i.e., ensuring that your solution actually brings something unique to the table.

    You already know what I’m going to say, right? Product managers cannot do their jobs properly in the absence of competitive intelligence. When you’re unfamiliar with your competitors, you’re ill-equipped to create something that stands out from the pack.

    By empowering your product managers with the insights they need to optimize your product roadmap, competitive intelligence can help your business succeed.

    4. Retain customers

    When you’re in a competitive market, the acquisition of a new customer is cause for celebration. Between sales, marketing, and — albeit indirectly — product management, it takes a tremendous team effort to create and close a deal in the context of intensifying competition.

    But just because you’ve acquired a customer does not mean you’ve insulated them from your competitors. That you’ve secured a yearlong or even a multi-year contract means little to the other companies in your market — they still want that customer’s business. It may not happen immediately, but you better believe that new customer of yours will be hearing from at least one of your competitors in the not-so-distant future.

    This, of course, is why you have a customer success team — to ensure that your customers continue to be your customers. Among many other things, customer success reps need to be prepared to respond in an effective manner when one of their accounts voices an interest in exploring alternative solutions in the market.

    Competitive intelligence plays a critical role in these situations. CS reps with a strong understanding of your competitive landscape are well-equipped to prove to their accounts that your solution is still the best available option.

    By empowering your CS reps with the insights they need to retain customers, competitive intelligence can help your business succeed.

    5. Inform long-term business strategy

    All the while — as sales reps win deals, marketers improve positioning, product managers optimize roadmaps, and CS reps retain customers — executive leaders work to produce and implement long-term business strategy.

    Note that this responsibility extends far beyond the scope of product strategy (although that is the primary focus for someone in the position of VP Engineering or Chief Product Officer). In addition to helping their direct reports do their jobs as effectively as possible, executive leaders work to answer questions like these:

    • How are we going to continuously grow revenue and market share over the long term?
    • Are there adjacent markets we can break into? If so, what do those markets look like?
    • In what ways are we at risk? What can we do to shield ourselves from those factors?

    The role of competitive intelligence in answering these questions may not be obvious, but it is undeniable. You can’t make a plan to grow market share without understanding who your competitors are and how they’ve positioned themselves in the eyes of prospective buyers. Similarly, you can’t make a plan to break into an adjacent market without understanding who your competitors would be. And finally, you can’t make a plan to mitigate risks without understanding the ways in which your competitors are poised to exploit your weaknesses.

    By empowering your executive leaders with the insights they need to produce and implement long-term strategy, competitive intelligence can help your business succeed.

    Revenue is the ultimate goal of competitive intelligence

    Competitive intelligence is an investment, and just like any other investment, it’s only worthwhile insofar as it yields a return. Through each of the five avenues we’ve explored here today, CI does, in fact, yield a return — and we’ve got the data to back it up.

    According to the latest edition of the State of Competitive Intelligence Report, 61% of businesses have seen revenue growth as a direct result of their investment in CI. Amongst businesses with advanced CI programs — that is, businesses that are fully capitalizing on the potential of CI — that figure jumps to 83%.

    The takeaway is clear: Embrace competitive intelligence and you’ll be rewarded with positive results on the bottom line.

    Author: Conor Bond

    Source: Crayon


  • What Does Being Data-Driven Really Mean? A F1 Racing Analogy

    What Does Being Data-Driven Really Mean? A F1 Racing Analogy

    Did you watch the Hungarian F1 race Sunday July 31st? Max Verstappen qualified 10th on Saturday but won the race. Lewis Hamilton started 7th and ended second. The pole sitter, George Russel ended up third. The two Ferrari drivers, who qualified 2nd and 3rd and favored to perform well, ended up 4th and 6th respectively. Something went array for Ferrari, but what?

    What is Strategy?

    This is yet another story about what strategy really means. On lap 21 the TV camera panned to the corners before the main straight. The two Ferrari drivers of Carlos Sainz and Charles Leclerc were under 1 second apart chasing down the leader, George Russel.  As the camera followed the leading three cars, It looked like the Ferrari’s were about to overtake the leader. Then we heard over the Ferrari radio, ‘box, box, box’. Box means ‘head to the pits’. Just as Ferrari speed and tire management looked like it was paying off and they might take the lead while on the track, someone at Ferrari decided to take the cars out of the battle. What?  Why?

    As the race unfolded things went from bad to worse. One of the Ferrari’s put on hard tires. All the other front runners were on soft or medium tires. A couple of other cars were on hard tires but their race pace was off and they were not competitive. Putting on hard tires didn’t make sense. From there on, the Ferrari lost position and was uncompetitive with the top drivers.

    Post-Race Review

    In the post-race interviews the real problem surfaced. Christian Horner, team principle of the winning Red Bull, gave it away. It seems that on the previous day, in warmer weather, the hard tire did offer higher performance than soft or medium tires. That was what the data said then.

    When Mattia Binotto was interviewed, the Ferrari team principle noted that his cars “didn’t perform as expected”. Ferrari had decided to put on hard tires on race say (Sunday) using data from Saturdays qualifying session. Christian’s critical killer phrase was this: “You only had to look up from the screen.”

    Here is the story in my words. Ferrari were relying too much on data alone to inform or dictate their strategy. Red Bull and Mercedes all used data, but they added a human element. They had looked up from their screens and saw how the hard tires were actually performing in the cooler weather. Thus they discounted the data due to different conditions. Ferrari didn’t. Ferrari assumed the data was reliable, trusted. And so it was. But the data didn’t apply to the actual race conditions of the day!

    What Being Data-Driven Really Means

    Conclusion: to be data-driven does not mean to (only) use data or use data all the time to automate every decision. Some decisions need to be augmented: Machines or humans using data is not always enough. Sometimes you need to look up or away from the data.

    So to be data-driven does not mean to (only) use data. I like to say that to be data-driven really means “to help business leaders ask smarter questions of the business and environment around them”. Ferrari didn’t ask a smarter question. They asked a question from the previous day and got an answer for the previous day. The other teams asked a different, smarter question. And ignored the data and expanded their data set. (Toto Wolff quote)

    The Ferrari drivers, who should have been on the podium at the end of the race, finished in the doldrums.

    Author: Andrew White

    Source: Gartner

  • Why do B2B companies struggle to find customized Market Intelligence?

    Why do B2B companies struggle to find customized Market Intelligence?

    As a company operating in a B2C environment, life is easy.

    More explicitly, acquiring the right market information is easy. Countless reports filled with rich consumer insights are available at your fingertips. These reports, which cover topics like market size, consumer profiles, competitors, and trends, are easily accessible through sales and marketing professionals. With the right approach, available information can also be directly translated into clear insights on a strategic level. In the boardroom, market intelligence serves as a reliable sparring partner, setting the direction for strategic actions.

    Unfortunately, the opposite is the case for B2B companies.

    Their markets can often feel like a massive black box filled with blind spots. Also, the majority of leading market research companies focus on producing market reports for B2C companies, because the required data is significantly more convenient to obtain and more widely available. Besides, B2C companies are more willing to invest in market intelligence reports, due to the better overall quality of the data and insights.

    However, possessing the right intelligence is also vital for B2B players, especially in the fast changing and dynamic business environment they are operating in. Having access to information about market size, competitors, and industry trends can make the difference between staying on top of your league or to be disrupted.

    Existing market reports for B2B companies are difficult to put to direct action, as they are extremely standardized and frequently based on extrapolations of historical figures. Aside from the inaccuracies, these reports, in general, only provide you with insights about the past, whereas trustworthy market intelligence also helps you to be proactive instead of reactive, with respect to the near future.

    Another issue with these reports is the phenomena of ‘information overload’. Decision makers drown in huge market research reports filled with endless pie charts and tables. By the time they reach page 299, any actionable insight is definitely lost, and the reader is left behind frustrated.

    Sounds familiar?

    Luckily, there are several methods through which professionals in a B2B environment can start creating their own customized market intelligence.

    Today’s world offers one enormous advantage; the availability of rich and infinite open-source intelligence: OSINT. Endless bits and pieces of information are available on the open web; hidden in databases, social media content, trade journals and news articles. Connecting all the pieces of the puzzle in a smart way leads to a better understanding of your market.

    Another method of creating tailor-made market intelligence is through (predictive) modelling. Key factor is defining which variables affect the topic you want to clarify. Take for example market sizing. Some variables might be less obvious than others. Illustrative for B2B companies is that they often act as a shackle in the middle of a value chain. Also, business-to-business products and their applications are more multifaceted compared with their business-to-consumer counterparts. It can be necessary to count back from end volumes of a product and combine this with market characteristics to estimate the market size of a specific commodity.

    The illustrations mentioned above are just two plain examples of techniques that can be valuable. Obviously, many more methods and tools are available. The trick is in finding the right combination of methods and tools. As well as in-depth understanding of how to determine validity.

    However, the bottom line remains unchanged: by combining outcomes of different techniques proper market intelligence can be gathered, even in a B2B environment. Aside, it is important to periodically update your data and insights with new figures and trends. Check and double check your data model with industry experts and internal sources.

    By building market intelligence in a systematic and continuous way, insight in your market keeps increasing and the black B2B box can be whitened step-by-step.

    Author: Kees Kuiper

    Source: Hammer Intel

EasyTagCloud v2.8