When is the last time you took stock of your customer base? Two marketing and data experts say it’s due for a systematic review.
In this edition of Author Talks, McKinsey’s Anita Balchandani chats with Peter Fader and Michael Ross about their new book, The Customer-Base Audit: The First Step on the Journey to Customer Centricity (Wharton School Press, November 2022), cowritten by Bruce Hardie. Fader, a marketing professor at the Wharton School, and Ross, chief retail scientist at EDITED, have combined data science and long-term thinking to re-center the customer’s perspective in business decision making and provide a universal tool for assessing the health of your buyer base. An edited version of the conversation follows.
Why did you write this book now?
Peter Fader: It’s a combination of different factors. One comes from the data side. Along with our third coauthor, Bruce Hardie, at the London Business School, I had been pushing this idea of customer-base analysis and understanding your customer base at a deep, granular, rigorous level for quite some time. We developed a lot of forecasting models and mathematical tools to help enable that.
It begins with an audit, with a formal looking backward at the customers before we look forward and make forecasts. Part of it relates to the academic research, and part of it relates to a lot of the industry work that I’ve been doing. I’ve been writing other books on customer centricity about how not all customers are created equal. If we understand the differences and leverage them, we can do very well for ourselves and our customers.
Before we take that for granted, before we just believe that all customers are not created equal, we need to convince ourselves of that, and there’s no better way to do that than with a customer-base audit.
Michael Ross: When you’re in a business that’s growing through customer acquisition and retention, if you don’t have a fundamental understanding of how much of your growth is coming from your existing customer base and how many new customers you need to acquire to deliver on your business plan, you get into a world of pain.
I’ve had a 20-year journey of recognizing how critical it is to understand customer-base growth in customer-centric businesses. I think during COVID-19, businesses that had historically thought about customers through the lens of channels recognized, “You know what? Channels are not how customers think.” Unless you start understanding how your customers behave, you have a very poor understanding of what’s driving your long-term growth.
I think the timing [for this] is fantastic, because as we come out of COVID, every customer-centric business on the planet is trying to understand, “What does this new reality look like?” You have to look through the lens of the customer.
Every business does some customer analysis. That’s a truism, but what I think is missing is a systematic, structured audit that consists of a set of foundational analyses that leave nowhere to hide, and that allows you to understand at a very deep level how customers are behaving.
What are the most important consumer analyses from this book?
Michael Ross: The first one would be the customer cohort chart: the C3. That really is, I would say, the number-one analysis. It highlights and breaks down how your revenue in each year builds up from customers acquired in every previous period. It gives you a very clear view of the extent to which your growth is being driven by your existing base versus new customer acquisition.
Number two for me is looking at the distribution of customer value. When you look at a distribution of customer value, you will often see that a high-value customer—what we’d call a top-decile customer—can be worth 40 or 50 times a low-value customer. The notion of an average customer may be mathematically true, but it doesn’t really exist in practice or is extremely unrepresentative of a customer base.
That’s a very, very powerful analysis because it motivates a business to think about the decisions and actions that they’re currently taking based on the idea of an average customer, whether those are marketing decisions, operational decisions, service decisions, etcetera. How do you reorient decisions that are better aligned to customer value?
The third most important analysis for me, and this is a hard one, is the time between first and second purchase. Understanding the time between first and second purchase is incredibly helpful. It is a very good discipline to understand, “Why do customers only buy once? How can we get them to come back? How can we get them to come back faster?” That drives a lot of very good behaviors.
How far along the customer-base journey are different companies and industries right now?
Peter Fader: Ultimately, we hope that this book will be a “North Star” for all industries and all companies, and that it gets them to effectively do the same kinds of analyses so we can make more apples-to-apples comparisons across seemingly unrelated businesses.
That’s a real goal of ours, so managers can no longer say, “Well, we’re different. You can’t compare us to anybody else.” We’ve seen very different capabilities from one company to another, whether it’s because of tradition, or whether it’s because of the data collection technology they have at their fingertips.
For instance, in the gaming industry, they know every game you play, how often, and who with. It’s easy to look at some of those longitudinal slices for customers, whereas in other settings—in consumer packaged goods, for instance, or in quick-service restaurants—it’s easy to get a one-time look to know how many transactions were made, but it’s hard to link them together over time for particular kinds of customers.
What is holding businesses back from conducting better customer-base audits?
Michael Ross: If you go into any business and ask who the natural owner of this sort of customer analysis is, it’s typically the CMO [chief marketing officer]. That’s not a problem in and of itself, but a lot of this analysis, if it’s done, is done to solve marketing use cases rather than being used as more of a strategic unlock.
The opportunities to use customer analysis to change behavior go way, way beyond what is in the marketing domain—it goes to how we think about profitability, how we align channels, how we align the service propositions, how we align the operational experience, and how we understand which elements of products or categories are good for acquiring and retaining customers.
The key to this is engaging a CEO, and that’s challenging because CEOs’ agendas are packed. There are lots of things competing to get on onto their radar screens. A key to change the conversation and change business behavior is to say, “How does this become part of a weekly conversation or a monthly conversation?” For me, I think a key driver is how you make this sort of customer conversation part of the regular cadence.
One business leader I was talking with recently said, “Michael, [customer analysis] is part of our training pack, it’s just that it’s right at the end of our training pack, and therefore we never get to it.” One of the actions I recommended was to put it at the front of the training pack so that it was the first thing they talked about on a Monday morning, rather than it being in the appendix.”
How are companies grappling with changing consumer behaviors amid existing challenges like inflation and downtrading?
Peter Fader: We’re riding a longer-term wave. In general, companies are becoming more interested in what customers are doing, how they differ from one another, and how they change over time. There’s been, overall, a rising tide of desire to understand customers and weave them into a variety of business decisions.
In particular, there’s a real focus on value these days. Whether it’s within the company or outside, investors and other stakeholders want to know what kind of ROI they’re getting on different kinds of marketing activities, product development partnerships, and channel strategies, and how much more we’re able to get out of customers: Are they staying with us longer? Are they buying more often? Are they spending more when they do? These kinds of questions are coming front and center.
The idea of looking at a group of customers now versus, say, last period—whether that’s a quarter or a year ago—is one of the lenses we discuss in the book. Whether we want to evaluate the impact of a pandemic, a holiday season, or a potential recession, we’ve got that covered.
Michael Ross: Many businesses are asking these questions of how customers are behaving and how that behavior is changing. What we often see is a go-to segmentation.
There’s typically some sort of persona segmentation—young mothers or older customers, for example—and the challenge I see with that approach is that those segments are, by definition, a bit vague. The membership of segments changes over time, and you end up with an incredibly confusing and complex analysis to understand if the segment is performing less well or if the membership of the segment is just changing.
We believe very firmly that there’s something foundational about cohorts. When we say “cohort,” we mean a group of customers that were acquired in a period, whether that’s a monthly cohort, a quarterly cohort, or an annual cohort. The 2021 cohort is a group of customers who made their first purchase in 2021.
What’s powerful about a cohort is that the membership of that group never changes. Once someone is in a cohort, they’re always in that cohort. Therefore, you can track the behavior of that cohort over time and understand if it’s growing or declining.
Not only is this sort of membership immutable, but if you look at the entire customer base, every customer is in a cohort. Cohorts completely cover the customer base. For me, that makes it a very compelling and powerful way to answer questions about customer behavior. It’s not that demographics and persona-led segments aren’t helpful. For me, they are at a different level to a cohort analysis, which I would say is foundational.
What advice do you have for CEOs who are navigating turbulent times?
Peter Fader: Don’t relegate the customer analyses to someone who is a level or two below you. You, as the CEO, need to care, to dig in, and to be held accountable for the behavior of your customers. It hasn’t been that way, historically.
The customer is your most important asset, whether it’s showing up on the balance sheet or not. So understand those customers at a deep, granular level. Look at them through different lenses, and you’ll see your business very differently.
Michael Ross: If we take anything from what’s happened over the past few months, it’s the primacy of understanding profitability. Acquiring customers at any cost and keeping them happy at any cost is not a long-term recipe for success.
Therefore, truly understanding how you build a sustainable growth plan based on cost-effective acquisition and retention of customers is absolutely key. Profits and losses of the individual customer is the new unit of analysis for every business.
Peter Fader: It’s fascinating to see just how different products are when we look at them through the lens of customers. Instead of asking, “Which products do we sell the most of?” ask, “Which products are disproportionately appealing to the customers who have been with us the longest and buy most often?”
It’s not a one-to-one match: there are some products that might be relatively small but are uniquely appealing to customers who appear to be the most valuable.
Once we start looking at the actions we take, whether it’s with products, channels, or other activities, through the lens of customer value, it gives us a lot of strong advice about how to manage and develop products and other tactical elements.
Michael Ross: The data set we started off with is amazingly simple. It’s a list of transactions with a date, an amount, and a product ID. What surprised me is just how many analyses it’s possible to do with that sort of data. I like to think of myself as pretty analytical. I like to think I’m good at this sort of thing, but I was genuinely blown away by just how much it’s possible to squeeze out of these data sets.
We’re not using sophisticated machine learning or artificial intelligence—quite the opposite. What we’re doing is a set of descriptive analyses, but it’s amazing how complex some of these things are in practice. I look back and think, “Wow, this is stuff that I wish I’d known 25 years ago.” It all seems pretty obvious when it’s in the book, but it really isn’t.
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This article first appeared in https://www.mckinsey.com/
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