Customer Loyalty Trends 2026: What's Actually Working for Program Managers

What is working in loyalty in 2026 is activating the members a program already has: predictable earning combined with bounded surprise, design matched to each stage of membership, personalization the data can support, and a few KPIs measured against a control.

If a loyalty program has run the same earn-and-burn mechanic for three years, declining engagement is often a design problem as well as a market one. Some members leave for reasons a program cannot control: Deloitte reports that "4 in 10 Americans now exhibit deal-driven, cost-conscious, or trade-down behaviors across industries." But many drift when the program stops delivering value that feels relevant to how they behave, and that part is fixable. The two problems have different fixes. This guide is for program managers responsible for an existing program, whether optimizing a points structure, evaluating a new mechanic or making the internal case for a refresh, and each section covers a design challenge and what a program manager can do about it.

Key Takeaways

What does the 2026 loyalty landscape look like?

Loyalty membership is nearly universal among US online adults, so for most established programs the harder measure is how many members are active.

Forrester reports that 90% of online adults in the US belong to at least one loyalty program (Consumer Benchmark Survey, 2024), which means few consumers are new to loyalty programs. Enrollment still matters where a program reaches only a small share of its own customers, but for most established programs the harder measure is activity. Deloitte's 2025 Consumer Loyalty Program Survey found that "the average consumer enrolls in eight loyalty programs, yet actively participates in only five." Members are spreading their attention across many programs, so for most established programs the bigger opportunity is activating the members they already have.

That shift changes where investment goes: from acquisition mechanics toward personalization, automation and re-engagement of existing members. Three design shifts are worth watching, each covered below in terms of what a program manager can implement: running loyalty and promotions together rather than as separate programs, treating gamification as behavioral design rather than decoration, and separating genuine personalization from personalized messaging style. The guide to running loyalty and promotions on separate platforms covers the first in detail.

Why do transactional programs lose members over time?

Purely transactional programs create conditional engagement: members take part when the earn rate justifies the purchase and drift away when it does not.

Most programs are built on a simple logic: earn points on purchases, redeem for discounts or products. It works in the short term because it gives a clear, immediate reason to join. But a fixed schedule, where members know exactly what each purchase earns, gives nothing to hold attention once a member has decided whether the earn rate is worth it.

Programs that sustain engagement tend to combine predictable elements (points on purchase, tier progression) with variable ones that members cannot fully predict. Research offers qualified support: in four studies with real but small rewards, Shen, Fishbach and Hsee (Journal of Consumer Research, 2015) found that people "invest more effort, time, and money to qualify for an uncertain reward" than for a certain one of higher expected value, but only when they focus on the process of pursuing it rather than on the reward itself. Members who treat the program mainly as a discount may value predictability more, so test variable elements against a control before rolling them out. In practice this can be bonus point events with varying triggers, surprise recognition for a milestone, or tiered challenges with different reward outcomes. It does not require a rebuild, only identifying where the current mechanics are entirely predictable and adding controlled variability there.

What do members value at each stage?

Loyalty members value different things by stage: progress and head starts for newer members, status protection for members who already hold a tier.

A member who has just enrolled is in a different position from one who has been active for two years and is 200 points from a tier upgrade. Three principles help with design decisions:

• Goal gradient. Effort tends to rise as a goal gets closer. In a study of a real café reward program, Kivetz, Urminsky and Zheng (Journal of Marketing Research, 2006) found members "purchase coffee more frequently the closer they are to earning a free coffee." Make progress visible, and set thresholds and milestones that create frequent near-goal moments rather than one distant target. The same study found that purchase rates reset to a lower level after a reward is earned, so show the next goal as soon as a member redeems.
• Head starts. The same paper reported a field experiment in which customers given a 12-stamp card with 2 "bonus" stamps completed the 10 required purchases faster than those given a regular 10-stamp card. Onboarding can apply the same idea by framing the first goal so members start partway along it, for example a welcome balance that counts toward the first reward threshold.
• Status protection. Members who hold a tier tend to work to keep it, so tier expiry or requalification can help retain high-tier members. The lever cuts both ways: in research by Wagner, Hennig-Thurau and Rudolph (Journal of Marketing, 2009), "loyalty intentions are indeed lower for demoted customers than for those who have never been awarded a preferred status." Warnings before requalification deadlines, grace periods or a soft landing one tier down reduce that risk. Status protection is a weaker fit for new members, who have nothing to lose yet.

Matching the principle to the stage avoids designing one campaign and expecting it to work across the whole member base.

How should non-transactional rewards be used?

Use experiential and non-transactional rewards when they reinforce a specific behavior for a specific segment and can be delivered consistently, not because they are fashionable.

Forrester suggests giving members "exclusive access to benefits such as limited-release products, first access to deals, and member-only events," but experiential rewards only pay off when the experience is genuinely different and relevant to the members receiving it. Three questions come first:

• Who is it for? A VIP event for the top 5% of members by spend is a different decision from early access for all active members.
• What behavior does it reinforce? An experience awarded for enrollment rewards acquisition; one awarded for cross-category purchase rewards category expansion.
• Can it be delivered consistently? Experiences that are announced and then fulfilled unevenly can damage trust, so scope what can be executed reliably.

Sustainability-linked rewards, such as charitable donations tied to purchases, follow the same logic: they are most likely to resonate with segments that already show values-driven behavior, so test them with those segments before offering them program-wide.

What does gamification research support?

Gamification works when it rests on behavioral mechanisms such as visible progress, head starts and bounded variable rewards, and is less likely to change behavior when it is a cosmetic layer of badges and leaderboards.

• Visible progress. Progress bars, tier trackers and challenge meters apply the goal gradient. Progress must be meaningful; a bar that barely moves gives members little reason to return.
• Head starts. Challenges that begin with partial credit ("you have already earned 1 of 5 stamps for joining this challenge") apply the head-start effect.
• Bounded variable rewards. Bonus events, mystery rewards and spin-to-win games give members a reason to return between purchases. Keep them bounded so rewards feel possible, not arbitrary, and remember that a chance-based reward tied to a purchase needs a free alternative method of entry.

An example. For National Pizza Month, Brandmovers ran 31 Days of DiGiorno, a gameboard on which users completed tasks such as uploading receipts for DiGiorno purchases, taking a survey and referring a friend, earning one sweepstakes entry per space, plus a weekly "Spin to Win" wheel for a chance at prizes. The mix of fixed tasks and a weekly chance element was designed to give fans reasons to come back across the month.

Common failure patterns include notification overload, stacked challenges with no recovery time, and leaderboards where most members have no realistic path to a ranking. A practical sequence for adding a challenge: define the target behavior, set a duration that fits purchase frequency, give a head start at enrollment, show milestones along the way, reward completion, and measure completion and purchase behavior against a control group before scaling. The guide to gamification in loyalty programs covers mechanic design and the rules for chance promotions in more depth.

What does personalization require?

Personalization depends on clean member data more than on algorithms, so segmentation and behavioral triggers come before predictive models.

A sophisticated model on sparse or inconsistent data can underperform simple segmentation on clean data. Personalization typically works at three levels:

• Offer personalization. Different rewards or bonus events for different segments based on purchase history. Recency, frequency and monetary value (RFM) segments can be built in many standard CRM tools without data-science support.
• Communication personalization. Timing, channel and content based on behavior, such as a reactivation message after a period of inactivity or a reminder when a member is close to a tier threshold.
• Predictive personalization. Models that estimate who is likely to churn, upgrade or respond to an offer. They need enough history, clean data and either data-science support or a platform with built-in predictive analytics, so they are rarely the right starting point.

An example. Brandmovers built an activity-based loyalty program for a large CPG nutritional brand in which influencers earn points and rewards for engaging with the brand across social media, as well as for purchases, through missions and challenges. It is an example of offer design built around one segment's behavior, using simple rules rather than predictive models. The BLOYL™ program recorded a 62% engagement rate and a 3+ increase in average transactions per user (disclosed by Brandmovers).

Data capture is therefore a design decision, not only a technology one. Zero-party data (preferences members state directly) and first-party behavioral data (purchases, redemptions, channel activity) are what make personalization possible.

What privacy rules apply to loyalty data?

In the US, California's privacy law sets the most detailed rules for loyalty programs, including a notice of financial incentive, and several other states have their own consumer privacy laws.

• Notice of financial incentive. Under California's Civil Code section 1798.125, a business covered by the law (generally one with annual gross revenue above an inflation-adjusted $25 million, or that buys, sells or shares the personal information of 100,000 or more consumers or households, as defined in section 1798.140) that offers rewards in exchange for personal information must give a notice of financial incentive and obtain opt-in consent, and may not use incentive practices that are "unjust, unreasonable, coercive, or usurious in nature"; California's Attorney General sent loyalty program operators notices of alleged noncompliance on this point in a January 2022 sweep.
• Disclosure at enrollment. Tell members plainly what data is collected, how it is used and whether it is shared.
• Data minimization. Collect what the program will use; building profiles beyond what the program can act on adds risk without benefit.
• Access and deletion. Under Civil Code section 1798.130, businesses must respond to verifiable consumer requests within 45 days, extendable once by another 45 days with notice.
• Opt-out of sale or sharing. If member data goes to advertising or coalition partners, give members a clear way to opt out.
• Other states. Virginia, Colorado, Connecticut, Texas and other states have their own consumer privacy laws. Some address loyalty programs directly: Virginia's law permits different prices or service tied to "voluntary participation in a bona fide loyalty, rewards, premium features, discounts, or club card program," and Colorado's rules set conditions for bona fide loyalty programs. Check the rules where members live.

Promotions add separate rules: sweepstakes and instant wins need official rules, a free alternative method of entry and attention to state requirements. The guide to promotions compliance covers them. This is general information, not legal advice.

What should program managers measure?

Start with metrics you can collect reliably and act on: active member rate, redemption rate, repeat purchase frequency and incremental spend against a control group.

• Active member rate: the share of enrolled members with at least one qualifying transaction in the past 90 days, or a period that fits purchase frequency. High enrollment with a low active rate is not a retention success.
• Redemption rate: the share of members who redeemed at least once in the past 12 months (some programs instead track points redeemed as a share of points issued, so pick one definition and keep it). Low redemption can mean the threshold is too high, the rewards are not relevant or members forget or do not know how to redeem; in Deloitte's survey, "40% of all respondents admit to sometimes forgetting to redeem." Each cause has a different fix, and higher redemption also means higher program cost, so read the rate alongside reward cost. The guide to redemption rates covers them.
• Repeat purchase frequency: qualifying transactions per active member per period, to show whether the program reinforces buying or simply records it.
• Incremental spend: spend among members compared with a matched control group. Without a randomized test this is directional, not causal, because members who join may already buy more, but it is actionable.

An example. A leading Canadian regional distributor runs its Culture Club program for its own business customers on BENGAGED™, Brandmovers' B2B channel incentives platform. The program recorded a 25% average sales increase among enrolled customers vs. 5% among non-enrolled (disclosed by Brandmovers). Because customers chose to enroll, the gap shows a difference between groups rather than proof of cause, but it is a useful starting point for a case to leadership, stronger when paired with each group's sales before enrollment.

Add a yearly cohort comparison of first-year members with established ones. The loyalty KPI dashboard guide covers formulas.

A 2026 program audit checklist

Audit the current program before investing in new mechanics, personalization or analytics, then fix in sequence: compliance gaps first, then mechanics before personalization, and data before analytics.

BLOYL, Brandmovers' loyalty platform, includes gamification modules such as scratch-offs, gameboards, instant wins and challenges, a dynamic rules engine that can reward specific behavioral actions, A/B testing against a control group, predictive churn analytics and bidirectional CRM and CDP data flows. See the loyalty platform overview for details.

Conclusion

Loyalty in 2026 rewards treating the program as a design-and-measurement discipline rather than a trend-adoption exercise. Membership is nearly universal, so for most established programs the biggest opportunity is the members they already have. The mechanics that sustain engagement, bounded variable rewards, stage-appropriate design and personalization the data can support, can often be added to an existing program without a full rebuild, and most of the core metrics can be reported from a standard CRM, though measuring incremental spend needs a deliberately maintained control group. Start with the audit, then fix compliance gaps first, mechanics before personalization and data before analytics.

Sources

• Forrester, "Consumers Crave More Than Discounts From Loyalty Programs" (January 23, 2025)
• Deloitte, "Reshaping loyalty programs in an era of value seeking" (January 12, 2026)
• Wagner, Hennig-Thurau and Rudolph, "Does Customer Demotion Jeopardize Loyalty?" Journal of Marketing (2009)
• Shen, Fishbach and Hsee, "The Motivating-Uncertainty Effect," Journal of Consumer Research (2015)
• Kivetz, Urminsky and Zheng, "The Goal-Gradient Hypothesis Resurrected," Journal of Marketing Research (2006)
• California Civil Code section 1798.125 (financial incentives)
• California Civil Code section 1798.140 (definitions)
• California Civil Code section 1798.130 (responding to requests)
• Code of Virginia, section 59.1-578 (controller duties)
• Colorado Privacy Act Rules, 4 CCR 904-3, Rule 6.05 (loyalty programs)
• California Attorney General, loyalty program notices (January 28, 2022)
• Brandmovers, 31 Days of DiGiorno case study
• Brandmovers, nutritional CPG loyalty program case study
• Brandmovers, distributor B2B loyalty program case study