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Build customer profiles from your research data
Three to five clear customer profiles showing who they are, what they need, how they buy and what puts them off.
When to use it
After a survey or when refreshing your audience understanding
Step by step
Prepare an anonymous summary
Export your survey or sales data and remove names, emails and any other details that identify a person. Summary tables or grouped answers work well.
Ask for evidence-based profiles
Paste the data into your AI assistant with this prompt.
Prompt to copyFrom this anonymised customer data, identify 3 to 5 distinct customer groups. For each give: a descriptive name, roughly what share of customers they represent, who they are, what they need most, how and where they buy, what would make them buy more, what puts them off, and the evidence from the data behind each point. Don't invent details that aren't supported; mark anything you're inferring as 'assumption'. Finish with which group looks most valuable to target and why. [paste data]Check against what you know
Compare the profiles with your own analysis and with frontline colleagues' experience, and adjust anything that doesn't ring true.
Make them easy to share
Put each profile on one slide or one page so marketing and product teams can keep them handy.
What you get
Weekday Rushers (about 35%): 25 to 40, working full time, buy on their phone at lunchtime. Need: speed and no surprises at checkout. Put off by: delivery fees shown late. Evidence: 62% of mobile orders placed 12 to 2pm. Assumption: likely to respond to a subscription.
Tips
- Never paste raw customer records with personal details; use grouped or anonymised data and follow your company's data rules.