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"Buy Till You Die": New Perspectives on E-Commerce Buying Patterns

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Professor Peter Fader
The Wharton School, University of Pennsylvanian
Co-Director, Wharton Customer Analytics Initiative

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"Buy Till You Die": New Perspectives on E-Commerce Buying Patterns

  1. 1. 1 “Buy Till You Die”: New Perspectives on E-Commerce Buying Patterns Professor Peter Fader The Wharton School, University of Pennsylvania Co-director, Wharton Customer Analytics Initiative faderp@wharton.upenn.edu www.petefader.com Twitter: @faderp  CPA (cost per acquisition)  Big mistake!  Would you use it for other kinds of acquisition activities (e.g., employees, technology, lawyers)?  Firms should focus instead on:  VPA  Value per acquisition, i.e., customer lifetime value (CLV) What metric is most used to gauge and guide firms’ customer acquisition activities?
  2. 2. 2 Marketing Science, Sept./Oct. 2011, p. 837-850 “Customers acquired from Google on average have a higher lifetime value (mean CLV at $1,002) than customers acquired from other channels (mean CLV at $808). The difference is even larger for those whose first-time purchase was off-line (mean CLV at $1,226 versus $959, respectively)…” What is CLV? The present value of the future (net) cash flows associated with the customer How to calculate CLV? It depends on the business setting… A forward-looking concept, not to be confused with (historic) customer profitability
  3. 3. 3 Classifying business settings Consider the following two statements regarding the size of a company’s customer base:  Based on numbers presented in a news release that reported Vodafone Group Plc’s results for the year ended 31 March 2014, we see that Vodafone UK had 11.7 million “pay monthly” customers at the end of that period.  In his “Q2 2014 Earnings Conference Call” the CFO of Amazon made the comment that “[a]ctive customer accounts exceeded 250 million,” where customers are considered active when they have placed an order during the preceding twelve-month period. 5 Contractual vs. noncontractual settings  In a contractual setting, we observe the time at which customers “die”  In a noncontractual setting, the time at which a customer becomes inactive is unobserved The challenge of noncontractual settings: How do we differentiate between those customers who have ended their relationship with the firm versus those who are simply in the midst of a long hiatus between transactions?
  4. 4. 4 Noncontractual transaction data The RFM classification Within the direct marketing literature, there is a strong tradition of classifying customers on the basis of RFM:  Recency: date of the customer’s last transaction  Frequency: how many times the customer has bought from us in a specified time period  Monetary Value: average value of transactions in a specified time period Our objective is to develop a model to generate forward- looking estimates of CLV as a function of RFM in a noncontractual continuous-time setting: E(CLV)=f(R, F, M)
  5. 5. 5 Case study: High-growth cosmetics retailer Consider a cohort of 4,127 new customers who were acquired between February 14th and August 14th 2014:  We track their initial and subsequent purchases over a 12-month period.  For testing purposes, split the 12 months into two periods of equal length:  group customers on the basis of recency and frequency in months 1–6 (“calibration period”)  compute average total spend for each group in months 7-12 (“holdout period”)  Ultimately we use all 12 months for CLV analysis Starting point for model building Assume that the amount spent per transaction is independent of the transaction process. ⇒ our model of buyer behavior can be separated into:  a sub-model for the transaction flow (recency, frequency)  a sub-model for revenue per transaction 10
  6. 6. 6 Distribution of M by F 11 Modeling the transaction flow (“Buy Till You Die”) Transaction Process:  While active, a customer purchases with a “random” per- period purchase probability  Purchase probabilities vary across customers Dropout Process:  Each customer has an unobserved dropout propensity  Dropout propensities vary across customers This is known as the “BG/BB” model (Fader, Hardie and Shang 2010)
  7. 7. 7 “BG/BB” model likelihood function For a randomly chosen individual, 13 Implementation in Excel More details available here: http://brucehardie.com/notes/010/. And for R users: http://cran.r-project.org/web/packages/BTYD/
  8. 8. 8 Model performance (1 of 4): Calibration-period histogram Model performance (2 of 4): Tracking cumulative repeat transactions
  9. 9. 9 Model performance (3 of 4): Tracking week-by-week repeat transactions Model performance (4 of 4): Conditional expectations (holdout period)
  10. 10. 10 Modeling the spend process  The dollar value of a customer’s given transaction varies randomly around his average transaction value  Average transaction values vary across customers but do not vary over time for any given individual  The distribution of average transaction values across customers is independent of the transaction process. 19 Spend model 20 More details can be found at: http://brucehardie.com/notes/025/
  11. 11. 11 Distribution of average transaction value 21 Turning to CLV: “Iso-Value” curves 22
  12. 12. 12 CLV predictions 23 Margin = 60%, 15% discount rate M = $20 M = $50 CLV predictions 24 Margin = 60%, 15% discount rate M = $20 M = $50
  13. 13. 13 The “Increasing Frequency” paradox Expected (discounted) number of future purchases 4.5 2.5 x Cust B xx x x Cust A 25 xx x x Average E(CLV ) by RFM segment
  14. 14. 14 Total E(CLV ) by RFM segment 27 CLV by Acquisition Channel 28
  15. 15. 15 Summary: substantive observations  There is a highly nonlinear relationship between recency/frequency and future transactions  The “increasing frequency” paradox is a common phenomenon  The underlying process for monetary value appears to be fairly stationary and independent of recency and frequency  A thorough analysis of the customer base requires careful consideration of the “zero class”  Iso-value curves can be used to identify customers with different purchase histories but similar CLVs  Some acquisition channels yield higher CLV customers! 29 Philosophy of model building  Keep it as simple as possible (while also ensuring validity)  Minimize cost of implementation  Use of data summaries (e.g., RFM)  Use of readily available software (e.g., Excel)  Purposively ignore the effects of covariates (customer descriptors and marketing activities) so as to highlight the important underlying components of buyer behavior.  But of course bring them afterwards to understand their incremental effects  Can’t ignore endogeneity 30
  16. 16. 16 Further reading Schmittlein, David C., Donald G. Morrison, and Richard Colombo (1987), “Counting Your Customers: Who They Are and What Will They Do Next?” Management Science, 33 (January), 1–24. Fader, Peter S., Bruce G.S. Hardie, and Ka Lok Lee (2005a), “RFM and CLV: Using Iso-value Curves for Customer-Base Analysis,” Journal of Marketing Research, 42 (November), 415-430. Fader, Peter S., Bruce G. S. Hardie, and Ka Lok Lee (2005b), “’Counting Your Customers’ the Easy Way: An Alternative to the Pareto/NBD Model,” Marketing Science, 24 (Spring), 275–284. Fader, Peter S., Bruce G. S. Hardie, and Jen Shang (2010), “Customer- Base Analysis in a Discrete-Time Noncontractual Setting,” Marketing Science, 29 (6), 1086-1108. 31
  17. 17. 17 Professor Peter Fader faderp@wharton.upenn.edu www.petefader.com Twitter: @faderp Wharton Customer Analytics Initiative: http://wcai.wharton.upenn.edu/ “Customer Centricity: Focus on the Right Customers for Strategic Advantage” http://bit.ly/FaderCC Discussion