Won't people just say they'd cancel over any increase?

Some will, which is why the follow-ups matter. The interviewer asks what would actually have to change for them to leave, and the reasoning separates the reflexive "I'd cancel" from a genuine reservation price.

Should I test a specific number?

You can. Change the 20 percent to whatever increase you are considering, or run parallel studies with different numbers to matched segments. The follow-ups explain the reaction either way.

By default respondents are anonymous: no name, email, or IP is stored. Anonymity is especially valuable for pricing, because people answer money questions more honestly when the answer is not tied to their account.

What do the results look like?

Themes with supporting quotes and counts, the value-rating breakdown, full transcripts, and a plain-language question box for asking things like "what did customers say already feels overpriced?"

Templates / Pricing survey

Customers · Adaptive interview template

Pricing survey template

Pricing surveys are famous for lying. Ask what someone would pay and they answer strategically, low if they think it moves the price, high if they want to seem serious. The number is almost useless on its own; the reasoning behind it is where the pricing decision lives.

This template asks how customers rate the value for money, then poses the one question that separates talk from behavior: what they would do if the price went up 20 percent, and why. The AI interviewer follows the reasoning: which parts they would pay more for, what already feels overpriced, and where the line actually sits. You end up with the logic behind willingness to pay, not just a distribution of made-up numbers.

When to use it

Before a planned price increase, to hear how each segment would react
When designing or repackaging tiers and you need to know what carries value
When a feature feels underpriced or a plan feels bloated and you want the why
Segmented by plan, to compare how new and long-tenured customers weigh value

Running it well

Ask about behavior, not a magic number. "What would you do" and "why" beat "what would you pay" every time, because people answer the second one strategically.
Segment before you read. What a two-year customer will tolerate and what a trial convert will tolerate are different studies wearing the same questions.
Read the gap between the value rating and the reaction to the increase. "Excellent value" plus "I'd leave" means they love it and cannot afford it, which is a packaging problem, not a value one.
Keep it anonymous. People are far more honest about money when their answer is not attached to their account.

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.

Multiple choice

How would you rate the value for money?

ExcellentGoodFairPoor

Value is the ratio behind price, and this rating anchors it before any number is discussed. It sets up the interviewer to ask a "Poor" and an "Excellent" very different follow-up.

Open text

If the price increased 20%, what would you do and why?

A behavioral question beats a willingness-to-pay guess. Asking what they would do, and why, surfaces the real reservation price and whether it is anchored to value, budget, or an alternative.

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 you'd probably stay. What would have to get worse before a price increase actually pushed you out?"

"You mentioned one part feels overpriced. Which part, and what would make it feel worth it?"

"You'd start looking at alternatives. Which one comes to mind first, and what does it charge?"

Live demo

Play the pricing 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

Won't people just say they'd cancel over any increase?: Some will, which is why the follow-ups matter. The interviewer asks what would actually have to change for them to leave, and the reasoning separates the reflexive "I'd cancel" from a genuine reservation price.
Should I test a specific number?: You can. Change the 20 percent to whatever increase you are considering, or run parallel studies with different numbers to matched segments. The follow-ups explain the reaction either way.
Is it anonymous?: By default respondents are anonymous: no name, email, or IP is stored. Anonymity is especially valuable for pricing, because people answer money questions more honestly when the answer is not tied to their account.
What do the results look like?: Themes with supporting quotes and counts, the value-rating breakdown, full transcripts, and a plain-language question box for asking things like "what did customers say already feels overpriced?"

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