User Segmentation AI Prompts

Segment users based on behavioral patterns in this product. Behavioral data: {{behavior_data}} (event logs: user_id, event_type, timestamp, session_id) Segmentation goal: {{goal}} (personalization, intervention targeting, resource allocation) 1. Feature engineering for segmentation: Create behavioral features per user over the last {{window}} days: - Recency: days since last active - Frequency: sessions per week - Depth: average actions per session - Breadth: number of distinct features used - Tenure: days since account creation - Core action rate: % of sessions with {{core_action}} 2. RFM-style segmentation (rule-based, interpretable): Apply percentile-based segmentation on Recency, Frequency, and Depth: - Champions: recent, frequent, deep engagement - At-risk: previously frequent but declining - Dormant: not active in > 30 days - New users: tenure < 14 days - Casual: low frequency, low depth 3. Cluster-based segmentation (data-driven): - Apply k-means clustering on the behavioral features - Test k = 3, 4, 5, 6 clusters; select using silhouette score - Profile each cluster: mean values for each behavioral feature - Name each cluster with a business-friendly label based on its profile 4. Segment stability: - How stable are segments over time? (Do users move between segments frequently?) - A good segment is both meaningful and stable 5. Segment sizing and value: - Count and % of users in each segment - Revenue, retention, or other outcome metric per segment - Which segment represents the highest business value? 6. Recommended actions per segment: - Champions: retain and leverage as advocates - At-risk: trigger a win-back flow - Dormant: re-engagement campaign or sunset - New users: accelerate activation Return: feature engineering code/SQL, RFM segment definitions, cluster profiles, segment sizing table, and recommended actions.

Identify and analyze the power users of this product to understand what drives exceptional engagement. Engagement data: {{engagement_data}} Power user definition: {{definition}} (top 10% by usage frequency, or specific behavior threshold) 1. Power user identification: - Define power users quantitatively: users who {{criterion}} in the last 30 days - What % of total users are power users? - What % of total activity or revenue do power users account for? (Often 80% of value from 20% of users) 2. Power user profile: - Demographics: tenure, acquisition channel, plan type, company size (if B2B) - Behavioral fingerprint: which features do they use most? What is their typical session pattern? - Onboarding: did they complete onboarding differently? How quickly did they activate? - First week behavior: what did power users do in their first 7 days that non-power users did not? 3. The aha moment: - Is there a specific action in the first week that strongly predicts becoming a power user? - Compute: % of power users who completed {{action}} in week 1 vs % of all users - This is the aha moment candidate - the action to optimize for in onboarding 4. Power user journey: - Map the typical sequence of feature adoption for power users - At what tenure do most users reach power user status? - Is there a specific feature or workflow that accelerates the journey? 5. Implications for product and growth: - How can onboarding be redesigned to guide more users toward the power user path? - Which acquisition channels produce the most power users? (Not just the most users) - What does retaining power users require? (Are they at risk of churning for any reason?) Return: power user definition and sizing, behavioral profile, aha moment analysis, journey map, and product/growth implications.