Dynamic Pricing
“What rate maximizes revenue for this room tonight?”
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A real-world example
What rate maximizes revenue for this room tonight?
Hotels leave 8-15% of potential revenue on the table through suboptimal pricing. Revenue managers update rates daily based on occupancy forecasts and competitor rates, but miss the demand signals embedded in search patterns, event calendars, weather, and competitor sellout cascades. For a hotel chain with 50,000 rooms at $150 ADR, a 5% RevPAR improvement generates $137M in additional annual revenue.
How KumoRFM solves this
Graph-powered intelligence for travel and hospitality
Kumo connects rooms, bookings, competitor rates, events, and weather into a hospitality demand graph. The GNN learns pricing dynamics from the full market network: how a competitor selling out at a lower rate creates demand spillover, how event combinations (conference + concert) create non-linear demand spikes, and how weather forecasts shift booking velocity by room type. PQL predicts revenue-maximizing rates per room type per night, updating as demand signals change.
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
ROOMS
| room_type | inventory | base_rate | floor | view |
|---|---|---|---|---|
| King Standard | 120 | $159 | 2-8 | City |
| King Deluxe | 60 | $219 | 9-15 | Ocean |
| Suite | 20 | $389 | 16-20 | Ocean |
BOOKINGS
| booking_id | room_type | check_in | rate_paid | channel |
|---|---|---|---|---|
| BK5001 | King Standard | 2025-03-07 | $179 | Direct |
| BK5002 | King Deluxe | 2025-03-07 | $245 | OTA |
| BK5003 | Suite | 2025-03-07 | $420 | Direct |
COMPETITORS
| hotel | room_type | rate | availability | date |
|---|---|---|---|---|
| Hotel Alpha | Standard | $169 | Sold out | 2025-03-07 |
| Hotel Beta | Standard | $189 | 4 left | 2025-03-07 |
| Hotel Gamma | Deluxe | $255 | Available | 2025-03-07 |
EVENTS
| event_id | name | type | attendees | date |
|---|---|---|---|---|
| EVT101 | Tech Conference | Conference | 8,000 | 2025-03-07 |
| EVT102 | Beach Music Festival | Concert | 15,000 | 2025-03-08 |
WEATHER
| date | condition | high_temp_f | rain_prob |
|---|---|---|---|
| 2025-03-07 | Sunny | 78 | 5% |
| 2025-03-08 | Sunny | 82 | 10% |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(BOOKINGS.rate_paid, 0, 1, days) FOR EACH ROOMS.room_type
Prediction output
Every entity gets a score, updated continuously
| ROOM_TYPE | CURRENT_RATE | OPTIMAL_RATE | OCCUPANCY_PRED | REVPAR_DELTA |
|---|---|---|---|---|
| King Standard | $159 | $199 | 98% | +$38 |
| King Deluxe | $219 | $269 | 95% | +$44 |
| Suite | $389 | $449 | 90% | +$48 |
Understand why
Every prediction includes feature attributions — no black boxes
King Standard rooms -- March 7, 2025
Predicted: Optimal rate: $199 (98% predicted occupancy, +$38 RevPAR)
Top contributing features
Competitor Hotel Alpha sold out
Spillover demand
30% attribution
Tech Conference (8K attendees)
Business travel surge
26% attribution
Current booking velocity
+45% vs same DOW
19% attribution
Weather (sunny, 78F = leisure demand)
Favorable
14% attribution
Days until check-in (short window)
6 days
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 hotel chain with 50,000 rooms generates $137M in additional annual revenue through 5% RevPAR improvement. Kumo's demand graph captures competitor sellout cascades, event combinations, and weather-driven booking velocity that daily rate updates miss.
Related use cases
Explore more travel & hospitality 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.
