Ad Revenue Optimization
“Which ad slot maximizes revenue without increasing churn?”
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A real-world example
Which ad slot maximizes revenue without increasing churn?
Ad-supported streaming tiers must balance revenue per viewer against ad fatigue that drives cancellations. Too many ads and subscribers downgrade or leave; too few and revenue per viewer drops. For a platform with 15M ad-supported subscribers generating $8 ARPU, a 10% improvement in ad load optimization adds $144M in annual revenue without increasing churn.
How KumoRFM solves this
Graph-powered intelligence for media platforms
Kumo connects subscribers, ad impressions, content, and watch sessions into a graph that models the revenue-churn tradeoff per subscriber. The GNN learns each subscriber's ad tolerance based on viewing patterns, engagement depth, content type, and historical responses to ad load changes. PQL predicts optimal ad slots per session, balancing incremental revenue against churn risk for each individual subscriber.
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 | ad_tier | avg_session_min |
|---|---|---|---|
| SUB301 | Ad-supported | Standard | 55 |
| SUB302 | Ad-supported | Light | 32 |
| SUB303 | Ad-supported | Standard | 78 |
AD_IMPRESSIONS
| impression_id | subscriber_id | ad_slot | revenue | timestamp |
|---|---|---|---|---|
| AI501 | SUB301 | Pre-roll | $0.045 | 2025-03-01 20:00 |
| AI502 | SUB301 | Mid-roll-1 | $0.038 | 2025-03-01 20:15 |
| AI503 | SUB302 | Pre-roll | $0.042 | 2025-03-01 14:30 |
CONTENT
| content_id | type | genre | duration_min |
|---|---|---|---|
| SER401 | Series | Drama | 48 |
| MOV501 | Movie | Comedy | 95 |
WATCH_SESSIONS
| session_id | subscriber_id | content_id | ads_shown | completed |
|---|---|---|---|---|
| WS701 | SUB301 | SER401 | 3 | True |
| WS702 | SUB302 | MOV501 | 2 | False |
| WS703 | SUB303 | SER401 | 4 | True |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(AD_IMPRESSIONS.revenue, 0, 1, days) FOR EACH SUBSCRIBERS.subscriber_id WHERE BOOL(SUBSCRIBERS.is_cancelled, 0, 30, days) = False
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | OPTIMAL_ADS_PER_SESSION | PREDICTED_DAILY_REV | CHURN_RISK |
|---|---|---|---|
| SUB301 | 3 | $0.128 | Low |
| SUB302 | 1 | $0.042 | High |
| SUB303 | 4 | $0.172 | Low |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB302 -- Ad-supported Light tier
Predicted: Optimal: 1 ad per session ($0.042 daily, churn risk: High)
Top contributing features
Session abandonment after 2+ ads
68% rate
33% attribution
Average session duration
32 min
24% attribution
Days since plan downgrade consideration
12 days
19% attribution
Content type engagement depth
Low
14% attribution
Similar subscribers' churn rate at 2+ ads
22%
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 platform with 15M ad-supported subscribers adds $144M in annual revenue by optimizing ad load per subscriber. Kumo balances revenue against individual churn risk, showing some subscribers tolerate 4 ads while others leave after 2.
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.
