As of 13 August 2026, AI can analyse your pricing survey results.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

   How to actually do it

   1. Open the survey platform export or spreadsheet and make a clean copy that retains the exact question wording, response labels and respondent-level rows.
   2. Add the current price, any tested prices, the business decision, recruitment method, fieldwork dates and known exclusions to the survey context.
   3. Remove direct identifiers and confidential free text that the analysis does not need, then paste the cleaned table and context into the prompt.
   4. Ask the chatbot to produce the requested analysis, including denominators, data-quality checks, segment definitions and a table of key figures.
   5. Compare every count, percentage and chart in the response with the source spreadsheet, checking the denominator and confirming that skipped or invalid responses were handled correctly.
   6. Test the recommendation against actual sales, conversion, retention or customer-support evidence, then write the chosen pricing action and its measurement plan yourself.

   Prompt

   Analyse the pricing survey data below as a product strategist. Use only the information supplied here and do not invent missing figures, customer characteristics or market facts.
   
   Survey context:
   - Product or service: [describe it]
   - Current price and pricing model: [enter it]
   - Proposed prices tested: [enter them, or say none]
   - Business decision to make: [describe the pricing decision]
   - Target customer and intended market: [describe them]
   - Recruitment method and sample source: [describe how respondents were recruited]
   - Fieldwork dates: [enter them]
   - Known exclusions, duplicate handling or data-quality issues: [describe them, or say none known]
   
   Survey questions and response data:
   [Paste the exact questions and the spreadsheet export or a clearly labelled table here. Keep column names and response values unchanged.]
   
   Produce the analysis in this order:
   1. State what the data can and cannot answer. Distinguish stated preference from observed purchasing behaviour and do not claim causation.
   2. Check the dataset for missing values, duplicates, inconsistent labels, impossible entries and small subgroups. List each issue and how it affects interpretation.
   3. Give the response count for each relevant question and calculate percentages from the supplied data. Show the denominator for every percentage and say when a question was skipped.
   4. Summarise price acceptance and objections overall, then by meaningful segments supported by the data. Do not create segments that are not present.
   5. Identify contradictory responses, possible acquiescence or anchoring effects, sampling bias and other limitations. Separate evidence from interpretation.
   6. Compare the tested prices or price bands only if the survey design supports that comparison. Do not estimate demand, revenue or willingness to pay beyond the data.
   7. Give three decision options, with the evidence supporting and weakening each option, followed by the additional evidence that would most reduce uncertainty.
   8. End with a short recommendation labelled 'Recommendation for human decision'. Make clear that it is a planning input, not a forecast. Include a table of every key figure so I can compare it with the original data.
   
   Use plain British English. Show calculations clearly enough for a non-specialist to reproduce them. Flag anything I need to verify rather than filling gaps with assumptions.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.