Engagement Scoring
“For each user, what will their total engagement hours be over the next 30 days?”
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A real-world example
For each user, what will their total engagement hours be over the next 30 days?
Binary churn models tell you who might leave but not how engaged they are. A user logging in once a month looks "active" but is already disengaging. Continuous engagement scores let product and CS teams intervene on a gradient — before the binary signal fires. For a SaaS platform with 200K users, a 10% lift in engagement correlates to $18M in upsell revenue.
How KumoRFM solves this
Relational intelligence for customer retention
Kumo predicts a continuous engagement score — total session hours over the next 30 days — by learning from session depth, feature adoption sequences, support ticket patterns, and how engagement spreads through organizational graphs. Unlike rule-based health scores, Kumo captures the compound dynamics that precede engagement shifts.
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
USERS
| user_id | plan | signup_date | company |
|---|---|---|---|
| U201 | Enterprise | 2024-02-10 | Acme Corp |
| U202 | Pro | 2024-06-15 | Bolt Inc |
| U203 | Enterprise | 2023-11-01 | Crest Labs |
SESSIONS
| session_id | user_id | duration_min | features_used | timestamp |
|---|---|---|---|---|
| S3001 | U201 | 45 | dashboard,reports | 2025-02-28 |
| S3002 | U202 | 12 | dashboard | 2025-03-01 |
| S3003 | U203 | 68 | reports,api,exports | 2025-03-02 |
SUPPORT_TICKETS
| ticket_id | user_id | priority | status | timestamp |
|---|---|---|---|---|
| T701 | U201 | Low | Resolved | 2025-02-15 |
| T702 | U202 | High | Open | 2025-02-28 |
| T703 | U203 | Medium | Resolved | 2025-01-20 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(SESSIONS.DURATION_MIN, 0, 30, days) FOR EACH USERS.USER_ID
Prediction output
Every entity gets a score, updated continuously
| USER_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| U201 | 2025-03-05 | 22.4 hrs |
| U202 | 2025-03-05 | 3.1 hrs |
| U203 | 2025-03-05 | 38.7 hrs |
Understand why
Every prediction includes feature attributions — no black boxes
User U202 — Bolt Inc
Predicted: 3.1 hours (low engagement predicted)
Top contributing features
Session duration trend (30d)
-54%
32% attribution
Open high-priority tickets
1 unresolved
26% attribution
Features used per session
1.2 avg
20% attribution
Company-wide engagement trend
Declining
13% attribution
Days since last API call
18 days
9% 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 platform with 200K users that lifts engagement by 10% through targeted interventions unlocks $18M in upsell revenue and reduces churn-driven ARR loss by 30%.
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.
