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Spotlight on the loyalty economy: How to value a company by analyzing its customers

In the weeks leading up to the initial public offering of apparel retailer Revolve Group, in June 2019, investors struggled to come up with a fair valuation. Several recent IPOS — most notably those of the ride-hailing firms Uber and Lyft — had been disappointing. Revolve had delayed its IPO for months because of a downturn in the stock market. Despite the headwinds, its IPO was priced at $1.2 billion — and it exploded by an additional 89% on its first day of trading, making it one of the best first-day IPO performances of 2019. What happened, and why did investors originally fail to see just how strong a firm Revolve was?

Revolve’s premium valuation stemmed from the firm’s strong underlying fundamentals. This strength was less about top-line revenue growth and more about strong customer-unit economics: Simply put, Revolve not only acquired its customers profitably but retained them for many years, and that meant its longerterm profit potential was larger than its revenue growth to date had implied.

Revolve’s IPO success illustrates the movement toward customer-driven investment methodologies. Using customer metrics to assess a firm’s underlying value, a process our research has popularized, is called “customer-based corporate valuation” (CBCV). This approach is driving a meaningful shift away from the common but dangerous mindset of achieving “growth at all costs” — and bringing a much higher degree of precision to the new loyalty economy.

In this article, we explain how executives and investors can use the principles of CBCV to better understand and measure the value of a firm. The methodology works whether the company features a subscription-driven revenue stream (think of Netflix) or a base of active customers who place discretionary orders every so often (think of Uber). We also discuss how companies can benefit from providing investors with more of the right kinds of customer data — and how investors can avoid being fooled by vanity metrics.

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The premise behind CBCV is simple. Most traditional financial-valuation methods require quarterly financial projections, most notably of revenue. Recognizing that every dollar of revenue comes from a customer who makes a purchase, CBCV exploits basic accounting principles to make revenue projections from the bottom up instead of from the top down. This brings more focus to how individual customer behavior drives the top line.

What do we need to implement CBCV? In addition to the usual financial statement data, two things are required: a model for customer behavior (what we call the customer-base model), and customer data that we feed into it. The model consists of four interlocking submodels governing how each customer of a firm will behave. They are:

1. The customer acquisition model, which forecasts the inflow of new customers.

2. The customer retention model, which forecasts how long customers will remain active.

3. The purchase model, which forecasts how frequently customers will transact with a firm.

4. The basket-size model, which forecasts how much customers spend per purchase.

Bringing these models together enables us to understand the critical behaviors of every customer at a firm — who will be acquired when, how much they’ll spend over time and so on. Summing up all the projected spends across customers gives us our quarterly revenue forecasts. Together, these models can produce much more precise estimates of future revenue streams — and from that, one can make much better estimates of what a company is really worth.


Although the CBCV methodology may seem daunting, it’s relatively simple to get going.

Imagine you’re the founder of a young, fast-growing, subscription-based meal-kit company. In its first four months of operation, your company generated $1,000, $2,500, $4,500 and $7,000 in total revenues respectively. You’d like to understand what this means for future revenues and the overall viability of your business. As a start, you want to forecast revenue in month five.

Let’s suppose that active customers pay a flat fee of $100 per month for meal kits delivered over the course of the month, and that the company acquired 10, 20, 30 and 40 customers, respectively, in its first four months of operation (100 in total). Half the acquired customers churned out in their first month; all customers who did not churn out in the first month have remained.

The first step in forecasting month five revenue is to figure out how much revenue will come from retained customers. Of the 100 customers acquired over the first four months, half, or 50, will still be with the company in month five if historical retention trends persist. Thus, the portion of month five revenue from retained customers is $5,000 (50 x $100). The next step is to forecast how much revenue will come from new customers. Assuming that acquisition trends continue, you can expect an additional 50 customers, representing $5,000 of revenue. By adding up the two forecasts, you arrive at a total monthly revenue of $10,000.

Using the CBCV approach, revenue numbers no longer exist in a vacuum. Instead, they are a direct function of a small set of behavioral drivers — in this example, total customers acquired, retention dynamics and average revenue per user.


The richness of the insights that can be derived from CBCV depends on how much access the person performing the analysis has to internal company data. A corporate executive would have full visibility of all customer data. A private equity investor assessing an acquisition target would typically have access to transactional and customer relationship management data. For subscription firms, that would include the length of contracts, periodic payments and observable churn; for nonsubscription firms, it would include the timing and size of each individual purchase.

For those on the outside looking in — hedge funds, Wall Street analysts and others — detailed customer data might be impossible to obtain on a regular basis. They may, however, have access to the firm’s customer cohort chart, or C3, which tracks revenue by acquisition cohort over time and shows how total customer spending changes as each cohort ages. Many large firms have begun to disclose their C3. A firm’s C3, along with the number of active customers and the total number of orders, is sufficient to give investors a good understanding of customer behavior.

If a firm can’t or won’t release its C3, investors should press it to reveal four key metrics: the number of active customers, gross acquired customers over the most recent period, revenue and the number of orders.


Few companies currently provide all the data outsiders need to perform CBCV, for a variety of reasons. First, disclosure of customer metrics is voluntary, and companies feel little to no pressure to make them available. Second, there is little consensus about which customer metrics are the most informative and how those metrics should be calculated and reported. And finally, policymakers and regulators have been largely silent about these issues, leaving disclosure to companies’ discretion.

Unfortunately, executives often fear that additional disclosure could put them at a competitive disadvantage or open them up to potential litigation. Successful firms worry about how investors will react if the metrics they’re disclosing start going in the wrong direction. In the absence of investor pressure and regulatory standards, firms can arbitrarily choose which metrics to disclose, generally selecting those that paint an overly rosy picture for the investment community.

If you are an investor, don’t ignore the customer-related metrics that may be tucked away in financial reports; seek them out. If the data you need isn’t disclosed, demand it, or find alternative sources. If you’re an executive and you aren’t currently disclosing your customer metrics, start thinking about the story they would tell if disclosure were required. If you would not be proud of your metrics as they stand, this is your opportunity to improve the health of your customer base.

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