Renewal Prediction
“Which subscriptions will renew at their next renewal date?”
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A real-world example
Which subscriptions will renew at their next renewal date?
Renewal forecasting based on CSM gut feel or simple usage thresholds misses 40% of at-risk accounts. By the time a customer signals non-renewal, it is often too late to intervene. For a SaaS company with $500M ARR, improving renewal prediction accuracy by 15% protects $25M in at-risk revenue.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo learns renewal signals from the full relational graph — usage event sequences, support ticket escalation patterns, multi-seat adoption curves, and how renewal behavior propagates through industry and partner networks. Unlike threshold-based models, Kumo captures compound signals that emerge weeks before the renewal decision.
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
SUBSCRIPTIONS
| subscription_id | customer_id | plan | mrr | renewal_date |
|---|---|---|---|---|
| SUB401 | C501 | Enterprise | $12,500 | 2025-04-15 |
| SUB402 | C502 | Pro | $3,200 | 2025-04-01 |
| SUB403 | C503 | Enterprise | $18,000 | 2025-05-10 |
RENEWALS
| renewal_id | subscription_id | amount | timestamp |
|---|---|---|---|
| RN201 | SUB401 | $150,000 | 2024-04-15 |
| RN202 | SUB402 | $38,400 | 2024-04-01 |
| RN203 | SUB403 | $216,000 | 2024-05-10 |
USAGE_EVENTS
| event_id | subscription_id | feature | count | timestamp |
|---|---|---|---|---|
| UE501 | SUB401 | API calls | 12,400 | 2025-03-01 |
| UE502 | SUB402 | Dashboard views | 340 | 2025-03-01 |
| UE503 | SUB403 | Data exports | 890 | 2025-03-02 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(RENEWALS.*, 0, 30, days) > 0 FOR EACH SUBSCRIPTIONS.SUBSCRIPTION_ID
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIPTION_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| SUB401 | 2025-03-05 | True | 0.91 |
| SUB402 | 2025-03-05 | False | 0.28 |
| SUB403 | 2025-03-05 | True | 0.85 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscription SUB402 — Pro plan ($3,200 MRR)
Predicted: False (28% renewal probability)
Top contributing features
Feature adoption breadth (30d)
2 of 12 features
31% attribution
Active seats vs licensed seats
3 of 10
24% attribution
Support ticket escalation trend
+3 P1 tickets
20% attribution
Similar-industry renewal rate
45%
14% attribution
Champion user login frequency
-60%
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: A SaaS company with $500M ARR that improves renewal prediction accuracy by 15% protects $25M in at-risk revenue — giving CS teams the lead time to intervene 6-8 weeks before the renewal decision.
Related use cases
Explore more retention 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.
