AI-Powered Personas & Market Research: The Engineer's Guide

ai has completely changed the way we do market research for the first time, you don't need guesswork and you don't have to make assumptions the tools you already have can systematically gather all the intelligence you need step 1 mining your support data your support inbox is a goldmine here's how to extract insights using ai the support analysis prompt ai prompt analyze these support conversations and 1\ identify common pain points 2\ group issues by user role/type 3\ extract feature requests 4\ note language patterns used 5\ flag satisfaction indicators support data \[paste 10 20 support conversations] use this with chatgpt to analyze the customer support inbox exports look for patterns in common complaints feature requests user types language used bonus points build a custom gpt step 2 sales call intelligence turn your sales calls into persona insights the sales call analysis prompt ai prompt review these sales call transcripts and identify 1\ common objections and how they vary by role 2\ key pain points mentioned 3\ feature requirements by segment 4\ decision criteria across roles 5\ budget discussions and patterns transcripts \[paste 3 5 call transcripts] 😎 pro tip use tools like zapier or make to automate the transcripts export and feed them to ai for analysis step 3 review mining leverage g2 and capterra reviews systematically the review analysis prompt ai prompt analyze these product reviews and 1\ group feedback by user role 2\ identify primary use cases 3\ extract key benefits mentioned 4\ list common complaints 5\ note competitive comparisons reviews \[paste 20 30 reviews] capterra com review for archbee step 4 competitor intelligence turn competitor reviews into strategic insights the competitive analysis prompt ai prompt compare these competitor reviews and 1\ map feature gaps vs our product 2\ identify underserved needs 3\ list common switch triggers 4\ extract pricing feedback 4\ note market positioning differences competitor reviews \[paste competitor reviews] step 5 product usage patterns transform analytics into persona insights the usage pattern prompt ai prompt analyze this product usage data and 1\ identify user archetypes based on behavior 2\ map feature adoption patterns 3\ highlight engagement differences by role 4\ note common friction points 5\ list success indicators usage data \[paste usage metrics] step 6 website behavior analysis turn web analytics into buyer journey insights the web behavior prompt ai prompt review this website behavior data and 1\ map common user journeys 2\ identify drop off points 3\ list high engagement content 4\ note conversion patterns 5\ extract search intent signals analytics data \[paste analytics export] step 7 community intelligence mine community discussions for deeper insights the community analysis prompt ai prompt analyze these community discussions and 1\ list recurring questions 2\ identify expertise levels 3\ map common challenges 4\ note solution approaches 5\ extract terminology used discussion data \[paste community threads] building your persona framework now, synthesize all this data into clear personas the persona synthesis prompt ai prompt using all the analyzed data, create detailed personas including 1\ role and responsibilities 2\ key pain points and goals 3\ decision criteria 4\ common objections 5\ preferred channels 6\ language patterns 7\ success metrics previous analysis \[paste your ai analysis results] creating your market research engine build this system set up automatic data collection from all sources create regular ai analysis cycles update personas quarterly feed insights back to product and marketing the research update prompt ai prompt compare this new data with our existing insights and 1\ identify emerging trends 2\ note changing preferences 3\ flag new opportunities 4\ highlight shifts in behavior 5\ suggest strategy adjustments new data \[paste new data] previous insights \[paste previous analysis] your ai research engine should grow smarter with each cycle feed it reliable data, ask thoughtful questions, and use its insights to refine and build for the next iteration that's how you engineer market understanding