Behavioral User Segmentation AI Prompt

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.