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Lokai estimated a 42% increase in customer lifetime value for a charity-affiliated subscription cohort during a five-month test. The same cohort showed a 13% nominal retention improvement and a 23% relative retention improvement compared with similar products that did not feature a charity affiliation.
This is one company case study, not a universal cause marketing benchmark. Its value is the test design: Lokai compared cohorts, kept price and timing aligned, measured retention, and translated the result into an estimated lifetime value effect.
Lokai sells bracelets, rings, and related products, including subscription offerings. Charitable partnerships have long been part of the brand, but not every product or customer experience used the same cause-related message.
Lokai wanted to determine whether a visible charity affiliation in product marketing and checkout correlated with stronger subscription retention. The company compared a charity cohort with a control cohort using similar bracelets, the same price point, and the same five-month period.
The total tested population was 3,020 customers. The charity-affiliated cohort included 393 customers, and the control cohort included 2,627. The original analysis reported a 95% confidence interval of plus or minus 0.5 percentage points. Teams evaluating the result should also review assignment, cohort balance, acquisition source, and churn definitions before generalizing it.
The charity-affiliated cohort showed a 13% nominal improvement in retention and a 23% relative improvement in retention. Based on that observed retention difference, Lokai estimated a 42% increase in customer lifetime value for the charity cohort.
Nominal and relative improvement answer different questions. A nominal change is the difference between two retention rates in percentage points. A relative change divides that difference by the control rate. Teams should publish both when possible so readers can understand the baseline.
Estimated lifetime value also depends on the model. Retention, purchase frequency, order value, contribution margin, discounts, and servicing costs can all change the result. The Lokai customer story provides the approved summary of the program and should remain the reference for public claims.
A charitable choice can make a recurring relationship feel more personally relevant. Customers see a continuing connection between their subscription and a cause, which may add meaning beyond the product itself. That explanation is plausible, but the test result alone does not prove a single psychological mechanism.
Program design matters. The nonprofit should fit the audience and brand, the charitable promise should be easy to understand, and the customer should be able to see what action creates value for the cause. Vague claims or an unrelated partner can weaken trust instead of strengthening it.
Lokai's broader giving experience lets customers support causes connected to the brand's balance-focused mission. Companies considering a similar program can review Change's loyalty program solution for nonprofit choice, embedded giving, donation records, and impact communication.
Start with one falsifiable question, such as whether a charity-affiliated subscription improves 90-day retention. Define the primary metric, observation window, eligible population, exclusions, and minimum sample before launch. Do not change the success metric after seeing the result.
Use random assignment when practical. If the program relies on customer choice or pre-existing cohorts, document likely selection effects. Keep price, product, acquisition channel, promotion, and timing as comparable as possible, then report baseline rates, absolute change, relative change, confidence intervals, and sample size.
Track charitable delivery as a separate outcome. The business result does not show whether funds reached the intended nonprofit accurately and on time. Change's donation infrastructure can connect the customer action to the nonprofit selection, transaction ledger, receipt logic, and disbursement record.
Repeat the test across another period, product, or audience before treating the estimated lift as a planning assumption. Examine whether the effect persists after the initial campaign, whether higher retention changes support or fulfillment costs, and whether customers understand the charitable mechanics.
Review the program with legal and finance teams. The Federal Trade Commission's advertising guidance emphasizes truthful, non-deceptive marketing, while state charitable promotion laws can require agreements, filings, disclosures, records, and reports. The experiment and public claims should use the same donation formula.
Scale the operational controls with the customer experience. Add nonprofit approval, eligibility monitoring, calculation tests, refund rules, payout reconciliation, impact reporting, and a clear support path. A successful experiment should become a repeatable program, not a claim detached from the underlying evidence.
Implementation review. A team replicating the Lokai analysis should write a measurement plan before exposing customers to the campaign. Specify the eligible products, subscription terms, acquisition channels, charity message, checkout placement, observation window, retention definition, lifetime value formula, planned exclusions, and minimum sample. Preserve that version with the test results.
Check whether the cohorts were comparable at baseline. Useful fields include first-order value, discount, acquisition cost, channel, geography, device, tenure, product, and prior charitable engagement. If customers self-select into the charity experience, the result can reflect pre-existing differences as well as the program itself. Report that limitation rather than describing correlation as pure causation.
Calculate both customer and program economics. Higher retention can create more gross revenue, but the business should also account for donation expense, software and processing costs, discounts, fulfillment, returns, support, and contribution margin. A program that raises lifetime value but reduces margin may still be worthwhile, but leadership should see the full tradeoff.
Repeat the analysis after enough time has passed to observe the intended behavior. Compare the original estimate with realized revenue and retention, then update the model. Publish only the result that the data supports, including sample size and period. The charitable report should separately reconcile eligible transactions, donation commitments, completed payouts, and nonprofit confirmation so the commercial case does not overshadow delivery to the cause.
Plan the public case study while designing the experiment. Obtain permission to use the company and nonprofit names, identify which data can be disclosed, and agree on how results will be reviewed. Keep the original analysis, queries, and definitions so a future editor can reproduce the number. If later data changes the estimate, update the article rather than continuing to circulate a headline that no longer reflects the complete observation period.
No. The 42% figure was an estimate from one five-month test. Other brands, products, audiences, and campaign designs can produce different results.
Nominal lift is the percentage-point difference between cohorts. Relative lift compares that difference with the control cohort's rate.
The test included 3,020 customers: 393 in the charity-affiliated cohort and 2,627 in the control cohort.
Choose one primary metric tied to the decision, such as subscription retention over a defined period. Treat conversion, order value, and engagement as secondary metrics unless the experiment is designed around them.
Yes. Report the customer behavior result separately from funds committed, funds delivered, payout timing, and any exceptions affecting the nonprofit.
This article provides general information, not legal, tax, or accounting advice. Consult qualified advisers about your specific program and jurisdictions.
