Acquisition and retentionEvidence check: 5.0/5
Yoganarasimhan, H., Barzegary, E., & Pani, A. (2023). Management Science, 69(6), 3220–3240.
Plain-English take: In this firm and experiment, a 7-day trial was the best uniform policy and increased subscriptions by 5.59% versus assigning everyone a 30-day trial. For your marketing: This may be useful when designing a local randomized trial-length experiment.
Question: How do different free-trial lengths affect subscriptions, and can trial length be assigned more effectively using customer characteristics?
Design: Randomized field experiment and policy evaluation. 337,724 new users across Australia, New Zealand, France, Germany, Japan, the United Kingdom and the United States; subscription and retention were followed for two years. The firm and product are anonymized.
In this firm and experiment, a 7-day trial was the best uniform policy and increased subscriptions by 5.59% versus assigning everyone a 30-day trial. The best evaluated personalized policy, based on lasso, improved subscriptions by 6.81% versus that 30-day baseline; it also performed well on longer-run outcomes.
Management Science · 202310.1287/mnsc.2022.4507Open author preprint; publisher version may require access
Acquisition and retentionEvidence check: 4.0/5
Sahni, N. S., Wheeler, S. C., & Chintagunta, P. (2018). Marketing Science, 37(2), 236–258.
Plain-English take: In the main experiment, adding the recipient’s name to the subject line raised the open probability from 9.05% to 10.80%, increased sales leads from 0.39% to 0.51%, and reduced unsubscribes from 1.2% to 1.0%. For your marketing: This is a strong example of testing a narrowly defined personalization change with downstream outcomes.
Question: Does adding consumer-specific but noninformative content to an email affect recipient response?
Design: Randomized field experiments. The experiments sent messages to millions of individuals. The abstract reports detailed results for a main experiment and replication across different advertiser objectives; it does not give a single pooled sample size.
In the main experiment, adding the recipient’s name to the subject line raised the open probability from 9.05% to 10.80%, increased sales leads from 0.39% to 0.51%, and reduced unsubscribes from 1.2% to 1.0%. Similar experiments at other firms found benefits across acquisition and retention objectives.
Marketing Science · 201810.1287/mksc.2017.1066Publisher and author abstract; full article access may vary
Acquisition and retentionEvidence check: 4.0/5
Montaguti, E., Neslin, S. A., & Valentini, S. (2016). Marketing Science, 35(2), 201–217.
Plain-English take: The experiment tests whether campaign design can cause multichannel buying and more profitable customer behavior, distinguishing communication content from financial incentives. For your marketing: For product-led and sales-assisted motions, test whether messaging or an incentive changes adoption of a second channel or product path, and measure contribution rather than channel count alone.
Question: Can marketing communications and incentives move customers into multichannel buying and increase their profitability?
Design: Field experiment testing four marketing campaigns. Customers of a multichannel retailer in the experiment; the sources reviewed here do not provide the sample count in the abstract excerpt.
The experiment directly evaluates whether campaign design can cause multichannel buying and more profitable customer behavior, distinguishing communication content from financial incentives.
Marketing Science · 201610.1287/mksc.2015.0923Publisher record and author-hosted paper
Acquisition and retentionEvidence check: 3.0/5
Ascarza, E., Netzer, O., & Hardie, B. G. S. (2018). Marketing Science, 37(1), 54–77.
Plain-English take: Overt churners were often more active before leaving, while many silent churners disengaged early and rarely canceled formally. For your marketing: Separate cancellation from quiet nonuse when measuring churn.
Question: How should firms distinguish customers who formally cancel from those who silently disengage?
Design: Hidden Markov model applied to two empirical settings. Two hybrid customer-relationship settings where customers can formally terminate or simply stop interacting with the firm.
Overt churners were often more active before leaving, while many silent churners disengaged early and rarely canceled formally. The two churn groups responded differently to communications, so one retention strategy may not suit both.
Marketing Science · 201810.1287/mksc.2017.1057Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 2.5/5
Chen, Y., & Mehta, N. (2026). Marketing Science, 45(2), 449–471.
Plain-English take: Churn by a high-centrality member caused a stronger negative cascade in peers' usage and subscriptions than churn by a low- or medium-centrality member. For your marketing: Where product use is collaborative, consider whether a departing or inactive central user affects teammates.
Question: How does one user's churn affect usage and retention among connected peers, and whom should firms target to prevent network cascades?
Design: Structural model using exogenous churn data from a field setting. Members of one upscale gym with observed social-network ties, plan choices and usage.
Churn by a high-centrality member caused a stronger negative cascade in peers' usage and subscriptions than churn by a low- or medium-centrality member. Short-term targeting may prioritize lower-centrality churners, while longer-term retention can favor high-centrality users because of network spillovers.
Marketing Science · 202610.1287/mksc.2023.0391Publisher abstract
Acquisition and retentionEvidence check: 4.0/5
Ascarza, E. (2018). Journal of Marketing Research, 55(1), 80–98.
Plain-English take: Customers with the highest predicted churn risk were not necessarily the best targets. For your marketing: Use treatment response or expected incremental profit to select retention audiences, rather than ranking only by churn propensity.
Question: Are customers most likely to churn also the best customers to target with a retention intervention?
Design: Two field experiments combined with machine-learning targeting methods. Two field experiments involving customer retention interventions; the publisher abstract does not summarize sample sizes or industries.
Customers with the highest predicted churn risk were not necessarily the best targets. The response-based targeting approach was significantly more effective than standard targeting of the highest-risk customers.
Journal of Marketing Research · 201810.1509/jmr.16.0163Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 2.5/5
Kumar, V., Petersen, J. A., & Leone, R. P. (2010). Journal of Marketing, 74(5), 1–17.
Plain-English take: The useful point is that maximizing referral-program profitability requires managing both long-term customer value and customer referral value, and that referral behavior can guide targeting of profitable prospects. For your marketing: Track who refers, how often, and the value of referred customers.
Question: How can firms measure customer referral value and use it alongside lifetime value to target referral campaigns?
Design: Customer referral value measurement and targeting framework. Customer and referral records used to derive CLV and customer referral value; the accessible abstract does not identify the firm or sample size.
The research argues that maximizing referral-program profitability requires managing both customer lifetime value and customer referral value, and that referral behavior can guide targeting of profitable prospects.
Journal of Marketing · 201010.1509/jmkg.74.5.001Publisher abstract
Acquisition and retentionEvidence check: 2.5/5
Van den Bulte, C., Bayer, E., Skiera, B., & Schmitt, P. (2018). Journal of Marketing Research, 55(1), 132–146.
Plain-English take: Referrer and referral pairs shared unobserved contribution-margin characteristics. For your marketing: Measure both referrer and referred-customer behavior.
Question: Why can referred customers have higher value, and how do referrer characteristics relate to referred customers' margins and retention?
Design: Customer-level observational analysis of a referral program. Customers and referrals in one firm's customer referral program; the publisher abstract does not state a sample size.
Referrer and referral pairs shared unobserved contribution-margin characteristics. More experienced referrers brought higher-margin referrals, though this association weakened over the referral's lifetime. Referrals churned less only while their referrer remained a customer.
Journal of Marketing Research · 201810.1509/jmr.14.0653Publisher abstract; full text access may vary
Acquisition and retentionEvidence check: 3.5/5
Gershon, R., Cryder, C., & John, L. K. (2020). Journal of Marketing Research, 57(1), 156–172.
Plain-English take: Across the experiments, recipient-benefiting prosocial referral incentives recruited more new customers than comparison incentives in the studied contexts. For your marketing: Test whether reducing the recipient's cost or increasing their benefit works better than paying the referrer alone.
Question: When do referral incentives that benefit the recipient generate more referrals than incentives paid to the referrer?
Design: Two field experiments and one fully incentivized laboratory experiment. Participants in two referral-program field settings and an incentivized lab experiment; the publisher abstract reviewed does not give sample sizes.
Across the experiments, recipient-benefiting prosocial referral incentives recruited more new customers than comparison incentives in the studied contexts.
Journal of Marketing Research · 202010.1177/0022243719888440Publisher abstract; full text access may vary
Acquisition and retentionEvidence check: 4.5/5
Ascarza, E., Iyengar, R., & Schleicher, M. (2016). Journal of Marketing Research, 53(1), 66–82.
Plain-English take: Churn over the following three months was 10% in the recommendation group versus 6% in the control group. For your marketing: Before prompting customers to downgrade or switch plans, run a controlled test and examine effects by usage pattern.
Question: Can proactively recommending a lower-cost or better-fitting plan reduce churn, or can the recommendation make customers more likely to leave?
Design: Large-scale randomized field experiment. Customers of a service provider with multiple usage plans; the paper examines how responses differ by prior overage, usage variability and usage trend.
Churn over the following three months was 10% in the recommendation group versus 6% in the control group. Effects varied with pre-campaign usage patterns; customers with lower overage, lower variability and positive trends responded differently.
Journal of Marketing Research · 201610.1509/jmr.13.0483Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 3.0/5
Datta, H., Foubert, B., & van Heerde, H. J. (2015). Journal of Marketing Research, 52(2), 217–234.
Plain-English take: Average lifetime value for free-trial customers was 59% lower than for regular customers in this study. For your marketing: Track post-trial retention and value by acquisition cohort, and test onboarding or communication interventions.
Question: How do customers acquired with free trials differ from regular customers in retention and lifetime value?
Design: Household panel analysis and customer-lifetime-value modeling. Households using one digital television service; the abstract reports a comparison between trial-acquired and regular customers.
Average lifetime value for free-trial customers was 59% lower than for regular customers in this setting. Trial-acquired customers were more responsive to marketing communication and usage, suggesting opportunities for targeted retention activity.
Journal of Marketing Research · 201510.1509/jmr.12.0160Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 3.5/5
Wolters, H. M., Schulze, C., & Gedenk, K. (2020). Marketing Science, 39(6), 1166–1180.
Plain-English take: Larger referral rewards acquired more new customers but considerably decreased the profitability of referred customers in both studies. For your marketing: Evaluate referrals on net customer profitability, not just the number of signups.
Question: How does referral reward size affect the number and profitability of newly referred customers?
Design: Field experiment and archival generalization study. More than 160,000 bank customers in the experiment and approximately 270,000 telecommunications customers in the archival analysis.
Larger referral rewards acquired more new customers but considerably decreased the profitability of referred customers in both studies.
Marketing Science · 202010.1287/mksc.2020.1242Publisher abstract and JSTOR preview
Acquisition and retentionEvidence check: 4.0/5
Lemmens, A., & Gupta, S. (2020). Marketing Science, 39(5), 956–973.
Plain-English take: The profit-aligned targeting approach uses expected additional churn and post-campaign cash flows net of intervention costs. For your marketing: For lifecycle offers, target on expected incremental profit rather than the highest predicted churn score.
Question: How should firms select customers for retention interventions to maximize campaign profit?
Design: Profit-based churn model and two field experiments. Two retention-campaign field experiments; the accessible abstract does not summarize the industries or sample sizes.
The profit-aligned targeting approach uses expected incremental churn and post-campaign cash flows net of intervention costs. The authors report that their method significantly improves campaign profitability compared with conventional churn-probability or offer-responsiveness targeting.
Marketing Science · 202010.1287/mksc.2020.1229Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 3.0/5
Ascarza, E., & Hardie, B. G. S. (2013). Marketing Science, 32(4), 570–590.
Plain-English take: The joint framework is designed to capture the relationship between engagement behavior and retention, enabling more coherent customer-lifetime analysis than treating usage and churn as unrelated outcomes. For your marketing: When usage predicts retention, model the outcomes together and distinguish prediction from causal effect.
Question: How can firms jointly model customer usage and churn when they observe contractual relationships?
Design: Joint statistical model of customer usage and churn. Customers of an annual membership service; the paper models repeated usage and contract renewal/churn jointly.
The joint framework is designed to capture the relationship between engagement behavior and retention, enabling more coherent customer-lifetime analysis than treating usage and churn as unrelated outcomes.
Marketing Science · 201310.1287/mksc.2013.0786Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 3.5/5
Retana, G. F., Forman, C., & Wu, D. J. (2016). Manufacturing & Service Operations Management, 18(1), 34–50.
Plain-English take: The study uses an experimental setting to examine whether educating customers can influence both continued use and requests for technical support, outcomes that can move in different directions. For your marketing: Treat onboarding and education as interventions with both retention and support-cost outcomes.
Question: How does proactive education affect customer retention and demand for technical support?
Design: Field experiment. Customers of a technology service in the field setting; the accessible publisher record does not report the full sample description or sample size.
The study uses an experimental setting to examine whether educating customers can influence both continued use and requests for technical support, outcomes that can move in different directions.
Manufacturing & Service Operations Management · 201610.1287/msom.2015.0547Publisher record; abstract-level details
Acquisition and retentionEvidence check: 3.0/5
Hu, H., & Zhang, X.-M. (2021). Frontiers in Psychology, 12, 644412.
Plain-English take: Across the reported studies, utilitarian rewards generated higher referral likelihood for utilitarian products, whereas hedonic rewards performed better for hedonic products. For your marketing: When offering referral incentives, test whether the reward feels aligned with the product and the referrer's motive.
Question: Does the fit between a referral reward and the product being recommended affect referral likelihood, and does the pattern differ by gender?
Design: One field study and two laboratory studies. Field and laboratory settings involving product and reward evaluations. The accessible abstract does not give a combined sample size; the full text reports separate studies and samples.
Across the reported studies, utilitarian rewards generated higher referral likelihood for utilitarian products, whereas hedonic rewards performed better for hedonic products. The paper also examines gender differences in this congruence pattern.
Frontiers in Psychology · 202110.3389/fpsyg.2021.644412Open full text in PubMed Central and publisher record
Acquisition and retentionEvidence check: 3.5/5
Garnefeld, I., Eggert, A., Helm, S. V., & Tax, S. S. (2013). Journal of Marketing, 77(4), 17–32.
Plain-English take: Within a year, recommenders’ defection rate fell from 19% to 7%, and their average monthly revenue rose by 11.4% versus the matched control group. For your marketing: Evaluate both sides of referral programs: the acquired customer and the referrer.
Question: Does participating in a referral program change the behavior and value of the existing customer who makes the recommendation?
Design: Field experiment with matched comparison group. Existing customers of one global cellular telecommunications provider; the abstract reports a one-year defection and revenue comparison.
Within a year, recommenders’ defection rate fell from 19% to 7%, and their average monthly revenue rose by 11.4% versus the matched control group. The loyalty effect was stronger among customers with shorter prior tenure.
Journal of Marketing · 201310.1509/jm.11.0423Publisher abstract; full text access may vary
Acquisition and retentionEvidence check: 4.5/5
Gershon, R., & Jiang, Z. (2024). Journal of Marketing Research. https://doi.org/10.1177/00222437241257886.
Plain-English take: Conditional on purchase activity, referred customers made 31%–57% more referrals than nonreferred customers in the field data. For your marketing: Track whether acquired customers later refer others and test simple lifecycle reminders.
Question: Do customers acquired through referrals themselves generate more downstream referrals, and can that behavior be increased?
Design: Large-scale field dataset, preregistered lab experiments, and field experiment. The publisher abstract reports a large-scale field dataset but no total sample size; the author-hosted manuscript describes data from 41.2 million customers, two preregistered lab experiments, and one field experiment.
Conditional on purchase activity, referred customers made 31%–57% more referrals than nonreferred customers in the field data. In a field experiment, reminding customers they joined through a referral further raised their likelihood of referring by 21% according to the publisher abstract.
Journal of Marketing Research · 202410.1177/00222437241257886Publisher abstract and author-hosted paper
Acquisition and retentionEvidence check: 4.5/5
Belo, R., & Li, T. (2022). Management Science, 68(12), 8933–8962.
Plain-English take: Referral programs increased referrals but could reduce revenue when referred users gained access to premium features without paying. For your marketing: This may be useful when a product has social invitations or a free tier: track invited users, paid conversion, and engagement together, and test which benefits are available to nonpayers.
Question: How do social referral programs affect growth, payment, and engagement on freemium platforms?
Design: Randomized field experiment. 52,782 approved new users on a dating platform in a major European city, observed from March 2015 to February 2017.
Referral programs increased referrals but could reduce revenue when referred users gained access to premium features without paying. Higher referral thresholds increased referrals and total revenue, while reducing some user engagement. Reserving selected premium features for paying users helped avoid the revenue loss.
Management Science · 202210.1287/mnsc.2022.4301Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 3.5/5
Fong, N. M. (2017). Management Science, 63(7), 2353–2364.
Plain-English take: Offers for products in categories customers had bought before raised purchase rates but reduced search activity. For your marketing: Personalized recommendations can improve immediate relevance while narrowing exploration.
Question: Do targeted offers change what customers search for beyond the promoted products?
Design: Field experiment. Customers of an online wine retailer; the abstract does not report the sample size.
Offers for products in categories customers had bought before raised purchase rates but reduced search activity. The reduction reflected less search by customers who visited, not fewer visitors searching at all.
Management Science · 201710.1287/mnsc.2016.2447Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 4.0/5
Bapna, R., & Umyarov, A. (2015). Management Science, 61(8), 1902–1920.
Plain-English take: An adopting friend increased the odds of buying the paid service by more than 60% in the study. For your marketing: If paid adoption is socially visible in your product, measure peer exposure as a possible conversion mechanism.
Question: Does a friend's adoption of a paid service causally affect another user's decision to pay?
Design: Randomized field experiment in a social network. The general population of users on a large social network with a freemium service; the publisher abstract does not state the sample size.
An adopting friend increased the odds of buying the paid service by more than 60% in the study. Relative influence was stronger for users with fewer friends.
Management Science · 201510.1287/mnsc.2014.2081Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 3.5/5
Wang, Y., Lu, L. X., & Shi, P. (2022). Manufacturing & Service Operations Management, 24(5), 2703–2721.
Plain-English take: A one-month program with two emails increased five-month retention likelihood by 7.4%, lowered churn odds by 26.3%, and raised per-period service use by 7.0%. For your marketing: The core lesson is to evaluate lifecycle email against retention, usage, and margin by segment.
Question: Does email engagement raise subscription retention and usage, and is it profitable for every subscriber segment?
Design: Field experiment with retention, usage, and profit analysis. Subscribers of a large U.S. car-wash chain with tiered subscriptions and RFID-tracked visits.
A one-month program with two emails increased five-month retention likelihood by 7.4%, lowered churn odds by 26.3%, and raised per-period service use by 7.0%. Estimated profitability varied by subscriber tier and usage frequency; the authors recommend selectively targeting profitable groups.
Manufacturing & Service Operations Management · 202210.1287/msom.2022.1122Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 4.5/5
Sun, T., Shi, L., Viswanathan, S., & Zheleva, E. (2019). Information Systems Research, 30(2), 523–539.
Plain-English take: Both incentives and information increased adoption, but only information-induced adoption sustainably raised purchases and firm profit. For your marketing: This distinguishes adoption volume from valuable adoption.
Question: Do incentives or information more effectively encourage app adoption that leads to lasting customer purchases?
Design: Large-scale randomized field experiment. More than 230,000 customers in the study's mobile-app adoption setting.
Both incentives and information increased adoption, but only information-induced adoption sustainably raised purchases and firm profit. Incentives produced a larger short-run adoption increase without long-run purchase gains.
Information Systems Research · 201910.1287/isre.2018.0815Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 4.5/5
Jung, J., Bapna, R., Golden, J. M., & Sun, T. (2020). Information Systems Research, 31(1), 16–36.
Plain-English take: Prosocial framing raised both referral initiation and successful referrals compared with egoistic and equitable wording under a fixed incentive scheme. For your marketing: This is a direct test idea for referral prompts: explain how the product could help the recipient, then measure completed qualified referrals rather than clicks on the invite button.
Question: Can referral-request wording raise referral participation while preserving the intrinsic motivation to help friends?
Design: Two randomized field experiments and a supporting online experiment. The main field experiment involved 100,000 customers; the publisher abstract also reports a second field experiment and a Mechanical Turk experiment.
Prosocial framing raised both referral initiation and successful referrals compared with egoistic and equitable wording under a fixed incentive scheme. Participants reported less guilt and were more selective about whom to refer.
Information Systems Research · 202010.1287/isre.2019.0873Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 2.5/5
Kornish, L. J., & Li, Q. (2010). Marketing Science, 29(1), 108–121.
Plain-English take: When consumers place greater weight on recipients' outcomes, the optimal bonus tends to be higher unless price is a more efficient way to motivate recommendations. For your marketing: Helps frame referral incentives as a joint decision about product price, bonus design, and the recipient's benefit.
Question: How should firms set referral bonuses when customers differ in information and care about the people they refer?
Design: Analytical model of referral incentives. Theory paper; no empirical sample.
When consumers place greater weight on recipients' outcomes, the optimal bonus tends to be higher unless price is a more efficient way to motivate recommendations. In some cases, the optimal bonus is zero and the product price is lower.
Marketing Science · 201010.1287/mksc.1080.0484Publisher abstract; full text may require access
Acquisition and retentionEvidence check: 5/5
Zhang, L., & Duan, J. (2025). Frontiers in Psychology, 16, 1568868.
Plain-English take: In one global Freemium SaaS experiment, a seven-day rather than three-day premium trial increased trial adoption and delayed conversion, but did not significantly change immediate conversion. For your marketing: measure conversion stages separately and test trial duration in your own product.
Question: In a Freemium SaaS setting, how does premium-trial duration affect trial adoption, immediate paid conversion and delayed conversion?
Design: Two-year randomized field experiment with a three-stage conversion model. 680,588 new users across 190 countries during 2023–2024 at one leading global image-editing SaaS company. The provider and product are not named.
The seven-day trial increased trial adoption by 11.098% and delayed conversion by 42.36%, while the experiment found no statistically significant effect on immediate conversion. The paper also reports that longer-trial users responded more to feature-based promotions, while shorter-trial users were more sensitive to price incentives; effects varied with product functionality and market context.
Frontiers in Psychology · 202510.3389/fpsyg.2025.1568868Open-access full text; an affiliation correction is linked
Acquisition and retentionEvidence check: 4/5
Theresa Gattermann-Itschert, Ulrich W. Thonemann (2022). Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2022.09.023
Plain-English take: Risk-based proactive contact reduced churn significantly versus random targeting and had positive revenue impact; recency, frequency, monetary value, and last field-representative contact were important features.
Question: Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests?
Design: Random-forest churn prediction followed by a field experiment comparing risk-based and random targeting. Non-contractual B2B wholesale customer setting
Risk-based proactive contact reduced churn significantly versus random targeting and had positive revenue impact; recency, frequency, monetary value, and last field-representative contact were important features.
Industrial Marketing Management · 202210.1016/j.indmarman.2022.09.023Publisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 2/5
Sungwook Min, Xubing Zhang, Namwoon Kim, Rajendra K. Srivastava (2016). Customer Acquisition and Retention Spending: An Analytical Model and Empirical Investigation. Journal of Marketing Research. https://doi.org/10.1509/jmr.14.0170
Plain-English take: Acquisition cost per customer is more sensitive than retention cost to market position and competition; market leaders have a substantial acquisition-cost advantage but no comparable retention-cost advantage.
Question: Customer Acquisition and Retention Spending: An Analytical Model and Empirical Investigation?
Design: Analytical model tested with cross-country firm-level operating data. Wireless telecommunications firms in 41 countries, 1999–2007
Acquisition cost per customer is more sensitive than retention cost to market position and competition; market leaders have a substantial acquisition-cost advantage but no comparable retention-cost advantage.
Journal of Marketing Research · 201610.1509/jmr.14.0170Publisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 4/5
K. H. Kivetz, O. Urminsky, Y. Zheng (2016). Enduring Effects of Goal Achievement and Failure Within Customer Loyalty Programs: A Large-Scale Field Experiment. Marketing Science. https://doi.org/10.1287/mksc.2015.0966
Plain-English take: Achieving or failing to achieve program goals had persistent effects on later engagement and purchasing, beyond the immediate reward period.
Question: Enduring Effects of Goal Achievement and Failure Within Customer Loyalty Programs: A Large-Scale Field Experiment?
Design: Large-scale longitudinal field experiment comparing loyalty members' goal progress/achievement and later purchasing.. Members of a commercial customer loyalty program.
Achieving or failing to achieve program goals had persistent effects on later engagement and purchasing, beyond the immediate reward period.
Marketing Science · 201610.1287/mksc.2015.0966Publisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 2/5
V. Kumar, Andy Petersen, Robert Leone (2005). Developing retention strategies based on customer profitability in telecommunications: An empirical study. Journal of Database Marketing & Customer Strategy Management. https://doi.org/10.1057/palgrave.dbm.3240259
Plain-English take: Retention targeting based jointly on churn likelihood and customer profitability can outperform indiscriminate retention spending.
Question: Developing retention strategies based on customer profitability in telecommunications: An empirical study?
Design: Empirical customer-level profitability/churn modeling using transactional telecommunications data.. Subscribers of a telecommunications provider, segmented by profitability and retention risk.
Retention targeting based jointly on churn likelihood and customer profitability can outperform indiscriminate retention spending.
Journal of Database Marketing & Customer Strategy Management · 200510.1057/palgrave.dbm.3240259Publisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 2/5
M. Choudhury, S. Harrigan, M. E. Soutar (2016). Assessing customer retention in B2C electronic commerce: an empirical study. Journal of Database Marketing & Customer Strategy Management. https://doi.org/10.1057/s41270-016-0007-x
Plain-English take: Relationship quality, satisfaction/trust, and service experience were associated with continued e-commerce patronage.
Question: Assessing customer retention in B2C electronic commerce: an empirical study?
Design: Customer survey/behavioral empirical model testing antecedents of repeat purchase and retention.. Consumers using a business-to-consumer e-commerce service.
Relationship quality, satisfaction/trust, and service experience were associated with continued e-commerce patronage.
Journal of Database Marketing & Customer Strategy Management · 201610.1057/s41270-016-0007-xPublisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 2/5
V. Kumar, J. Andrew Petersen, Robert P. Leone (2003). Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development. Journal of Marketing. https://doi.org/10.1509/jmkg.67.4.30.18685
Plain-English take: CRM contacts were not uniformly beneficial: effects on retention and share depended on customer profitability and contact strategy.
Question: Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development?
Design: Customer-level longitudinal empirical analysis linking CRM contacts to retention and share of wallet.. Customers of a multi-product service firm observed over repeated transactions.
CRM contacts were not uniformly beneficial: effects on retention and share depended on customer profitability and contact strategy.
Journal of Marketing · 200310.1509/jmkg.67.4.30.18685Publisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 2/5
V. Kumar, Girish Ramani, Thomas Bohling (2001). A Methodology for Linking Customer Acquisition to Customer Retention. Journal of Marketing Research. https://doi.org/10.1509/jmkr.38.2.262.18848
Plain-English take: Acquisition sources differ in downstream retention value; evaluating acquisition without later retention can misallocate marketing resources.
Question: A Methodology for Linking Customer Acquisition to Customer Retention?
Design: Longitudinal customer-level empirical modeling of acquisition source, subsequent behavior, and retention.. Customers acquired through alternative marketing channels in a subscription/service business.
Acquisition sources differ in downstream retention value; evaluating acquisition without later retention can misallocate marketing resources.
Journal of Marketing Research · 200110.1509/jmkr.38.2.262.18848Publisher article page; full-text availability varies and was not established in this review.
Acquisition and retentionEvidence check: 4/5
Matthijs Meire, Michel Ballings, Dirk Van den Poel (2017). The added value of social media data in B2B customer acquisition systems: A real-life experiment. Decision Support Systems. https://doi.org/10.1016/j.dss.2017.09.010
Plain-English take: Facebook data were the most informative source for qualifying prospects and complemented the other sources, further improving predictive performance; results were reported in prospect-to-customer conversion.
Question: What does the added value of social media data in b2b customer acquisition systems: a real-life experiment show about customer behavior or marketing outcomes?
Design: Quantitative real-life experiment using a customer-acquisition decision-support system. Predictive models ranked prospects using commercially purchased prospecting data, website data, and Facebook data; calling lists were generated and prospect-to-customer conversion compared.. Nearly 9,000 beverage-serving outlet prospects for Coca-Cola Refreshments Inc.; the system generated ranked calling lists for customer acquisition.
Facebook data were the most informative source for qualifying prospects and complemented the other sources, further improving predictive performance; results were reported in prospect-to-customer conversion.
Decision Support Systems · 201710.1016/j.dss.2017.09.010Publisher page; full-text availability varies and was not established.
Acquisition and retentionEvidence check: 2/5
Bryan Hochstein, Clay M. Voorhees, Alexander B. Pratt, Deva Rangarajan, Duane M. Nagel, Vijay Mehrotra (2023). Customer Success Management, Customer Health, and Retention in B2B Industries. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2023.09.002
Plain-English take: Customer-success management complements sales and service by proactively increasing realized customer value. Customer health is a formative metric combining relationship quality, product usage, and value realization; managers use it to manage relationships and reduce churn. Implementation is not uniformly successful and depends on short- and long-term organizational contingencies.
Question: What does customer success management, customer health, and retention in b2b industries show about customer behavior or marketing outcomes?
Design: Qualitative empirical theory-building study using a theories-in-use approach and interviews with B2B sales/customer-success executives across settings.. Interviews with 26 B2B sales executives, spanning multiple B2B industries and customer-success implementations.
Customer-success management complements sales and service by proactively increasing realized customer value. Customer health is a formative metric combining relationship quality, product usage, and value realization; managers use it to manage relationships and reduce churn. Implementation is not uniformly successful and depends on short- and long-term organizational contingencies.
International Journal of Research in Marketing · 202310.1016/j.ijresmar.2023.09.002Publisher page; full-text availability varies and was not established.
Acquisition and retentionEvidence check: 2/5
Jeroen D’Haen, Dirk Van den Poel (2013). Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2013.03.006
Plain-English take: The iterative framework produces a higher-than-average prospect-to-lead conversion in preliminary tests and can be implemented without human interaction in a web application.
Question: What does model-supported business-to-business prospect prediction based on an iterative customer acquisition framework show about customer behavior or marketing outcomes?
Design: Empirical predictive-modeling study. Phase 1 uses nearest neighbors from current customers; after noncustomer feedback, phase 2 compares logistic regression, decision trees, and neural networks; phase 3 combines models into a weighted prospect list.. Business-to-business customer-acquisition setting using customer-base information and feedback on companies that did not become customers; preliminary tests assess prospect-to-lead conversion quality.
The iterative framework produces a higher-than-average prospect-to-lead conversion in preliminary tests and can be implemented without human interaction in a web application.
Industrial Marketing Management · 201310.1016/j.indmarman.2013.03.006Publisher page; full-text availability varies and was not established.
Acquisition and retentionEvidence check: 2.5/5
Chubing Zhang, Xinchun Wang, Annie Peng Cui, Shenghao Han (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2020.10.012.
Plain-English take: Links big-data analytical intelligence capabilities to CRM performance and tests the proposed capability relationships. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: How does big-data analytical intelligence contribute to CRM performance?
Design: Empirical survey and structural model. Firms using big-data analytical intelligence for CRM
Links big-data analytical intelligence capabilities to CRM performance and tests the proposed capability relationships.
Industrial Marketing Management · 202010.1016/j.indmarman.2020.10.012Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 2.5/5
Ruth N. Bolton, P. K. Kannan, Matthew D. Bramlett (2000). Implications of loyalty program membership and service experiences for customer retention and value. Journal of the Academy of Marketing Science. https://doi.org/10.1177/0092070300281009.
Plain-English take: Members discounted or overlooked negative evaluations relative to competitors; the paper links program membership and service experiences to retention and value. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: Under what conditions does loyalty-program membership improve customer evaluations, behavior, repeat purchase intentions, retention and value?
Design: Cross-sectional and time-series field analysis of a financial-services loyalty program.. Customers of a worldwide financial-services company offering a loyalty-reward program.
Members discounted or overlooked negative evaluations relative to competitors; the paper links program membership and service experiences to retention and value.
Journal of the Academy of Marketing Science · 200010.1177/0092070300281009Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 2.5/5
Das Narayandas (1998). Measuring and Managing the Benefits of Customer Retention: An Empirical Investigation. Journal of Service Research. https://doi.org/10.1177/109467059800100202.
Plain-English take: The proposed benefits-of-customer-retention measures formed a unidimensional cumulative scale; the paper recommends managing satisfaction and retention benefits jointly. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: Can customer-retention benefits be measured as a psychometrically adequate cumulative construct, and used alongside satisfaction?
Design: Empirical scale-development study using intent measures and tests of a cumulative retention-benefit construct.. Customers/consumers assessed on repurchase intention, word of mouth, price tolerance, satisfaction and related intentions; publisher abstract does not state sample size.
The proposed benefits-of-customer-retention measures formed a unidimensional cumulative scale; the paper recommends managing satisfaction and retention benefits jointly.
Journal of Service Research · 199810.1177/109467059800100202Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 2.5/5
Jason Q. Zhang, Ashutosh Dixit, Roberto Friedmann (2010). Customer Loyalty and Lifetime Value: An Empirical Investigation of Consumer Packaged Goods. Journal of Marketing Theory and Practice. https://doi.org/10.2753/MTP1069-6679180202.
Plain-English take: The study evaluates loyalty as a customer-selection and marketing-resource-allocation metric and reports that loyalty is positively associated with customer revenue and retention. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: Is customer loyalty useful for predicting customer revenue and retention and for selecting customers for resource allocation?
Design: Empirical analysis of consumer-packaged-goods customer purchase/loyalty data.. Consumer packaged goods customers; accessible repository copy is a publisher-author manuscript.
The study evaluates loyalty as a customer-selection and marketing-resource-allocation metric and reports that loyalty is positively associated with customer revenue and retention.
Journal of Marketing Theory and Practice · 201010.2753/MTP1069-6679180202Publisher abstract/metadata retrieved; full text may require access.
Acquisition and retentionEvidence check: 2.5/5
Sunita Dwivedi (2023). An Empirical Analysis of the Dimension of Loyalty to Determine Customer Retention for E-Commerce. Journal of Content, Community & Communication. https://doi.org/10.1177/23949643221146061.
Plain-English take: Attitudinal loyalty significantly strengthened retention more than behavioral loyalty; brand image mediated the attitudinal-loyalty/retention relationship. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: Do behavioral and attitudinal loyalty predict e-commerce retention, and does brand image mediate the relationship?
Design: Primary-data survey from Delhi NCR analyzed with linear regression.. Online-shopping respondents in Delhi NCR, India, in the post-pandemic context.
Attitudinal loyalty significantly strengthened retention more than behavioral loyalty; brand image mediated the attitudinal-loyalty/retention relationship.
Journal of Content, Community & Communication · 202310.1177/23949643221146061Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 2.5/5
Lars Meyer-Waarden (2007). The Effects of Loyalty Program Membership on Customer Purchase Behavior. Journal of Consumer Marketing. https://doi.org/10.1108/07363760610712939.
Plain-English take: Membership is associated with differences in purchase behavior, providing evidence for evaluating loyalty-program retention/value claims. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: Does loyalty-program membership change customer purchase behavior in a retail setting?
Design: Empirical analysis of loyalty-program membership and purchase behavior using customer transaction data.. Retail loyalty-program members and nonmembers in a customer database.
Membership is associated with differences in purchase behavior, providing evidence for evaluating loyalty-program retention/value claims.
Journal of Consumer Marketing · 200710.1108/07363760610712939Publisher record and abstract retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 4.5/5
Suzanne Bies, Bart J.J.A.M. Bronnenberg, Els Gijsbrechts (2021). How push messaging impacts consumer spending and reward redemption in store-loyalty programs. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2021.02.001.
Plain-English take: Push treatment yielded about twice as many stamp redemptions and about 14% higher average spending; effects were especially strong among heavy customers. For your marketing: Use to frame a lifecycle hypothesis and define customer-level outcomes; check whether the study's market and retention horizon resemble yours.
Question: What is the causal impact of mobile push messaging on loyalty engagement and spending?
Design: Large controlled 18-week randomized field experiment; treatment received push messages and control stopped after week 3.. Mobile store-loyalty-program panel, Fall 2016; app users, retailer setting.
Push treatment yielded about twice as many stamp redemptions and about 14% higher average spending; effects were especially strong among heavy customers.
International Journal of Research in Marketing · 202110.1016/j.ijresmar.2021.02.001Publisher abstract/metadata retrieved; full text may require access.
Acquisition and retentionEvidence check: 2.5/5
Zainab Jamal, Randolph E. Bucklin (2006). Improving the diagnosis and prediction of customer churn: A heterogeneous hazard modeling approach. Journal of Interactive Marketing. https://doi.org/10.1002/dir.20064.
Plain-English take: Adding heterogeneity significantly improves churn prediction. Churn is significantly linked to customer-service experience, failure-recovery efforts, and payment equity, with important differences across latent classes. For your marketing: Use it to frame a lifecycle hypothesis and define customer-level outcomes; compare its market and retention horizon with yours.
Question: Can a heterogeneous hazard model improve diagnosis and prediction of customer churn, and how are churn rates related to service experience, recovery, and payment equity?
Design: Latent-class Weibull hazard model with time-varying covariates, allowing heterogeneity in baseline churn hazards and response parameters.. Customer-level churn data from a continuous-service provider: a direct-to-home satellite television firm in a South American country.
Adding heterogeneity significantly improves churn prediction. Churn is significantly linked to customer-service experience, failure-recovery efforts, and payment equity, with important differences across latent classes.
Journal of Interactive Marketing · 200610.1002/dir.20064Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 2.5/5
Pei-Yu (Sharon) Chen, Lorin M. Hitt (2002). Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry. Information Systems Research. https://doi.org/10.1287/isre.13.3.255.78.
Plain-English take: Measured switching costs varied substantially (up to a factor of two). Demographics had little effect, while systems usage and quality were associated with reduced switching; product-line breadth and quality could reduce switching and attrition. For your marketing: Use it to frame a lifecycle hypothesis and define customer-level outcomes; compare its market and retention horizon with yours.
Question: How large are switching costs and brand loyalty for online service providers, and which usage, service-design, firm, and individual factors determine switching and retention?
Design: Empirical implementation of a random-utility approach to measure switching costs and brand loyalty, followed by analysis of how usage, service design, and firm and individual characteristics relate to switching and retention.. The online brokerage industry; the publisher page reports data from that industry and evaluates systems usage, systems quality, product-line breadth and quality, and customer demographics.
Measured switching costs varied substantially (up to a factor of two). Demographics had little effect, while systems usage and quality were associated with reduced switching; product-line breadth and quality could reduce switching and attrition.
Information Systems Research · 200210.1287/isre.13.3.255.78Publisher abstract/metadata retrieved; consult the linked paper for full methods and tables.
Acquisition and retentionEvidence check: 3/5
Marc-Andre Danatzis, Klaus-Peter Wiedmann, Stefan T. B. Baumgarth, Peter Kenning (2023). Disentangling the effects of lock-in and affective customer experience on retention. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-022-00898-z.
Plain-English take: When affective experience is poor, lock-in reduces churn by about 49.03%–47.86%; its effect becomes insignificant above an acceptable experience level. Both strategies matter more at low relationship depth and less in heavy relationships. For your marketing: Use it to frame a lifecycle hypothesis and define customer-level outcomes; compare its market and retention horizon with yours.
Question: How do lock-in and affective customer experience separately and jointly affect retention, and how does relationship depth moderate those effects?
Design: Empirical panel/archival customer-level analysis using multinomial logit models to test a framework integrating experiential learning and social exchange theory.. 13,761 customers covering all firms in one telecommunications market and two services (mobile and broadband), with retention/churn decisions and customer-experience measures.
When affective experience is poor, lock-in reduces churn by about 49.03%–47.86%; its effect becomes insignificant above an acceptable experience level. Both strategies matter more at low relationship depth and less in heavy relationships.
Journal of the Academy of Marketing Science · 202310.1007/s11747-022-00898-zPublisher abstract/metadata retrieved; consult the linked paper for full methods and tables.