Retention / Loyalty
Loyalty Program Mistakes to Avoid
// a program nobody understands rewards nobody
By Fracto Solutions
October 30, 2026
The short answer
Most loyalty programs underperform for avoidable reasons: rules too complex to understand, rewards too hard to reach, margin given away for loyalty customers already had, launching with no promotion, and never measuring results. Keep it simple and attainable, model the economics, promote it, and track repeat-purchase behavior, not just sign-ups.
Are the rules too complicated to explain?
If you cannot state the earn-and-redeem rule in one sentence, most customers will never engage with it. We should flag one tension honestly though: the firm-level evidence does not support simplicity as an absolute.
The practical case for simplicity is strong. A rule a customer can hold in their head gets used; one that needs a table does not. Keep one earning action, one clear conversion rate, and a redemption step that takes a single click at checkout.
The complication is that across 322 public companies introducing programs between 2000 and 2015, designs with tiers or earning mechanisms, which are strictly more complex, produced significantly larger gains in sales and gross profit than simpler ones. So the rule is not “simpler is better” but “explicable is better”. A tiered program with a one-sentence summary beats both a confusing points scheme and an oversimplified one with nothing to aim at.
We have previously called complexity the most common loyalty killer. We have no data ranking failure causes, so we have dropped the superlative. It is a common failure mode, which is enough reason to avoid it.
Is the first reward too far away?
This is the mistake with the clearest experimental evidence behind it, and the cheapest fix. Perceived distance to the first reward changes completion rates substantially even when the actual effort required is identical.
Nunes and Dreze ran a field experiment with 300 car-wash customers in which both groups had to buy exactly eight washes. One group received a ten-space card with two stamps already filled in; the other an eight-space card with none. Completion was 34% against 19%. Nothing changed except the perceived starting position. So grant points or status at enrollment rather than starting everyone at zero.
The companion finding is about the next reward rather than the first. Kivetz, Urminsky and Zheng analyzed 949 completed ten-stamp cafe cards, roughly 10,000 purchases, and found interpurchase time shortening by a mean 0.7 days, about a 20% acceleration, as the reward neared. And in a 329-respondent study, 56% accepted a promotional offer when close to a reward against 38% when far from it. Distance suppresses response; proximity accelerates it.
An earlier version of this page gave exactly this advice as our own judgment. It is published research with samples attached, and stating it that way is both more useful and more honest.
Is the reward budget going to the wrong customers?
Usually, unless you design against it. A program on default settings routes most of its rewards to the customers least likely to change their behavior, which is the largest and quietest source of margin leakage in loyalty.
Liu’s longitudinal study of a convenience-store franchise found that the program’s heavy buyers “benefited most from the program, but their spending did not grow over time,” while light and moderate buyers genuinely increased frequency and transaction size. Meyer-Waarden and Benavent add that heavier customers also enroll earlier. Put together: your best customers sign up first, claim the most, and change the least.
The design response is to target incrementality. Weight rewards toward the behaviours you actually want more of, such as a second purchase inside a window, a category the customer has not tried, or a subscription conversion, rather than toward raw spend. And model the effective reward rate at full redemption against gross margin, remembering that outstanding points are a deferred-revenue liability under ASC 606 and IFRS 15 rather than a float.
On point expiry, loyalty points sit outside the US federal gift-card five-year floor, since Regulation E expressly exempts loyalty, award and promotional cards. That gives you latitude on terms, and it also means your expiry and devaluation rules live in your own contract and under general unfair-terms law, so write them plainly and do not change them retroactively.
Did you launch it quietly?
A program nobody knows about rewards nobody. Enrollment also is not engagement, and the two get conflated constantly.
Promote it where the decision happens: at account creation, on the post-purchase page, in the order confirmation, and as a persistent but unobtrusive element showing current balance and distance to the next reward. That last element is doing the goal-gradient work, so it earns its place on the page.
We used to write that plenty of well-designed programs fail because nobody joins. The nearer real figure is about engagement rather than enrollment: one industry study put active participation at 39.5% of memberships. So the gap to watch is not only sign-up but what share of members ever earn and redeem anything. Measure both.
Are you measuring it the wrong way?
Almost certainly, if you are comparing members to non-members. That is the standard advice, it is what an earlier version of this page recommended, and it is confounded by self-selection in a way that will credit the program for results it did not produce.
Here is the problem. Meyer-Waarden and Benavent found that “heavier, more frequent store customers enroll in the loyalty program earlier” and that “buying behavior changes only slightly after buyers join the program”, with small loyalty gains eroding six to nine months after joining. Leenheer and colleagues found that once self-selection is explicitly modeled, the effect of membership on share of wallet is only small and positive, far below the naive member-versus-non-member gap. Taylor and Neslin matched rewarded to non-rewarded customers on baseline sales within $9 and ran a switching regression specifically to correct for self-selection. Your best customers join first, so the raw gap mostly measures who signed up.
Three designs that do work, in ascending order of rigour. Compare members to non-members matched on pre-enrollment purchase behavior. Compare each member to their own pre-join baseline, which controls for selection directly. Or hold back a randomised share of the enrollment invitation and compare invited to uninvited, which is the only approach that gives a clean causal read. The third costs nothing but discipline at launch, and it is worth insisting on before the program goes live.
Then judge it on the right number. Incremental gross margin after reward cost, not sign-ups, not member revenue share, and not member average order value. Expect profit to lag sales: the 322-firm study found gross-profit effects arriving a quarter after the sales effect and being much smaller. A program that looks like a success in month two may just be accruing a liability you have not recognized yet.
We keep loyalty simple, attainable, promoted, and measured.
We strip the rules down to something customers grasp instantly, set a first reward that’s actually reachable, model the economics so margin holds, promote enrollment at the right moments, and measure incremental repeat behavior, so the program builds profit instead of leaking it.
It’s the same measurement-first approach behind our lifecycle and conversion optimization work, where we rebuild the buying path and measure the result .
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Frequently asked questions
Why do loyalty programs underperform?
The documented failure modes are more structural than the usual list suggests: competitive saturation neutralizing the benefit where most rivals run a program, launch effects fading within months, and rewards concentrating on heavy buyers whose behavior does not change. Add the avoidable design errors, which are rules nobody can explain and a first reward that feels unreachable.
How close should the first reward be?
Close enough to feel already underway. In a 300-customer car-wash field experiment, 34% completed a card that started with two stamps already filled in against 19% that started empty, for exactly the same eight required purchases. Grant progress or status at enrollment, and keep the next reward near, since purchases accelerated about 20% as a reward approached across 949 cafe cards.
Should I compare members to non-members to see if it works?
No. Your best customers join first, so that gap mostly measures self-selection rather than program effect, and research that models the selection explicitly finds the real effect on share of wallet is only small. Instead compare members to non-members matched on pre-enrollment behavior, compare each member to their own pre-join baseline, or hold back a randomised share of the enrollment invitation.
How do I stop the program leaking margin?
Reward the behavior you want more of rather than raw spend, because the heaviest claimers are often the customers who were going to buy anyway: one longitudinal study found heavy buyers benefited most while their spending did not grow. Then model the effective reward rate at full redemption against gross margin, and treat outstanding points as the deferred-revenue liability they are under ASC 606 and IFRS 15.
Sources
Nunes & Dreze (2006), “The Endowed Progress Effect,” Journal of Consumer Research 32(4):504-512. Car-wash field experiment, 300 customers, identical eight paid washes required either way: 34% completed a ten-space card given two stamps already filled in, against 19% given an eight-space card with none. Artificial advancement toward a goal raised completion by framing alone. Peer-reviewed study (Oxford) on loyalty program mistakes to avoid
Kivetz, Urminsky & Zheng (2006), “The Goal-Gradient Hypothesis and Its Discontents,” Journal of Marketing Research 43(1):39-58. Cafe-card field data: 949 completed ten-stamp cards, roughly 10,000 purchases, with interpurchase time shortening by a mean 0.7 days (t = 2.6, p < .05), about a 20% acceleration, as the reward neared. A companion frequent-flyer study of 329 respondents found 56% accepted a promotional offer in the near-goal condition against 38% in the distant-goal condition (chi-square = 10.7, p < .001). Peer-reviewed study (SAGE) on loyalty program mistakes to avoid
Liu (2007), “The Long-Term Impact of Loyalty Programs on Consumer Purchase Behavior and Loyalty,” Journal of Marketing 71(4):19-35. Longitudinal convenience-store franchise data: the program’s heavy buyers “benefited most from the program, but their spending did not grow over time,” while light and moderate buyers genuinely increased purchase frequency and transaction size. This is the measured version of the criticism that programs pay the already-loyal for behavior they already had. Peer-reviewed study (SAGE) on loyalty program mistakes to avoid
Meyer-Waarden & Benavent (2009), Journal of the Academy of Marketing Science 37(3):345-358. “Heavier, more frequent store customers enroll in the loyalty program earlier,” and “buying behavior changes only slightly after buyers join the program,” with small loyalty gains eroding six to nine months after joining. This self-selection is why comparing members to non-members overstates program effect. Peer-reviewed study (Springer) on loyalty program mistakes to avoid
Leenheer, van Heerde, Bijmolt & Smidts (2007), International Journal of Research in Marketing 24(1):31-47. Once self-selection into the program is explicitly modeled, the effect of loyalty-program membership on share of wallet is small and positive, far below the naive gap between members and non-members. The methodological point that follows: a member-versus-non-member comparison is not a measurement of the program. Peer-reviewed study (ScienceDirect) on loyalty program mistakes to avoid
Revenue recognition for loyalty points: ASC 606 and IFRS 15. Points granted with a sale are a material right and therefore a separate performance obligation: part of the transaction price is allocated to the points and deferred until redemption, with breakage recognized in proportion to the pattern of redemptions (ASC 606-10-55-46 to 55-48) or only once further redemption becomes remote. Under 55-49, amounts state escheat law requires to be remitted are a liability rather than revenue. IFRS 15 allocates on the same standalone-selling-price basis (IFRS 15.77 to 80). RevenueHub on gift card breakage
Regulation E, 12 CFR §1005.20(b) (exemptions). The federal gift-card rules, including the five-year minimum before funds may expire, expressly exclude “a loyalty, award, or promotional gift card.” So loyalty points are not covered by that five-year floor, and point expiry is governed instead by your own terms and by general unfair-terms and consumer-protection law. US Regulation E gift card rules
Shopify, “Ecommerce Loyalty Program: 7 Ways to Build One,” published March 4, 2024. Vendor marketing content, cited only for what it contains: the seven program types (cash-back, points, referral, tiered, paid, subscription and hybrid) and their definitions. It carries no cost-to-serve data, so we no longer cite it for the claim that repeat customers cost less to sell to, which it does not support. Shopify guide to loyalty programs