​​​​Why Most Loyalty Programs Ignore Behavioural Science

The disconnect between loyalty programs and human behaviour

Most loyalty programs are built around transactions, not behaviour. Points per purchase, spend thresholds, and blanket discounts dominate program design. While these mechanics are easy to implement and measure, they largely ignore how users actually make decisions inside products.

Behavioural science focuses on why people act the way they do, especially under constraints like limited attention, delayed gratification, and cognitive bias. Loyalty programs often skip this layer and assume that higher rewards automatically lead to better engagement. In practice, this assumption breaks down quickly.

The result is a large number of programs that look active on dashboards but fail to change user behaviour in meaningful or lasting ways.

Over-reliance on rational user assumptions

Treating users as purely rational actors

Many loyalty programs assume users evaluate rewards logically, calculate value accurately, and act in their long-term interest. This is rarely true. Users respond to cues, shortcuts, and emotional triggers more than optimal calculations.

For example, a 5 percent cashback sounds rationally better than a small fixed reward, but users often respond more strongly to immediate, visible benefits than percentage-based ones.

Ignoring this leads to reward structures that make sense internally but feel abstract or irrelevant to users.

Assuming more rewards equals more loyalty

Increasing reward value does not linearly increase engagement. Beyond a point, users stop noticing incremental gains. Behavioural science shows that perceived value matters more than actual value.

Programs that rely only on higher payouts end up competing on cost instead of influence, increasing burn without improving retention.

Metrics-driven design replaces behaviour-driven design

Optimizing for redemption, not behaviour change

Redemption rate is one of the most common loyalty metrics. While useful, it does not explain whether behaviour actually changed. A user redeeming points once does not mean they formed a habit or increased lifetime value.

Behavioural science emphasizes measuring before-and-after actions. Many programs skip this and treat redemption as success, even when usage drops once rewards stop.

Short-term campaign thinking

Loyalty initiatives are often launched as campaigns with fixed timelines. Behaviour change, however, requires consistency and reinforcement over time.

Campaign-based thinking prioritizes quick spikes instead of sustained patterns. Behavioural design requires systems that learn and adapt, not isolated reward bursts.

Simplicity wins, but programs add complexity

Cognitive overload in reward structures

Tier systems, expiry rules, bonus conditions, and exclusions often pile up over time. While each rule may have a business justification, the combined effect overwhelms users.

Behavioural science highlights that users disengage when effort exceeds perceived benefit. Many loyalty programs unknowingly cross this threshold.

Simple, predictable systems outperform complex ones, even when the rewards are smaller.

Poor feedback loops

Feedback reinforces learning. Many programs delay feedback by weeks or hide progress behind dashboards users never check.

When users do not see immediate consequences of their actions, learning does not occur. Behavioural science stresses the importance of tight feedback loops, which most loyalty systems fail to implement.

Behavioural science is seen as optional, not foundational

Treated as a UX concern, not a growth lever

Behavioural principles are often limited to UI tweaks or onboarding copy. Core reward logic remains unchanged.

This separation prevents teams from using incentives as behavioural tools. Rewards should shape action sequences, not just decorate interfaces.

Lack of cross-functional ownership

Behavioural design sits between product, growth, and analytics. When ownership is unclear, it gets deprioritized.

Teams default to familiar constructs like discounts and points because they are easy to justify internally, even if they are ineffective.

What applying behavioural science actually looks like

Rewarding actions, not outcomes

Behavioural systems focus on leading indicators such as setup completion, frequency, and consistency. Outcomes like revenue follow later.

Programs that reward effort and progress build habits faster than those that reward only end results.

Designing for timing and context

When a reward is triggered matters as much as what it is. Behaviourally informed programs trigger incentives at moments of hesitation, drop-off, or high effort.

Context-aware rewards outperform generic ones because they align with user intent.

Measuring persistence, not just response

Instead of asking whether a reward was redeemed, teams should ask whether behaviour persisted after rewards reduced.

This shift separates short-term engagement from real loyalty.

Why this gap persists

Ignoring behavioural science is not due to lack of evidence. It persists because transactional models are easier to explain, cheaper to test, and simpler to operate.

Behaviour-driven systems require experimentation, cross-team alignment, and patience. Many organizations choose predictability over effectiveness.

Why this matters for product and growth teams

Loyalty programs shape behaviour whether teams intend them to or not. Ignoring behavioural science does not make systems neutral, it makes them inefficient.

For product and growth teams, applying behavioural principles is the difference between incentives that inflate metrics and incentives that change behaviour. Programs that understand users outperform those that only reward spend.

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