Overview
Auto-Segmentation analyzes your schema data to discover natural customer segments. Instead of manually defining segments based on assumptions, you select a schema and a set of fields, and the AI identifies clusters of customers with similar characteristics. Navigate to AI > Auto-Segmentation in the sidebar.How It Works
Choose the schema table that contains the customer data you want to segment (e.g.,
customers, credit_profiles). The schema must have been created in the Data module and contain rows.Select the fields to include in the segmentation analysis. Choose numeric and categorical fields that are likely to differentiate customer groups — for example,
age, income, total_spend, region, account_type.Select 3 to 8 fields for the best results. Too few fields produce trivial segments; too many dilute the signal and slow analysis.
Click Discover Segments. The AI analyzes the data and identifies clusters of customers with similar characteristics.
Dual-Tier Analysis
| Tier | When Used | Method |
|---|---|---|
| LLM | Default, or when ML Worker is unavailable | Samples up to 1000 rows and uses the LLM to identify patterns and propose segment definitions |
| ML Worker | When connected and dataset exceeds 5K rows | Runs K-Means clustering on the full dataset with silhouette scoring to determine the optimal number of clusters |
Interpreting Segment Cards
Each segment card displays:- Segment name — An AI-generated descriptive name (e.g., “High-Value Loyalists”, “Price-Sensitive New Users”)
- Size — Number of customers in the segment and percentage of total
- Key characteristics — The defining attributes of the segment, showing how its averages compare to the population
- Distinguishing features — Which fields most differentiate this segment from others
- Suggested actions — AI-generated recommendations for how to target this segment
High-Value Loyalists — 2,340 customers (18%)
- Average spend: 480 population avg)
- Average tenure: 4.2 years (vs. 1.8 population avg)
- Primary channel: Email (72%)
- Suggested: Premium offers, loyalty rewards, lower contact frequency
Applying Segments
When you click Apply on a segment:- A recommendation is created in the AI Insights dashboard
- The recommendation includes the segment definition (field conditions)
- Applying from Insights creates a draft qualification rule that identifies customers matching the segment criteria
Field Selection Tips
- Numeric fields work best — Income, age, spend, tenure, and score fields produce clearer clusters
- Limit categorical fields — High-cardinality categoricals (e.g., zip code) add noise. Prefer broad categories like region or account type
- Exclude IDs — Do not include customer_id, email, or other unique identifiers as segmentation fields
- Include behavioral data — If you have interaction summaries (total_clicks, avg_response_rate), include them for behaviorally meaningful segments
Advanced Parameters
Each segmentation run can be fine-tuned using the Advanced Parameters panel on the segmentation page. Expand the panel to adjust:| Parameter | Default | Description |
|---|---|---|
| Min Clusters | 2 | Fewest groups to split customers into |
| Max Clusters | 8 | Most groups customers can be split into |
| Algorithm | kmeans | Clustering method (kmeans or dbscan) |
| Included Features | All | Which attributes to consider (null = all) |
Large Dataset Warning
When the selected schema contains 5,000 or more rows, a confirmation dialog appears before analysis begins. The dialog shows:- Accuracy comparison — ML Worker uses K-Means with silhouette scoring on the full dataset vs. LLM sampling
- Estimated cost — Token count and approximate cost if proceeding with LLM
- Speed comparison — ML Worker processes locally in seconds vs. LLM round-trip