Extract STP insights from reviews into segmentation, targeting, and positioning outputs.
npx skills add https://github.com/TimLai666/skills --skill review-mining-stp
Please help me install this Agent Skill. Skill: review-mining-stp Source: https://github.com/TimLai666/skills/tree/main/review-mining-stp Command: npx skills add https://github.com/TimLai666/skills --skill review-mining-stp
Reviews, support tickets, app feedback, and other text data are rich with insights but challenging to structure and act upon. This Skill enforces a strict workflow to convert raw text into Segmentation -> Targeting -> Positioning -> Strategy outputs, enabling reliable evidence-based decision making.
Use the review-mining-stp skill to process a canonical set of scored artifacts and produce STP outputs.
High-intent search queries and answers about installing and using this skill.
To extract STP insights from customer reviews, you must process raw text data through a strict scoring workflow to generate segmentation, targeting, and positioning artifacts. This converts feedback into traceable evidence for strategy planning.
Generating a positioning scorecard from app feedback requires canonical input artifacts like review_scoring_table.csv, attribute_catalog.csv, and ideal_point.json. These structured files provide the scored foundation needed for the analysis.
Yes, you can use pandas and scikit-learn within a reproducible workflow contract to automate STP analysis from textual feedback. The pipeline processes structured artifacts to output segmentation variables and targeting datasets.
The best way to map brand positioning against an ideal point is by applying a formal scorecard approach using brands.json and ideal_point.json. This evaluates your brand relative to customer expectations using verbatim evidence.
STP analysis from review text relies on a strict workflow contract to ensure downstream analytics are anchored to verbatim evidence. Without it, converting raw text into reliable segmentation and targeting outputs becomes inconsistent.
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