How To Value A Service Company

In the weeks leading up to the initial public offering of dress retailer Revolve Group, in June 2019, investors struggled to come with a fair valuation. Several contempo IPOs—nearly 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.two billion—and it exploded by an additional 89% on its first day of trading, making information technology ane of the best beginning-24-hour interval IPO performances of 2019. The spike brought the company's valuation to roughly 4.5 times its revenue over the previous 12 months—5 times the multiple of its clothes-retailing peers and more akin to that of a technology company. What happened, and why did investors originally neglect to see just how strong a house Revolve was?

Revolve's premium valuation was non a fluke. Information technology stemmed from the firm's stiff underlying fundamentals, which were non fully appreciated by the underwriters who ready the IPO price. This strength was less virtually top-line revenue growth and more about strong customer-unit economics: Simply put, Revolve not but acquired its customers profitably merely retained them for many years, and that meant its longer-term turn a profit potential was larger than its revenue growth to date had implied.

Revolve'south 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 only dangerous mindset of "growth at all costs" toward revenue immovability and unit economics—and bringing a much higher degree of precision, accountability, and diagnostic value to the new loyalty economic system.

In this article, we explain how executives and investors tin can use the principles of CBCV to ameliorate understand and measure the value of a firm. The methodology works whether the visitor features a predictable, subscription-driven revenue stream (think of Netflix and Verizon) or a base of active customers who place discretionary orders every so oft (think of Uber and Walmart). We too discuss how companies can benefit from providing investors with more of the correct kinds of customer information—and how investors can avoid being fooled by vanity metrics that appear to be useful indicators of customer behavior only aren't as meaningful as they might remember.

A More Precise Way to Forecast Revenue

The premise behind CBCV is simple. Nearly 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. Although this may seem like a radical divergence from traditional frameworks, that'due south non the example: CBCV only brings more than focus to how individual client behavior drives the superlative line.

What practise we need to implement CBCV? In addition to the usual financial statement data, two things are required: a model for customer beliefs (what nosotros phone call the customer-base model), and customer information that nosotros feed into it. The model consists of iv interlocking submodels governing how each customer of a house will comport. They are:

the customer acquisition model, which forecasts the inflow of new customers
the client retentiveness model, which forecasts how long customers will remain agile
the buy model, which forecasts how frequently customers will transact with a firm
the basket-size model, which forecasts how much customers spend per purchase

Bringing these models together enables the states to empathise the critical behaviors of every client at a firm—who will be acquired when, how much they'll spend over fourth dimension, and so on. Summing upwards all the projected spends across customers gives u.s. our quarterly revenue forecasts. Together, these models can produce much more precise estimates of future revenues streams—and from that, i can make much amend estimates of what a company is actually worth.

This basic model is universal, no affair what kind of business a company is in. Exactly how it is specified, nevertheless, depends on the company's business model—in particular, on whether the company is subscription-based or not. At a subscription-based business concern, such as a gym or a telecommunication firm, managers generally know how much customers will spend each calendar month, and they are able to directly observe when customers churn out, considering they literally cancel their contracts and close their accounts. This simplifies how the retention and purchasing submodels are built.

Most companies, all the same, are characterized by discretionary (that is, nonsubscription) purchasing and unobservable customer churn. If you take an Amazon business relationship but decide never to buy from the company once again, for example, it's difficult for anyone inside or outside Amazon to immediately recognize that. Marketers telephone call this latent attrition. Bookkeeping for information technology requires more-complicated submodels, but marketers take developed methods for predicting it extremely well.

Peeking Inside the Black Box

Although this methodology may seem daunting, it's relatively simple to get going, and it tin exist refined and extended as appropriate for particular business contexts.

Let's peek inside the black box through an example. Imagine that yous're the founder of a young, fast-growing, subscription-based meal-kit company. In its first 4 months of functioning, your company generated $1,000, $ii,500, $4,500, and $7,000 in total revenues respectively. Y'all would like to sympathise what this means for future revenues and the overall viability of your business organization. As a commencement, y'all desire to forecast acquirement in month five.

Let'south suppose that agile 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 starting time four months of operation (100 in total). Half the acquired customers churned out in their start month; all customers who did non churn out in the first month take remained.

The first step in forecasting calendar month five revenue is to figure out how much acquirement will come from retained customers. Of the 100 customers acquired over the offset four months, half, or fifty, will all the same be with the company in month 5 if historical retention trends persist. Thus, the portion of calendar month five revenue from retained customers is $5,000 (50 10 $100). The next step is to forecast how much revenue will come from new customers. Bold 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 full monthly revenue of $x,000.

Using the CBCV approach, revenue numbers no longer be in a vacuum. Instead, they are a direct part of a small set of behavioral drivers—in this case, total customers acquired, retention dynamics, and average revenue per user (ARPU). This framework makes acquirement forecasting easier and serves as a diagnostic, helping managers and investors understand where the value creation is coming from (and what questions to ask when results are out of line with expectations).

Teenie Harris Archive/Getty Images
Knuckles Ellington signing autographs in Pittsburgh, 1946

Of form, few companies will accept such simple models and corking patterns as our meal-kit example. Our purpose here is to outline the general mechanics of the approach, every bit extensions of it follow naturally. Suppose, for instance, that your firm has tiered pricing (it too offers a second plan that delivers twice as many meals a calendar month for $189). In that case, you would need to business relationship for variable ARPU from period to period. If the firm allows customers to skip deliveries or make discretionary purchases, you lot would need to track order frequency and average spend per gild. If the firm pivots to sell meals à la carte instead of on a subscription basis, you'll need to use a model that predicts how oftentimes customers will identify orders. These extensions add complexity to the model, simply the bones process to incorporate them would exist the same equally in the instance above. If you want to extend the fourth dimension horizon beyond month 5, you lot can repeat the calculation for multiple months. That gives you lot a long-term acquirement forecast, which is vital for corporate valuation.

For an in-depth discussion of the CBCV methodology in complex scenarios, see our academic papers "Valuing Subscription-Based Businesses Using Publicly Disclosed Client Information" (Periodical of Marketing, October 2016) and "Customer-Based Corporate Valuation for Publicly Traded Not-Contractual Firms" (Periodical of Marketing Research, March 2018).

Looking at Customers from Within and Outside

The richness of the insights that tin can exist derived from CBCV depend on how much access the person performing the analysis has to internal company data. A corporate executive would have full visibility of all client information. A private equity investor assessing an acquisition target would typically have access to transactional and CRM information. For subscription firms, that would include the length of contracts, periodic payments, and observable churn; for nonsubscription firms, information technology would include the timing and size of each individual purchase. Access to other behavioral data, demographics, marketing touchpoints, service interactions, and the like would further enrich the CBCV assay.

For those on the outside looking in—hedge funds, Wall Street analysts, regulators, and others—detailed client data might be incommunicable to obtain on a regular basis. They may, nevertheless, accept access to the house's customer accomplice chart, or C3, which tracks revenue by acquisition cohort over time and shows how total customer spending changes as each accomplice ages. (For an example, run into the exhibit "C3: A New Tool for Corporate Valuation.") Many big, reputable firms (both subscription and nonsubscription) have begun to disembalm their C3, amid them Slack Technologies, Dropbox, Lyft, and luxury marketplaces the RealReal and Farfetch. A firm's C3, forth with the number of agile customers and the full number of orders, is sufficient to give investors a good understanding of customer behavior.

The customer cohort chart is straightfoward: full acquirement, period by period, broken down by customer acquisition accomplice. Though its format may vary, the C3 is increasingly finding its style into companies' public filings, giving investors a powerful tool for gauging what a visitor is actually worth. This example of a C3 comes from Revolve Clothing'south 2019 SEC filing.

If a firm tin't or won't release its C3, investors should press information technology to reveal four central metrics: the number of active customers (in total and the percentage from tenured customers, or customers who have been with the firm for over 12 months); gross acquired customers over the well-nigh contempo period; revenue (total and pct from tenured customers); and the number of orders (total and percentage from tenured customers).

While nosotros would strongly encourage firms to disclose more, having iii or four years' worth of these disclosures (from past filings) is enough to run a CBCV model and assess the overall health of a company's customer base of operations, admitting with greater uncertainty near future revenues.

Trending Toward Transparency

Few companies currently provide all the information outsiders need to perform CBCV, for a variety of reasons. Kickoff, disclosure of customer metrics is voluntary, and companies experience little to no pressure to make them available. 2d, in that location is piddling consensus well-nigh which client metrics are the almost informative and how those metrics should be calculated and reported. And finally, policy makers and regulators have been largely silent nearly these issues, leaving disclosure to companies' discretion.

Unfortunately, executives oft have a "less is more than" mentality regarding disclosure. They fear that additional disclosure, however aggregated the numbers may be, could put them at a competitive disadvantage or open them upward to potential litigation or regulatory scrutiny. Successful firms worry about how investors will react if the metrics they're disclosing start going in the wrong management. And customer-level forecasting often remains siloed in the marketing department; managers in finance and related functions are unaccustomed to incorporating customer behaviors in their revenue forecasts and are more than comfortable using traditional methods.

In the absence of investor pressure and regulatory standards, firms can arbitrarily choose which metrics to disembalm, generally selecting those that pigment an overly rosy picture for the investment community. The metrics are often defined improperly, based on faulty assumptions, or framed incorrectly.

Think about the story your customer metrics would tell if disclosure were required.

Consider Peloton, which sells high-end home-exercise equipment and monthly subscriptions to streaming-video fettle classes. When it filed its pre-IPO S-i, in August 2019, it chose to disclose its customer lifetime value (CLV) per subscriber, boasting a CLV of $iii,593 in its virtually contempo fiscal year. To its credit, Peloton also disclosed the underlying formula it used to compute its CLV, just that formula left much to exist desired. The nearly glaring problem was that it did not account for the time value of money, and instead simply added more than than xiii years' worth of future cash flows without discounting them. Applying fifty-fifty a small-scale discount rate would slash its CLV by more 50%—a driblet with significant implications for the health of the customer base. As more firms voluntarily disclose customer metrics, analysts must be vigilant well-nigh vetting data that may be misleading or is mostly window dressing.

Although Peloton's metrics are far from perfect, they nevertheless represent an encouraging shift toward transparency around customers that will exist good for shareholders, companies, and customers. Shareholders will increasingly rely on customer data to evaluate potential investments every bit more purchases are made online and traditional brick-and-mortar metrics, such every bit same-store sales, decline in relevance. Executives tin can use client data to build the case for investing in activities that volition generate long-term value for the firm and to communicate to shareholders the impact of those investments on CLV and other long-term metrics. Customers will be treated equally strategic assets whose value should be cultivated over the long term. This mindset volition be a welcome change from the status quo, in which shareholders, lacking the information needed to assess long-term customer profitability, compensate by pushing firms to hit brusque-term functioning measures.

Until the CBCV revolution fully takes agree, what does all this mean for you lot? If you are an investor, don't ignore the customer-related metrics that may exist tucked away in financial reports; actively seek them out. If the data you need isn't disclosed, demand it, or discover alternative sources that tin serve every bit effective proxies. Focusing on unit economics volition almost certainly reveal opportunities y'all can exploit.

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 y'all would not be proud of your metrics every bit they stand, this is your golden opportunity to refocus on and improve the health of your customer base of operations in the dark. It may not be long earlier marketplace participants demand sunlight.

A version of this commodity appeared in the January–February 2020 effect of Harvard Concern Review.