Cancellation Prediction
“Will this reservation be cancelled?”
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A real-world example
Will this reservation be cancelled?
Hotels experience 20-40% cancellation rates, with last-minute cancellations (within 48 hours) being the most costly. Empty rooms from cancellations cost $50-200 per night in lost revenue. Overbooking to compensate risks costly walks ($200-500 per walked guest). For a chain with 50,000 rooms at 30% cancellation rate, accurate cancellation prediction enables optimal overbooking that recovers $40-70M annually without increasing walk rates.
How KumoRFM solves this
Graph-powered intelligence for travel and hospitality
Kumo connects reservations, guests, properties, weather, and events into a booking graph. The GNN learns cancellation patterns from the full reservation network: how booking lead time interacts with rate type, how weather forecast changes trigger leisure cancellations, how group bookings create correlated cancellation risk, and how guest history predicts individual cancellation behavior. PQL predicts cancellation probability per reservation, enabling overbooking decisions that maximize revenue within walk-rate constraints.
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
RESERVATIONS
| reservation_id | guest_id | room_type | check_in | rate_type |
|---|---|---|---|---|
| RES701 | GST101 | King Standard | 2025-03-14 | Non-refundable |
| RES702 | GST102 | King Deluxe | 2025-03-14 | Flexible |
| RES703 | GST103 | Suite | 2025-03-15 | Flexible |
GUESTS
| guest_id | past_cancellations | total_bookings | loyalty_tier |
|---|---|---|---|
| GST101 | 1 | 15 | Gold |
| GST102 | 4 | 8 | None |
| GST103 | 0 | 22 | Platinum |
PROPERTIES
| property_id | name | market | avg_cancellation_rate |
|---|---|---|---|
| HTL201 | Beachfront Resort | Miami | 32% |
| HTL202 | Convention Center Hotel | Orlando | 28% |
WEATHER
| market | date | forecast | change_from_yesterday |
|---|---|---|---|
| Miami | 2025-03-14 | Rain/Storms | Was: Sunny |
| Miami | 2025-03-15 | Partly Cloudy | No change |
EVENTS
| event_id | market | name | status | date |
|---|---|---|---|---|
| EVT201 | Miami | Beach Music Fest | Confirmed | 2025-03-15 |
| EVT202 | Orlando | Tech Summit | Postponed | 2025-03-14 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(RESERVATIONS.status = 'Cancelled', 0, 14, days) FOR EACH RESERVATIONS.reservation_id
Prediction output
Every entity gets a score, updated continuously
| RESERVATION_ID | GUEST | CHECK_IN | CANCEL_PROB | RISK_TIER |
|---|---|---|---|---|
| RES701 | GST101 | 2025-03-14 | 0.15 | Low |
| RES702 | GST102 | 2025-03-14 | 0.72 | Critical |
| RES703 | GST103 | 2025-03-15 | 0.05 | Low |
Understand why
Every prediction includes feature attributions — no black boxes
Reservation RES702 -- Guest GST102, Flexible King Deluxe Mar 14
Predicted: 72% cancellation probability (Critical)
Top contributing features
Flexible rate type (no cancellation penalty)
Flexible
28% attribution
Guest historical cancellation rate
50% (4 of 8)
25% attribution
Weather forecast change (sunny to storms)
Negative shift
21% attribution
No loyalty tier (low switching cost)
None
15% attribution
Booking lead time (long = higher cancel)
45 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 recovers $40-70M annually by optimizing overbooking based on per-reservation cancellation predictions. Kumo's booking graph connects guest history, weather shifts, and event status changes to predict cancellations before they happen.
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.
