#5 How to Use Data to Design a Personality

Now that the standout virtual-character concepts have been refined through qualitative feedback, Phoebe and Maikel jump into the numbers phase—where data, not gut feeling, decides which designs go live.

Key take-aways

- From shortlist to scorecard – Why you first narrow to a small set of high-potential designs, then pit them head-to-head in structured tests rather than betting the budget on a boardroom favorite.

- Quant 101 for character design – Crafting perception surveys in tools like Qualtrics, defining traits (trust, energy, professionalism) in testable language, and keeping sessions under 20 minutes so attention—and data quality—stay high.

- Participants that matter – Sourcing 100-300 respondents who match the target market, building demographic diversity, and spotting red-flag responses (five-minute completions, straight-line scores) before they skew results.

- Hybrid methods – Using “think-aloud” moderated surveys with a sub-sample to capture the why behind the sliders, linking qualitative nuance to quantitative scale.

- Pilot before you plunge – Quick internal dry-runs expose confusing wording, broken sliders, or survey fatigue before hundreds of users see it.

- Making the data talk – Cleaning noisy responses, running correlation and subgroup analysis (e.g., slower speech → higher trust among 60-plus drivers), and translating findings into concrete visual, vocal, or personality tweaks.

- Knowledge that compounds – Each quantified insight becomes part of a growing, cross-project playbook that lets future teams start smarter and align faster.

If Episode 3 covered inspiration and Episode 4 covered refinement, Episode 5 shows how to turn user perception into hard evidence—so the AI character that finally ships is the one your audience actually wants.