AI-Powered Market Research Without the Focus Groups
Traditional audience research takes 4–6 weeks, costs $30k+, and draws from a self-selected sample of people willing to fill out a survey for $5. By the time you read the report, your assumptions have already moved on and the data is stale.
HOOKiQ runs simulated discussions with 5–100 AI personas calibrated to your target demographic, reading like a real Reddit or Twitter thread. You get sentiment breakdowns, recurring themes, objection patterns, and language preferences in minutes.
How it works
Define the topic or question you want audience perspective on: positioning, naming, category framing, anything.
Specify the demographic: age range, market, profession, interests, or paste an existing customer description.
Read the simulated discussion thread, segmented sentiment, and the recurring concerns and language patterns that surfaced.
Common questions
Should I use this instead of survey research?
No, use it before. Surveys give you statistical confidence on specific questions. Simulation gives you directional signal on which questions to ask. Run simulation first to refine the hypothesis, then run surveys to validate.
What are the AI personas based on?
Each persona is built from two ingredients: the demographic parameters you give it (age, location, profession, interests, beliefs) and the AI's training knowledge of how real people with those characteristics talk and behave online. No real person's data is used, and there is no database of individuals we draw from. The personas are statistical composites built from the AI's training on public-domain patterns (Reddit, Twitter, news, blogs, books), shaped by the targeting parameters you specify.
How does AI persona simulation compare to social listening?
Social listening tells you what's already being said about your product or category. Simulation tells you what would likely be said about your hypothetical campaign, product, or statement. Both are useful. They answer different questions.
Can the personas reflect cultural nuance?
Up to a point. The model is calibrated to general demographic patterns and online discussion norms. For deep cultural-specific work (e.g., regional Australian, specific ethnic communities, niche subcultures), supplement simulation with real audience input from those groups.
What sample sizes are useful?
15–30 personas for clear directional signal. 50+ when you want to surface long-tail reactions or compare sub-segments. Smaller sample sizes are fine for binary 'does this land or not' questions.
How do I know the simulated reactions are realistic?
The model is grounded in patterns from actual Reddit and Twitter discussions of adjacent topics. You can calibrate against known reactions: run a simulation of a campaign that already launched and compare to the real response. Most teams find the simulation surfaces the same objection patterns that surfaced in reality, plus a few they missed.