12 items tagged "actionable insights"

  • Applying Market Intelligence in six steps

    Applying Market Intelligence in six steps

    A marketing intelligence process is like the third-eye which allows you to see all that is happening in the market. It’ll allow you to reach your target audience, learn new business tactics, understand why market leaders succeed and what they do that makes them successful, in addition to identifying trends, even the ones which aren’t so obvious. Here’s how you build a market intelligence process in 6 steps.

    1. Identify your competitors

    This seems like a simple enough step, but all is not as it seems. You likely have a good grasp on who your direct competitors are, but do you know who your indirect competitors are? Your direct competitors sell or market the same products as your business, and while indirect competitors might not do that, they still compete with your business. For example, let’s say your product is an energy drink, then your direct competitors are other organizations that sell energy drinks. However, your customer might just as easily choose a carbonated beverage instead of an energy drink, which makes the organization producing that beverage your indirect competitor. You can identify direct and indirect competitors through some market research, customer feedback, and monitoring online portals. And, of course, you can (and should) use market intelligence to do that. Once you’ve identified both your direct and indirect competitors, you can move on to deciding the metrics you wish to measure.

    2. Choose the metrics you wish to measure

    The metrics an organization chooses to measure depend on their goals, and the strategies they deploy to achieve that goal. Organizations generally fall in two categories - ones that are brand-focused, and others that are performance-focused. Brand-focused organizations give more weightage to the aspects of their brand, their category, and their competitors. Performance-focused organizations, on the other hand, give more weightage to demand generation, and their sales efforts. Naturally, it is these respective metrics that they should focus on to gain a competitive advantage.

    Brand-focused organizations should measure and pay attention to metrics such as brand advocacy, affinity, appeal, association, awareness, loyalty, perception, personality, reputation, recall, preference, strength, sentiment, salience, trust, usage and of course, competitors’ performance & tactics. Pay special attention to the kind of content your audience likes. They should use market intelligence to continuously collect information pertaining to these metrics, and deliver them to their marketing teams in the form of daily alerts and weekly or monthly reports.

    Performance-focused organizations should measure and pay attention to critical sales metrics such as their competitors’ annual recurring revenue, sales budget, average revenue per user, win rates, conversion rates, acquisition channels, sales tactics, and the like. A market intelligence process that allows your sales team to constantly be aware of these metrics should be put in place. Integrating your sales enablement tool to your market intelligence system is a great way to streamline things in this case.

    3. Understand how to use market intelligence effectively

    In 2021, almost every business uses market intelligence in some form or another. From a small company that does basic or unstructured research using the internet on their target market and competitors, to huge enterprises that pay millions of dollars for data on their competitors and the markets. Neither of these organizations is using market intelligence effectively. In fact, 50% of organizations don’t know how to use M&CI properly in decision-making. When an organization creates a market intelligence process, there are 3 things they should look out for to ascertain its ROI.

    - Data costs

    - Labor costs

    - Cost of poor decisions

    Now, the company that does basic research has no data cost, as surfing the internet costs nothing. Little to no labor costs are incurred, as there’s no team of analysts decoding data that is fetched. However, the cost of poor decisions is probably immense, which is why this company is still a small company even after being in the market for a long time.

    On the other hand, the enterprise-sized organization is paying through their noses for data, labor costs to analyze that data are probably high too, as the organization likely has teams of analysts for this specific job. However, their cost of poor decisions is really low, which explains why they are an enterprise-sized business. They do, however, hemorrhage money in labor and data which could be saved with a more effective MI process.

    A balanced approach would be to use a market intelligence software, which will incur moderate data costs, incur moderate labor costs as a modest amount of analysis is required, while saving you from the cost of poor decisions entirely.

    4. Perform a market and competitive analysis

    The next step would be to perform a market and competitive analysis. Using the insights gleaned from your MI process, design a market and competitive analysis that can be shared with your organization’s stakeholders for easy interpretation. Bear these things in mind when doing so:

    - Provide a context
    Not everyone in your organization may be used to understanding how numbers and visual representations in the analysis work. Next to every statistic in the analysis, provide some context about what these insights mean for the organization, whether good or bad. Adding a benchmark to measure statistics would be a good idea too.

    - Provide recommended actions
    Statistics in themselves are no good if you or your stakeholders don’t know what to do with them. Every statistic is either an opportunity or a threat that must be taken advantage of or dealt with. Describe a plan of action as to what should be the appropriate response to every statistic you put in your analysis.

    - Provide Proof
    Although your stakeholders are not going to doubt the information you put in the analysis, it is always better to furnish them with specific resources for better understanding. Also, they might have to explain it to a customer, client, or another stakeholder in the future, so an attached resource to any statistic or a methodology on how you reached a conclusion is a must.

    - Keep it short
    The stakeholders in your organization, particularly the leadership, are busy people who have a schedule to stick to. Lengthy analyses that take hours to comprehend will waste their valuable time, and more likely not be paid adequate attention to. So skip the granular details, and provide information that can be quickly consumed and understood.

    Keeping these things in mind when designing a market and competitive analysis will ensure your organization makes the most of it.

    5. Deliver market intelligence throughout the organization

    To ensure that market intelligence is utilized effectively throughout the organization, certain things need to be taken into consideration.

    - It gets to the right stakeholders

    - It gets to them in a timely manner

    - It is easy to understand

    Doing all of this requires figuring out an appropriate delivery process. Doing this manually is labor-intensive and prone to faults, even if you use a CMS. Markets are highly-dynamic, and the number of insights you get each day, each week and each month can be overwhelming. Then there’s the question of turning them into daily insights as well as weekly, monthly, and/or quarterly reports for the stakeholders to understand the trends better. Finally, you need to send them to the right stakeholders. Not difficult, but laborious.

    6. Transform insights into action

    The goal of market intelligence is for a business to be able to make smart and strategic decisions with the information it provides. This generally means more sales, better products or services, a larger market share, more customers, more brand awareness within the target audience, in addition to other business objectives the organization might have. For this to happen, intelligence, strategy and action need to have a direct link, in order to be defined as a process. Organizations need to establish this link on their own, as market intelligence is just one piece of the puzzle. The process should ideally look like this:

    - The market intelligence process provides insights

    - Those insights are given a context by your market intelligence team, if you have one, or by the stakeholders themselves in case you don’t

    - The information is translated into specific business questions, that need to be answered with strategies

    - Strategies should be formulated after determining the best course of action in the present and future market landscape

    - These strategies should be communicated to everyone involved in their execution

    - Actions should be taken based on these strategies

    If you follow this process from insight to action accurately, the results will speak for themselves.

    Conclusion

    Today’s world is data-driven, and organizations that use a market intelligence process are able to take full advantage of it. Similarly, an inefficient market intelligence process, or worse no process at all, can quickly become a burden on an organization. The market intelligence process described above will hopefully give you some ideas on how to set up a similar system for your own organization, and enable you to be more competitive. Another option that can prove be invaluable, is hiring an external Market Intelligence team specialized in the ins and outs of the MI process.

    Author: Malay Mehrotra

    Source: Contify

  • Gaining competitive intelligence through the websites of your competitors  

    Gaining competitive intelligence through the websites of your competitors

    According to Crayon's 2021 State of Competitive Intelligence Report, 99% of survey respondents found value in monitoring their competitors' website changes. Between messaging and team changes, pricing and product updates, and myriad other insights, your competitors' websites are gold mines of actionable intel. Specifically, competitor website analysis enables you to:

    • Better understand your target market
    • Identify problems you might have otherwise missed
    • See what your audience responds well to—and what they don’t
    • Fine-tune your company's branding
    • Identify new opportunities and potential market gaps

    In this blog, we will explore the process and questions you should ask to identify website changes, discover the why behind your rivals’ updates, and utilize those insights to better position your company's solution.

    Identify the breadth & depth of updates

    The first step to a successful website analysis is to identify what has changed. Take a look at the copy, design, and audience segment to better understand the breadth and what areas need analyzing. For example, say your competitor in the food retail industry changes its homepage headline from “Your Favorite Local Grocery Store” to “Your Favorite Grocery Store Now Shipping Worldwide.” This is a clear shift in direction for the company, showing its efforts to expand globally and reach a new customer base. Recognizing key messaging changes like this one can help you better understand your company’s positioning in comparison to competitors.

    Understanding the depth of these changes is critical as well. A couple of minor edits are likely nothing to sweat over, but a complete website overhaul is a tell-tale sign that a branding pivot is in play. After you’ve noted exactly what changed and its scope, you can start analyzing to more deeply understand your competitor.

    Now, let’s dive into some specific examples of things to look out for.

    Research job changes

    When jobs are added to or removed from a website, this could be an indicator that the company is growing their team, has granted an employee a promotion, has terminated an employee, or is expanding into new verticals or product lines. These changes shed light on what particular department the company is focusing on—and how extensive their budget is. 

    For some insight on hiring, check out team pages, office/contact pages, and general career pages as they can be very revealing. 

    More specifically, determining any management changes via the leadership page is one of the most important places to gather valuable insight of all. Often, a change in command on the executive team means some updates are afoot, and it can provide intel into where resources are being allocated. For example, a new head of product marketing almost always guarantees a refresh to messaging and positioning. Likewise, a new head of sales or product often puts their stamp or spin on the business’s core offering, translating to site updates.

    After investigating, if there was an addition to the leadership team, determine where they came from—LinkedIn is a helpful tool for discovering this. Did they hire someone from B2B or B2C? Do they have mostly Fortune 500 experience? Are they an industry expert? This background can help you predict positioning changes your rival might take based upon their new leaders’ experience.

    Pinpoint product or feature upgrades

    Being fluent in your competitors’ solutions and features is key, especially for product marketers and sales teams. Below is a specific list of elements to keep in mind when scoping out their offerings:

    • Product name changes 
    • Supporting image or screenshot updates 
    • Specific features or benefits listed 
    • Pricing (if available) 
    • Videos and demos 
    • Free trial or freemium offering (SaaS only)

    Step one is checking the company’s homepage. Ask yourself—what are they featuring? Do they display all their products equally, or are they pushing one solution in particular? The homepage can teach you what they find most important in their services at that particular moment.

    Next, navigate over to the press/media section to determine any recent announcements such as new products, features, or partnerships. This page can be an indicator of how big of a splash they are trying to make and how important this change is to the organization.

    The third step is (you named it!) diving into the product and offerings page. This is where you can explore this checklist in more detail and evaluate the entire scope of services.

    If you notice a product name change, for example, take a look at what part of it changed. While it may seem minute at a glance, it can be pretty telling of what the company wants to emphasize. Did they add more actionability to the name to create a sense of urgency? Was an adjective descriptor added, signifying there was confusion previously that needed clarification? Are they incorporating more marketing buzzwords? Answering these questions will help you understand where their strengths and weaknesses lie when it comes to conveying their product offering to their target audience.

    When scanning the images and videos on that page, take note of how the product is displayed differently. Do the images and videos show more details or fewer now? Did they incorporate more customer case studies or partner with a company on a video? If screenshots of the actual product are available, be sure to share them with your product team.

    Detail modifications can signal product changes as well. Was the website previously feature driven as opposed to benefit focused? Are they listing specific use cases now that weren’t previously shown? This subtly communicates that the product marketing team felt that there was an ambiguity that needed clearing up.

    Analyze messaging & positioning modifications

    Be sure to take a holistic approach when reviewing the site, noting all product marketing messaging updates. While these changes may sometimes be harder to spot, use the following checklist as a guide to where these edits may have manifested:

    • Audience
      • Has the primary audience changed? 
      • Are they speaking to multiple industries/verticals or new ones?
      • Does their messaging target buyers and decision-makers or end-users?
      • Who might their ICP be and has it changed?
    • Tone
      • Has the tone changed to be more formal, casual, or bold? 
      • Are they speaking in benefits, use cases, or features?
      • What are their calls-to-action?

    Once these questions are answered, you will have gathered great intelinto how the company is trying to position itself and who exactly they are trying to appeal to. Messaging is truly the key to all brands’ identities and missions.

    Utilize your new insight 

    With all the insight you’ve gained, compare your company's website performance and pages to your competitors’, noting all strengths and weaknesses. Use the competitive analysis as motivation to improve your site, add new features or details, and deliver a better overall user experience.

    For those who enjoy gathering analytics and technical data, your research may have even sparked ideas on how to improve your company’s UX Design, keyword optimization, and more. No matter which way you cut it, this insight is another tool in your competitive intelligence toolbox to help your business succeed.

    Always be in the know

    Positioning and promoting your company's solution without considering the competition can only get you so far. By staying in tune with your competitors’ site changes, you will have a much deeper understanding of their priorities, targets, and overall direction. Not only does this insight help your company better position itself in the market, but it allows you to deduce your competitors’ next moves—keeping you one step ahead.

    Take your time when scrolling through your rivals’ sites, remember to bring your newfound competitive intelligence back to your team, and be sure to have this guide open to assist you along the way—it’s here to lend a helping hand! 

    Author: Mackenzie Colcord

    Source: Crayon

  • How to generate relationship intelligence and use it to your advantage

    How to generate relationship intelligence and use it to your advantage

    There’s more to prospect contact data than phone numbers, job titles, and company pain points.

    In your CRM and other communication tools you can find valuable information, known as relationship intelligence, that goes beyond the surface level. 

    Let’s say a current customer forwards one of your product emails to several procurement officers — this could indicate a change in their spending budget. 

    But what if I’m already using sales, lead, or market intelligence? Do I really need to add another type of intelligence to my data strategy?

    Relationship intelligence broadens your outreach potential by connecting the dots that are laid out by other types of intelligence. Using your CRM system (and supplemental data from a provider), you can find new opportunities close to those you’re already working with.

    What is relationship intelligence data?

    Relationship intelligence is a type of data that’s stored in CRM databases, and it’s used to gain new insights on current and potential customers. Data points in relationship intelligence come from company interactions, or in other words, customer-facing communications.

    Companies can use relationship intelligence to append records within their existing database and clean inaccurate data — and possibly create a new organizational chart.

    Relationship intelligence vs. Customer intelligence

    Relationship intelligence creates family tree-like branches between professionals, and at each end are bits of customer intelligence.

    Customer intelligence is customer-centric, while relationship intelligence is connection-centric: 

    Customer intelligence:

    • Phone numbers & email addresses
    • Reporting structure
    • Job titles
    • Social media handles

    Relationship intelligence:

    • Outreach campaign targets
    • Number of support tickets submitted
    • Amount of renewal cycles
    • Social media traffic

    Benefits of relationship intelligence data

    By adding on to existing data about sales, leads, and customers, relationship intelligence helps sales reps and marketers to achieve the following:

    • Reduce the amount of prospect research.
    • Find the right prospects for a deal close.
    • Gain deeper knowledge of their prospects.
    • Personalize their campaign messages and pitches.
    • Reach new buyers before competitors.
    • Improve relationships with current customers.

    Looking at the list above, it’s no wonder that 77% of B2B sales and marketing professionals believe personalized experiences make for better customer relationships. 

    Having a one-size-fits-all approach to using intelligence in sales and marketing efforts can drastically worsen their success, so understanding relationships is an important advantage.

    Tools to build relationship intelligence

    Relationship intelligence tools help fill in the gaps in contact databases and act on new intelligence gained.

    Consider these solutions to build relationship intelligence:

    Customer relationship management (CRM)

    As a staple to any many organizations, a CRM system may have untapped potential. To dig up relationship intelligence, companies can unify customer data storage, integrate with other applications used for customer interactions, and import third-party data.

    Data provider or data collector

    In-house data collection is ideal for saving resources, but is time-consuming if it’s your sole data source. Time spent on data collection and organization takes away from important sales and marketing-oriented tasks.

    If you have room in your budget, data providers can add to your existing database.

    Data visualizer

    Looking over lines and lines of data can put anyone to sleep. And it makes it difficult to create a full, 360-degree view of your leads and customers if you can’t see it. Data visualization tools take your data and create graphs and charts, that let people digest information more easily.

    Email automation

    Your email inbox is where a majority of communications occur. There is so much information to bank on in your emails, such as job titles, phone numbers, events, and company names. 

    When your email system is synced with your CRM, this valuable data can be easily captured and stored for future customer engagement.

    Next steps in building better relationships with intelligence

    Sales and marketing teams can leverage relationship intelligence from professional interactions, such as emails and account management activity. This can improve sales and marketing efforts by going beyond basic contact information.

    Take that valuable information, and put it into your next outreach strategy for more sales opportunities.

    Author: Rayana Barnes

    Source: Zoominfo

  • How to use data science to get the most useful insights out of your data

    How to use data science to get the most useful insights out of your data

    Big data has been touted as the answer to many of the questions and problems businesses have encountered for years. Granular touch-points should simplify making predictions, solving problems, and anticipating the big picture down the road. The theory behind data science is a law of large numbers; similar to quantum physics, when we try to predict or analyze data lakes to draw a conclusion, it can only be a probability. Data cannot simply be read, it’s like a code that needs to be cracked.

    There’s an incredible amount of insight that can be gleaned from this type of information, including using consumer data to better inform their strategies and bottom lines. But the number of businesses that are actually implementing actionable steps from their data is minimal. So, how can companies ensure that they’re effectively managing the data they’re collecting in order to improve business practices?

    Identify what you’re looking to learn

    Too many companies invest heavily in software and people in a quest for big data and analytics without truly defining the problems that they’re looking to solve. Business leaders expect to instantly throw a wide net over all datasets, but they won’t necessarily get something useful in return.

    Take, for example, a doctor that spent over a year and a half implementing a new system that was supposed to give his colleagues meaningful medical insights.

    After collecting the data without truly defining the problem they wanted to solve, they ended up with the following insight: “Those who have had cancer have had a cancer test.” This, obviously, is a true statement culled from the data. The problem is it’s useless information.

    The theory behind data science was never meant for small data sets, and scaling to do so comes with a host of issues and irregularities; however, more data doesn’t necessarily mean better insights. Knowing what questions to ask is as important for a company as having the best tools for thorough data analysis.

    Prepare your data to be functional

    They say practice makes perfect, but with data science, practice makes permanent if you’re doing it the wrong way.

    The systems that companies use to keep track of data don’t have a lot of validation. Once you start diving into big data for insights, you realize there’s a whole layer of “sanitization” and transformation that needs to happen before you can start running reports and gleaning useful information.

    We’ve seen major companies doing data migration, but with an accuracy rate of 53%. Imagine if you went to the doctor mentioned in the previous section and he admitted his recommendations were only 53% correct. We can make a big bet you’re not going to that doctor anymore.

    To get quality data, you have to understand what quality data looks like. The human element and the machine have to work together; there needs to be an actionable balance. Data sources are constantly in flux, grabbing from new inputs from the outside world, ensuring a useful level of quality on the data coming in is critical or you’ll get questionable results.

    Depend on a reliable tech solution

    Once you have a clear path of checks and balances to ensure you’re on the right track, establishing a minimum viable product — potentially with a more efficient outsourced team — is what will truly drive actionable results. It makes sure the assumptions and projections derived from the insights are continually up to date, and looks from different angles to anticipate major trend changes.

    It’s important to see the big picture, but also be able to change a model’s behavior if it’s not delivering the most valuable insights. Whatever solution you settle on might not necessarily be the most sophisticated, but as long as it’s providing the answers to the right questions, it will be more impactful than something complex and obscure.

    When companies employ tools to untangle their stores of data without having a deep understanding of the limitations of data science, they risk making decisions based on faulty predictions, resulting in detriment to their organization. That means higher costs, incorrect success metrics and errors across marketing initiatives.

    Data science is still evolving very quickly. Although we will never get to the point that we can predict everything accurately, we will get a better understanding of problems to provide even more useful insights from data.

    Author: Luming Wang

    Source: Insidebigdata

  • More and more organizations are basing their actions on their data

    More and more organizations are basing their actions on their data

    Many corporations collect data but don't end up using it to inform business decisions. This has started to shift.

    All in all, 2020 will go down as one of the most challenging and impactful years in history. It will also be known as one of the most transformative, with companies and individuals adjusting quickly to the new normal in both work and play, with a 'socially distant' way of life changing how people interact and communicate.

    ven amidst the chaos, we saw an influx of existing technologies finding new industry opportunities, such as videoconferencing tools, streaming platforms such as Netflix, telehealth applications, EdTech platforms, and cybersecurity, to name a few. All of these technologies are powered by one fundamental thing, yet this entity isn't being tapped to its full potential by SMBs and enterprises alike.

    That thing is data, collected by companies with the intent to inform business decisions and better understand and serve their customers. However, from what I have seen, more than 80 percent of data that businesses generate goes unused. This will drastically change in the next three years, with the majority of the data consumed being put to use.

    What's driving this trend

    Data generation was already a hot topic prior to the COVID-19 pandemic with a projected 59 zettabytes (ZB) of data created, captured, and copied over the last year according to IDC. This trend has only accelerated with the pandemic as companies are fast-tracking digital transformation initiatives. Adding to this, the ongoing health crisis is resulting in the avoidance of face-to-face interactions during the workday, causing digital interactions to increase tenfold. This has created even more data through connectivity tools and applications.

    Companies have realized that analyzing this data can help leaders make better-informed decisions rather than relying on gut feeling. Data has become so important to companies' success that according to Gartner, by 2022, 90 percent of today's corporate strategies will unequivocally list information as a critical enterprise asset and analytics as an essential competency. Leading organizations know that in order to drive success in their industry, they have to leverage data and analytics as a competitive differentiator, fueling operational efficiencies and innovation.

    Setting up for success

    Though the majority of data collected by businesses currently goes to waste, there are more tools emerging to help companies unify consumed data, automate insights, and apply machine learning to better leverage data to meet business goals.

    First, it's important to take a step back to evaluate the purpose and end goals here. Collecting data for the sake of having it won't get anyone very far. Companies need to identify the issues or opportunities associated with the data collection. In other words, they need to know what they're going to do with every single piece of data collected.

    To determine the end goals, start by analyzing and accessing different types of data collected to determine if it was beneficial to the desired outcome or has the potential to be but wasn't leveraged. This will help identify any holes where other data should be tracked. This will also help hone the focus on the more important data sets to integrate and normalize, ultimately making data analysis a more painless process that produces more usable information.

    Next, make sure the data is useful - that it's standardized, integrated across as few tech platforms as possible (i.e., not a different platform for every department or every function), and that the collection of specific data follows company rules and industry regulations.

    Finally, use data in new ways. Once your organization has integrated data and technology solutions, the most meaningful insights can often only be found using multidimensional analytics dashboards that take data from two previously siloed functions to understand how pulling a lever in one area affects costs or efficiencies in another.

    Using data to streamline business processes and lower costs

    One industry that's collecting data and using it efficiently to optimize business processes is the telematics industry. Before the digital transformation era, fleet managers and drivers had to rely on paper forms for vehicle inspections or logging hours of service. Now, many telematics-driven companies are relying on connected operations solutions to collect, unify, and analyze data for a variety of tasks such as improving fuel management, driver safety, optimized routing, systematic compliance, and preventive maintenance.

    We have seen fleets with hundreds of assets switch from other out-of-the box telematics solutions, to a more business-focused solution, which allows them to leverage data insights from their connected operations and realize meaningful improvements and costs savings. One such client recently reported saving $800,000 annually in field labor costs, an annual savings of $475,000 in fleet maintenance and repairs, and they've seen compliance with their overdue maintenance reduction initiative go from around 60 percent to 97 percent. It's clear that data contains the answers to an organization's challenges or goals. The question remains whether the organization has the tools to unearth the insights hidden in its data.

    Empowering decision makers through data

    The most important piece to the entire data chain is ensuring the right data insights get into the hands of decision makers at the right time. What use is accurate, analyzed data if it goes unused - as most of today's data does? Including the right stakeholders from across all business functions in the data conversations may unearth current challenges, as well as new opportunities that may have not otherwise been known. This is a step that many companies are now recognizing as crucial for success, which is why we will see more data consumed and put to use over the next three years.

    If they haven't already, executives and decision-makers at all levels should start looking at business operations through a data-centric lens. Companies that recognize and act on the fact that their competitive edge and profit growth lies in the insights hidden in their operational data can expect to see immediate ROI on their efforts to mine their data for golden insights. If they're not doing something about this now, they might just be in a race to the bottom.

    Author: Ryan Wilkinson

    Source: TDWI

  • The key to implementing analytics successfully into your business

    The key to implementing analytics successfully into your business

    Today’s analytics are so powerful and accessible that they can drive value-based decision-making at all levels of the organization. No matter your industry, applications for analytics are available for any department—marketing, HR, operations, and others—enabling every team member to access and leverage insights they understand.

    Most senior leaders believe achieving these results means transformative changes, not only to enterprise technologies but also to the fundamental ways their employees do their jobs. This simply is not the case. Real-world evidence supports the often-counterintuitive idea that new analytics technologies must adapt to employees’ existing (good) habits when it comes to decision-making, rather than employees changing their habits to accommodate unfamiliar tools.

    This approach does not reduce the complexity of both analytics implementation and user adoption. After all, “advanced analytics can drive value only if employees use them to make decisions,” as McKinsey describes: “[But] The more familiar and intuitive a model is, the more likely it is to gain acceptance... integrating the analytics into core processes can make new systems feel like a natural extension of existing ones, rather than an abrupt change.”

    Here we take a closer look at how analytics can align with existing, effective workflows, thereby helping employees in their decision-making roles rather than forcing them to change. We will explore the key elements that make up a successful modern analytics environment as well.

    Modern Analytics Must “Fit In” With Existing Best Practices

    recent Korn Ferry report identifies one of the greatest workforce challenges of 2021: “needed capabilities are changing faster than it’s possible for organizations to shift their workforces.” That’s why leading companies are attempting to bring facts-based decisions into all levels of the organization. They are “democratizing” access to advanced analytics as part of a “business decision supply chain.”

    The business decision supply chain can be defined generally as a sequence of processes by which raw data is transformed into trusted, actionable insights that everyday business users can leverage for each of their own unique purposes. In the right environment, these insights not only provide the best value for each employee’s decision-making but are also easy for that employee to access and understand—no heavy technical training required. The result is twofold:

    1. Employees are happier—they are better at their jobs with less guessing and manual work.
    2. Results per employee are better across the enterprise, driving business value and resiliency.

    Making employees happy seems like a small goal for enterprise technology, but it’s actually critical to securing consistent use of analytics tools throughout the organization. The most powerful analytics are useless if users don’t feel the need or desire to use them.

    The Three Key Elements to Successful Analytics

    Good employees don’t live in a vacuum, either. They are aware of the business challenges surrounding their organizations and are most often open to some degree of change. With that in mind, we’ve identified three key elements that contribute to a successful, data-driven decision-making environment: in the subject areas of peopleprocesses, and technology. If you’re early in analytics adoption, consider how to approach these three factors as you begin.

    1. Help People Decide and Adapt to Market Changes

    In practice, individual teams and the initiatives with which they are most familiar shape analytics success for organizations. Leading companies are working to instill a profound sense of trust in analytics capabilities among their employees as a result.

    But business leaders must proactively understand the needs of those specific teams before deciding which data resources are right for them. They should maintain the essential goal of helping those employees in their decision-making—rather than forcing change alongside new technology adoption—as they move forward.

    Korn Ferry highlights an example of this change in their report, namely the changing role of a hypothetical salesperson. With fewer in-person visits to clients, her focus has shifted to become more data-focused: Today, she builds compelling proposals for clients using analytics. But while the salesperson’s focus has shifted, her responsibilities and KPIs have not fundamentally changed. She simply has more resources at her disposal, even if the broader business climate has reduced her opportunities to engage clients face-to-face.

    2. When It Comes to Processes, Integrate and Improve

    In practice, integrating analytics is about keeping “what works” for employees—processes they prefer and that drive results. For example, marketers can transition away from subjective decision-making and begin thinking strategically based on a wide variety of data-driven insights. Their responsibilities have not changed, but the ways in which they drive value are more accurate, and they are more confident.

    McKinsey cites another example—that of a mining company where dispatchers and operators were subjected to an alarm they most often ignored. That’s because the alarm did not fit into their existing workflow and therefore served no practical purpose. It was by “embedding the alert mechanism directly into the [particular] monitoring system” that those workers found it useful as the alert coordinated with their preferred processes.

    3. Make Sure Your Analytics Technology Has “Legs to Stand On”

    As you consider analytics platforms, remember this: All the best capabilities in the world won’t matter if employees don’t use the tools. How employees access and use analytics are therefore foundational to adoption and ultimately, ROI. Analytics environments that lend themselves to this level of adoption are critical as a result.

    Realize Business Value, and Employee Success

    New technologies always involve learning requirements. But the fundamental advantages of existing processes and goals that employees have come to value must be enhanced, not eliminated. Data leaders must consider both user and business needs and then assign data resources and self-service capabilities to employees with those in mind. That’s how the business decision supply chain becomes truly successful, transforming data into real business value.

    Author: Omri Kohl

    Source: Pyramid Analytics

  • The struggle of B2B companies to find customized Market Intelligence

    The struggle of B2B companies to find customized Market Intelligence  

    As a company operating in a B2C environment, life is easy. More explicitly, acquiring the right market information is a rather direct process. 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 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 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, B2B products and their applications are more multifaceted compared with their B2C 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 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 doing this

    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: Egbert Philips

    Source: Hammer Market Intelligence

  • The transformation of raw data into actionable insights in 5 steps

    The transformation of raw data into actionable insights in 5 steps

    We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways organizations tackle the challenges of this new world to help their companies and their customers thrive.

    In a world of proliferating data, every company is becoming a data company. The route to future success is increasingly dependent on effectively gathering, managing, and analyzing your data to reveal insights that you’ll use to make smarter decisions. Doing this will require rethinking how you handle data, learn from it, and how data fits in your digital transformation.

    Simplifying digital transformation

    The growing amount and increasingly varied sources of data that every organization generates make digital transformation a daunting prospect. But it doesn’t need to be. At Sisense, we’re dedicated to making this complex task simple, putting power in the hands of the builders of business data and strategy, and providing insights for everyone. The launch of the Google Sheets analytics template illustrates this.

    Understanding how data becomes insights

    A big barrier to analytics success has been that typically only experts in the data field (data engineers, scientists, analysts and developers) understood this complex topic. As access to and use of data has now expanded to business team members and others, it’s more important than ever that everyone can appreciate what happens to data as it goes through the BI and analytics process. 

    Your definitive guide to data and analytics processes

    The following guide shows how raw data becomes actionable insights in 5 steps. It will navigate you through every consideration you might need to make about what BI and analytics capabilities you need, and every step of the way that leads to potentially game-changing decisions for you and your company.

    1. Generating and storing data in its raw state

    Every organization generates and gathers data, both internally and from external sources. The data takes many formats and covers all areas of the organization’s business (sales, marketing, payroll, production, logistics, etc.) External data sources include partners, customers, potential leads, etc. 

    Traditionally all this data was stored on-premises, in servers, using databases that many of us will be familiar with, such as SAP, Microsoft Excel, Oracle, Microsoft SQL Server, IBM DB2, PostgreSQL, MySQL, Teradata.

    However, cloud computing has grown rapidly because it offers more flexible, agile, and cost-effective storage solutions. The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3, Hadoop and Microsoft Azure.

    An effective, modern BI and analytics platform must be capable of working with all of these means of storing and generating data.

    2. Extract, Transform, and Load: Prepare data, create staging environment and transform data, ready for analytics

    For data to be properly accessed and analyzed, it must be taken from raw storage databases and in some cases transformed. In all cases the data will eventually be loaded into a different place, so it can be managed, and organized, using a package such as Sisense for Cloud Data Teams. Using data pipelines and data integration between data storage tools, engineers perform ETL (Extract, transform and load). They extract the data from its sources, transform it into a uniform format that enables it all to be integrated. Then they load it into the repository they have prepared for their databases.

    In the age of the Cloud, the most effective repositories are cloud-based storage solutions likeAmazon RedShift,Google BigQuery, Snowflake, Amazon S3, Hadoop, Microsoft Azure. These huge, powerful repositories have the flexibility to scale storage capabilities on demand with no need for extra hardware, making them more agile and cost-effective, as well as less labor-intensive than on-premises solutions. They hold structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video).  Sisense provides instant access to your cloud data warehouses.

    3. Data modeling: Create relationships between data. Connect tables

    Once the data is stored, data engineers can pull from the data warehouse or data lake to create tables and objects that are organized in more easily accessible and usable ways. They create relationships between data and connect tables, modeling data in a way that sets relationships, which will later be translated into query paths for joins, when a dashboard designer initiates a query in the front end. Then, users, in this case, BI and business analysts, can examine it, create relationships between data, connect and compare different tables and develop analytics from the data.

    The combination of a powerful storage repository and a powerful BI and analytics platform enables such analysts to transform live Big Data from cloud data warehouses into interactive dashboards in minutes. They use an array of tools to help achieve this.Dimension tables include information that can be sliced and diced as required for customer analysis ( date, location, name, etc.). Fact tables include transactional information, which we aggregate. TheSisense ElastiCube enables analysts to mashup any data from anywhere. The result: highly effective data modeling that maps out all the different places that a software or application stores information, and works out how these sources of data will fit together, flow into one another and interact.

    After this, the process follows one of two paths:

    4. Building dashboards and widgets

    Now,developers pick up the baton and they create dashboards so that business users can easily visualize data and discover insights specific to their needs. They also build actionable analytics apps, thereby integrating data insights into workflows bytaking data-driven actions through analytic apps. And they define exploration layers, using an enhanced gallery of relationships between widgets.

    Advanced tools that help deliver insights include universal knowledge graphs and augmented analytics that use machine learning (ML)/artificial intelligence (AI) techniques to automate data preparation, insight discovery, and sharing. These drive automatic recommendations arising from data analysis and predictive analytics respectively. Natural language querying puts the power of analytics in the hands of even untechnical users by enabling them to ask questions of their datasets without needing code, and to tailor visualizations to their own needs.

    5. Embed analytics into customers’ products and services

    Extending analytics capabilities even further, developers can create applications that they embed directly into customers’ products and services, so that they become instantly actionable. This means that at the end of the BI and analytics process, when you have extracted insights, you can immediately apply what you’ve learned in real time at the point of insight, without needing to leave your analytics platform and use alternative tools. As a result, you can create value for your clients by enabling data-driven decision-making and self-service analysis. 

    With a package like Sisense for Product Teams, product teams can build and scale custom actionable analytic apps and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage.

    Author: Adam Murray

    Source: Sisense

  • Toucan Toco breidt team in Amsterdam uit met Tim Bosman en Elisabet Queralto Garzon

    Toucan Toco breidt team in Amsterdam uit met Tim Bosman en Elisabet Queralto Garzon

    Toucan Toco, specialist in data storytelling, breidt het team in Amsterdam uit met Tim Bosman en Elisabet Queralto Garzon. Bosman richt zich in zijn nieuwe rol voor Toucan Toco op Business Development. Queralto Garzon vervult de positie van Project Manager. Met de uitbreiding van het team kan Toucan Toco concreet invulling geven aan het verwezenlijken van de groei-ambities op de Nederlandse markt.

    Met data storytelling wil Toucan Toco niet alleen managers, maar ook medewerkers voorzien van inzichten waarmee zij gefundeerde beslissingen kunnen nemen. Het van origine Franse bedrijf opende ruim een jaar geleden een eigen kantoor in Amsterdam. Onlangs werd Yann Toutant aangesteld als Country Manager voor het neerzetten van een stevige structuur waarmee het bedrijf een goede uitgangspositie heeft om sterke groei te realiseren. De uitbreiding van het team met Tim Bosman en Elisabet Queralto Garzon is hiervoor een eerste stap.

    Tim Bosman is een ervaren commercieel manager met een achtergrond in finance, vastgoed en business management. Met zijn kennis van zowel verkoop als management en een sterke focus op bedrijfsbeheer voegt hij waardevolle expertise toe aan het Nederlandse team. “Om succesvol te kunnen zijn zou data breed toegankelijk moeten zijn en niet alleen voorbehouden blijven aan een kleine groep mensen”, zegt Tim Bosman, business development manager bij Toucan Toco. “Ik vind het een mooie uitdaging om de expertise en toegevoegde waarde van Toucan Toco naar Nederlandse organisaties te brengen zodat iedereen kan profiteren van sterke inzichten en op basis daarvan daar betere beslissingen kan nemen.”

    Elisabet Queralto Garzon heeft de nodige ervaring opgedaan als project manager op zowel de internationale als de Nederlandse markt, onder meer bij Effectory. Daar begeleidde ze onder andere een project bij Ikea, waarbij ze verantwoordelijk was voor de succesvolle implementatie van HR software in meer dan 50 landen. Ze zal deze ervaring nu inzetten om de Nederlandse klanten van Toucan Toco te ondersteunen. 

    “De oplossing van Toucan Toco is ontzettend slim en tegelijkertijd zo eenvoudig als het gebruiken van een app op je smartphone”, zegt Elisabet Queralto Garzon, project manager en customer success manager bij Toucan Toco. “Ik zie er naar uit om klanten te helpen het volledige potentieel van de oplossing optimaal te gaan gebruiken.” 

    12 miljoen euro groeikapitaal 

    Toucan Toco is opgericht in 2014 heeft inmiddels kantoren in de Benelux, Frankrijk, Spanje, Italië en Amerika. Onder de ruim 100 klanten die gebruikmaken van de oplossingen van Toucan Toco bevinden zich onder meer Engie, Heineken, Sodexo en Renault Nissan. In november 2019 ontving het bedrijf in een eerste investeringsronde 12 miljoen euro groeikapitaal. Met het geld wil Toucan Toco onder meer de aanwezigheid in de Benelux-markt uitbreiden en de ambitie voor marktleiderschap verwezenlijken.

    Auteur: Yann Toutant 

    Bron: Toucan Toco

  • Why AI requires market reseachers to generate actionable insights

    Why AI requires market reseachers to generate actionable insights

    Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses.

    Artificial intelligence combines information from a variety of different sources, analyzes the material instantly, and acts on the insights derived from those data. In effect, artificial intelligence is designed by humans with intentionality and reaches conclusions based on its instant analysis.

    The future for artificial intelligence is bright. Aside from automating repetitive tasks and the near future of driverless vehicles, TechTalks blog names several industries that are being positively affected by the deployment of artificial intelligence today. Some of these are listed below:

    • Education
    • Healthcare
    • Human resources
    • Marketing
    • Supply chain management
    • Customer service and experience
    • Logistics
    • Cybersecurity

    At its core, artificial intelligence is, according to an introduction from an MIT artificial intelligence course, "Making computational models of human behavior." Since we believe that humans are intelligent, therefore models of intelligent behavior must be artificial intelligence. It is true that artificial intelligence applications make fewer errors than humans.

    Therein lies a fundamental challenge to the efficient use of artificial intelligence going forward. There are not enough people who can make other businesses understand the vision of machine powered progress in the world. In other words, there are not enough people who know how to operate machines which think and learn by themselves. More worrisome, there are fewer people who can interpret the artificial intelligence output to make actionable use of the outcomes. More ominously, developing an efficient artificial intelligence system is currently too difficult to achieve, in practice.

    Who then, is in a position to make artificial intelligence more actionable? To become the bridge between the algorithms and the impact?

    The answer lies with the emerging field of data or analytics translators. As defined by Google, “A data translator is a conduit between data scientists and executive decision-makers.” They are specifically skilled at understanding the business needs of an organization and are data savvy enough to be able to talk tech and distil it to others in the organization in an easy-to-understand manner. In an article for Forbes, Bernard Marr writes, “Forget Data Scientists And Hire A Data Translator Instead.”

    Marketing research professionals are uniquely poised to fill in this gap. Like the merging of qualitative research with quantitative analysis, the new data/analytics translation is a mixture of traditional marketing research skills and the continuing expansion of bandwidth. Artificial intelligence algorithms are expanding exponentially. This creates a tsunami of consumer, machine learning, and social media data. So many tools, so much data. One message is coming through clear as a bell: clients and C-suite executives want to hear the story.

    Artificial Intelligence, Big Data, and the Problem with Insights

    Predictive analytics is a cousin to marketing research. The former targets endeavors such as investments, commercial and security applications of advanced analytics, including text mining, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, sector timing, and fraud detection.

    Predictive analytics and marketing research are two distinct fields. Artificial intelligence is, by definition, a subset of predictive analytics. However, both industries employ data-scientists. Marketing research firms regularly mine corporate databases in order to write up conclusions. I myself have done so for Burger King, Pfizer, the Ohio State University Medical Center, the Cheesecake Factory, REI Adventures (a large adventure travel company) as well as voter targeting for a presidential campaign.

    Below I summarize the project path these endeavors take.

    Artificial intelligence professionals calculate results to maximize model efficiency—the Data and Information side of the chart. They are not equipped to present detailed yet summated reports.

    Marketing research reporting skills, combined with sophisticated analytical firepower, position marketing research professionals to interpret for the C-Suite that deluge of data. This takes into account the right side of the project path as well—that of Knowledge and Information. It also opens the door for a researcher to grow into the role of strategic consultant, now commonly referred to as an analytics or data translator.

    Open Source Power: The R-Project

    For readers who are not familiar with open source statistical software, R is a free-of-charge programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. While there is a steep learning curve with R, marketing research professionals can certainly learn to use it.

    In the past, one had to purchase expensive SAS licenses or many SPSS modules to achieve the firepower that is now available for free on the internet. Below is a list of a few of the thousands of open-source modules contained in the R-Project. My list includes some of the most well-known and commonly practiced algorithms used in artificial intelligence:

    • Bayesian Inference
    • CHAID Trees
    • Feature Selection Regression
    • General Linear Models
    • Logistic Regression
    • Machine Decision List Functions
    • Neural Networks

    There are many, many more.

    These days any marketing research firm can partner with a data-scientist, and thereby offer to its clients not only a research report, but the capability to mine corporate databases with a sophistication provided by artificial intelligence companies. Each of the above-mentioned algorithms can be utilized in day-to-day marketing research. Training for these additional skills is also open-sourced. Dozens of free, short, online courses on, say, Coursera can train any experienced marketing researcher how to analyze and distill analytic output.

    Bringing Analytics Translation into the Mainstream

    Thus far, we have demonstrated that the tools and bandwidth for marketing researchers to perform artificial intelligence or database mining are readily available today, and will be increasingly so over time.

    So: how might the marketing research industry best exploit this new frontier?

    Marketing researchers are experts at constructing questionnaires and summarizing results. Artificial intelligence people, by contrast, are not. While they can certainly calculate the most efficient marketing mix or product placement, when it comes to summarizing findings and presenting them to a CMO, marketing researchers win hands-down.

    Why? Because marketing researchers are trained in inductive reasoning, a crucial component of a project report—the Knowledge and Wisdom side of the graphic. This is the C-Suite deliverable. We are natural data translators.

    Summing Up

    We can indeed do it all. We take the data, process it with high-power, open-source software; we summarize the results and provide our clients with the ability to leverage strategic thinking. We fuse sophisticated artificial intelligence capabilities with marketing research story-telling prowess to offer cogent and compelling conclusions. We are data translators, the “must-have” role for the future.

    Despite its increasing sophistication, artificial intelligence will always require human eyes.

    Author: Michael Lieberman

    Source: Market Research Blog

  • Why CI should cover the entire competitive landscape, not just the players

    Why CI should cover the entire competitive landscape, not just the players

    In April 2010, Mars, one of the world’s largest privately owned businesses, embarked on a breakthrough initiative. For the next year, Jessica Eliasi, then the director of Competitive Intelligence at Mars Chocolate, travelled the world running 'competitive simulation' games with local market teams from Russia to Mexico to Turkey to England.

    These simulations were not some computer-based hypothetical games. They were intense, intelligence-based, role-playing immersion workshops that got leaders to see the market from a different and unfamiliar perspectives.

    Such games have become more popular among leading edge corporations. But Jessica’s approach was still unique. While large consulting firms push expensive 'war games' at the leadership level, Jessica ran cheap and quick local games based on local market dynamics. She then fed the results as market intelligence input into a senior leadership competitive game. The workshops brought the 'voice of the markets' to Mars’ leadership’s doorstep.

    By connecting the dots across a series of markets, brands and competitors, Jessica identified the key global insights that provided both risks and opportunities for the global firm.  She brought her on-the-ground experiences to life through a 'game' that was played with the business unit’s top management team, pressure-testing some closely held beliefs. The insights and the workshops have since influenced how Mars assesses risks and opportunities and develops strategy.

    This is just one example of how Mars is trying to create and sustaining agility through competitive intelligence (CI).

    Simply and clearly put, CI is a perspective on changing market conditions. This means identifying risks and opportunities early enough to allow the company to adapt its strategy or in extreme cases, change it. That simple definition forcefully delineates it from all other information, data, and research services. Information alone is not a perspective on change, information does not automatically lead to insight. Yet the vast majority of companies and executives confuse these two to the detriment of their performance.

    The popular literature is filled with definitions and images of competitive intelligence taken from the realm of the government and the military. These cause more damage to the discipline than if management was simply ignorant. They focus the discipline on competitors ('the enemy' in military parlance) instead of the market as a whole, the entire competitive arena. They talk about intelligence 'collection', as if more searches are the essence of perspective. In recent years with the big data craze, collecting digital data has replaced strategic intelligence. Many companies either waste millions on massive databases or research projects that don’t yield useful insight, or throw the first available junior marketing or information specialist at the job and push it down to tactical product level, missing out on the true value of competitive intelligence as a purveyor of strategic change.

    Used properly, CI leads to greater strategic agility: the ability to adapt to changing market circumstances. To become more agile, start by rethinking your competitive intelligence process. That means having a clear definition of scope and role, as well as following a few simple steps such as mandating intelligence reviews at critical decision stages, ensuring the CI analyst has direct access to and input into strategic meetings and reviews, and smartly tapping an informal internal community of practice.

    The essence of the competitive intelligence perspective is the view of the competitive set as a whole. Consider the example of Pratt and Whitney, a United Technology company. The commercial engine division, under the leadership of Stephen Heath (since retired) and Todd Kallman spent two intensive days in 2006 'war gaming' P&W’s strategy as its two bigger rivals, GE and Rolls Royce, divided the market between them. Looking at the market dynamic between Airbus, Boeing, GE, and Rolls Royce led to P&W deploying a breakthrough strategy for their new Geared Turbofan (GTF) engine. Looking at each competitor separately would have made it so much harder to translate the collected information into actionable insights.

    If you want your company to become more agile, start by rethinking the design of your intelligence process. Focus on building a strategic early warning capability so you don’t miss the big picture.

    Author: Benjamin Gilad

    Source: Harvard Business Review

  • Why competitive benchmarking is key to successful competitive intelligence

    Why competitive benchmarking is key to successful competitive intelligence

    Most businesses know their own strategy through and through, and recently, more companies than ever before are investing in competitive intelligence. The real secret to success is combining those two knowledge sources – monitoring your own strategy alongside that of your competitors. This brings us to a concept called competitive benchmarking, which is critical to competitive analysis success. Keep reading to learn how to benchmark your company against your competition for a successful competitive analysis. 

    What is competitive benchmarking? 

    Let’s start with the basics. Chances are, you have transparency across your organization, so you have insight into what product is working on, which campaigns marketing is launching, and how your revenue is performing. As you conduct a competitive analysis, you’re gathering similar information on your competition. You’re gathering product information, revenue information, marketing campaign information, and more about your competitors.

    Now, you need to know how you compare to your competitors. Where do you stand within the market? This is called competitive benchmarking – comparing your company performance against your competition to measure how you stack up. You can then identify gaps, similarities, and adjust your strategy accordingly. Let’s dive into how you can gather your competitive intelligence data and benchmark your own company alongside your competition. 

    Monitor your competitive landscape 

    It’s great to know every move your competitors make, if possible, even before they make a move. But we’re not psychics, right? So how are we going to know what our competitors are going to do before they make a move? You need to pick up on smaller signals, or breadcrumbs, to gain insight into what their next major move may be. 

    In order to best analyze your competitive landscape, you should look for relative changes in investment. It’s common practice that companies will ramp up certain strategies such as content, social media, or campaigns when they have a big announcement coming up. Are they posting a lot of content around a specific product feature? Are they targeting a certain persona more than another? Which positions are they recruiting for? Discovering the answers to these questions, and similar questions are great tactics for gaining competitive insight. These small competitive insights can give you insight into your competitor’s overall strategy. Based on where your competitors are investing, you can make predictions as to where they’re heading next. 

    In addition to monitoring what your competitors are doing, it’s important to monitor what your competitors’ customers are saying about the solutions they use. There’s a lot we can learn from our competitors’ customers, including what they like and dislike about the product they’re using.

    Take note of the positives and negatives highlighted in the customer feedback. This information can be compared to what your customers are saying about your own company. Where are you struggling where your competitors are excelling? You can use the positive feedback from your competitors’ customers to learn, and you can use the negative reviews to better align yourself with the needs of your market. This information is valuable for all teams within your company. Not only is it beneficial to product managers, because they can develop your product accordingly, but it’s beneficial for account executives and marketers alike. 

    Leveraging third-party reviews is a great way to gain insight into your competitors’ strengths and weaknesses. This piece of your analysis can help you measure where you need to improve and where you lead the charge within your industry. 

    Break into untapped opportunities 

    Now that you’ve monitored your competitive landscape and gathered competitive insights, you need to insert your own company into the analysis. Measure your company performance in the same way you measure your competition. That way, you can see where you crush the competition, and identify where there is room for you to break into untapped opportunities. If your competitor is excelling in a specific area, it may be harder to knock them out of their top spot right away. However, filling the gaps in the market will help you gain the competitive edge you’re looking for. 

    The great part about tracking yourself alongside your competitors is that you can identify where gaps are in the market. If you can find the gaps within your market, you can break into that space and serve an unmet need. This also gives you the opportunity to lead the charge in new areas within your market. This is a great way to grab the leads and opportunities that your competitors are missing out on. You’re now offering your target audience something of value that they’re unable to get from your competitors, giving you a unique competitive advantage. 

    Make sure your competitive analysis is actionable 

    Conducting competitive intelligence research is time-consuming, especially when you’re also putting together a formal competitive analysis. To get the most out of your efforts, you want to ensure that you’re turning insights into action. 

    The important thing to remember about competitive intelligence is that it’s not only about what your competitors are doing; it’s about how you compare to your competitors, and how you can turn your insights into action to best serve your market. The key to a successful competitive analysis is benchmarking yourself alongside your competitors, and identifying the gaps in your market to showcase your solution and position yourself as a market leader. 

    Author: Emily Dumas

    Source: Crayon

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