Content Demand Forecasting
“How much will this new show be watched?”
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A real-world example
How much will this new show be watched?
Studios invest $200M+ per year in original content with limited ability to predict which titles will perform. Marketing spend is allocated uniformly rather than proportional to predicted demand. Content that underperforms wastes production budget; content that overperforms gets under-marketed. For a studio producing 50 originals per year, accurate demand forecasting saves $30-50M in misallocated production and marketing spend.
How KumoRFM solves this
Graph-powered intelligence for media platforms
Kumo connects content metadata, genres, creators, similar titles, and trailer engagement into a graph. The model learns demand signals from the content graph: how a creator's track record interacts with genre trends, how trailer engagement converts to viewership by subscriber segment, and how similar titles' performance trajectories predict new content demand. Predictions are available before launch, enabling optimized marketing spend and scheduling.
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
CONTENT
| content_id | title | genre | budget | creator_id |
|---|---|---|---|---|
| NEW01 | Dark Protocol | Thriller/Sci-Fi | $45M | DIR001 |
| NEW02 | Summer House | Rom-Com | $18M | DIR002 |
| NEW03 | Iron Circuit | Action | $60M | DIR003 |
GENRES
| genre | trend_score | avg_completion | subscriber_penetration |
|---|---|---|---|
| Thriller/Sci-Fi | Rising | 68% | 32% |
| Rom-Com | Stable | 75% | 28% |
| Action | Declining | 62% | 35% |
CREATORS
| creator_id | name | avg_viewership_m | hit_rate |
|---|---|---|---|
| DIR001 | J. Nakamura | 8.2M | 60% |
| DIR002 | S. Okafor | 4.5M | 40% |
| DIR003 | R. Zhang | 12.1M | 45% |
SIMILAR_TITLES
| content_id | similar_to | similarity_score | viewership_m |
|---|---|---|---|
| NEW01 | Title-X | 0.89 | 9.4M |
| NEW02 | Title-Y | 0.82 | 5.1M |
| NEW03 | Title-Z | 0.91 | 14.2M |
TRAILER_VIEWS
| content_id | views_7d | completion_rate | social_shares |
|---|---|---|---|
| NEW01 | 4.2M | 72% | 180K |
| NEW02 | 1.8M | 65% | 45K |
| NEW03 | 6.1M | 58% | 320K |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(WATCH_HISTORY.minutes_watched, 0, 30, days) FOR EACH CONTENT.content_id WHERE CONTENT.release_date > '2025-03-01'
Prediction output
Every entity gets a score, updated continuously
| CONTENT_ID | TITLE | PREDICTED_VIEWERS_30D | CONFIDENCE |
|---|---|---|---|
| NEW01 | Dark Protocol | 11.2M | High |
| NEW02 | Summer House | 4.8M | Medium |
| NEW03 | Iron Circuit | 9.6M | Medium |
Understand why
Every prediction includes feature attributions — no black boxes
Content NEW01 -- Dark Protocol (Thriller/Sci-Fi)
Predicted: 11.2M viewers in first 30 days (High confidence)
Top contributing features
Trailer engagement conversion rate
72% completion
28% attribution
Creator J. Nakamura track record
8.2M avg
24% attribution
Genre trend score
Rising
20% attribution
Similar title performance
9.4M viewers
17% attribution
Social share velocity (first 7 days)
180K shares
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 studio producing 50 originals per year saves $30-50M by accurately forecasting demand before launch. Kumo's content graph connects creator track records, genre trends, and trailer signals to predict viewership with enough lead time to optimize marketing and scheduling.
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.
