1 Web Appendix OPERATIONALIZATION OF CUSTOMER METRICS FOR NETFLIX AND VERIZON WIRELESS Netflix reports all required customer metrics (profit contribution, acquisition cost, retention cost, and retention rate) quarterly (Wiesel, Skiera, and Villanueva 2008). For Verizon, information on the company's wireless business, which contributes the vast majority of its profits, is almost as extensive. We calibrate our model using these public data. Determining Profit Contribution and Acquisition Cost To determine the profit contributions for Netflix, we follow Gupta, Lehmann, and Stuart (2004) and Libai, Muller, and Peres (2009), who separate customer-specific costs from indirect costs by regressing operating costs on the number of customers, then using the constant of the regression as the indirect cost. This approach presumes that indirect costs are indeed fixed and do not vary over time, an assumption that can be unsuitable for companies whose indirect costs vary for some reason that is unrelated to customer development and is not included in our regression. In our analysis, however, the basic regression without additional variables produces plausible and remarkably stable results as more observation periods join the analysis for the later years. Profit per customer is then profit contribution divided by the number of customers in the respective period. In its annual reports, Netflix (2008) claims that it does not run marketing campaigns aimed at customer retention, so we follow Wiesel, Skiera, and Villanueva (2008) and calculate
2 the acquisition cost per customer by dividing the total annual marketing expenses by the number of new paying customers per year. For Verizon, we follow the same approach but extend the regression to include the number of new customers acquired in a given period as an independent variable. The corresponding coefficient then indicates the acquisition cost, which is not explicitly reported in Verizon's public reports. Calculation of Retention Rate For Netflix, we calculate customer churn for paying customers (i.e., customers not in a free trial period) by dividing the number of lost paying customers in period t by the average total number of paying customers during the period: (W1) Churn t LostCustomerst 1rt Customerst1 Customerst 2. For Verizon Wireless, we use the churn rate specified in the company's quarterly reports. Calculation of Lifetime Value. To calculate the lifetime value of a current subscriber (CLV current ), we combine information about the subscriber's average quarterly profit contribution (p), retention rate (r), and discount rate (d) in Equation W2: (W2) CLV current p r t.5 t t. t.5 t0 1 dt Because we rely on discrete formulations, we take care to incorporate the timing of revenues, associated costs, and customer churn properly. In Equation W2, we assume that customers pay and churn steadily during the periods (t), though we could modify this assumption in several
3 ways to indicate that customer payments and churn take place at the beginning or the end of a period only (for alternative models, see Donkers, Verhoef, and de Jong 2007; Hogan et al. 2002). Wiesel, Skiera, and Villanueva (2008) argue that the calculation represented by Equation W2 may lead to underestimations of the value of the customer base if customer retention rates are very heterogeneous, in which case Equation W2 should include a factor that adjusts for the heterogeneity of retention rates across customers. Unfortunately, modeling heterogeneity requires more detailed customer data that are not publicly available in our case but that may be available for internal use. Neglecting this heterogeneity might have some effect on the absolute values we derive, but it should not significantly affect our relative comparisons, because the possible errors likely cancel out each other (Wiesel, Skiera, and Villanueva 2008). Equation W2 also can be simplified and rewritten as Equation W3 if the profit contribution, retention rate, and discount rate are stable. This assumption receives support from the lack of clear positive or negative trends in the historical patterns of the input metrics, which matches prior findings for other companies (Gupta, Lehmann, and Stuart 2004). (W3) CLV current.5.5 1d r p. 1d r The lifetime value of future customers must also account for customer acquisition costs (acqc). The lifetime value of future customers (CLV future ) is: (W4).5.5 1d r CLV future p acqc. 1d r
4 Prediction of Future Customers for Verizon Wireless To account for the acquisition of Alltel Wireless by Verizon Wireless in 2009, we extend the formula for predicting future customers in Equation 4. The additional Alltel customers simply join Verizon's existing customer base in its public reports, so we have no means of distinguishing them; we adapt Equation 4 to include a dummy variable for the Alltel acquisition (alltel) that extends the asymptote α upward by αalltel for the first quarter of 2009 and thereafter: (W5) N net t alltel alltel. 1 exp t OPERATIONALIZATION OF CUSTOMER METRICS FOR VERIZON WIRELINE The available information on customer metrics for the wireline business that Verizon operates in 13 U.S. states is less extensive than that for the company's wireless business. Although Verizon reports the net development of switched access lines quarterly, gross newly acquired customers per period, and thus customer churn, are unknown. Yet Verizon reports customer and financial information for wireline and wireless separately, so the accuracy of our calculations for the wireless business (i.e., the majority of company profits) is not affected. For the wireline segment, we use available data to make separate predictions for the development of switched access lines and the corresponding margins, without considering customer acquisition costs or customer churn. For growing companies with high acquisition costs and a high churn rate, this approach will likely produce upward-biased results. Although acquisition costs and the churn rate are unknown, Verizon s wireline business is declining, so the potential upward bias created by the lack of detailed customer data in our calculations should be
5 relatively small. This segment also constitutes only a small part of the total profit, which further decreases its likely influence on our overall assessment of the company. OPERATIONALIZATION OF FINANCIAL METRICS Calculation of Cost of Capital We calculate the beta for the capital asset pricing model (CAPM) using up to five years of available data on the companies' historic monthly stock prices and quotes for the S&P 500 index. The market premium equals the average difference between the annualized return from the S&P 500 and three-month U.S. Treasury bills over 30 years. For Verizon, we find relatively low betas (from.6 in 2009 to 1.38 in 2007). The weighted average cost of capital (WACC) further benefits from Verizon's financial leverage and the comparably low cost of debt. When incorporating the company's five-year average tax rate of 20.8% as a tax shield, we calculate annual discount rates between 7.7% in 2009 and 12.2% in 2007. Netflix s betas were higher and generally stable between 2004 and 2007, ranging from 1.95 to 2.05. In 2003, the beta was only 1.48, likely due to the lack of historical data from the young company. In 2008, Netflix clearly outperformed the market, and its beta decreased to 1.60. Netflix's combined corporate and state tax rate without reductions is 40% (Netflix 2008). Corresponding annual discount rates range from 10.5% in 2008 to 15.3% in 2004, a plausible result. Calculation of Non-Operating Assets Netflix explicitly reports working capital, so we can calculate non-operating assets by subtracting working capital from cash, cash equivalents, and short-term investments. Verizon
6 does not publicly report working capital. We make the conservative assumption that all cash resources are required as working capital (and thus excluded from non-operating assets); thus, we consider only Verizon's short-term investments and its other minority holdings in unconsolidated businesses as non-operating assets.
7 REFERENCES Donkers, Bas, Peter C. Verhoef, and Martijn G. de Jong (2007), "Modeling CLV: A Test of Competing Models in the Insurance Industry " Quantitative Marketing and Economics, 5 (2), 163-90. Gupta, Sunil, Donald R. Lehmann, and Jennifer A. Stuart (2004), "Valuing Customers," Journal of Marketing Research, 41 (February), 7-18. Hogan, John E., Donald R. Lehmann, Maria Merino, Rajenda K. Srivastava, Jacquelyn S. Thomas, and Peter C. Verhoef (2002), "Linking Customer Assets to Financial Performance," Journal of Service Research, 5 (1), 26-38. Libai, Barak, Eitan Muller, and Renana Peres (2009), "The Diffusion of Services," Journal of Marketing Research, 46 (April), 163-75. Netflix (2008), Netflix Annual Report 2007. Los Gatos, CA: Netflix. Wiesel, Thorsten, Bernd Skiera, and Julian Villanueva (2008), "Customer Equity An Integral Part of Financial Reporting," Journal of Marketing, 72 (March), 1-14.