Customer Intelligence
Understand your customers at the individual level. AI profiles, segments, and lifetime value predictions.
Not all customers are equal. Some will buy once and disappear. Others will become loyal advocates worth 50x their first purchase.
Customer intelligence helps you tell them apart.
Customer Profiles
What is in a Profile
Every identified customer has a profile:
| Section | Data |
|---|---|
| Identity | Email, phone, external ID (hashed) |
| Demographics | Location, device, browser |
| Value Metrics | Total spent, order count, AOV |
| Behavior | Page views, sessions, engagement |
| Journey | All touchpoints and conversions |
| Predictions | LTV, churn risk, next purchase |
Customer Profile Example
| Metric | Value |
|---|---|
| Customer ID | cust_abc123 |
| First Seen | 2024-06-15 |
| Total Orders | 5 |
| Lifetime Value | $487 |
| Avg Order Value | $97.40 |
| Days Since Last Order | 23 |
| Predicted LTV | $1,240 |
| Churn Risk | Low (12%) |
| Segment | Platinum |
Customer Journey Timeline
| Date | Event | Details |
|---|---|---|
| Jun 15 | First Visit | Facebook Ad |
| Jun 15 | Email Signup | Newsletter popup |
| Jun 18 | First Purchase | $89 |
| Jul 02 | Second Purchase | $124 |
| Aug 15 | Third Purchase | $156 |
| Oct 01 | Email Click | Sale announcement |
| Oct 03 | Fourth Purchase | $118 |
AI-Powered Segmentation
Automatic Segments
AI creates segments based on behavior:
| Segment | Criteria | % of Customers |
|---|---|---|
| Diamond | Top 1% by LTV | 1% |
| Platinum | Top 5% by LTV | 4% |
| Gold | Above-average value | 15% |
| Silver | Average value | 30% |
| Bronze | Below-average value | 50% |
Behavioral Segments
| Segment | Behavior Pattern |
|---|---|
| Frequent Buyers | 4+ purchases in 90 days |
| Big Spenders | AOV 2x+ above average |
| Window Shoppers | Many sessions, no purchase |
| Cart Abandoners | Added to cart, did not buy |
| Loyalists | 12+ months, repeat purchases |
| At Risk | Previously active, now dormant |
Custom Segments
Create segments with your own rules:
****
javascript // API: Create a custom segment POST /api/v1/segments { "name": "VIP Product Buyers", "conditions": [ { "field": "totalSpent", "operator": "gte", "value": 500 }, { "field": "productCategory", "operator": "contains", "value": "premium" } ] } ****Lifetime Value Prediction
How LTV is Calculated
AI predicts future customer value using:
| Factor | Weight |
|---|---|
| Purchase history | High |
| Purchase frequency | High |
| Average order value | Medium |
| Engagement patterns | Medium |
| Time since last purchase | Medium |
| Product mix | Low |
LTV Distribution
See how customer value is distributed:
| LTV Range | Customers | Revenue Share |
|---|---|---|
| $1,000+ | 2% | 25% |
| $500-999 | 8% | 22% |
| $200-499 | 20% | 28% |
| $50-199 | 35% | 18% |
| Under $50 | 35% | 7% |
Insight: Top 10% of customers generate 47% of revenue.
Using LTV Predictions
| Use Case | Action |
|---|---|
| Acquisition | Set CPA targets based on predicted LTV |
| Retention | Invest more in high-LTV customers |
| Win-back | Focus on high-LTV churned customers |
| Personalization | Tailor offers by predicted value |
Churn Risk Analysis
Churn Risk Scoring
Every customer gets a churn risk score:
| Risk Level | Score | Meaning |
|---|---|---|
| Critical | 80-100% | Likely already gone |
| High | 60-79% | Needs immediate attention |
| Medium | 40-59% | Monitor closely |
| Low | 20-39% | Healthy engagement |
| Minimal | 0-19% | Highly engaged |
Churn Signals
| Signal | Risk Impact |
|---|---|
| Days since last visit | High |
| Declining purchase frequency | High |
| Support complaints | Medium |
| Email unengagement | Medium |
| Reduced session duration | Low |
Churn Prevention
| Risk Level | Recommended Action |
|---|---|
| Critical | Personal outreach, special offer |
| High | Win-back email campaign |
| Medium | Re-engagement automation |
| Low | Standard nurture flow |
Customer API Reference
Get Customer Profile
****`bash GET /api/v1/customers/{customerId}
Response: { "id": "cust_abc123", "email": "hashed_email", "metrics": { "totalSpent": 487, "orderCount": 5, "avgOrderValue": 97.40, "predictedLTV": 1240, "churnRisk": 0.12 }, "segments": ["platinum", "frequent_buyer"], "firstSeen": "2024-06-15", "lastSeen": "2024-10-03" } ****`
Recap
Here is what you learned:
- Customer profiles - Complete view of each customer
- AI segmentation - Automatic grouping by value and behavior
- LTV prediction - Know what customers will be worth
- Churn risk - Identify at-risk customers early
Your best customers are hidden in your data. Customer intelligence helps you find and keep them.
Next step: Learn about attribution models to understand how customers discover you.
Key Takeaways
- 1Individual customer profiles with full journey history
- 2AI segments customers by behavior and value
- 3Predict customer lifetime value
- 4Identify at-risk customers before they churn
Frequently Asked Questions
How is customer data protected?
When do customers get profiles?
How often is LTV updated?
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