Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

Daniel M. McCarthy, Peter S. Fader, & Bruce G.S. Hardie
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Key Takeaways

​External stakeholders (e.g., investors, competitors, suppliers, regulators) can make better short- and long-term revenue forecasts by modeling customer acquisition, retention, and spend using firms’ quarterly customer metric disclosures. 

Customers are long-lived assets, yet current accounting rules do not allow firms to treat them as such. 

Using our customer modeling framework, firms can not only estimate the overall valuation of firms, they can also estimate the expected lifetime value of newly acquired customers and perform customer-based segmentation.

​Article Snapshots: Executive Summaries from the Journal of Marketing​

We develop a framework for linking the value of a firm’s customers to the overall value of the firm, incorporating key empirical realities associated with how customers are acquired and retained, with an underlying valuation model that lives up to the high standards of financial professionals.


Our research is motivated by a desire to show financial professionals that publicly disclosed customer data are very informative for corporate valuation purposes, and by linking customer data to firm valuation, we hope to get marketing departments the visibility they deserve within the C-suite. The gap in the literature to date is that the underlying models of customer acquisition and retention do not adequately reflect empirical realities associated with these behaviors (e.g., the effects of seasonality and customer tenure), and the valuation model used is not up to the standard of finance professionals.


We propose a novel model for how customers are acquired, retained, and spend while they are active. We showed how this model can be estimated with quarterly data reporting total customers acquired and lost, ending customer counts, and revenues. We then apply this framework to data from two public companies, Dish Network and Sirius XM. 


We show through a rolling prediction analysis that we can make very accurate long-term predictions of future customer metrics using the proposed model. Our valuation estimates for the two publicly traded companies studied, Dish Network and Sirius XM, are very close to then-current stock prices. We infer that the average Dish Network customer has a very long expected lifetime (5.5 years) and is quite valuable, with a future lifetime value of $1,426 after he/she has been acquired. 


We show that investors can make better stock price and revenue predictions for companies disclosing simple customer metrics. Knowing this, investors should use our model when analyzing firms that disclose these metrics and should demand that these metrics be disclosed for firms who do not disclose them already. Correspondingly, managers will be more accountable than ever for the drivers of a healthy customer base—namely, how many customers are acquired, how long those customers are retained for, and how profitable they are while alive.

Questions for the Classroom

  • What aspects of how customers are acquired and retained should we make sure to include in our model for them? 

  • Given data on the number of customers acquired and lost each quarter, how can we estimate the parameters of a customer acquisition/retention model?

  • How can we compute customer lifetime value using a customer-based valuation model?

Article Citation

McCarthy, Daniel M., Peter S. Fader, & Bruce G.S. Hardie (2017), “Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data,” Journal of Marketing,  81 (1), 17-35.

Daniel McCarthy is a doctoral candidate, Department of Statistics, The Wharton School, University of Pennsylvania (e-mail:

Peter S. Fader is Frances and Pei-Yuan Chia Professor of Marketing, The Wharton School, University of Pennsylvania (e-mail:

Bruce G.S. Hardie is Professor of Marketing, London Business School, University of London (e-mail:

Author Bio:

Daniel M. McCarthy, Peter S. Fader, & Bruce G.S. Hardie
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