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By the time a study is fielded, analyzed and presented, the launch has often moved on. Teams end up deciding on instinct, or testing in front of real customers where mistakes are expensive.
One survey wave or interview round can take weeks and limits how many ideas get tested.
Hard-to-reach segments are underrepresented, so the loudest customers shape the decision.
Teams test one price or one message when the real question is which of ten will work.
Research sits in a deck instead of shaping the next product or campaign decision.
Every synthetic persona starts with real data, and every important result is checked against real people. Simulation widens what you can test; it does not replace listening to customers.
Run interviews, diary studies and task observation to learn how customers decide, what they need and where they struggle.
Design and field surveys, concept tests and conjoint studies, with AI speeding up drafting, coding of open answers and analysis.
Build personas from real interview, survey, loyalty and behavioral data, so each simulated customer reflects a real segment, including hard-to-reach ones.
Test how customer groups respond to a launch, price change, campaign or policy, comparing many options side by side before anything ships.
Compare simulated results with real responses, state a confidence level for each finding, refresh personas as new data arrives and confirm high-stakes calls with live customers.
We gather real research and first-party data and define the segments that matter.
We build synthetic personas and run the scenarios your team needs to compare.
We validate key findings with real customers and refresh the personas as new data arrives.
Scenarios that once needed a new study run in hours.
Teams compare many prices, concepts and messages instead of one.
Personas represent groups that small samples miss.
Every persona starts with real research data.
Each finding carries a stated level of confidence.
Weak ideas fail in simulation, not in the market.
Personas refresh as new data arrives.
Product, marketing and leadership work from the same evidence.
Point One Zero practice
Fashion and retail brands need consumer feedback faster than a survey cycle allows, especially ahead of a seasonal launch. We specified a synthetic persona platform for fashion and retail consumer testing, with personas built from real shopper data and results checked against real responses. The approach was built into the research plan for a 90-day backpack launch.
AI models of customer segments, built from real interview, survey and behavioral data, that answer questions and react to scenarios the way those customers are likely to. They are useful for exploring options quickly, and their answers should be checked against real people for important decisions.
It can estimate responses, especially when personas are built from data about the same customers. We compare options side by side, state a confidence level for each result and confirm high-stakes findings with live customers.
Accuracy depends on how well the personas are grounded in real data and how closely the question matches what that data covers. We measure it rather than assume it: simulated answers are compared with real responses, and each finding carries a confidence level.
No. Real interviews and surveys ground the personas, simulation explores far more scenarios than a single study could, and real customers confirm the decisions that matter.
Yes. Loyalty, purchase, survey and product usage data can ground personas in your own customers, under your governance and consent rules. The data stays inside the boundary you approve.
Bring a concept, a price or a message. We will build personas from your data and show how each segment is likely to respond.
Plan a consumer studyOr email us at admin@pointonezero.com.