Templates / Post-purchase survey
Customers · Adaptive interview template
Post-purchase survey template
The window right after a purchase is the only time a buyer can still remember the hesitation. A week later they have forgotten the tab they left open, the shipping line that made them pause, and the competitor they were comparing you against. Most post-purchase surveys waste that window on a satisfaction star and a coupon.
The questions in this template are built to recover the decision, not rate it. One asks what nearly stopped them from buying, because the objection they overcame is the one your next buyer will not. The AI interviewer then follows the specific hesitation: someone who says "shipping cost" gets asked what number would have made them abandon, while someone who says "I wasn't sure it would fit" gets asked what would have reassured them.
Because the keyword here is about the questions themselves, this template leans on question design: the anchors are chosen so the follow-ups have somewhere to go.
When to use it
- On the order confirmation page, immediately after checkout completes
- Emailed a day after delivery, when the product is in hand
- After a first purchase from a new customer, to learn how they found you
- Following a high-consideration purchase where buyers compared alternatives
Running it well
- Trigger it fast. The recall you want decays within days, so on-page or next-day beats a weekly digest.
- Do not bribe the answer with a discount in the same breath. The coupon reframes the survey as a transaction and flattens the honesty.
- Read the "Nothing, it was an easy decision" transcripts too. Easy buyers tell you what your positioning already gets right.
- Embed the first question directly in the confirmation email so a reply starts the interview without a click.
The questions, and why they're shaped this way
These are the anchor questions. You can edit, reorder, or add to them; the AI interviewer handles the depth between them.
What almost stopped you from completing this purchase?
The reason someone almost did not buy is the reason someone else will not. Each option points the interviewer at a different hesitation, so the follow-ups recover the objection in the buyer's words.
What finally convinced you to go ahead?
Asked after the hesitation is named, this surfaces the exact reassurance that worked: a review, a return policy, a spec. That is the line to put higher on the page for the next visitor.
How did you first hear about us?
Left open rather than a channel list because buyers describe attribution in ways analytics cannot: a friend, a specific video, a search for a problem. The transcripts show the language they used to find you.
What the interviewer asks next
Follow-ups aren't scripted. The AI reads each answer and probes what a researcher would. Depending on what a respondent says, it might ask:
"You said the shipping cost almost stopped you. What number would have made you close the tab?"
"You mentioned a review convinced you. Do you remember what it said, or where you read it?"
"You said you were comparing us to another option. What did they have that made you hesitate?"
Live demo
Play the post-purchase survey interview
This is the real interviewer, not a recording. Answer as yourself and watch the follow-ups adapt to what you write.
Prefer a full window? Open the demo.
Common questions
- Which post-purchase survey questions actually matter?
- The two that pay for the survey are what almost stopped them and what convinced them. Ratings tell you the mood; those two tell you the objection to answer and the reassurance to promote. The AI follow-ups turn each into specifics.
- When should the survey fire?
- As close to the purchase as you can. On the confirmation page catches the decision; a next-day email catches the product in hand. Both beat waiting a week, when the hesitation is already forgotten.
- Will asking about hesitation create buyer's remorse?
- Framed as helping you improve rather than second-guessing them, it does not. Respondents can skip any question, and the ones who almost did not buy are usually glad to explain what nearly went wrong.
- What do the results look like?
- Themes with supporting quotes and counts, the breakdown of what almost stopped buyers, full transcripts of every interview, and a plain-language box for asking things like "what convinced people who almost left over price?"
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