How to test a loyalty program before you commit to it

Aug 21
3 min read

Updated: 4 days ago

Introduction

Most loyalty schemes are launched, not tested. Someone decides on points or stamps, it goes live everywhere at once, and a year later nobody can say whether it changed behaviour or simply gave discounts to customers who were already loyal.

Running it as a test first costs almost nothing and answers that question before you build anything around it.

1. How to test a loyalty program: decide what you are actually testing

A test needs a single claim. "Loyalty will improve retention" is not one — it is a hope.

Write it as something that can be wrong: *offering a tenth visit free will raise repeat purchase rate among first-time customers.* Now you know which customers matter, which mechanic is in question, and which number settles it.

If you cannot phrase the claim that way, you are not ready to test — you are still choosing what to try.

2. Pick one mechanic, not a scheme

Test the simplest version of the idea. A stamped card, a manual note at the till, a code in a follow-up message. Nothing that requires an app, an integration or a supplier.

The point of the test is to learn whether the *incentive* changes behaviour. Building infrastructure first means paying for the answer before you have it, and it makes abandoning the idea feel like waste rather than learning.

3. Define the comparison group

This is the step that makes it a test rather than an anecdote.

Run the mechanic with one defined group and not another: one location and not the other, one shift and not the rest, customers whose surname falls in one half of the alphabet. Anything that splits your customers without selecting for how loyal they already are.

Without a comparison you will measure the season, not the scheme. Repeat rates move on their own, and a rise during a busy month proves nothing.

4. Set the number and the period before you start

Decide in advance which figure decides it — repeat purchase rate, visits per customer, or the gap between purchases — and record it for both groups before the test begins.

Then set an end date. Match it to your purchase cycle: long enough for a second purchase to be plausible, short enough that you will act on the result. For most businesses that is one to three months.

Deciding the measure afterwards is how a test becomes a justification.

5. Cost the reward at margin

Work out what the reward costs you in gross profit, and what behaviour change would be needed to cover it.

A free item costing $4 against nine purchases at $6 and a 40% margin earns $21.60 and spends $4 — sound. Change the margin, the price or the number of purchases and it may not be. Do this arithmetic before the test, because it tells you whether a *successful* test is even worth scaling.

6. Read the result honestly

Three outcomes, and two of them are useful.

The number moved in the test group and not the comparison group: the mechanic works, and you now know roughly by how much.

Neither moved: the mechanic does not change behaviour here. That is a real finding and it has saved you the cost of building it.

Both moved: something external changed — a season, a competitor, a promotion. The test is inconclusive, not positive.

The trap is the third case read as the first, which is why the comparison group matters.

7. Scale only what the test earned

If it worked, expand the same mechanic before adding sophistication. Tiers, apps and points economies can come later, once the simple version has proven the behaviour exists.

If it did not work, stop. A scheme kept running because removing it would look like an admission is the most expensive kind of loyalty programme — it costs margin every month and produces nothing.

Conclusion

State a claim that can be wrong, test the simplest possible mechanic, split your customers into a test and a comparison group, and fix the measure and the end date before you begin.

Cost the reward at margin first, read the comparison honestly, and scale only what the result actually supports. Treating loyalty as a test rather than a launch is the difference between knowing it works and hoping it does.