10 items tagged "data collection"

  • 5 best practices on collecting competitive intelligence data

    5 best practices on collecting competitive intelligence data

    Competitive intelligence data collection is a challenge. In fact, according to our survey of more than 1,000 CI professionals, it’s the toughest part of the job. On average, it takes up one-third of all time spent on the CI process (the other two parts of the process being analysis and activation).

    A consistent stream of sound competitive data—i.e., data that’s up-to-date, reliable, and actionable—is foundational to your long-term success in a crowded market. In the absence of sound data, your CI program will not only prove ineffective—it may even prove detrimental.

    By the time you’re done reading, you’ll have an answer to each of the following:

    • Why is gathering competitive intelligence difficult?
    • What needs to be done before gathering competitive intelligence?
    • How can you gather competitive intelligence successfully?

    Let’s begin!

    Why is gathering competitive intelligence difficult?

    It’s worth taking a minute to consider why gathering intel is the biggest roadblock encountered by CI pros today. At the risk of oversimplifying, we’ll quickly discuss two explanations (which are closely related to one another): bandwidth and volume.


    CI headcount is growing with each passing year, but roughly 30% of teams consist of two or fewer dedicated professionals. 7% of teams consist of half a person—meaning a single employee spends some of their time on CI—and another 6% of businesses have no CI headcount at all.

    When the responsibility of gathering intel falls on the shoulders of just one or two people—who may very well have full-time jobs on top of CI—data collection is going to prove difficult. For now, bandwidth limitations help to explain why the initial part of the CI process poses such a significant challenge.


    With the modern internet age has come an explosion in competitive data. Businesses’ digital footprints are far bigger than they were just a few years ago; there’s never been more opportunity for competitive research and analysis.

    Although this is an unambiguously good thing—case in point: it’s opened the door for democratized, software-driven competitive intelligence—there’s no denying that the sheer volume of intel makes it difficult to gather everything you need. And, obviously, the challenges of ballooning data are going to be compounded by the challenges of limited bandwidth.

    Key steps before gathering competitive intelligence

    Admittedly, referring to the collection of intel as the initial part of the CI process is slightly misleading. Before you dedicate hours of your time to visiting competitors’ websites, scrutinizing online reviews, reviewing sales calls, and the like, it’s imperative that you establish priorities.

    What do you and your stakeholders hope to achieve as a result of your efforts? Who are your competitors, and which ones are more or less important? What kinds of data do you want to collect, and which ones are more or less important?

    Nailing down answers to these questions—and others like them—is a critical prerequisite to gathering competitive intelligence.

    Setting goals with your CI stakeholders

    The competitors you track and the types of intel you gather will be determined, in part, by the specific CI goals towards which you and your stakeholders are working.

    Although it’s true that, at the end of the day, practically everyone is working towards a healthier bottom line and greater market share, different stakeholders have different ways of contributing to those common objectives. It follows, then, that different stakeholders have different needs from a competitive intelligence perspective.

    Generally speaking:

    • Sales reps want to win competitive deals.
    • Marketers want to create differentiated positioning.
    • Product managers want to create differentiated roadmaps.
    • Customer support reps want to improve retention against competitors.
    • Executive leaders want to mitigate risk and build long-term competitive advantage.

    Depending on the size of your organization and the maturity of your CI program, it may not be possible to serve each stakeholder to the same extent simultaneously. Before you gather any intel, you’ll need to determine which stakeholders and goals you’ll be focusing on.

    Segmenting & prioritizing your competitors

    With a clear sense of your immediate goals, it’s time to segment your competitive landscape and figure out which competitors are most important for the time being.

    Segmenting your competitive landscape is the two-part job of (1) identifying your competitors and (2) assigning each one to a category. The method you use to segment your competitive landscape is entirely up to you. There’s a number of popular options to choose from, and they can even be layered on top of one another. They include:

    • Direct vs. indirect vs. perceived vs. aspirational competitors
    • Sales competitiveness tiers
    • Company growth stage tiers

    We’ll stick with the first option for now. Whereas a direct competitor is one with which you go head-to-head for sales, an indirect competitor is one that sells a similar product to a different market or a tangential product to the same market. And whereas a perceived competitor is one that—unbeknownst to prospects—offers something completely different from you, an aspirational competitor is one that you admire for the work they’re doing in a related field.

    Once you’ve categorized your competitors, consider your immediate goals and ask yourself, “Given what we’re trying to do here, which competitors require the most attention?” The number of competitors you prioritize largely depends on the breadth of your competitive landscape.

    Identifying & prioritizing types of intel

    One final thing before we discuss best practices for gathering intel: You need to determine the specific types of intel that are required to help your stakeholders achieve their goals.

    To put it plainly, the types of intel you need to help sales reps win deals are not necessarily the same types of intel you need to help product managers create differentiated roadmaps. Will there be overlap across stakeholders? Almost certainly. But whereas a sales rep may want two sentences about a specific competitor’s pricing model, a product manager may want a more general perspective on the use cases that are and are not being addressed by other players in the market. In terms of gathering intel, these two situations demand two different approaches.

    It’s also important to recognize the trial-and-error component of this process; it’ll take time to get into a groove with each of your stakeholders. Hopefully, their ongoing feedback will enable you to do a better and better job of collecting the data they need. The more communicative everyone is, the more quickly you’ll get to a place where competitive intelligence is regularly making an impact across the organization.

    5 best practices for gathering competitive intelligence

    Now that we’ve covered all our bases, the rest of today’s guide is dedicated to exploring five best practices for gathering competitive intelligence in a successful, repeatable manner.

    1. Monitor changes to your competitors’ websites

    [According to the State of CI Report, 99% of CI professionals consider their competitors’ websites to be valuable sources of intel. 35% say they’re extremely valuable.]

    You can make extraordinary discoveries by simply monitoring changes on your competitors’ websites. Edits to homepage copy can indicate a change in marketing strategy (e.g., doubling down on a certain audience). Edits to careers page copy can indicate a change in product strategy (e.g., looking for experts in a certain type of engineering). Edits to customer logos can indicate opportunities for your sales team (e.g., when a competitor appears to have lost a valuable account).

    The examples are virtually endless. No matter which specific stakeholders and goals you’re focused on, frequenting your competitors’ websites is a time-tested tactic for gathering intel.

    2. Conduct win/loss analysis

    [According to the State of CI Report, 96% of CI professionals consider win/loss analysis to be a valuable source of intel. 38% say it’s extremely valuable.]

    Although win/loss analysis—the process of determining why deals are won or lost—is a discipline in its own right, it’s often a gold mine of competitive intelligence. The most effective method of collecting win/loss data is interviewing customers (to find out why they bought your solution) and prospects (to find out why they didn’t buy your solution). You’ll find that these conversations naturally yield competitive insights—a customer mentions that your solution is superior in this respect, a prospect mentions that your solution is inferior in that respect, etc.

    Through the aggregation and analysis of your customers’ and prospects’ feedback, you’ll be able to capitalize on some tremendously valuable intel.

    3. Embrace internal knowledge

    [According to the State of CI Report, 99% of CI professionals consider internal knowledge to be a valuable source of intel. 52% say it’s extremely valuable.]

    This may seem counterintuitive, but it’s true: Your stakeholders themselves are amazing sources of competitive intelligence. In fact, as you read above, more than half of CI pros say internal knowledge (a.k.a. field intelligence) is an extremely valuable resource. 

    Sales reps are often speaking with prospects, and marketers, customer support reps, and product managers are often speaking with customers. Across these conversations with external folks, your colleagues learn about your competitors in all kinds of useful ways—product features, pricing models, roadmap priorities, sales tactics, and so on.

    Some of the best ways to gather internal knowledge include listening to calls with prospects and customers, reviewing emails and chat messages, and combing through CRM notes.

    4. Find out what your competitors’ customers are saying

    [According to the State of CI Report, 94% of CI professionals consider their competitors’ customers’ reviews to be valuable sources of intel. 24% say they’re extremely valuable.]

    If you found yourself wondering how one might fill in the gaps between pieces of internal knowledge, look no further: By reading reviews written by your competitors’ customers, you can uncover tons of previously unknown intel.

    And if your initial instinct is to head straight for the scathing reviews, make no mistake—there’s just as much to learn from your competitors’ happy customers as there is from their unhappy customers. Let’s say, for example, that nearly every single positive review for one of your competitors makes mention of a specific feature. This is a critical piece of intel; as long as you’re lacking in this area, your rival will boast a concrete point of differentiation.

    5. Keep your eye on the news

    [According to the State of CI Report, 96% of CI professionals consider news to be a valuable source of intel. 38% say it’s extremely valuable.]

    Product launches, strategic partnerships, industry awards—there’s no shortage of occasions that may land your competitors in the news. Typically, media coverage is the result of a press release and/or other public relations tactics, but that may not always be the case. (In certain industries, media coverage is very common—whether it’s solicited or not.)

    Regardless of why a competitor is in the news, it’s almost always an opportunity to gather intel. In the case of a product or feature launch, you can learn about the positioning they’re trying to establish. In the case of a partnership, you can learn about the kinds of prospects they’re trying to connect with. And in the case of an award, you can learn about the ways in which they’re trying to present themselves to prospects.

    Author: Conor Bond

    Source: Crayon

  • 5 types of market data to collect when expanding your business

    5 types of market data to collect when expanding your business

    When planning to expand a business to another area globally or locally, you need a lot of information to make this move as secure and smart as possible. There is no point in making a significant strategic move if you do not have enough information about the market demands. But what kind of information will you need for this? Here is a short checklist you can go through.

    1. First and foremost, you have to know more about your potential customers: what they need and desire. You need to know more about how they buy and where they buy. Of course, it is essential also to know more about their culture and habits. Find out what their values are and what motivates and makes them tick. How do they live their everyday lives? Focus on understanding, what are the challenges they face and how your products or services could help them to solve these problems or needs.
    2. You should also be familiar with your target country’s legislation and regulations. This way you’ll be prepared and can avoid potentially costly mistakes. In addition, take into account the customs and habits of your customers in your marketing and advertising.
    3. If you are going to set up let’s say an office, a restaurant, or a hotel in another country, you need to know what the economic and educational situation of the target country is. What is the employment rate, and is there qualified staff available to hire? Is there enough purchasing power and demand for your products and services in the country?
    4. Logistical information is also highly important. You need to know more about the geographical facts that can affect logistics and infrastructure. If you plan to build a new production plant, how to get power for your facilities, machines and production? How will transportation take place? Also, you should pay attention to the political climate, because it can affect your business in a multitude of ways.
    5. You should also be aware of the competitive environment. Focus on finding out who are the biggest rivals in your field and what they are doing in the same business area. This will tell you how you should position yourself in the markets? Is there a gap in the market that your company’s product or service could fill?

    It is market intelligence that you need when planning to expand your business to other locations or business areas. You need to focus on external factors that could have an impact on your plans and business opportunities. As you can see from the checklist above, a lot of information is needed in order to turn it into useful market insights.

    Author: Joakim Nyberg

    Source: M-Brain

  • Data storytelling: 5 best practices

    Data storytelling: 5 best practices

    Learn how to hone both your verbal and written communication skills to make your insights memorable and encourage decision-makers to revisit your research.

    You’ve spent months collecting data with your insights team or research vendors, and you’ve compiled your research into a presentation that you think is going to blow your audience away. But what happens after you’ve finished presenting? Do your stakeholders act on the insights you’ve shared, or do they move on to their next meeting and quickly forget your key takeaways and recommendations?

    If you want to avoid the latter, it’s important to consider how you can make the biggest possible impact while presenting and also encourage your stakeholders to revisit your research after the fact. And that requires you to hone both your verbal and written communication skills.

    In other words: practice your storytelling.

    Research shows that combining statistics with storytelling results in a retention rate of 65-70%. So, how do you take advantage of this fact when presenting and documenting your insights?

    Below are five best practices to help you present insights through stories – and encourage your stakeholders to revisit those stories as they make business decisions.

    Tailor the message to your audience

    To maximize the impact of your story, you have to consider who’s hearing it.

    When you’re presenting to someone in finance, try to cover how your findings can help the company save money. When you’re talking to Marketing or Sales, explain how the information can drive new leads and close more deals. When you’re talking to the product development team, explain how they can deliver a better solution.

    The more you can address your audience’s concerns in the language they use and the context they understand, the bigger the impact your story will have.

    Ask yourself:

    1. How much does my audience already know about the subject I’m covering?
    2. How much time do they have to listen to what I’m saying?
    3. What are their primary concerns?
    4. What type of language do they use to communicate?
    5. Are there preconceptions I need to address?

    If your insights are applicable to multiple groups across the organization, it’s worth thinking about how you can tweak the story for each audience. This could mean writing different sets of key takeaways and implications for different groups or altering the examples you use to better align with each audience’s interests.

    Follow the structure of a story

    While stories come in various shapes, sizes, tones, and genres, they all have a few things in common – one of those being a similar structure.

    Think about how a movie is typically divided into three acts. Those acts follow this general structure:

    1. Setup: We’re introduced to the protagonist, and they experience some kind of inciting incident (i.e., the introduction of conflict or tension) that propels the story forward.
    2. Confrontation: The protagonist works to achieve a goal but encounters obstacles along the way.
    3. Resolution: The protagonist reaches the height of their conflict with an antagonist and achieves some kind of outcome (whether it’s the protagonist’s desired outcome or not will depend on the type of story).

    Here’s a (fictional) example of an insights-driven story that follows this structure:

    1. The insights team for a beverage company shares a recorded interview with a real customer, who we’ll call Raquel. Raquel talks about how she loves getting together for backyard barbecues with friends. She says that she used to always drink beer at these barbecues but has recently decided to stop drinking.
    2. Raquel goes on to say that she doesn’t really like soda because she thinks it’s too sweet, but she will often pick one up at barbecues because she wants to have a drink in her hand.
    3. After playing this interview, the insights team presents findings from their latest study into young women’s non-alcoholic beverage preferences. They use Raquel’s story to emphasize trends they are seeing for canned beverages with lower sugar or sweetener contents.

    By framing your data and reports in this narrative structure, you’re more likely to keep your audience interested, make your findings memorable, and emphasize how your findings relate to real customers or consumers. This is a great way to get business decision-makers to invest in and act on your insights.

    Put your editor’s hat on

    When you have managed or been directly involved with a research project, it can be tempting to include every fascinating detail in your presentation. However, if you throw extraneous information into your data story, you’ll quickly lose your audience. It’s important to put yourself in the mindset of your audience and ruthlessly edit your story down to its essential ingredients.

    According to Cinny Little, Principal Analyst at Forrester Research, you should focus on answering the audience’s two primary questions: “What’s in it for me?” and “Why do I need to care?”

    You should also keep your editor’s hat on when documenting your key recommendations or takeaways for a report. Studies show that people can only hold about four items in their conscious mind, or working memory, at any one time. If you include more than three or four recommendations, your audience will have a harder time retaining the most important information.

    Find your hook

    When presenting, don’t think you can start slow and build up excitement – research suggests you only have about 30 to 60 seconds to capture your audience’s attention. After that, you’ve lost them.

    And getting them back won’t be easy.

    That’s why you need a hook – a way to start your story that’s so engaging and compelling your audience can’t help but listen.

    According to Matthew Luhn, a writer, story consultant, and speaker who has experience working with Pixar, The Simpsons, and more, a compelling hook is at least one of the following:

    • Unusual
    • Unexpected
    • Action-filled
    • Driven by conflict

    When sharing your research, you could hook your audience by leading with a finding that goes against prevailing assumptions, or a specific example of a customer struggling with a problem that your product could solve. Find a hook that evokes a strong emotion so that your story will stick with listeners and drive them to make decisions.

    Experiment with your story medium

    If you present your research to a room (or Zoom meeting) full of stakeholders once and then move on, you’re limiting the reach, lifespan, and value of that research. At a time when so many teams have become decentralized and remote work is common, it’s more important than ever to preserve your data stories and make them accessible to your stakeholders on demand.

    At the most basic level, this could mean uploading your presentation decks to an insights management platform so that your stakeholders and team members can look them up whenever they want. However, it’s also worth thinking about other mediums you can translate your stories into. For example, you might publish infographics, video clips from customer interviews, or animated data visualizations alongside your reports. Think about the supporting materials you can include to bring the story to life for anyone who wasn’t in the room for the initial presentation.


    ​​By applying the best practices above, you can take the data and reports that others often find dry (no matter how much you disagree) and turn them into compelling, engaging, and persuasive stories.

    This process of developing and distributing insights stories will enable you and your team to have a more strategic impact on your company as a whole by demonstrating the potential outcomes of making decisions based on research.

    Author: Madeline Jacobson

    Source: Greenbook

  • 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

  • No Question Research as a solution to common data collection issues

    No Question Research as a solution to common data collection issues

    Research projects are challenging enough, without having to handle issues with respondent fraud. 'No Question Research' utilizes different data sources and provides a solution to handling false responses found in data collection.

    About two years ago, Tony Costella from Heineken posted a compelling piece on the GreenBook Blog, ‘Everybody Lies'. Intriguing and at the same time a clear call to action for all of us, researchers at agency or client-side, to address the fact that people lie, unintentionally or not. We trust people’s claims about their feelings or behavior, whilst they are often clueless. Tony’s message: we should build a toolbox with new approaches and methods, and add behavioral measures to our way of working.

    ‘No Question Research’ – passive and automated data collection; behavioral, transactional or social media data, etc. – provides an answer to many of these challenges and is booming in the past decade. This becomes clear when we look at the revenue spend in the industry. Deep diving into the Global Market Research Report 2019, Ray Poynter has pointed out that ‘No Question Research’ represents already half (39 US$ billion) of the revenue spend in 2018, a steady increase over time (+44% growth vs 2014) and the sole contributor to the growth of our industry.

    ‘No Question Research’ is on the rise and is there to stay. It’s based on pure data streams or observations and in one way or another it is directly derived from real consumer behavior, giving us a vast amount of (passively collected) data. Whether it’s word of mouth, click behavior, shop behavioral measures through sensors, sharing moments in the consumers’ life; letting go frustration in Instagram stories…, and that makes it so valuable. No questions are asked.

    With the speed technology is evolving, more data streams will become available and will be used to tap into people’s behavior in order to learn, understand & predict better. And with this, it’s likely that non-question research and analytics (AI & predictive modeling) will take a more prominent place in the blend of sources that will be used in the data and insights industry.

    Digitizing how people behave & buy

    As an example, look at the online retail industry. There is a consensus about the fact that a unique and engaging customer retail experience is the way brick-and-mortar stores can add value to the consumer journey. Although data analytics have become the norm to uncover the online customer journey, the practice is still in its infancy in the offline world. In the digital space, e-commerce companies use big data and artificial intelligence to predict and influence online customer behavior without the customer even being aware. In the digital space, something as seemingly trivial as changing the color or location of the 'buy' button on a website has a direct impact on sales. Enabling companies to set clear goals and KPI’s on every single element of the digital shop journey. Online, consumer behavior is monitored closely and tracked continuously resulting in a data lake, enabling the creation of algorithms to predict consumer behavior, set clear KPI’s and conduct A/B testing. No questions asked.

    Compared to the digital space, brick-and-mortar retail stores live in the analytical dark ages and rarely consider customer behavior data and metrics, other than sales. So why does physical retail not follow suit? The truth is very few retailers know exactly how the shopper experience in their stores really works and what makes their customers tick.

    Why? Because at best, companies conduct shopper studies to question people about their shop experience, observe them and try to understand how to create impact. But it’s simply impossible for people to tell you exactly how and why they behaved in a certain way. Data collected through sensors is giving an objective answer to that; non-disputable clear objective behavioral data streams.

    At IIeX Europe, I will showcase how sensor data (GDPR compliant) will give insights possible store layout optimizations and merchandise productivity, based on real consumer behavior. On top of this, the technology is used to measure and understand store marketing performances, even labor management, and proactive loss prevention, or sales and traffic predictions through artificial intelligence.

    No Question Research, the holy grail?

    Of course, ‘No Question Research’ is just one of the many elements in the mix. But for sure, there will be a clear elimination of waste (eg nonrelevant researches, questions which people can not answer) and focus on the right blend of methods, tools and data streams. In fact, the deep understanding of the ‘why’ will still be covered by qualitative (question) market research and will alongside the rise of ‘No Question Research’ grow, to make the vast amount of data insightful & relevant.

    Author: Wim Hamaekers

    Source: Greenbook Blog

  • Overcoming data challenges in the financial services sector  

    Overcoming data challenges in the financial services sector

    Importance of the financial services sector

    Financial services industry plays a significant role in global economic growth and development. The sector contributes to the creation of amore efficient flow management of savings and investments and enhance risk management of financial transaction activities for products and services. Institutions such as commercial and investment banks, insurance companies, non-banking financial companies, credit and loan companies, brokerage firms, trust companies offer a wide range of financial services and distribute them in the marketplace. Some of the most common financial services are credits, loans, insurances and leases, distributed directly by insurance companies and banks, or indirectly via agents and brokers.

    Limitations and challenges in data availability

    Due to the important role of financial services in the global economy, it is expected that the financial services market is professional and highly developed, also in terms of data availability. Specifically, a well-designed database is expected to be available, where a wide range of information is presented and can be collected regarding the certain industries. However, reality does not meet these expectations.

    Through assessments of various financial service markets, it has been observed that data collection is a challenging process. Several causes contribute to this situation. Lack of data availability or poor data availability, data opacity, consolidated information from market or annual reports, as well as different categorization schemes of financial services are some of the most significant barriers. Differences in the legal framework among countries have a major impact on the entry and categorization of data. A representative example which applies in this case, is the different classification schemes and categorization of financial services across countries. Specifically, EU countries are obligated to publish data of financial service lines under certain classification scheme and pre-defined classes, which in many cases, differs from the classification schemes or classes of non-EU countries, contributing to an unclear, inaccurate overview of the market. The identification and understanding of each classification scheme are necessary to avoid double counting and overlapped data. In addition, public institutions often publish data, revealing part of the market and not presenting the actual market sizes. Lastly, it has also been observed that some financial services have different definition across countries, which influences the complexity of the data collection and assessment of the financial services market.

    Need for a predictive model

    In order to overcome the challenges of data inconsistency and poor, limited or non-existent data availability and to create an accurate estimation of the financial services market, it is necessary to develop a predictive model which analyzes a wide range of indicators. A characteristic example is the estimation of the global financial services market conducted by The World Bank. An analysis model, based on both derived and measured data information, was created, to address limited data inputs challenges.

    An analysis model for the assessment of the financial services markets, created by Hammer, takes into consideration both, collection of qualitative and quantitative data from several sources as wells as predictive indicators. In previous assessment of the certain financial services markets, data information was collected by publications, articles, reports from public financial services research institutions, country’s financial services associations and association groups and private financial services companies. Field’s experts opinion also constituted a significant source of information. The model included regression and principal component analysis, where derived data were produced based on certain macroeconomic factors (such as country population, GDP, GDP per sector, unemployment rate), trade indicators, economic and political factors.  

    The selection of the indicators and analysis model depends on the type of the financial service product and relative market that we want to assess. In addition, based on model analysis, it is possible to identify and validate correlations between a set of predictive indicators that have been considered as potential key drivers of the specific markets. To conclude with, it is possible to identify the sizes of the financial services markets, with the support of an advanced predictive analysis model which can enable and enhance comparability and consistency of data across different markets and countries.

    Author: Vasiliki Kamilaraki

    Source: Hammer, Market Intelligence

  • Questionnaire design: garbage in, garbage out

    Questionnaire design: garbage in, garbage out

    One of the most useful ways to collect data when conducting market research is via the use of HUMINT (Human Intelligence). Data can be collected via in-depth qualitative interviews or large scale quantitative surveys. In this blog, the focus is on the latter. Quantitative surveys are a great method to gather representative information about a specific target group.

    In order to obtain valuable input for analysis you need a well-designed questionnaire. If your questionnaire isn’t well designed, you will have trouble getting valuable results out of it: ‘garbage in, garbage out’. This blog will provide some guidance on how to prevent garbage input (and thus garbage output) when designing a questionnaire for a quantitative survey.

    ‍Research purpose and KPI’s

    When you plan to conduct a quantitative survey, you need a clear view on what you want to achieve after collecting and analyzing the results: the purpose of your survey. Ask yourself the following questions during the questionnaire design:

    • What is the intended use of the insights resulting from the survey? What should the survey results clarify to make a substantiated decision?

    The answer to these questions will not result into survey questions yet, but rather into measurable Key Performance Indicators (KPI’s), e.g.: market share, brand awareness, buying criteria, satisfaction etc. Also, keep in mind that if you want to show the final results in a data visualization tool (e.g. PowerBI, Tableau) that the KPI’s are easy to visualize and to understand for the user.

    ‍Creating key questions

    Once you have a clear purpose to conduct a survey and awareness which KPI’s you need to measure, it is time to create the key questions. These questions provide a result for the sample of respondents. While creating questions there will probably pop up all kinds of secondary questions. It is vital to clearly distinguish the key questions measuring your KPI’s from the ‘nice to know’ questions to position the key questions in such a way that they can be answered unbiased. From those ‘nice to know’ questions, only keep those questions that really add value.

    A longer questionnaire leads to distracted, less motivated respondents.

    ‍Respondent profile

    A key aspect of analysis of a quantitative survey is segmentation. When you analyze the results of the survey, it is beneficial to segment your sample to identify significant differences between types of respondents. This is why your questionnaire should start with questions that create a respondent profile. Obvious questions that come to mind are demographics like age and gender, but it is important not to miss out on any indicator where a segmentation of the sample could be useful.

    If we would for example conduct a survey among 100 CEO’s of European companies, it could be useful to segment the CEO’s by personal indicators like how long they have been in the position of CEO, how long they have worked at that particular company, what other jobs they have had etc. It could also be useful to segment the companies they work for on indicators like company size, the sector they are active in, whether they operate B2B or B2C etc.

    ‍Crystal clear questions

    One thing that should be present throughout the entire questionnaire is clarity for the respondent. Be sure that it is 100% clear what you mean to the respondent, every single question. You can achieve this by providing context with text, images or video for any question or term that may possibly be interpreted in multiple ways

    A good way to provide context about specific terms is by adding a sentence right after a question where you have used a difficult or multi-interpretable term. For instance if you ask the open question: which products of term X can you name? You should follow with: By term X we mean A, B and C, excluding X, Y and Z.

    Besides providing clarity about definitions, it is also important to provide clarity on what is expected from the respondent. For instance: ranking buying criteria on importance on a scale of 1-  5, answering with a number, multiple answers being possible etc. Make this explicit! It will not only provide clarity to the respondent but also make life easier once you analyze the results.

    A proper routing will also provide clarity to the respondent and make the analysis easier. Think thoroughly about which answers will lead to skipping or jumping to other questions. If I ask the 100 CEO’s whether they have heard of Hammer and I want to follow up with a question about their perception of Hammer, I should be sure that the respondents who have never heard of Hammer skip the follow-up question about perception!

    ‍Preventing bias

    In order to get valid results out of your survey, it is key to have your respondents answer questions as unbiased as possible. The two most common ways bias can sneak into your questionnaire are by:

    • Loaded wording: when you ask your respondents about sentiment it is key to ask the question as neutral as possible. Avoid adding loaded words about your topic. A question like: ‘How beautiful do you find logo X?’ is a no go for instance. Instead, you could ask ‘What is your opinion about logo X?’ Rate from 1 – not beautiful at all to 5 – very beautiful
    • Order of text and questions: quite often in surveys questions are asked about a topic that has already had attention earlier. Be sure to avoid giving any information that sends the respondent in a certain direction about a topic you will ask later on.

    ‍Questionnaire feedback

    Last, but not least, be sure to always obtain feedback before approaching respondents. No matter how carefully you designed your questionnaire, it is very common to overlook minor mistakes that are easily made. Secondly, it is important to gain feedback from a content perspective. Are all KPI’s properly measured? Are there any questions missing? Or are there questions in you questionnaire that should be removed? It is best to have your draft questionnaire checked by three types of people:

    • Stakeholders: those people who benefit from a successful survey. At Hammer, when we conduct a survey assigned by a client, we align the questionnaire narrowly with their demands. In the end, they have to make decisions supported by the results of the survey.
    • Experts: those people who have the knowledge and experience to improve your questionnaire by viewing it from their perspective. This can be either experts on the topic or experts on the research method. If I want to conduct a survey among dairy farmers for instance, I will ask both a dairy/agriculture expert from our network and one of my experienced co-workers with market research knowhow.
    • People part of the target group: asking feedback from someone who fits the criteria of your target group is invaluable. This is a great check to find out if the questionnaire is completely clear to respondents. Running the questionnaire by a potential respondent can also be used as a pilot to see whether the types of answers that come out of it are analyzable.

    ‍Designing a solid questionnaire can easily be underestimated. Make sure your research objective is well defined, as well as the responding KPI's. Make the questionnaire as clear as possible for the respondent, prevent bias and ask for feedback from different types of stakeholders.

    Author: Jasper Reintjens

    Source: Hammer, Market Intelligence

  • The 4 steps of the big data life cycle

    The 4 steps of the big data life cycle

    Simply put, from the perspective of the life cycle of big data, there are nothing more than four aspects:

    1. Big data collection
    2. Big data preprocessing
    3. Big data storage
    4. Big data analysis

    All above four together constitute the core technology in the big data life cycle.

    Big data collection

    Big data collection is the collection of structured and unstructured massive data from various sources.

    Database collection: Sqoop and ETL are popular, and traditional relational databases MySQL and Oracle still serve as data storage methods for many enterprises. Of course, for the open source Kettle and Talend itself, big data integration content is also integrated, which can realize data synchronization and integration between hdfs, hbase and mainstream Nosq databases.

    Network data collection: A data collection method that uses web crawlers or website public APIs to obtain unstructured or semi-structured data from web pages and unify them into local data.

    File collection: Including real-time file collection and processing technology flume, ELK-based log collection and incremental collection, etc.

    Big data preprocessing

    Big data preprocessing refers to a series of operations such as “cleaning, filling, smoothing, merging, normalization, consistency check” and other operations on the collected raw data before data analysis, in order to improve the data Quality lays the foundation for later analysis work. Data preprocessing mainly includes four parts

    1. Data cleaning
    2. Data integration
    3. Data conversion
    4. Data specification

    Data cleaning refers to the use of cleaning tools such as ETL to deal with missing data (missing attributes of interest), noisy data (errors in the data, or data that deviates from expected values), and inconsistent data.

    Data integration refers to the consolidation and storage of data from different data sources in a unified database. The storage method focuses on solving three problems: pattern matching, data redundancy, and data value conflict detection and processing.

    Data conversion refers to the process of processing the inconsistencies in the extracted data. It also includes data cleaning, that is, cleaning abnormal data according to business rules to ensure the accuracy of subsequent analysis results.

    Data specification refers to the operation of minimizing the amount of data to obtain a smaller data set on the basis of keeping the original appearance of the data to the maximum extent, including: data party aggregation, dimension specification, data compression, numerical specification, concept layering, etc.

    Big data storage

    Big data storage refers to the process of using memory to store the collected data in the form of a database in three typical routes:

    New database cluster based on MPP architecture: Using Shared Nothing architecture, combined with the efficient distributed computing model of MPP architecture, through column storage, coarse-grained indexing and other big data processing technologies, the focus is on data storage methods developed for industry big data. With the characteristics of low cost, high performance, high scalability, etc., it has a wide range of applications in the field of enterprise analysis applications.

    Compared with traditional databases, its PB-level data analysis capabilities based on MPP products have significant advantages. Naturally, MPP database has also become the best choice for a new generation of enterprise data warehouse.

    Technology expansion and packaging based on Hadoop: Hadoop-based technology expansion and encapsulation is aimed at data and scenarios that are difficult to process with traditional relational databases (for storage and calculation of unstructured data, etc.), using Hadoop open source advantages and related features (good at handling unstructured and semi-structured data), Complex ETL processes, complex data mining and calculation models the process of deriving relevant big data technology.

    With the advancement of technology, its application scenarios will gradually expand. The most typical application scenario at present is to support the Internet big data storage and analysis by expanding and encapsulating Hadoop, involving dozens of NoSQL technologies.

    Big data all-in-one: This is a combination of software and hardware designed for the analysis and processing of big data. It consists of a set of integrated servers, storage devices, operating systems, database management systems, and pre-installed and optimized software for data query, processing, and analysis. It has good stability and vertical scalability.

    Big data analysis and mining

    From visual analysis, data mining algorithms, predictive analysis, semantic engine, data quality management, etc., the process of extracting, refining and analyzing the chaotic data.

    Visual analysis: Visual analysis refers to an analysis method that clearly and effectively conveys and communicates information with the aid of graphical means. Mainly used in massive data association analysis, that is, with the help of a visual data analysis platform, the process of performing association analysis on dispersed heterogeneous data and making a complete analysis chart. It is simple, clear, intuitive and easy to accept.

    Data mining algorithm: Data mining algorithms are data analysis methods that test and calculate data by creating data mining models. It is the theoretical core of big data analysis.

    There are various data mining algorithms, and different algorithms show different data characteristics due to different data types and formats. But generally speaking, the process of creating a model is similar, that is, first analyze the data provided by the user, then search for specific types of patterns and trends, and use the analysis results to define the best parameters for creating a mining model, and apply these parameters In the entire data set to extract feasible patterns and detailed statistics.

    Data quality management refers to the identification, measurement, monitoring, and early warning of various data quality problems that may be caused in each stage of the data life cycle (planning, acquisition, storage, sharing, maintenance, application, extinction, etc.) to improve data A series of quality management activities.

    Predictive analysis: Predictive analysis is one of the most important application areas of big data analysis. It combines a variety of advanced analysis functions (special statistical analysis, predictive modeling, data mining, text analysis, entity analysis, optimization, real-time scoring, machine learning, etc.), to achieve the purpose of predicting uncertain events.

    Help users analyze trends, patterns, and relationships in structured and unstructured data, and use these indicators to predict future events and provide a basis for taking measures.

    Semantic Engine: Semantic engine refers to the operation of adding semantics to existing data to improve users’ Internet search experience.

    Author: Sajjad Hussain

    Source: Medium


  • 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

  • What to Expect in Customer Experience and Market Research this Year?  

    What to Expect in Customer Experience and Market Research this Year?

    The Customer Experience (CX) discipline, including market research, is continuously changing, and being enhanced as more organizations understand how critical CX is to future growth. And even though CX and market research are key for business success, the programs are often in their early stages, even in modern offices.

    2022 will be the year CX and market research explode

    Customer data is too important to ignore. And the pandemic altered our normal routine enough to fundamentally change buying behavior, for both B2B and B2C – maybe forever. Modern businesses fall behind sometimes, especially with this year’s uneven return to the workplace. And even a good program can improve. The predictions in this article will help your team act with foresight to build the best and most-responsive organization possible in 2022.

    Prediction #1: Brand loyalty rebooted

    COVID-19 killed brand loyalty. Brand loyalty took a nosedive during the pandemic with 75% of consumers trying a new shopping behavior since the COVID-19 pandemic started. This includes trying new brands, new retailers, and new generics.

    During the pandemic, consumers vented their need for novelty by ordering new toothpaste, a different cereal, or engaging in a fully new routine. When consumers couldn’t browse grocery store aisles, getting new products delivered scratched the itch for something outside the routine.

    But the great brand realignment isn’t over. McKinsey reports that consumers intend to continue these new habits after pandemic restrictions ease. Eighty percent of consumers intend to continue use of private labels and almost as many intend to continue using new brands (73%) and new retailers (79%).

    Why loyalty matters

    Smart marketers and researchers know that a 5% increase in customer retention can increase company revenue by 25-95%. In short, it can be easier and more profitable to keep the customers you have than attract net-new customers. Loyalty matters because regular customers tend to be your best customers.

    If consumers tried new or private labels during the pandemic, it should then come as no surprise that Forrester reports that loyalty and retention marketing budgets increased by 30% in 2021. CMOs are attempting to shore up loyalty by putting the customer at the center of everything they do. Expect to see customer experience, marketing, and market research grow ever closer in 2022.

    Why consumers switch

    The pandemic forced consumers to change behavior, but that isn’t the full story around consumers’ lack of loyalty. It’s simply easier these days to produce products, purchase advertising, and find a receptive audience. This speed to market is part of the reason there are so many small direct-to-consumer brands appearing recently.

    How to earn wandering consumers back

    Some experts encourage a return to marketing and research basics to improve brand-customer relationships. At Alchemer, we recommend making that brand-customer relationship your highest goal. Growing relationships takes effort, but we’ve found that our best relationships typically lead to our best growth opportunities.

    So, we put customers and their feedback at the center of our work. Our recommendation to business and research leaders is to become customer-obsessed in 2022. It’s a huge task to orient away from a product-first or company-first mentality. But shifting to a customer-first mentality makes all the difference.

    Prediction #2: Surveys are just the start

    Customer experience programs are far more than surveys. Prepare for CX and research to get more complex in 2022. Surveys will always play a part in customer experience and market research. Surveys allow researchers to benchmark performance, capture post-purchase feedback, and develop a data cache with periodic surveys.

    In 2022, CX programs will grow far more comprehensive in scope. Executives have been trying (and often failing) to integrate customer feedback in a meaningful and profitable way – in a way that allows for quick customer interaction and issue resolution, not just data collection. That real-time relationship building has been difficult or impossible in the past, but no longer. Technical advances will finally allow CX managers and market researchers to contribute meaningfully to relationships in real-time.

    No playing around

    CX leaders need surveys to collect data, but then that data must feed into other systems, triggering automated workflows and providing solutions quickly.

    In fact, McKinsey declares that “survey-based systems can no longer meet the demands of today’s companies“, because they are:

    • Limited. Only 13% of CX leaders express full confidence that their measurement system provides a representative view of their customer base.
    • Ambiguous. Only 16% of CX leaders think that surveys allow them to address the root causes of performance.
    • Unfocused. Only 4% of CX leaders believe their CX measurement system enables them to calculate a decision’s return on investment (ROI).

    In fact, our prediction for 2022 is that customer data becomes one important piece of your larger CX and research program – a broader view empowered by connected data sets.

    Prediction #3: From data collection to data action

    Many CX and market research teams lack the data integration of their sales and marketing counterparts.

    Sales departments tend to operate using a customer relationship management (CRM) tool like Salesforce (SFDC) and, by 2022, have likely already integrated with other data sets like Jira for product or Salesloft for cadences. Likewise, marketing tends to run the marketing automation platform with data that syncs not only to Salesforce but other publishing or analytics tools.

    Integrated data is how sales and marketing get done. Less so for CX.

    In 2022, expect the CX and market research functions to evolve in many organizations. CX has collected numerous potential data sets just waiting to be leveraged:

    • Internal customer behaviors, transactions, and profiles
    • Third-party data on customer attitudes, purchase preferences and digital actions
    • Social media activity
    • IoT data collected in-store or on-location regarding customer health, usage, and sentiment
    • Net Promoter Score (NPS) or Customer Satisfaction Score (CSAT)

    We predict that this next year will see CX departments and market researchers evolve from data consumers to data contributors, sharing critical customer interactions throughout their journey.

    Learning how to share

    Customer data needs to flow to frontline employees and tools, like a company CRM, via an application programming interface (API). The API can serve as a catalyst for automated actions based on metrics like lead score.

    In a recent Forrester Consulting Thought Leadership Paper, commissioned by Alchemer, “Smoke And Mirrors: Why Customer Experience Programs Miss Their Mark“, only 29% of CX respondents say they can meaningfully act on the data they collect and only 17% say that feedback is communicated to the appropriate internal teams to act on it.

    Companies have invested heavily in creating customer programs that do a pretty good job of listening and analyzing feedback, but are terrible at responding to the input from their customers.

    Collecting and seeing data is one step. But acting on data is a trend Alchemer expects to see much more in 2022, a key differentiating feature for market research teams.

    Prediction #4: Automation embraced by employees

    Automation provokes anxiety in many people including market researchers. PwC reports that 60% of people are worried about automation putting many jobs at risk.

    In 2022, Alchemer predicts a shift in the way we view automation. We believe this coming year will herald a new relationship with automation; an acceptance of our human foibles and the automation we will now call upon to continue our evolution.

    Acceptance of automation should not come as a surprise. We know brands are becoming more transparent about data collection and market research. We know that consumers care less about businesses collecting particular pieces of information, like email addresses. And we know we cannot stop the onset of better software, better AI and ML, better…everything.

    Fear of automation is overstated

    In the United States, 52% of respondents received automation training within the past year and 94% credit the training with an improved job performance. Almost all respondents claim that automation makes them better at their job.

    But acceptance of automation doesn’t end at training. When considering future careers, 63% of workers see automation skills as critical to their career growth.

    So, let’s face it: We are cautiously optimistic about the future of technology. The same PwC study reveals that 64% of respondents say technology presents more opportunities than risks (only 9% disagree).

    Increasing human potential

    So, why are workers so bullish on automation these days? An early indicator could be exactly the type of work being automated – humans may be delighted that their repetitive, mundane work can be automated freeing more time for strategy and analysis.

    A second indicator appears to be a renewed focus on other skills. With the introduction of AI, executives are more likely to look at processes and roles, and to consider different modes of working. Many business leaders are incorporating training on uniquely human skills: 59 percent of AI practitioners reported that their organizations are focusing on “process skills, like active listening and critical thinking”.

    We may already be seeing this type of training pay off. People learned a lot of new skills during the COVID pandemic. And not just how to bake sour-dough bread. Four in five workers learned new skills from home during the pandemic (82%) and 72% report feeling more confident in their ability to do their job. While many experienced The Great Recession or Realignment, those of us reporting to work are feeling better about it than the recent past.

    In 2022, we finally embrace automation.

    Prediction #5: B2B more like B2C

    Many of us spent a good portion of the pandemic making purchases from our phones, downloading media onto our devices, and playing games online with family members. We were living B2C lives where purchases were only a click away.

    Now, as workers return or plan to return to work, we are back to living B2B lives during part of the day. We’re required to speak with Sales teams to get information. We access wonky vendor portals or employee intranets to find solutions.

    When will a software company homepage give us the same warm and fuzzy feelings as Netflix? 

    The pandemic changed business relationships forever

    The bar has been raised for customer experience and market research teams and how they develop relationships with consumers.

    As workers shifted to remote work, they found a lot to love. Self-serve options, common in B2C e-commerce, have grown for business buyers too. Top-of-funnel activities like identifying and evaluating new suppliers offer more self-service options – up in a new McKinsey study from 22% of respondents in August 2020 to 34% in February 2021.

    We don’t hate human interaction; we just want to complete our work. McKinsey shows that two-thirds of buyers prefer remote human interactions or digital self-service. A Forrester study found that 59% of B2B buyers and sellers prefer not to interact with a sales rep and 74% prefer buying directly from a website. The message: Let us do it ourselves.

    What do B2B consumers want?

    B2B consumers want everything that B2C consumers have – easy service, quick product purchase, digital content, and responsive support. Forrester Principal Analyst Kathy Contreras says, “[t]he future B2B buyer will expect buying experiences to be increasingly open, connected, intuitive, and immediate“.

    B2B buyers want access to information – access that is possible through ubiquitous digital channels, available anywhere, on any device. With more people confined to their homes this past year and more reliant than ever on digital tools, customer expectations for a frictionless experience have risen.

    “Personalization” is the term most often tossed around when discussing tactics for customer focus. But we predict a bigger focus than just adding someone’s first name to their email introductions.

    This personalization is different. This personalization is about the whole experience – the content, the product, the offer, the look, and feel. This personalization starts to sound a lot like B2C to CEO Gal Oron:

    “Performing analytics on customers’ product content interactions can help businesses better understand what each customer needs and tailor their experiences – from the content they’re served to the offers and promotions they receive – in a way that feels just as customized and relevant as their video streaming feeds.”

    Personalization is about providing answers for the consumer. If these experiences are as profitable as they seem – Epsilon says 80% of consumers are more likely to make a purchase when offered a personalized experience – then expect to see a lot more personalization in B2B sales during 2022.

    We predict personalized, relevant experiences for B2B buyers on digital channels to greatly increase as the lines between B2B and B2C continue to blur. It may not be Netflix, but perhaps it can come close.

    Author: Chris Benham

    Source: Greenbook

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