The economic downturn caused by the COVID-19 event has fundamentally changed the way businesses operate. In such uncertain times, it’s more important than ever to turn to customer data to guide your business strategy. That’s why Elicit has identified the six customer data questions you should be asking today. Below you will find a series of articles that will thoroughly explore each of these questions, including specific metrics and practical examples to consider in our new economic environment. As we continue to offer guidance and support to our clients, we encourage you to utilize and share this valuable information.


An Overview



The economic downturn caused by the COVID-19 event is affecting everyone: schools, hospitals, the Government, businesses, and especially customers. Whether your business is struggling, sustaining, or thriving, the world has fundamentally changed. Everything you knew or believed about your customers has likely been called into question. Business leaders can’t rely on just their instincts anymore, and historical data cannot be trusted to predict future customer behavior as it previously could. Yet in this environment, utilizing data to make decisions is more important than ever. As we prepare for a post-COVID-19 sense of normalcy to arrive (in whatever form that takes), business leaders should be spending as much time asking questions and collecting data as they are in reacting to and addressing the day-to-day challenges their businesses are facing.

Our decade of experience as trusted customer experts for some of the world’s most notable brands has enabled us to anticipate what is going to be most important in the weeks and months ahead as the world reacts and subsequently rebounds from this pandemic. Given the large number of unknowns related to the speed and extent to which recovery will happen, there are six critical customer related questions that leaders should be asking of their data right now:

  1. How are my core customer KPIs performing?
  2. What does my current customer portfolio look like, and how healthy is it?
  3. What external data should I be looking at?
  4. How should I rebalance the use of historical vs real-time data?
  5. How should I be measuring customer engagement today?
  6. How will I know when my customer portfolio is starting to stabilize?

An introductory overview of each of these topics is included below. Additionally, the Elicit Leadership Team will be releasing a series of follow-up articles that will explore each question in more detail, including specific metrics and practical examples to consider in our new economic environment.


The standard set of customer KPIs you have been tracking for years are still critically important. Yes, they’re probably volatile right now, which is why you should be giving them an even closer look. Consider where there are pockets of stability, where there are notable declines in performance, and where you see areas of growth. There is much to be learned by reflecting upon where your value proposition seems to be resilient and strong, what new customers seem to be attracted to, how and in what channels customers are engaging, and what things customers are leaving behind. For a more detailed look at question 1, click here.


Most companies are facing dramatic declines in revenue and have probably already considered the drivers of those changes through product- and geo-based lenses. However, given that not all customers contribute equally to the bottom line (i.e., some spend more than others), a customer-focused view of business performance is imperative. What do you know about who is shopping and why? Which customer groups are important now, and which have stopped engaging? With finite resources to spend—including investments of money, time, and people—companies that make smarter, data-driven decisions about how to allocate those resources across their customer portfolio will realize a higher return.


There’s no shortage of externally available data and analysis covering a wide variety of consumer, economic, and pandemic related topics. These data sources can be effective indicators of marketplace dynamics and likely trends, opportunities, and risk factors that should all be considered as you are planning your go-forward strategies and tactics. You’re probably already using geo-demographic, value-based, and other data overlays, and you should be asking yourself if it’s still relevant. Typically, under-utilized data sources include channel consumption, social listening, econometric data, and government policy data. Take another look at these types of data, as they may shed light on the external forces that will be influencing consumer behavior.


All of the historical customer related data at your disposal is likely a poor indicator of what is actually happening in your business right now. While some basic customer information will remain constant (like customer IDs and account information), much of it won’t in the new post-COVID-19 market. Nearly every customer’s behavior will be shifting based on adjusted life priorities and customer missions. Any previous patterns regarding marketing sensitivity, category preferences, channel engagement, and price sensitivity should now be thought of as pre-event, mid-event, and post-event, keeping in mind that the timing varies by customer and geography.


Most businesses have some measure of engagement that they’ve been tracking over time. These measures can be as simple as a customer satisfaction score or as complex as a scorecard that incorporates everything from channel engagement to purchase patterns to marketing responsiveness. Regardless of what has been done historically, it’s time to redefine what it means for a customer to be engaged. While some customers may not be in a position to purchase right now, you should be able to tell if they’re still consuming in the category, engaging with marketing, browsing and shopping digitally, and interested in your brand overall. This will enable you to maintain customer relationships during this period that will contribute to your ability to re-activate them as purchasing customers in the future.


Right now, in virtually every industry, customer behaviors are highly volatile, and businesses are asking when things will return to normal. But this is the wrong question. Few businesses will look exactly the same in the future as they did pre-COVID-19. What permits us to manage businesses effectively is stability and the predictability it affords. Businesses need to identify emerging customer patterns and determine at what point those patterns can be reliably applied to business decision making. Again, new real-time or episodic data should be captured and used to study the new reality, and to help determine whether that new reality is stabilizing or ephemeral. For example, even small changes in behaviors such as increases in browsing activity or full-price buying may indicate that buyers are returning. Understanding when to make short-term responses vs. adjust long-term plans depends upon knowing how to read the signals. The key is to avoid making assumptions and relying upon past heuristics until the data supports (or refutes) it.

The instability of the current economic environment is certainly intimidating, but the future of business is still bright. Since the entire global economy has been affected in some way or another, the economic impacts caused by COVID-19 will serve as the inflection point for businesses that can react and evolve, outpacing those that can’t. Leveraging your customer data has never been more important and those that do so will be the ones who will help establish the new standards for the future.




As we established in our overview article, there are six customer data questions you should be asking today. The first of these is: “How are my core customer KPIs performing?” Key performance indicators, or KPIs, are just that: the core subset of metrics that tell you how your business is performing. Customer KPIs measure performance in customer terms—reflecting business performance based on customer behaviors and activity.

Of course, revenue and profit are our main business objectives. But as our colleague Mark Gonzales has pointed out, revenue is not a KPI[i] because it is an outcome, or aggregate, of customer activity; and profit is the result of both customer and operational activity. We measure revenue and profit via our traditional P&Ls, but these need to be supplemented with behavior-oriented customer KPIs. This is because our primary goal is to understand and influence customer behavior to drive business performance—through changes in product, pricing, customer experience, and marketing messages. And now, more than ever, keeping a close eye on key customer behaviors is critical.



The foundational set of customer KPIs that leaders should be tracking is straightforward, and multiplied together they equal revenue:

  • Number of Customers
  • Transactions Per Customer
  • Spend per Transaction

Each of these represents a key behavior or set of behaviors. Every customer counted in Number of Customers has chosen to be our customer, a critical behavior! Transactions per Customer counts the number of times a customer has purchased with us. Finally, Spend per Transaction is, obviously, the average amount spent during each transaction, another key behavior. Importantly, these three KPIs can be driven individually as the objectives of business initiatives (e.g., attracting new customers, getting high-potential customers to visit more often, or getting customers to spend more per visit by adding an additional category or service).



This set of customer KPIs raises a new series of questions. Who is buying and who isn’t? Through which physical and online channels? What are they buying? What time periods are most important, and how are these KPIs changing over time? These questions lead to the groupings, or dimensions, that should be explored:

  • Customer Groups (segments)—who?
  • Channel and Geography—where?
  • Product Category—what?
  • Time—when?

The first question (“who”) must start with a clear definition of “customer” in your data systems[ii]. Next, customers must be grouped into meaningful segments that, collectively, represent all of your revenue, and in which a customer can belong to only one segment. If you have an existing segmentation that is well-established, use that. Or simply define basic “lifecycle” segments—e.g., new, active, best, lapsed, unknown—with simple rules for each that make sense in your business:

  • New = new to file within past 90 days
  • Best = top 10% of spenders in the past year
  • Lapsed = no spend in past 12 months
  • Active = all other known customers
  • Unknown = revenue not trackable to a known customer (e.g., cash transactions not tied to a loyalty account)

More advanced forms of segmentation will be touched upon in our upcoming articles, but this is a good place to start. The important thing is that all of your revenue is captured by your customer groups.

The “where” and “what” questions—channel, geography, and product category—underpin your P&L reporting today. Your data should be structured so the “who” can be viewed across these dimensions. This will enable you to understand your P&L in customer terms. For example, instead of “sales are down in District 5,” we can ask why sales are down in District 5, and see customer counts, visits, spend per transaction, and which product categories people are and aren’t buying, across our new, best, active, and unknown customers. It’s much more actionable knowing that sales are down in District 5 primarily because active customers aren’t shopping as often, and when they do, they aren’t buying in two key categories. This stops us from wasting time on new customer acquisition, for example, and focus on the root problem.

Time (the “when” dimension) is important for comparing performance between periods of time, including identifying trends and inflection points. As the pandemic plays out, looking for inflection points is probably the most important thing you don’t normally do, but need to now. This involves seeing points in time in your data in which behavior makes a dramatic shift to a new state. Inflection points introduce uncertainty, in that trends are broken and predictions get very difficult to make. The past literally stops explaining the present or predicting the future. 

Inflection points can occur at the intersection of any of the KPIs and other dimensions—a specific customer behavior, in a geography or channel, with a specific product category or set of categories. Once identified, inflection points must be pinned in your data, and used to define pre- and post-periods. They will mark significant changes—increases or decreases—in performance. You must be careful not to use pre-inflection point data in your diagnostics, predictions, or goal setting; it has become irrelevant, part of a time that no longer reflects current conditions.



Customer behavior can have a significant impact on profitability, and it is logical to ask if we should measure profit at the customer level. While it may seem straightforward, this concept raises complex questions about the impact of customer decisions vs. business decisions and actions made by your organization. For example, if a customer primarily buys clearance product, are they a good or bad customer? At first blush, they may generate low or negative margin via their purchases. But the reason that product is on clearance is that it didn’t sell at full price. Is that the clearance buyer’s fault? Further, if they don’t buy it, how much more will it cost to liquidate? Did that clearance purchase actually just save you money?

All businesses have examples like these, and it is important to carefully analyze root causes of customer profit variances. Further, studying differences in customer profitability can yield a lot of insight about your business. Accordingly, an additional KPI that can be important to track is:

  • Customer Margin Contribution

This is computed differently from one business to another, but the concept is to capture elements of net profit that are attributable to customers, such as product cost, discounts, costs of returns and cancellations, service fulfillment, customer support, ship-to-home costs, and loyalty program benefits. Some organizations attribute direct marketing costs to customers, others consider those company decisions. Allocations such as channel costs, labor, store overhead, paid media, and other operating costs not directly attributable to a customer can be problematic and should be avoided.

Customer Margin Contribution can be very useful, but because of its complexity, you might start with a simpler computation, such as Customer Net Margin, which is simply price paid (net of all discounts) minus product cost. You can add additional components of variable cost later as you mature your data and reporting capabilities.



In summary, businesses should be tracking customer business performance through a minimum of 3 KPIs and 4 dimensions, with an optional 4th KPI measuring a component of profitability. Even with this relatively concise set of variables, a lot of insight will result. The who, where, what, and when dimension questions can be answered with details in your data, ensuring that all purchase data contains the customer (if known), channel, location, product, and date-time details needed. Ideally, these KPIs and dimensions should be easily accessible and explorable through a customer KPI dashboard that enables filtering, drilling, and comparisons across these dimensions. Critical inflection points should be flagged. And as you identify important trouble spots or opportunities, you can charge your data science team with probing for deeper patterns for richer insight.

There are many more customer behaviors we can dig into, such as price sensitivity, mobile and online browsing behavior, engagement with services and customer support, media response, and brand and category propensities. But this starter set will help you understand the fundamental shifts underway in your business as we all navigate these unprecedented times.




The COVID-19 pandemic and subsequent economic crisis have caused companies to radically shift both their short-term operations and long-term planning. Businesses that have built strong customer data capabilities are evaluating their existing measurement frameworks and grappling with how to best leverage them in the midst of economic upheaval. Previously stable aspects of your customer base may now be shifting significantly.

Your customer portfolio is the casting of 100% of your revenue through the lens of you customer segments or groupings. It enables you to answer questions like “who are our best customers, and how do they interact with us?,” “what levers should we pull to cultivate relationships with our brand advocates?,” and “which of our lapsed customers are most likely to reengage?” Recognizing that you have distinct groups of customers with differing abilities to spend, degrees and channels of engagement, and preferences for how and why they transact with you, and adjusting your approach accordingly, is more critical than ever before.

While it may seem logical to redefine your customer portfolio based on shifting customer behaviors, doing so would be premature. A more effective strategy includes asking new questions using your existing customer portfolio definitions and making precise adjustments only when you are confident that a change you are observing will persist.

The sooner that executives can begin to ask questions like the ones outlined below and track changes throughout the remainder of the crisis, the more effective their short- and long-term customer portfolio management practice will be.



P&Ls broken out by geography, product line, or channel have been a staple of modern business management for centuries, but few have supplemented their performance tracking with a true customer P&L that ascribes revenue and profit to distinct customer groups. This gap has historically handicapped a company’s ability to set and execute a robust customer-centric strategy. Yet, by definition, all revenue comes from customers, and their choices of what, where, when, and how to buy shape our financial performance. Filling this gap is more critical than ever as businesses scramble to track day-to-day changes driven by the pandemic, adjusting their activities to maintain and pick up share wherever possible.

Customer portfolio management is the practice of managing a business based on a customer P&L grounded in a deep and rich understanding of your customers. The portfolio is made up of customers assigned to groups (or segments) aligned to their response to your business value proposition. The customer portfolio is intended to help businesses strategically optimize the value of the firm via decisions that optimize the collective value of their customers.

With finite resources to spend—including investments of money, time, and people—companies that make smarter, data-driven decisions about how to allocate those resources will realize a higher return. There are four investment options that can be applied to customer portfolio management:

Elicit has partnered with dozens of companies spanning retail, hospitality, transportation, and healthcare to build custom customer portfolio management practices. While each company’s practice is unique, the capabilities needed are relatively consistent, including, but not limited to identifying customers across purchases and interactions, collecting and integrating available customer data, and creating a structured customer portfolio based on tracked behaviors expressive of value and engagement.

Each capability exists on a maturity spectrum, starting with basic practices and improving over time. While many companies are still in the nascent stages of developing these capabilities, that should not hold you back from beginning core customer portfolio management practices leveraging what you have. Even a simple customer lifecycle can be used to design, deploy, and measure customer interactions as you navigate through market disruptions.



Creating a structured customer portfolio by defining the distinct groups in your customer base enables you to track the health of your business over time from a customer perspective. One simple structure to start with is a lifecycle that includes stages such as introduced, new, active, and lapsed, and it’s likely that you already have one defined. A well-defined lifecycle should capture as close to 100% of your company’s revenue as possible. Establishing KPIs for acquisition, engagement, development, retention, and reactivation, and tracking the performance of those KPIs over time, will tell you if your business is getting relatively healthier or weaker.

Right now, most companies’ customer portfolios are ailing, or at least shifting. Your goal should be to use your customer understanding to guide how you reallocate your finite resources and determine where to invest, divest, sustain, and redistribute in the midst of continuing portfolio changes, and make dynamic adjustments to your investments as economic conditions and business performance shifts and eventually rebounds. Leveraging your existing customer lifecycle definitions, begin by asking the following questions to inform your allocation decisions.


  1. Where is there stability in our active customer base? Consider which customers are still purchasing as they were before, which are purchasing differently than before, which have stopped purchasing but are continuing to demonstrate non-purchase engagement, and which appear to have disengaged entirely. This provides a new framework by which to understand active customer participation and guide your short-term active customer journeys. Comparing these behaviors to prior periods, and continuing to track them for the foreseeable future, will provide factual insight into the level of disruption among your active customers.
  2. How do we define our “best” customers? Traditionally, your best customers are those that spend the most, and are engaged either deeply or broadly with other aspects of your value proposition. While you should continue to maintain relationships with those customers, a different pocket of customers will likely emerge that are taking advantage of available products and services, advocating for your brand on social media and review platforms, and engaging with your communications. These customers should be uniquely recognized and observed as well. Many of the changes in customer engagement that have materialized will stick around, and these customers will provide insight into the performance of various value proposition elements and early indications of rebound.


  1. How are we acquiring new customers today? As long as the pandemic is affecting physical shopping, the importance of digital acquisition will be greater than ever before. You’ve likely already shifted your media spend accordingly, but you should also pay significant attention to commonalities between customers that you are able to acquire right now. Are they coming from specific geographic regions that have been similarly impacted epidemiologically or economically? Are they entering on similar product categories? Are there specific communication messages or channels that are proving more effective? Are there specific campaigns or offers that drive higher conversion? Dimensionalizing as many aspects of your conversion funnel as possible will enable improved acquisition investments and payback.
  2. Should we redefine post-acquisition activation? Driving a second purchase was often the primary goal for new customer journeys. Even though transactions are more critical than ever, many consumers are facing financial hardship or uncertainty, and pushing products can quickly lead to disengagement. Leveraging what you’re observing from your best customers, rethink your new customer journeys to focus more on non-purchase value drivers, like incentivizing profile creation or social media engagement.


  1. Which customers are exhibiting likely-to-lapse behavior? One impact of the drastic changes to purchase and non-purchase patterns is that the metrics and models you’ve used to track likelihood to lapse may not be as effective. It’s important to recognize that some customers will go dormant in the short-term, but that doesn’t mean that they’ve truly stopped doing business with you. For example, a customer that may be off her expected purchase cadence may still be actively browsing your website, or engaging with your social media posts, or simply waiting until she gets back to work. It will be important to come back to this population once your industry begins to rebound. Without changing your definition of a lapsed customer, supplement it with various lenses for tracking active brand engagement.
  2. Is there an opportunity to reengage lapsed customers? For some businesses, marketplace dynamics may actually present an opportunity to reengage lapsed customers. Perhaps their preferred store is closed, or cannot provide a product or service as quickly, conveniently, or cost-effectively as they’d like. Now is a good time to reach out to your lapsed customers and demonstrate why they should reconsider your company. According to a recent report by global research firm Kelton, 71% of consumers would prefer a familiar and comforting brand right now. If you can supplement familiarity with a value proposition designed to support convenience or value, you may be well positioned to recoup lost customers.


  1. Can we track consumers who are engaged with our brand, but haven’t transacted with us? Most companies interact with consumers that have introduced themselves to that company through email, social media engagement, a mobile app download, or product usage without purchase (think an airline traveler whose spouse purchases tickets on his behalf). Because customer IDs are designed primarily to track purchases and behaviors exhibited by purchasing customers, many companies are challenged in their ability to identify non-purchasing customers across platforms. Revisit your company’s approach to identifying and tracking introduced customers and ensure that there’s connectivity post-first purchase. Effectively linking and observing the behaviors of these consumers will enable a targeted engagement approach that could result in conversion down the road.
  2. What opportunities exist to drive a transaction or deepen engagement with these customers? Your pool of introduced customers is an often valuable and overlooked population. As a result of the identification gaps described above, many companies don’t have an explicit strategy for cultivating relationships and maximizing conversions with their introduced customers. Developing a strategy for deepening this engagement now—or if nothing else, collecting contact information for these customers so that you can engage with them in the future—will pay off as the marketplace reopens and consumer spending rebounds.

These examples only begin to touch upon the potential of customer portfolio management. Your portfolio can (and should) be much richer than a lifecycle, encompassing patterns of response to your product, price, experience, and marketing. This is undoubtedly a big undertaking for any business, especially challenging in the midst of a global pandemic and economic crisis. It requires deep coordination between your marketing, merchandising, analytics, technology, and finance teams, and often takes years to truly get right.

But there’s never been a more important time to drive customer-based decision making into your business. Beginning the journey can be relatively simple, and companies will reap its benefits immediately. Your products, locations, and channels aren’t changing nearly as rapidly as a result of the economic conditions as your customers are. Keeping a close eye on customer behavior, reflected through a structured customer portfolio, will enable you to more effectively navigate through the challenge, and will leave you well positioned for years of economic recovery.




The coronavirus has fundamentally shifted our behaviors and priorities as we rightly focus on our health and livelihood. As we grapple with the fear of the unknown, evolving government policy, and a changing labor market, people are realigning spending habits to meet these new priorities. This means customers are tightening belts in certain categories and boosting spend in others, causing unprecedented volatility within traditional internal customer data sources. While this data now needs to be monitored more closely than ever, in order to understand how customers are engaging with your company’s value proposition in this new normal, it is becoming increasingly important to look outside your organization for signals that unpack the “why” and “what should we do next.”

The most obvious and important external data to be tracking at this point are those related to COVID-19 itself—specifically, case and mortality rates by region, the rate of expanded access to testing, and the easing of government social distancing restrictions. Until the virus is contained, and for an unknown period of time after, we will continue to face market volatility. Your proverbial customer “health” is now, more than ever in our lifetime, dictated by the actual health of your customers and their communities, so first ground yourself in the realities your customers are facing.

Beyond public health data, though, you should be monitoring the pulse of your industry and consumer base through the use of external data. External data sources can be effective indicators of upcoming trends, opportunities, and risk factors that should be considered as you plan your go-forward strategy. There is no shortage of external data categories, and it can be a daunting task to identify which matter most. We propose you focus on four key sources to make sense of your customer needs and supplement what you know from your internal data. Those four are:

These sources can also be used to build reliable benchmarks that help you more accurately gauge whether your strategy is effective. Let’s step through these four core categories to ensure you are making informed decisions and staying responsive to fluctuating consumer needs.



Technically speaking, we are not in a recession yet, but in the last few months our global economy has taken a massive hit putting us on a trajectory to fall into a recession in the coming quarter. The economic indicators in this climate will unlock insight into your customers’ willingness and ability to spend, identify early signs of stabilization, and ultimately detect when we are emerging from this recession.

Economic metrics are classified into two general categories: leading indicators, which detect change before the broader economy displays a shift, and lagging indicators, which tell us more about what has already happened, confirm current trends, and enable more stable predictions about the future. With government policies shutting down entire industries and significantly dampening others, the traditional combination of leading and lagging economic data we relied on have become increasingly unstable. Since it’s likely your company does not have the luxury of waiting on sufficient data from lagging indicators to react, we suggest prioritizing leading indicators. Make sure to note the frequency with which the indicators are updated as some will be more relevant at different points in time. The following measures are examples of strong indicators to use for assessing customer spending power and early signs of stabilization:

  1. Jobless claims – Unlike the unemployment rate, jobless claims are reported weekly and are a measure of the number of people filing for unemployment insurance benefits. Tracking this metric at a state level will help you understand the basic economic health of your customer base.
  2. Consumer confidence index (CCI) – Reported monthly, CCI measures the degree of optimism consumers have in the current and future state of the U.S. economy.
  3. Weekly Economic Index (WEI) – This is one of the newest indicators reported weekly by the New York Federal Reserve in response to the rapidly changing economy stemming from the coronavirus; this measures a combination of consumer sentiment, jobless claims, contract and temporary employment, steel production, fuel sales, and electricity consumption.

What you might notice missing from this list are some of the traditional indicators such as the stock market and GDP. These are important economic measures but can also be misleading in unstable times. In the case of the stock market, activity is not always a true reflection of consumer sentiment, but instead a reflection of algorithmic reactions to the economy. As for GDP, that is only compiled quarterly, and can be misleading as it cannot decouple activity stemming from financial policies such as quantitative easing and other government spending decisions like the Payroll Protection Program.



It’s critical to know what is happening across your industry, how your performance and response measures up to competitors, as well as how consumer behaviors are reshaping the industry itself. Industry data can specifically help you understand the macro and micro trends, track early warning signs of impending market shifts, assess consumer engagement, and detect competitor reactions.

There are several types of data, but the most important to make sense of are industry news, public and private competitor performance data, and government actions including legislation, advisories, and industry-related policy changes. Industry news and competitor performance data will highlight if and how your competitors are shaking up the industry through innovation, creative new delivery models, and positioning. Conversely, it can also highlight where competitors are exhibiting signs that they are unable to adequately serve their customers, such as performance drops or store closures, and thus expose opportunities where you may be able to expand or restructure your offering to grow market share. As for government action, you are likely already monitoring this on a regular basis, but systematic monitoring of relevant government action such as health travel advisories, international travel restrictions, and mandatory business closures will help you stay ahead of potential negative impact to your business.

There are several subscription-based databases that can help you track this information, such as IBISWorld and Statista. We recommend researching which makes the most sense for your business and investment threshold. Another good source are trade associations. These can be industry-specific such as the American Medical Association and the U.S. Travel Association, or these can be cross-industry associations such as the U.S. Chamber of Commerce. Depending on your industry, you may have well-developed trade groups who provide up-to-date information on legislative advocacy efforts, relevant industry news, and general guidance in these uncertain times.



The abundance of idle time that the stay-at-home orders have afforded has resulted in a spike in consumer digital engagement and dialogue across social media platforms. Consumers are giving us insight into their mindsets around your brand and products. Social listening can help you unlock this insight and identify ways to boost their engagement and loyalty.

Social listening goes beyond the more straight-forward social monitoring which typically involves tracking campaign performance, product or brand mentions, and likes. Social listening is the process of taking thoughts shared online through conversations and mentions and translating that into action for your businesses. Relevant data for social listening includes products people like (and don’t like), issues they may be facing in your industry or with your brand specifically, sentiment about competitors and threats to your business, questions about relevant products or topics, and identification of key influencers for your target customer base. All of this data translates into valuable insight on how to expand your marketing reach and relevance, improve your product strategy, and mitigate threats to your business—all to better meet your customers’ evolving needs.

There are several social listening tools that stitch together data across various platforms and sources (e.g. Twitter, Facebook, Instagram, blogs, and forums) so you don’t need to worry about embarking on a complex data engineering adventure. These tools offer you the capability to search topics or keywords to analyze trends, assess brand sentiment, compare your digital presence to competitors, and identify influencers. While the actions you take resulting from these insights extend beyond digital marketing campaigns, insight from social listening can also help you improve your share of voice in a positive way and ultimately enhance your business performance.



You likely already leverage geodemographic data in some way. As a reminder, the premise behind geodemographic data is to derive insight based on the assumption that people who live in the close proximity to one another are more likely to have similar characteristics than people who live in different areas. As you can imagine, this view presents several weaknesses. Think about your own neighborhood as an example—sure, your neighbors are likely in a similar economic situation, perhaps even have children the same age as your own. However, they likely have different preferences, are buying different products, and interacting with brands in different ways than you. Geodemographic data glosses over these behavioral differences, and because it is often surreptitiously obtained, it can also be fraught with errors. Therefore, we don’t advise using it for targeted, personalized communications. In the aggregate, though, it can be useful for market-level analyses, especially in areas where your business has little insight into customer behaviors.

Geodemographic data can be especially helpful during our current crisis as it can help you understand how the economic climate and future decisions you make about your business may disproportionately affect your target market across geographies. For example, a low-cost grocery chain may use geodemographic data to decide where to proactively divert resources based on anticipated demand growth to preserve access to food for lower-income geographies. More generally, geodemographic data should be considered where you think market-level information will enable you to improve your response to customer needs and enhance their overall experience.

In closing, the most important indicators of how you should grow and protect your business are always found within your own internal data. However, these times are like no other. The collapse of the economy and the reaction of consumers to this ongoing public health crisis has likely turned your internal data into a hot mess of noise with high variability, and in some cases, may have even shut down aspects of your business. This means that a renewed emphasis on external data sources, such as those in the four categories outlined above, will be essential to provide clues about how shifting consumer patterns may impact your business going forward. Those businesses who learn how to effectively navigate and harness the power of external data to refine their post-COVID-19 go-to-market strategies, in conjunction with a concentrated focus on the shifting patterns in their own internal data, will be best positioned to emerge from this crisis in a way that is most relevant and meaningful to their customers.




If your business has been operating for more than a few years, chances are you have bucketloads of data that have enabled you to forecast business performance with a reasonable amount of confidence. Models help predict everything from product sales to promotion response to customer churn; they fuel product recommendations and email personalization. But the dramatic impacts of COVID-19 on consumer behaviors and the economy itself have now broken most of those models; past behavior can no longer be counted on to predict the future and should not be trusted to be effective.

Does this mean we abandon the use of data to inform decision making? Not at all. In fact, just as predictive models are broken, so are our heuristics and intuitions. We cannot safely rely upon our human expertise and judgment either, as it, too, is based on past experience that may no longer be relevant. This makes the use of customer data—hard facts we can rely on—more important than ever.

With consumer behavior in flux, we must turn, for now, from prediction to signal detection for help. Signal detection is the use of data to identify a shift in a historical pattern. It relies upon statistical techniques to differentiate meaningful changes in customer behavior from random patterns that are not significant or may be ephemeral.

Customer behavior is inherently complex and difficult to interpret and predict. Their decisions are influenced by many factors, some of which are more stable, and some of which change frequently, and which have varying levels of impact on their behavior. A customer’s lifestage, for example, changes relatively slowly, but significant life events (e.g., graduating from college, getting married, having children) tend to be inflection points with drastic shifts in customer behavior that settle into new patterns. While signal detection may not explain the underlying reasons behavior is changing, it can tell us that a meaningful change has occurred.

Used in this way, signal detection can also point to emerging patterns that indicate customer missions are changing. A “mission,” for our purposes here, is simply defined as an in-the-moment want or need; a goal to be achieved, or job to be done. Whereas there is an underlying human core need for food, a mission would be defined as a person’s search in the aisles of their local grocery store for the ingredients to make a meal. One way to view the role of your business in your customers’ lives is your effectiveness in helping them fulfill the missions for which you are relevant.

Customer missions can range from the functional (e.g., replacing a broken washing machine) to the emotional (e.g., signing up for a new video streaming service to lift one’s spirit). As mentioned above, they may result from a lifestage change, or be stimulated by an external event. At present, an external event of historical proportions is driving dramatic changes in customer missions. Our data may not fully explain the functional and emotional drivers motivating them, but they give us important clues.

Here’s a simple example: While we can reasonably expect basic needs like food, medicine, and shelter to remain relatively constant, the customer’s mission around these needs is shifting. Previously, one might have gone to the store as needed for medical supplies and toilet paper, whereas right now we’re on a preparedness mission and stocking up proactively. Our data will show out-of-pattern spending on toilet paper, hand sanitizer, and flour. On the surface, these might appear unrelated. But as humans, we recognize patterns here: in a pandemic, these are supplies we can’t do without, and we’re stocking up.

If you’re like most businesses, you don’t have a documented inventory of the typical missions of your customers. But studying your customers’ “jobs to be done” can be a powerful way to understand where you are and aren’t meeting their needs effectively. The pandemic is stimulating new customer missions and putting others on hold. If we start first with statistical observation of changes in behavior (signal detection), then run the results through our own lens of mission detection, we can arrive at significant insights.

Here’s an example from long before the current pandemic. In the mid 1990s, Target was introducing full-range grocery via the SuperTarget concept. The first SuperTarget store was opened in Omaha, NE, and merchandising analysts noticed some odd patterns in their data, including a lot of lawn mowers being purchased (yes, Target sold lawn mowers back then). In fact, it shortly became the top lawn mower store in the entire chain. Why would adding food cause a spike in the sales of lawn mowers? Talking to front-line employees (who observe customers directly every day and are an often under-utilized source of insight) yielded the answer: wives brought their husbands to the new store to check it out, and the men promptly wandered to more “male-oriented” departments while their wives shopped for groceries. After digging further into the data, they saw spikes in automotive supplies, tools, and home repair products as well.

Traditional product-oriented analysis would suggest adding inventory in these categories and calling it a day. But a customer mission lens causes one to probe deeper: what missions are these men on? Why wasn’t Target their destination for these missions in the past? Will this new trend be sustained, or will it die off as the novelty of the new store fades? And critically, what could we do now to permanently win these customers over as the long-term destination for these missions?

These opportunities are lurking in your data today. The obvious ones, such as the flour shelves being decimated in every grocery store, require no advanced analytics. We know what’s going on … or do we? Have grocery chains dug into who is buying all that flour (which segments), and, critically, who is re-buying, which indicates they’re actually baking with it and not just stashing it in the pantry? Have they studied what else they’re buying that would indicate how they’re using the flour? Can they guess at which customers are more advanced bakers vs. newcomers to bread-baking, for instance, and thought about how to serve their missions differently? Can they turn these pandemic-induced bakers into life-long bakers? Can bread baking be a gateway to other baking and cooking habits, supported and encouraged by grocery stores? How could partnerships with local chefs and bakeries, whose workers are currently furloughed, enrich the experience? And, critically, how would we effectively sub-segment all those flour buyers, and get the right messages to the right customers, supporting their missions and stimulating new ones?

Thus, signal detection has to include the who element—we have to look for micro-signals with pockets of customers, ask ourselves what they are telling us about those customers’ missions, talk to front-line employees or the customers themselves, and start experimenting with ideas to capitalize on what we’re learning.

All that long-term data you have can come into play, appropriately used. The grocery stores, for instance, can probably tell which customers are more advanced home cooks, or are vegetarian, or are doing a lot of grilling, etc. Even in a pandemic, those past behaviors are relevant to interpretation of the new data. But look more carefully than ever for the signals of change in that behavior. And after the new normal settles in, post-pandemic, which it inevitably will, remember those signal detection skills, continue to ask what missions they suggest. You’re building a skill that will be useful long past the current crisis.




Traditionally, companies define their “best” customers as those who spend the most with their brand. While purchase behavior is clearly one important measure of customer engagement, this transactional view of customer value is limiting. What about customers who are consistently advocating for your brand, have referred dozens of customers, or influenced others through detailed product reviews? What about customers who are giving you nearly 100% of their share of wallet, even if they’re spending less in total than customers who are only giving you a fraction of their category spend? A focus exclusively on spend is particularly short-sighted in our current economic situation, where customers’ ability and willingness to purchase are being directly influenced by factors out of their control.

Many companies supplement their customer spend monitoring with some form of customer engagement metric, typically captured in the form of survey responses that measure customer satisfaction or willingness to promote a company’s product or brand. While these responses can provide a valuable “in-the-moment” read on customer sentiment, you can’t make the assumption that checking a box that says “satisfied” on a questionnaire means customers are meaningfully engaging with different elements of your value prop. It’s time for a better approach to customer engagement measurement that considers the various ways customers can engage with you, the degree to which they’re engaging, and the relative value of those different types of engagement.

If the primary goals of engaging with your customers are to increase spend, retention, and satisfaction, you must begin with measuring—at the individual customer level—all of the ways that customers can engage with your company. This includes if they are responding to your outbound communications, how they are seeking out content and information, if they are adopting your products and services, and the degree to which they are advocating for your brand publicly. Only by studying as many aspects of customer engagement as possible can you truly understand the unique ways that each of your customers want to interact with your brand, which of those interactions have the most short- and long-term monetary value, and how to create truly personalized customer interaction strategies. As historical patterns of spend and behavior continue to change, now is the time to move the goalposts and create a better definition of customer engagement that looks beyond spend.



Setting purchase behavior aside for the moment, there are four categories of engagement that every business should be studying today.

When you reach out to a customer, does he or she respond in some way? This can include email opens and clicks, SMS reads and replies, survey responses, and any other signals that the customer has read, interacted, and/or replied to an outbound communication. Also make sure to consider other outbound communications that are particularly relevant or unique to your company, like sales associate outreach or mobile app push notifications. Responding when you reach out is an important indicator that customers are receptive to what you have to say.

Is the customer proactively coming to you for content or information? This can include direct website visits, mobile app downloads, and customer service interactions. We also consider the sharing of personal data as part of this category, since customers do so in order to seek out something from your company—even if it’s just a more personalized experience. A seeking action indicates that a customer thinks you may be able to address one of their needs.

Does the customer take advantage of your products and services? While this certainly includes breadth of products purchased, it also includes the degree to which customers take advantage of any supplemental services, programs (such as a loyalty program), and purchase channels. As you invest in new elements of your customer experience, identifying which customers take advantage of those features can help you measure the ROI on your investment. These customers may also be great allies in helping you refine new offerings before you release them to the entire customer base.

Is the customer talking about your brand? Engaging with you directly on social media, writing product reviews, and referring other customers to you are all important ways that customers can advocate for your brand. These forms of influencing can be even more valuable to your brand than that individual customer’s spend. Keep in mind that if customers are frequently talking about your brand, it may not always be positive. If a customer provides thoughtful reviews for every purchase, some of those may be negative. A critical review is still a form of engagement, and there is value to it. It gives your company an opportunity to learn and correct, and can help other customers decide if a product is right for them, potentially preventing additional negative experiences or costly product returns.

While the specific elements of engagement within the four categories outlined above will vary slightly for your company, the most important first step is to reimagine your definition of customer engagement by identifying the new relevant engagement metrics for your brand. Even if you do not yet have access to all of the data you are interested in right away, that is okay. Simply capturing a list of what you want to measure is a step in the right direction.

Also, keep in mind that customers are not likely to neatly fall into one of these engagement categories. It may be tempting to assume that your “best” customers are those who participate in all types of engagement. In reality, customers will have various degrees of engagement—some may engage deeply within a single category, while others may engage at a surface level across all four categories. Instead of trying to get every customer to engage in every way, recognize how customers want to engage and leverage those preferences and strengths to deepen your relationships and advance your brand.

As you become more skilled in this practice, you can overlay purchase behavior on top of these other forms of engagement to understand how various behaviors and engagement journeys correlate to revenue. With so much uncertainty still in the market, it may take time for you to map the relationship between engagement, purchase behavior, and bottom line performance, but now is the time to start building an updated view of engagement that will enable you to meaningfully interact with customers, even during a time where their ability and willingness to purchase is in flux.




If you are like most companies, over the past several months you have been working tirelessly to adjust to the new economic climate—changing how you do business, starting to reopen operations or planning for reopening, and modifying your expectations of “normal” customer behavior. The term “new normal” has become a common phrase to describe a general expectation that people will not return to the exact same behavioral patterns as before the pandemic. While the effect of the virus still has no end in sight, you still have to continue making informed, long-term decisions about your business. The question is, what signals within your data should you trust as more than merely temporary blips on the radar?

For example, panic buying at the outset of the pandemic disrupted our supply chain, so much so that toilet paper manufacturers are still working to level out the balance of supply and demand again. And just try to find disinfectant wipes at your local grocery store. However, we shouldn’t expect the true need for toilet paper and wipes to forever stay at the crazy levels of these early days—the pandemic didn’t transform us all into a nation of hoarders.

Conversely, we have also seen a massive shift in some aspects of consumer behavior since the crisis began, especially for those of us who are now working from home (e.g. telemedicine, online shopping with curbside pickup, Zoom meetings)—and these trends show no signs of slowing. You have likely already placed a heavier emphasis on your longer-term digital strategy to stay on top of this move. What other signals are you seeing that warrant your attention, and might indicate the beginning of a stabilization period? 

Actually, “stabilization” is a bit of a misnomer—for most industries, it will take quite some time to learn what the “new normal” means for your organization. But this does not mean you should wait to begin your analysis. If you were lucky enough to stay open during this crisis, or even if you’re just beginning to re-emerge in recent weeks, you have data about your customers and their current purchasing patterns. And you should be leveraging this asset to make smart, data-driven bets on the future direction of your organization.

First of all, those year-over-year same-store comps you’ve been using out of habit for years can be thrown right out the window for the next year or so now that the world has been disrupted. They’re only useful for the shock value at this point. Instead, as we outlined in one of our previous articles, “How Are My Core Customer KPIs Performing,” there are three core KPIs you should evaluate, namely: number of customers, transactions per customer, and spend per transaction. At a bare minimum, you should be capturing these KPIs on an ongoing basis and analyzing trends on a week-over-week and month-over-month basis. These metrics are often used by start-ups with less historical data, relying on more near-term sales patterns to evaluate the trajectory of the business. In effect, COVID-19 has knocked many companies back into start-up mindsets, such that a refocusing on these simpler metrics makes solid business sense.

But looking at these trends in aggregate isn’t nearly good enough—your customers are not all the same as each other. You should be looking for patterns within different dimensions that are relevant for your business and industry, including customer segments (if you have them), channel, geography/region, and product category. Different groups of customers may be interacting with your brand in different ways, and you want to learn from this behavior to guide your decision-making.

Keep in mind that for this level of customer analytics, the goal is not to challenge yourself to identify patterns that indicate a larger-scale, economic turnaround. Tracking macro-economic trends via the right external data sources, as we’ve also written about in “What External Data Should I Be Looking At,” can be helpful for that endeavor. But you don’t need to be a trained economist to evaluate when your business is emerging from the crisis. In this case, you’re hunting for micro-trends within the data you are collecting about your unique set of customers.

However, looking at historical activity to monitor customer behavior is just the starting point. Even with just a few weeks or months of data, you can begin leveraging machine learning techniques to predict what you might expect to see in the weeks and months ahead. And you don’t need to buy the fanciest AI solution on the market to do this—this is the realm of time series forecasting, a tried-and-true set of techniques that has been used by statisticians and data analysts for many years.

Time series forecasting, when done correctly for relevant customer groups, can help you decompose certain components of the data—seasonality, noise, and actual trend—that will allow you to make more informed decisions about which patterns are emerging, and which you will need to collect more data for before concluding there is actually a pattern. There are also modeling techniques (note: for any data scientists or stats nerds reading this, look up “Bayesian structural time series”) and supplemental methods that can help you quantify the uncertainty around future predictions of purchasing behavior.

Finally, remember that averages are evil. Even within customer cohorts that are similar in some respect (e.g. region, spend band, channel), individual customer behavior spans a distribution of values. For each KPI or behavioral attribute you are measuring and monitoring, you’ll want to review how the distribution is changing over time. A simple yet effective tool to do this is to capture not just the average, but the “five number summary”—the minimum, 25thpercentile, median, 75th percentile, and maximum values within the range of the variable measurement. Note that when the distribution of a particular variable is “skewed”—when there are a lot of small values such as low-frequency purchasers or a lot of high values like revenue from your best customers—the median is more effective to track over time than the average.

In conclusion, many of the new customer needs we are seeing warrant more than a short-term reaction from companies. In that sense, the “new normal” is less of a state of being that is eventually realized, and more of an evolution over time that you will need to adjust for based on data. The companies who can detect and react to the right signals will emerge from this crisis as victors. This is not survival of the fittest; rather, it’s the survival of the most adaptable.


We put an immense amount of thought into these six customer data questions you should be asking, but realize you probably have several additional questions on your mind. All of our authors have agreed to make themselves available to answer your specific questions, so please don’t hesitate to reach out.


Elicit is a world leader in customer science strategy and consulting. Our team of data scientists, technologists, and strategists conduct advanced analytics, data strategy, research, technology design and system integration, and business strategies that result in stronger customer engagement and increased profits.


These six authors have over 100 years of vast industry experience between them. And while they received the bylines, each of the articles written were reviewed and influenced by the collective brainpower of Elicit.

Mason Thelen

Chief Executive Officer

Chuck Densinger

Chief Operating Officer

Brooke Niemiec

Chief Marketing Officer

Jim Sawyer

Chief Scientist

Lauren Drexler

Sr. Engagement Director

Shelly Rosenblum

Director of Data Science