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From pm-copilot
Use this skill when the user asks about "attitudinal segmentation", "segmenting by attitude", "AI embracer vs skeptic", "how to segment our users beyond demographics", "psychographic segmentation", "behavioral segmentation", "how users feel about AI", or wants to go beyond demographic user segments to understand attitudinal and behavioral differences that affect product and marketing decisions.
npx claudepluginhub productfculty-aipm/pm-copilot-by-product-facultyHow this skill is triggered — by the user, by Claude, or both
Slash command
/pm-copilot:attitudinal-segmentationThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
You are helping the user move beyond demographic segmentation (role, company size, industry) to attitudinal segmentation — grouping users by how they think and behave, which predicts product adoption and communication effectiveness far better than demographics.
Identify and prioritize customer segments based on shared needs, behaviors, and contexts.
Segments users from feedback data into at least 3 distinct groups based on behavior, JTBD, and needs. Useful for analyzing diverse feedback, segmenting user bases, or building models.
Builds deep customer personas: distinguishes consumer vs shopper, maps Jobs-to-be-Done, purchase journey, and 4-stage insight validation. Useful before ad copy, campaign briefs, or product positioning.
Share bugs, ideas, or general feedback.
You are helping the user move beyond demographic segmentation (role, company size, industry) to attitudinal segmentation — grouping users by how they think and behave, which predicts product adoption and communication effectiveness far better than demographics.
Framework: Hilary Gridley (AI embracer vs. skeptic segmentation), Lenny Rachitsky (counterintuitive advice for AI products).
Key principle: "The most meaningful segmentation for AI products is attitudinal: AI embracers vs. AI skeptics. Skeptics can become superusers — but they require a completely different journey." — Hilary Gridley, Lenny's Newsletter (2024)
Read memory/user-profile.md and context/product/personas.md. Understand the existing segments and whether they're demographically or attitudinally defined.
For any product, but especially AI-powered products, segment users on these dimensions:
Adoption attitude: How quickly and enthusiastically does the user adopt new tools?
AI-specific attitude (especially relevant for PM Copilot):
Craft orientation (relevant for knowledge work tools):
Based on research, analytics, or intuition, estimate the distribution of each segment in the user base:
For AI-attitude segmentation:
If no data exists, structure a 3–5 question survey to measure attitudinal segments.
For each identified segment:
Product implications:
Retention implications:
Messaging implications:
Identify the conversion path from Skeptic → Neutral → Embracer:
What specific experience or moment converts a Skeptic to a Neutral user? (Usually: a single output that impresses them and matches their quality bar)
What converts a Neutral to an Embracer? (Usually: discovering a second or third use case that makes the tool feel indispensable)
Design the product experience to facilitate these conversions deliberately.
Produce:
Offer to update context/product/personas.md with attitudinal layer and save insights to memory/user-profile.md.