Customer Health Scoring
“For each account, what will their composite health score be over the next 30 days?”
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A real-world example
For each account, what will their composite health score be over the next 30 days?
Manual health scores built from weighted rules (usage > X = green, tickets > Y = red) miss 60% of accounts that churn. The rules are static, the weights are guessed, and they cannot capture the compound interactions between usage decline, support escalation, and billing friction. For a B2B SaaS with 5,000 accounts and $120K average ACV, a 10% improvement in health score accuracy saves $12M in preventable churn.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo predicts a continuous health score — the average of future health signals — by learning the compound relationships between product usage depth, support ticket patterns, billing events, and how health trends propagate across accounts in the same industry or CSM portfolio. The model continuously reweights signal importance rather than relying on static rules.
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 | company | plan | mrr | csm_id |
|---|---|---|---|---|
| A601 | Nexus Corp | Enterprise | $28,000 | CSM-12 |
| A602 | Orbit Tech | Growth | $8,500 | CSM-07 |
| A603 | Pinnacle Ltd | Enterprise | $45,000 | CSM-12 |
HEALTH_SIGNALS
| signal_id | account_id | score | signal_type | timestamp |
|---|---|---|---|---|
| HS801 | A601 | 82 | Product Usage | 2025-02-28 |
| HS802 | A602 | 54 | Support Health | 2025-03-01 |
| HS803 | A603 | 91 | Product Usage | 2025-03-02 |
SUPPORT_TICKETS
| ticket_id | account_id | priority | resolution_hours | timestamp |
|---|---|---|---|---|
| T901 | A601 | Medium | 4.2 | 2025-02-20 |
| T902 | A602 | Critical | 48.0 | 2025-02-25 |
| T903 | A603 | Low | 1.5 | 2025-01-15 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(HEALTH_SIGNALS.SCORE, 0, 30, days) FOR EACH ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| A601 | 2025-03-05 | 78.3 |
| A602 | 2025-03-05 | 41.7 |
| A603 | 2025-03-05 | 89.1 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A602 — Orbit Tech
Predicted: 41.7 (declining health score)
Top contributing features
Unresolved critical ticket age
8 days
33% attribution
Product usage trend (30d)
-38%
25% attribution
Active users vs licensed seats
4 of 15
19% attribution
CSM portfolio health trend
3 accounts declining
13% attribution
Billing payment delay trend
+12 days avg
10% 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 B2B SaaS with 5,000 accounts and $120K average ACV that improves health score accuracy by 10% saves $12M in preventable churn — replacing guesswork rules with learned, continuously updated account intelligence.
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.
