Coupon and deal sites: incremental growth or margin skim?
Coupon and cashback partners convert well because a shopper searching for a discount right before checkout is already close to buying. That's also why to doubt the credit they claim: last-click hands the win to whoever sits at checkout for demand your ads, SEO, or email already created.
AI can read your order data and flag the incrementality signals; a holdout test in your platform is what proves it.
Fastest path: one prompt, end to end
🤖 AI prompt — paste into ChatGPT / Claude
You are an incrementality analyst. I will paste coupon/cashback-affiliate orders below. Assess how incremental they are using ONLY the pasted data — do not browse or assume.
Columns I'll paste: order ID, coupon/cashback partner, time from first site visit to order, traffic source of the FIRST touch (if I have it), whether the buyer is new or returning, and the search term used (if I have it).
Analyze for these signals:
1. Already-decided demand — orders where the first touch was my own ad/SEO/email and the coupon partner only appeared at the last step. Count and % of the batch.
2. Brand-name-plus-coupon — orders whose search term is "[my brand] coupon/discount"; these buyers had already chosen me. Count and %.
3. Genuine new-to-brand — new customers whose first touch was the partner's own audience, not my marketing. Count and %.
4. Last-second-only — orders where the partner's touch arrives in the final seconds with no upstream touch logged.
Output ONE markdown table | Signal | Count | % of batch | Read |, then 2 sentences: is this partner likely incremental, and what holdout test to run next.
Do not guess or invent numbers. Every count must come from the rows I pasted; if a column needed for a signal is missing, mark that signal "can't assess — need [column]". If the sample is too small, say so.
Or do it in 4 steps
- Start from the incrementality question. As affiliate manager Matt McWilliams frames it: is the partner sending you buyers you'd never have reached, or just capturing people already at checkout? These partners sit at the last step by design, so pure last-click can credit them for orders another channel already won. Don't take that number at face value; measure it.
- Keep code hygiene tight. Unique codes per partner, no shared public codes, and monitor aggregator sites — the same discipline that stops coupon-leak fraud also keeps your incrementality read honest.
- Run a true holdout. Block the partner for a sample of traffic or a whole geo for a few weeks, then compare conversion volume against the exposed group. The difference is your real incremental lift — everything else is a sale you'd have made anyway.
- Pay by incrementality, don't ban. Pay coupon/cashback partners a lower default rate than content/creator partners, cap or exclude commission on already-in-cart or repeat-customer triggers, and keep the genuinely incremental partners. Done: your rate reflects measured lift, not last-click habit.
Worked example: reading a coupon holdout
Say you run a 2-week geo holdout. The exposed region (coupon partner active) does 1,000 orders; the held-out region (partner blocked), adjusted for its normal traffic, does 940.
| Line | Figure |
|---|
| Orders with partner active | 1,000 |
| Orders with partner blocked (traffic-adjusted) | 940 |
| Truly incremental orders | 60 (6%) |
| Orders the partner was credited for under last-click | 300 |
| So credited-but-not-incremental | 240 of 300 (80%) |
These are illustrative numbers to show the method, not benchmarks — run the test on your own data. If it comes out like this, you were paying commission on 300 orders but the partner only created 60. That doesn't mean ban them — it means pay a rate that matches the real lift, and exclude the already-in-cart triggers.
Re-test each partner quarterly.