Is keeping customers worth more than finding new ones?
Keeping customers pays: in classic Bain research, cutting defections raised profits sharply at the firms studied. Emailed offers lifted spending most among customers who had lapsed, mostly without the code being used. Simple rules like “hasn’t visited in 90 days” find lapsed customers as well as complex models do. But retention alone won’t grow a business. Loyalty mostly follows popularity, contacting dormant customers can prompt them to leave, and too many messages cost money.
What to do
- Tag customers who haven’t visited in 60 or 90 days, and send them one welcome-back offer.
- Make it a one-time welcome back, not a permanent discount.
- Test win-back campaigns against a small group you don’t contact, to see the real effect.
- Keep bringing in new customers too: loyalty follows popularity.
What the research says
- Independent researchOlder study
Zero Defections: Quality Comes to Services
Reichheld & Sasser, HBR 1990
Cutting defections by 5 percentage points raised profits by 85% in one bank's branch system, 50% at an insurance brokerage and 30% at an auto-service chain.
What it means for you: Losing a regular customer is expensive. Track who stopped coming as closely as new sign-ups.
Keep in mind: Modelled profits at a few US service firms in the 1980s, with nothing on what retention costs. The popular “25–95%” figure comes from a later article.
Read the sourceMethod and quote
Bain & Company analyses of customer profitability over the relationship lifetime in client service firms: a credit-card issuer (MBNA), a bank branch system, an insurance brokerage and an auto-service chain. Case analyses, not experiments.
“Reducing defections by just 5% generated 85% more profits in one bank's branch system, 50% more in an insurance brokerage, and 30% more in an auto-service chain.”
- Peer-reviewed or official
Do Targeted Discount Offers Serve as Advertising?
Sahni, Zou & Chintagunta, Management Science 2017
Emailed discount offers raised spending by $3.03 (+37.2%) during the offer window. 90% of the gain did not come through redemption of the offer, and the effect was larger for people who had not bought in the previous year.
What it means for you: Win-back offers work best on lapsed customers, and counting redeemed codes understates their effect.
Keep in mind: One US online platform, measured over a short promotion window.
Read the sourceMethod and quote
70 randomized email experiments at a large online ticket-resale platform, comparing recipients with randomized non-recipients.
“We find that the offers cause the average expenditure to increase significantly, by $3.03 (a 37.2% increase) during the promotion window. However, the redemption rate of these offers is low. Importantly, 90% of these gains are not through redemption of the offers… the effect of the offers is significantly higher among individuals who did not transact on the platform in the year before the offer was given.”
- Peer-reviewed or official
Regaining 'Lost' Customers
Kumar, Bhagwat & Zhang, Journal of Marketing 2015
Customers with a stronger first relationship are more likely to accept a win-back offer. The reason they left and the type of offer both shape whether a second relationship is profitable. The abstract gives no single headline figure.
What it means for you: Prioritise win-backs for customers who used to buy often, and tailor the offer to why they left.
Keep in mind: One service firm; the abstract gives no single effect size.
Read the sourceMethod and quote
Data on lost customers of a service firm with high attrition (the abstract does not name the industry). Econometric modelling of reacquisition, second-lifetime duration and profit per month. Observational.
“The study shows that the stronger the first-lifetime relationship with the firm, the more likely a customer is to accept the win-back offer.”
- Peer-reviewed or officialOlder study
Recapturing Lost Customers
Thomas, Blattberg & Fox, JMR 2004
The best win-back pricing was a low price to reacquire followed by higher prices once the customer is back.
What it means for you: A win-back discount should be a one-time welcome back, not a permanent price cut.
Keep in mind: One application, from 2004.
Read the sourceMethod and quote
Joint model of reacquisition and second-tenure duration, applied to one firm's lapsed customers. Observational.
“the optimal pricing strategy for their application involves a low reacquisition price and higher prices when customers have been reacquired.”
- Peer-reviewed or officialOlder study
Instant Customer Base Analysis: Managerial Heuristics Often 'Get it Right'
Wübben & Wangenheim, JM 2008
Simple rules such as "no purchase in X months means inactive" identified lapsed customers as well as sophisticated statistical models.
What it means for you: A simple “hasn’t visited in 60 or 90 days” rule finds lapsed customers as well as complex models do.
Keep in mind: Older research, from outside India and the UAE.
Read the sourceMethod and quote
Compares the Pareto/NBD and BG/NBD models with the simple heuristics firms use, on real customer-transaction data. Predictive comparison.
“The authors find that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for predictions regarding future purchases at the overall customer base level.”
- Peer-reviewed or official
Personalization in Email Marketing
Sahni, Wheeler & Chintagunta, Marketing Science 2018
Adding the recipient's name to the subject line raised opens by 20%, sales leads by 31% and cut unsubscribes by 17%.
What it means for you: Even light personalisation, like the customer’s name, lifts response and reduces opt-outs.
Keep in mind: Email rather than WhatsApp.
Read the sourceMethod and quote
Randomized field experiments with three companies, sending emails to millions of recipients. The effects held for both acquisition and retention campaigns.
“adding the name of the message recipient to the email's subject line increased the probability of the recipient opening it by 20% (from 9.05% to 10.80%), which translated to an increase in sales' leads by 31% (from 0.39% to 0.51%) and a reduction in the number of individuals unsubscribing from the email campaign by 17% (from 1.2% to 1.0%).”
What cuts the other way
Research that complicates the picture, and what to take from it.
- Peer-reviewed or officialOlder study
Double Jeopardy Revisited
Ehrenberg, Goodhardt & Barwise, Journal of Marketing 1990
Small brands have far fewer buyers, and those buyers are slightly less loyal. Loyalty mostly follows size rather than driving it.
What it means for you: Retention alone won’t grow a small business; you also need a steady flow of new customers.
Keep in mind: Older consumer-panel research, the basis of Byron Sharp’s How Brands Grow.
Read the sourceMethod and quote
Review of repeat-purchase panel data across many categories.
“In any given time period, a small brand typically has far fewer buyers than a larger brand. In addition, its buyers tend to buy it less often. This pattern is an instance of a widespread phenomenon called 'double jeopardy' (DJ).”
- Peer-reviewed or official
The Double Jeopardy in high street footfall
Graham, O'Rourke & Khan, J. Place Management & Development 2023
The double-jeopardy pattern holds for local high streets. Busier streets have more visitors who also visit more often.
What it means for you: Even on local high streets, loyalty mostly follows popularity.
Keep in mind: Nine London high streets, measured at street level rather than shop level.
Read the sourceMethod and quote
Footfall and survey data collected simultaneously on nine high streets in one London borough.
“The authors observe that Double Jeopardy constrains behavioural outcomes, that a simple model fits high street footfall data well and that its theoretical assumptions are supported.”
- Peer-reviewed or official
The Perils of Proactive Churn Prevention Using Plan Recommendations
Ascarza, Iyengar & Schleicher, JMR 2016
A proactive retention campaign raised churn from 6% to 10% over three months, because it lowered inertia and drew customers' attention to their usage.
What it means for you: Contacting dormant customers can prompt them to leave. Test win-back campaigns against a group you don’t contact.
Keep in mind: One proactive retention campaign at a mobile phone carrier.
Read the sourceMethod and quote
Large-scale randomized field experiment at a service provider that recommended cost-minimising pricing plans (tariffs) to customers.
“Whereas only 6% of customers in the control condition churned during the three months following the intervention, 10% did so in the treatment group.”
- Peer-reviewed or official
Retention Futility: Targeting High-Risk Customers Might Be Ineffective
Ascarza, JMR 2018
Customers at highest churn risk are not the best targets. Target the customers who are most responsive to the intervention.
What it means for you: The customers most likely to leave aren’t always the best ones to target; target those who respond.
Keep in mind: Measuring who responds needs a test with a control group.
Read the sourceMethod and quote
Two field experiments combined with machine learning.
“customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs.”
- Peer-reviewed or official
Unraveling the Personalization Paradox
Aguirre, Mahr, Grewal, de Ruyter & Wetzels, Journal of Retailing 2015
Click-through drops sharply when customers realise their data was collected without consent. Overt collection plus trust signals reverses the effect.
What it means for you: Personalise only with data customers knowingly gave you, and say so.
Keep in mind: Experiments with online ads.
Read the sourceMethod and quote
A Facebook field study, secondary campaign data and three experiments.
“…sharp drops in click-through rates when customers realize their personal information has been collected without their consent.”
Sources checked as of October 25, 2026. Links go to the original publication.