Customer Churn Prediction
“Which loyalty members will lapse?”
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A real-world example
Which loyalty members will lapse?
Loyalty programs cost $10-15 per active member annually to operate, yet 54% of loyalty memberships are inactive (Bond Brand Loyalty). A retailer with 10M loyalty members has 5.4M generating zero incremental revenue. More critically, 15-20% of active members lapse each year, and re-acquiring a lapsed member costs 5-7x more than retention. The signals of impending lapse are scattered: declining visit frequency, shrinking basket size, reduced email engagement, and competitors' promotional activity. By the time a member's status flips to 'inactive,' the retention window has closed.
How KumoRFM solves this
Relational intelligence built for retail and e-commerce data
Kumo connects loyalty members to their purchase history, browsing behavior, email engagement, store visits, promotional responses, and competitor pricing data. The model identifies that Member LM-2201 has dropped from weekly to bi-weekly visits, her basket size has shrunk 30%, she has stopped opening promotional emails, and a competitor just launched a rival loyalty program in her zip code. These relational signals surface lapse risk 45-60 days before inactivity, giving marketing teams time to deploy personalized win-back offers.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
LOYALTY_MEMBERS
| member_id | tier | join_date | points_balance | lifetime_spend |
|---|---|---|---|---|
| LM-2201 | Gold | 2021-03-15 | 12,400 | $8,420 |
| LM-2202 | Silver | 2023-08-01 | 3,200 | $2,100 |
| LM-2203 | Platinum | 2019-11-22 | 48,000 | $32,500 |
PURCHASE_HISTORY
| member_id | order_id | total | items | store_id | timestamp |
|---|---|---|---|---|---|
| LM-2201 | ORD-1001 | $42.30 | 6 | S-14 | 2025-08-28 |
| LM-2201 | ORD-1002 | $28.50 | 4 | S-14 | 2025-09-10 |
| LM-2203 | ORD-1003 | $187.20 | 12 | S-22 | 2025-09-14 |
EMAIL_ENGAGEMENT
| member_id | emails_sent_30d | opens | clicks | unsubscribed |
|---|---|---|---|---|
| LM-2201 | 8 | 1 | 0 | False |
| LM-2202 | 8 | 5 | 2 | False |
| LM-2203 | 8 | 7 | 4 | False |
VISIT_PATTERNS
| member_id | visits_30d | visits_60d | visits_90d | trend |
|---|---|---|---|---|
| LM-2201 | 2 | 5 | 10 | Declining |
| LM-2202 | 4 | 4 | 3 | Stable |
| LM-2203 | 6 | 7 | 7 | Stable |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(LOYALTY_MEMBERS.STATUS = 'lapsed', 0, 60, days) FOR EACH LOYALTY_MEMBERS.MEMBER_ID WHERE LOYALTY_MEMBERS.STATUS = 'active'
Prediction output
Every entity gets a score, updated continuously
| MEMBER_ID | TIER | LAPSE_PROB | LIFETIME_SPEND | RECOMMENDED_ACTION |
|---|---|---|---|---|
| LM-2201 | Gold | 0.78 | $8,420 | Personal Offer + Bonus Points |
| LM-2202 | Silver | 0.23 | $2,100 | Standard Email |
| LM-2203 | Platinum | 0.09 | $32,500 | No Action Needed |
Understand why
Every prediction includes feature attributions — no black boxes
Member LM-2201 (Gold tier)
Predicted: 78% probability of lapsing within 60 days
Top contributing features
Visit frequency declining (-60%)
10 to 2 in 90d
30% attribution
Basket size shrinkage
-30% avg order
24% attribution
Email engagement collapse
1 open, 0 clicks
20% attribution
Points redemption stalled
0 in 60d
15% attribution
Competitor loyalty launch in zip code
2 new programs
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Retain 25-35% of at-risk loyalty members with targeted interventions, recovering $15-30M in annual revenue from a 10M-member loyalty program.
Related use cases
Explore more retail & e-commerce use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
