Lewis (2006)
Michael Lewis, "Customer Acquisition Promotions and Customer Asset Value," Journal of Marketing Research 43, no. 2 (2006): 195–203. doi:10.1509/jmkr.43.2.195
What it found
At a newspaper and an online grocer, deeper acquisition discounts produced customers who repurchased less and were worth less. From the abstract: "a 35% acquisition discount results in customers with about one-half the long-term value of non-promotionally acquired customers."
Why it matters
The bigger the acquisition discount, the less the customers it buys are worth.
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Villanueva, Yoo & Hanssens (2008)
Julian Villanueva, Shijin Yoo and Dominique M. Hanssens, "The Impact of Marketing-Induced versus Word-of-Mouth Customer Acquisition on Customer Equity Growth," Journal of Marketing Research 45, no. 1 (2008): 48–59. doi:10.1509/jmkr.45.1.48
What it found
At a web-hosting firm, "marketing-induced customers add more short-term value, but word-of-mouth customers add nearly twice as much long-term value."
Why it matters
The acquisition channel predicts what a cohort will be worth.
Datta, Foubert & van Heerde (2015)
Hannes Datta, Bram Foubert and Harald J. van Heerde, "The Challenge of Retaining Customers Acquired with Free Trials," Journal of Marketing Research 52, no. 2 (2015): 217–234. doi:10.1509/jmr.12.0160
What it found
At a digital-TV provider, behavioral differences "cause the average customer lifetime value of free-trial customers to be 59% lower than that of regular customers." Free-trial customers do respond more to marketing and to usage.
Why it matters
A trial has to pay back on lifetime value, not on sign-ups.
Anderson & Simester (2004): the counterpoint
Eric T. Anderson and Duncan I. Simester, "Long-Run Effects of Promotion Depth on New versus Established Customers: Three Field Studies," Marketing Science 23, no. 1 (2004): 4–20. doi:10.1287/mksc.1030.0040
What it found
"Deeper price discounts in the current period increased future purchases by first-time customers … but reduced future purchases by established customers."
Why it matters
Discounts don't always bring worse customers; who receives the discount matters.
Byers, Mitzenmacher & Zervas (2012)
John W. Byers, Michael Mitzenmacher and Georgios Zervas, "The Groupon Effect on Yelp Ratings: A Root Cause Analysis," Proceedings of the 13th ACM Conference on Electronic Commerce (EC '12) (2012): 248–265. doi:10.1145/2229012.2229034
What it found
Merchants' Yelp ratings fall after they run Groupon deals. Across "over 7 million Yelp reviews," the authors test explanations and propose that deal customers are "real, unbiased customers" compared with inflated baseline reviews.
Why it matters
Customers who come in through deals behave differently, and so do the signals they leave.
Schmittlein, Morrison & Colombo (1987)
David C. Schmittlein, Donald G. Morrison and Richard Colombo, "Counting Your Customers: Who Are They and What Will They Do Next?" Management Science 33, no. 1 (1987): 1–24. doi:10.1287/mnsc.33.1.1
What it found
The Pareto/NBD model estimates "the probability that any particular customer is still active" from the number and timing of past purchases.
Why it matters
How to count live customers when there's no contract.
Fader, Hardie & Lee (2005)
Peter S. Fader, Bruce G. S. Hardie and Ka Lok Lee, "'Counting Your Customers' the Easy Way: An Alternative to the Pareto/NBD Model," Marketing Science 24, no. 2 (2005): 275–284. doi:10.1287/mksc.1040.0098
What it found
The BG/NBD model is "vastly easier to implement" (it fits in Excel) and gives "very similar results."
Why it matters
A practical forecast of repeat purchases and lifetime value.
Fader & Hardie (2007)
Peter S. Fader and Bruce G. S. Hardie, "How to Project Customer Retention," Journal of Interactive Marketing 21, no. 1 (2007): 76–90. doi:10.1002/dir.20074 · free copy
What it found
The shifted-beta-geometric model fits in a spreadsheet. In the paper's words, "the observed phenomenon of increasing retention rates is simply a sorting effect in a heterogeneous population."
Why it matters
A cohort's retention rises with age because the leavers leave first, not because anyone got more loyal. Read churn by cohort, not as one blended rate.
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Fader & Hardie (2010)
Peter S. Fader and Bruce G. S. Hardie, "Customer-Base Valuation in a Contractual Setting: The Perils of Ignoring Heterogeneity," Marketing Science 29, no. 1 (2010): 85–93. doi:10.1287/mksc.1080.0482
What it found
"at the cohort level, retention rates typically increase over time." Using a single retention rate "yields a downward-biased estimate" of the customer base's remaining value.
Why it matters
Valuing a subscription book with one blended churn rate gets it wrong.
Vaupel, Manton & Stallard (1979)
James W. Vaupel, Kenneth G. Manton and Eric Stallard, "The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality," Demography 16, no. 3 (1979): 439–454. doi:10.2307/2061224
What it found
Life tables that ignore differences in frailty "overestimate current life expectancy … while underestimating rates of individual aging." The frail die first, which changes what the survivors' averages seem to say.
Why it matters
The demography that Fader and Hardie brought to customers.
Gupta, Lehmann & Stuart (2004)
Sunil Gupta, Donald R. Lehmann and Jennifer Ames Stuart, "Valuing Customers," Journal of Marketing Research 41, no. 1 (2004): 7–18. doi:10.1509/jmkr.41.1.7.25084
What it found
Across five public firms, "a 1% improvement in retention, margin, or acquisition cost improves firm value by 5%, 1%, and .1%, respectively."
Why it matters
Retention is the biggest lever on firm value. The next question is whose retention.
Reichheld & Sasser (1990)
Frederick F. Reichheld and W. Earl Sasser Jr., "Zero Defections: Quality Comes to Services," Harvard Business Review 68, no. 5 (1990): 105–111. hbr.org
What it found
"Companies can boost profits by almost 100% by retaining just 5% more of their customers." The case examples are 85% more profit at a bank's branch system, 50% at an insurance brokerage and 30% at an auto-service chain.
Why it matters
The most-quoted retention claim in business. The figures are case examples from the article, not a general law.
Reinartz & Kumar (2002)
Werner Reinartz and V. Kumar, "The Mismanagement of Customer Loyalty," Harvard Business Review 80, no. 7 (2002): 86–94. hbr.org
What it found
Across four companies and more than 16,000 customers, many long-tenure customers "barely generated a profit," and many of the most profitable were short-term.
Why it matters
Tenure isn't value, so retention effort should follow value.
Venkatesan & Kumar (2004)
Rajkumar Venkatesan and V. Kumar, "A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy," Journal of Marketing 68, no. 4 (2004): 106–125. doi:10.1509/jmkg.68.4.106.42728
What it found
"Customers who are selected on the basis of their lifetime value provide higher profits in future periods than do customers selected on the basis of several other customer-based metrics."
Why it matters
Rank accounts on expected future value, not on past spend or count.
Ascarza (2018)
Eva Ascarza, "Retention Futility: Targeting High-Risk Customers Might Be Ineffective," Journal of Marketing Research 55, no. 1 (2018): 80–98. doi:10.1509/jmr.16.0163
What it found
"customers identified as having the highest risk of churning are not necessarily the best targets." Targeting by sensitivity to the intervention was "significantly more effective."
Why it matters
Aim retention at the customers the effort actually changes, weighted by value, not at the highest churn scores.
Read our explainer
Ascarza, Iyengar & Schleicher (2016)
Eva Ascarza, Raghuram Iyengar and Martin Schleicher, "The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment," Journal of Marketing Research 53, no. 1 (2016): 46–60. doi:10.1509/jmr.13.0483
What it found
"whereas only 6% of customers in the control condition churned during the three months following the intervention, 10% did so in the treatment group."
Why it matters
Retention outreach can wake up "sleeping dogs" who would otherwise have stayed.
Lemmens & Gupta (2020)
Aurélie Lemmens and Sunil Gupta, "Managing Churn to Maximize Profits," Marketing Science 39, no. 5 (2020): 956–973. doi:10.1287/mksc.2020.1229
What it found
Rank customers by the campaign's incremental effect on churn and on future cash flows, net of cost. "Two field experiments affirm that our approach leads to significantly more profitable campaigns than competing models."
Why it matters
Save the valuable customers, not just the ones most likely to leave.
Radcliffe & Simpson (2007); Siegel (2013): uplift's four groups
Nicholas J. Radcliffe and Rob Simpson, "Identifying Who Can Be Saved and Who Will Be Driven Away by Retention Activity," Stochastic Solutions white paper (2007), PDF; Eric Siegel, Predictive Analytics (Wiley, 2013).
What it found
Four groups:
- Persuadables: "made more likely to stay by treatment."
- Sure Things and Lost Causes: stay or leave "whether treated or not."
- Sleeping Dogs: "tend to be driven away by treatment."
Why it matters
Spend only where the effort changes the outcome. That applies to ads (brand search reaches sure things) as much as to retention.
Yang, Eckles, Dhillon & Aral (2024)
Jeremy Yang, Dean Eckles, Paramveer Dhillon and Sinan Aral, "Targeting for Long-Term Outcomes," Management Science 70, no. 6 (2024): 3841–3855. doi:10.1287/mnsc.2023.4881
What it found
Targeting Boston Globe discounts on a surrogate for long-term retention beat targeting on short-term proxies. "Over three years, our approach had a net-positive revenue impact in the range of $4–$5 million."
Why it matters
Early signals, validated against long-term value, used to decide whom to target.