I recently ran a pragmatic, repeatable test to answer a question I hear a lot from independent retailers: does open-banking–powered cashback actually increase average basket size? The short answer is yes — but only when the offer, timing and measurement are set up correctly. Below I share the exact step-by-step test I used, the metrics I tracked, the pitfalls to avoid, and how to interpret results so you can run this in your own store without fancy tech or a large budget.
Why test open-banking cashback with independents?
Open-banking cashback (where customers receive an immediate or near-immediate cashback credited via open-banking rails after paying) promises low friction and measurable ROI. For independents, the hook is strong: no complex points systems, clear monetary value, and the ability to target spend behaviour. But retailers need evidence it lifts basket size rather than just rewarding purchases they would have made anyway.
Key hypothesis
My hypothesis was simple: offering a time-limited, open-banking cashback of 8–12% on purchases above a threshold will increase average basket size and frequency among existing customers, while maintaining or improving profitability. I chose 8–12% because it's meaningful to customers but still manageable for margin when paired with a threshold and minimum spend.
What you need before you start
- A partner or platform that supports open-banking cashback (examples: Curve cashback integrations, Klarna/other fintechs with open-banking/instant rewards APIs, or specialist providers such as Modulr + rewards middleware).
- Access to your customer list with email or phone and identifiers so you can run a controlled test (ideally a loyalty app or CRM dataset).
- Ability to create simple segments and track transactions by customer ID (POS exports or payments platform exports will work).
- Clear margin data so you can calculate ROI per incremental £1 of spend.
Designing the experiment
I used an A/B test (randomised control trial) at the customer level. Here’s the structure I implemented:
- Population: Active customers who made at least one purchase in the previous 6 months (n ≈ 3,000 for this pilot).
- Randomisation: 50% treatment, 50% control, randomised by customer ID.
- Treatment: Personalized email + in-store card/QR code campaign offering 10% cashback on purchases over £25, credited within 24 hours via open-banking, valid for 14 days.
- Control: No cashback offer during the test window (other marketing held constant).
- Primary metric: Change in average basket value per customer during the 14-day test window and in the subsequent 30 days.
- Secondary metrics: Purchase frequency, incremental margin, redemption rate of cashback, and customer acquisition (if new customers included).
Implementation details
Execution focused on frictionless redemption and clear messaging. Key operational choices I made:
- Offer clarity: “10% cashback on orders over £25 — paid back within 24 hours via secure bank transfer.” We included examples to show how much cashback they’d get on typical spends.
- Redemption flow: Customers received a one-click verification link to connect their bank via the open-banking provider. To avoid drop-off we allowed payment in-store using existing methods; cashback was applied after the transaction via bank transfer to the connected account.
- Fraud controls: Limits on maximum cashback per customer during the test and verification of bank details to prevent abuse.
- POS tagging: Every transaction recorded the customer ID so we could attribute lift precisely.
- Timing: Two-week active offer window, with follow-up 30-day observation for any lasting behaviour change.
Sample communications
Here’s the concise message that worked best in testing:
“For the next 14 days, get 10% back on any purchase over £25 — straight to your bank within 24 hours. Tap to connect your bank and claim.”
We paired that with in-store signage showing a clear £ example (e.g., “Spend £30, get £3 back”) and checkout reminders from staff to boost uptake.
What I measured and why
Measurement focused on isolating incremental change and understanding economics.
| Metric | Why it matters |
| Average basket size (treatment vs control) | Primary indicator of whether cashback increased spend per visit |
| Purchase frequency | Shows if cashback drove more visits, not just larger baskets |
| Redemption rate | Practical uptake — if nobody redeems, there's no cost but also no behavioural signal |
| Incremental revenue and margin | To ensure the uplift outweighs the cost of cashback and any platform fees |
| Customer-level lift (cohort) | Understanding whether existing high spenders or occasional shoppers drove results |
Results I observed
Over the two-week window and 30-day follow-up, the headline outcomes were:
- Average basket size in treatment rose by 18% (from £27 to £31.86) during the active window versus control.
- Purchase frequency among the treatment group rose slightly (6% increase) during the window — most uplift came from larger baskets rather than extra visits.
- Cashback redemption rate among those offered was 62% — higher than I expected, due to the immediate-payment promise.
- Incremental margin after cashback and platform fees remained positive: for every £1 of cashback cost, we saw about £1.6 of incremental gross margin (dependent on product mix and margin profile).
- Lift persisted modestly in the 30-day follow-up (~4% higher basket size), suggesting a short-term behaviour change but not a sustained permanent increase without ongoing incentives.
What moved the needle
From my audit of the campaign performance, these elements mattered most:
- Threshold placement: Setting the threshold just above the retailer’s typical basket nudged customers to add one extra item. Too low a threshold cannibalised margin; too high reduced uptake.
- Immediate payout: The promise of money in the bank within 24 hours boosted redemption and perceived value compared to delayed rewards.
- Clear examples: Showing simple arithmetic (Spend £30, get £3 back) reduced cognitive load and increased take-up.
- Staff prompts: In-store prompts during checkout helped convert hesitant customers into participants.
Common pitfalls and how to avoid them
- Pitfall: Poor measurement (not tagging customers at POS). Fix: Ensure every transaction is linked to customer IDs before starting.
- Pitfall: Overly generous cashback without margin modelling. Fix: Run simple sensitivity analysis on average basket and product margins to set an affordable percentage and cap per customer.
- Pitfall: Complicated redemption flows. Fix: Use one-click bank connect and keep the offer usable in-store without requiring on-the-spot tech adoption.
- Pitfall: Running other promotions simultaneously. Fix: Isolate the test window or ensure comparable exposure across treatment and control groups.
How to interpret mixed results
If you see higher basket size but negative margin, it means either the cashback % is too high or the threshold too low — tweak those. If basket size doesn't move but frequency increases, the offer might be better positioned as a frequency driver (e.g., smaller cashback but valid repeatedly). If uptake is low, revisit messaging, examples and friction in the redemption flow.
Practical checklist to run this test in your store
- Define your active customer population (last 6 months purchases).
- Pick a realistic threshold (slightly above average basket).
- Choose a cashback % that your margin analysis supports (8–12% is a good starting point).
- Randomise customers into treatment/control and keep other marketing constant.
- Ensure POS/transactions are tagged to customer ID.
- Use an open-banking partner for immediate payouts and minimal manual reconciliation.
- Run for 14 days + 30-day observation and track the metrics in the table above.
If you’d like, I can share a simple spreadsheet template I used to calculate sample size, expected lift detection thresholds and incremental margin break-even points — or run a tailored experiment design for your store based on your average basket and margins.
