Customer Persona Generator

The problem to solve

The usual issue in sales follow-up is not a lack of customer data, but scattered data across chat logs, orders, and visit notes. It is hard to form a consistent view: who is making the decision, what the real pain point is, and whether the next step should be active follow-up or pause. @user_70c2f807/customer-persona-generator turns these materials into a structured customer persona instead of generic market labels.
It infers preference patterns, decision chains, communication styles, and deeper pain points from available data, with explicit confidence levels. Core capabilities include extracting purchase and communication preferences, identifying B2B decision-makers, influencers, and users, judging rational or emotional, fast or slow, independent or consensus decision styles, uncovering needs behind surface requests, and producing follow-up advice with do-not-list items. Execution runs in three steps: collect chat, order, and visit data and identify gaps; extract behavior patterns, pain points, and decision chain; output a persona card, risk notes, and next actions.

Scope and caveats

This skill fits one-to-one or small-scope customer analysis based on lawfully obtained interaction data. It is not intended for large-scale BI analysis, social sentiment monitoring, credit risk scoring, or survey design. Conclusions are limited when data is thin, and personas need periodic refresh. For B2B accounts, avoid relying on a single contact and try to identify hidden decision-makers.