Engagement Prediction
“How many minutes will this user watch today?”
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A real-world example
How many minutes will this user watch today?
Engagement is the leading indicator of subscriber health, ad inventory value, and content ROI. Platforms that can predict daily engagement per subscriber can optimize content scheduling, ad load balancing, and proactive retention. A platform with 40M subscribers that increases average daily engagement by 5 minutes generates $180M in additional ad revenue and prevents $30M in churn-related losses annually.
How KumoRFM solves this
Graph-powered intelligence for media platforms
Kumo connects subscribers, sessions, content, and schedules into a temporal graph. The model learns individual engagement rhythms: weekday vs. weekend patterns, binge triggers (new season drops), device transitions throughout the day, and how social viewing signals (household co-watching) amplify engagement. PQL forecasts daily minutes at the subscriber level, enabling personalized scheduling and ad load decisions.
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
SUBSCRIBERS
| subscriber_id | plan | avg_daily_minutes | preferred_time |
|---|---|---|---|
| SUB201 | Premium | 85 | Evening |
| SUB202 | Ad-supported | 42 | Afternoon |
| SUB203 | Standard | 110 | Night |
SESSIONS
| session_id | subscriber_id | device | duration_min | timestamp |
|---|---|---|---|---|
| SES401 | SUB201 | Smart TV | 62 | 2025-03-01 20:00 |
| SES402 | SUB202 | Mobile | 18 | 2025-03-01 14:30 |
| SES403 | SUB203 | Smart TV | 95 | 2025-03-01 22:00 |
CONTENT
| content_id | type | genre | avg_engagement_min |
|---|---|---|---|
| SER301 | Series | Drama | 45 |
| MOV401 | Movie | Action | 110 |
| SER302 | Series | Comedy | 28 |
SCHEDULES
| content_id | release_date | release_type | marketing_push |
|---|---|---|---|
| SER301 | 2025-03-05 | New Season | Heavy |
| MOV401 | 2025-03-01 | Premiere | Standard |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(SESSIONS.duration_min, 0, 1, days) FOR EACH SUBSCRIBERS.subscriber_id
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | DATE | PREDICTED_MINUTES | VS_AVG |
|---|---|---|---|
| SUB201 | 2025-03-05 | 142 | +67% |
| SUB202 | 2025-03-05 | 38 | -10% |
| SUB203 | 2025-03-05 | 125 | +14% |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB201 -- Premium plan, evening viewer
Predicted: 142 predicted minutes on March 5 (+67% vs avg)
Top contributing features
New season drop for followed series
SER301
35% attribution
Historical binge pattern on release days
2.1x avg
25% attribution
Day of week (Wednesday = peak)
Wed
17% attribution
Household co-viewing likelihood
High
13% attribution
Device preference (Smart TV = longer)
Smart TV
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 40M-subscriber platform that predicts daily engagement generates $180M in additional ad revenue by optimizing content scheduling and ad load. Kumo captures individual viewing rhythms, binge triggers, and social signals that aggregate models flatten away.
Related use cases
Explore more media & entertainment 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.
