| name | customer-segmentation |
| displayName | Customer Segmentation |
| tagline | Segment customers by behavior, purchase history, and lifetime value for targeted marketing. |
| description | Creates customer segments based on purchase behavior, engagement patterns,
and lifetime value. Implements RFM (Recency, Frequency, Monetary) analysis
and behavioral cohorts. Enables targeted marketing campaigns, personalized
recommendations, and churn prediction for each segment.
|
| department | ["E-commerce","Marketing","Data & Analytics"] |
| use_cases | ["Customer Analytics","Personalization","Retention Marketing"] |
| tools_required | ["Shopify MCP"] |
| agents_compatible | ["Claude / Claude Code","Cursor","Windsurf","ChatGPT","Any MCP-compatible agent"] |
| author | Webrix |
| verified | true |
| updatedAt | "2026-05-08T00:00:00.000Z" |
| version | 1.0.0 |
| exampleInput | Segment our Shopify customers by purchase behavior.
Use RFM analysis. Identify VIPs and at-risk customers.
|
| exampleOutput | Customer Segmentation — RFM Analysis
SEGMENTS (4,200 total customers)
Champions (8%): 340 customers — High R, High F, High M
Avg LTV: $1,240 Action: VIP rewards, early access
Loyal (15%): 630 customers — Med R, High F, Med M
Avg LTV: $680 Action: Loyalty program, upsell
At Risk (12%): 504 customers — Low R, Med F, Med M
Avg LTV: $420 Action: Win-back campaign, survey
New (20%): 840 customers — High R, Low F, Low M
Avg LTV: $85 Action: Onboarding sequence, welcome offer
Dormant (45%): 1,886 customers — Low R, Low F, Low M
Avg LTV: $45 Action: Re-engagement or sunset
KEY INSIGHTS
- Champions generate 35% of revenue (8% of customers)
- 504 at-risk customers represent $211K in potential churn
- New customer conversion to Loyal: 22% within 90 days
|
Customer Segmentation
Segment customers by behavior, purchase history, and lifetime value for targeted marketing.
Integrations: Shopify
When to Use
- The user wants to segment customers for targeted campaigns
- Customer lifetime value analysis is needed
- The user mentions "customer segmentation", "RFM", or "cohort analysis"
Steps
Step 1: Pull Customer Data
Extract purchase history, engagement data, and customer attributes.
Step 2: Apply RFM Analysis
Score customers on recency, frequency, and monetary value.
Step 3: Define Segments
Create actionable segments with recommended marketing strategies.
Output
Deliver:
- Customer segments with counts and characteristics
- Recommended actions per segment
- LTV analysis and churn risk assessment
