Account Churn Prediction
“Which accounts will not renew?”
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A real-world example
Which accounts will not renew?
B2B SaaS companies lose 5-15% of ARR annually to churn. For a $200M ARR company, each percentage point of churn reduction is worth $2M. CSM teams manage 50-80 accounts each and cannot manually monitor usage patterns across all of them. The churn signal is rarely in a single metric: it is in the combination of declining user engagement, increasing ticket volume, champion departure, and contract timing that unfolds over 60-90 days before renewal.
How KumoRFM solves this
Graph-learned product intelligence across your entire account base
Kumo connects accounts, users, feature usage, support tickets, and contracts into a relational graph. It learns that accounts where the primary champion has gone inactive, where 3+ users switched to a competitor's integration, and where ticket escalation rate doubled in the last 30 days churn at 8x the base rate. The model captures cross-account patterns: when accounts in the same industry vertical start reducing usage simultaneously, it signals a competitive threat that account-level models miss.
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
ACCOUNTS
| account_id | name | arr | contract_end | plan_tier |
|---|---|---|---|---|
| ACC001 | Acme Corp | $120,000 | 2025-06-30 | Enterprise |
| ACC002 | TechStart Inc | $24,000 | 2025-04-15 | Growth |
| ACC003 | Global Mfg | $360,000 | 2025-09-01 | Enterprise |
USERS
| user_id | account_id | role | last_active | is_champion |
|---|---|---|---|---|
| U001 | ACC001 | Admin | 2025-03-02 | Y |
| U002 | ACC001 | Viewer | 2025-02-10 | N |
| U003 | ACC002 | Admin | 2025-01-28 | Y |
FEATURE_USAGE
| usage_id | account_id | feature | events_30d | trend |
|---|---|---|---|---|
| FU01 | ACC001 | Dashboard | 1240 | Stable |
| FU02 | ACC001 | API calls | 8500 | +12% |
| FU03 | ACC002 | Dashboard | 45 | -68% |
TICKETS
| ticket_id | account_id | priority | category | created_date |
|---|---|---|---|---|
| TK01 | ACC002 | P1 | Bug report | 2025-02-25 |
| TK02 | ACC002 | P2 | Feature request | 2025-02-28 |
| TK03 | ACC001 | P3 | How-to question | 2025-03-01 |
CONTRACTS
| contract_id | account_id | start_date | end_date | auto_renew |
|---|---|---|---|---|
| CON01 | ACC001 | 2024-07-01 | 2025-06-30 | Y |
| CON02 | ACC002 | 2024-04-15 | 2025-04-15 | N |
| CON03 | ACC003 | 2024-09-01 | 2025-09-01 | Y |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(CONTRACTS.RENEWED = 'N', 0, 90, days) FOR EACH ACCOUNTS.ACCOUNT_ID WHERE CONTRACTS.END_DATE <= '2025-09-01'
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | ARR | RENEWAL_DATE | CHURN_PROB |
|---|---|---|---|
| ACC001 | $120,000 | 2025-06-30 | 0.12 |
| ACC002 | $24,000 | 2025-04-15 | 0.84 |
| ACC003 | $360,000 | 2025-09-01 | 0.06 |
Understand why
Every prediction includes feature attributions — no black boxes
Account ACC002 -- TechStart Inc, $24K ARR
Predicted: 84% churn probability at renewal
Top contributing features
Champion last active
35 days ago
30% attribution
Dashboard usage trend (30d)
-68% decline
24% attribution
P1 tickets (last 30d)
1 unresolved
19% attribution
Auto-renew status
Disabled
15% attribution
Peer accounts in vertical churning
2 of 5 similar
12% 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: A $200M ARR SaaS company that identifies at-risk accounts 90 days before renewal and intervenes effectively reduces net revenue churn by 30%, saving $8M annually. Kumo detects champion departure, usage decay, and cross-account competitive signals that health-score spreadsheets miss.
Related use cases
Explore more B2B SaaS 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.
