Booking Prediction
“Will this browsing session result in a booking?”
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A real-world example
Will this browsing session result in a booking?
Online travel platforms convert only 2-4% of sessions into bookings. The 96-98% that don't convert represent a massive opportunity: even moving conversion from 3% to 3.5% is a 17% revenue increase. Traditional models use session-level features but miss the user journey graph: how search patterns evolve across sessions, how price sensitivity varies by trip context, and how property-user affinity signals predict intent. For an OTA with $5B in gross bookings, a 0.5% conversion lift generates $83M in incremental revenue.
How KumoRFM solves this
Graph-powered intelligence for travel and hospitality
Kumo connects users, searches, property views, bookings, and property attributes into a travel graph. The GNN learns booking intent from the full user journey: how search refinement patterns signal high intent, how price sensitivity interacts with property attributes, and which user-property pairings have the highest conversion probability. PQL predicts booking probability per session, enabling real-time personalization and targeted incentives for high-intent sessions.
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
USERS
| user_id | loyalty_tier | past_bookings | avg_booking_value |
|---|---|---|---|
| USR001 | Gold | 12 | $340 |
| USR002 | None | 0 | N/A |
| USR003 | Silver | 5 | $220 |
SEARCHES
| search_id | user_id | destination | dates | guests | timestamp |
|---|---|---|---|---|---|
| SRC401 | USR001 | Miami | Mar 14-17 | 2 | 2025-03-01 10:00 |
| SRC402 | USR002 | NYC | Apr 5-7 | 1 | 2025-03-01 11:30 |
| SRC403 | USR003 | Miami | Mar 14-16 | 2 | 2025-03-01 14:00 |
VIEWS
| view_id | search_id | property_id | time_on_page_s | photos_viewed |
|---|---|---|---|---|
| VW601 | SRC401 | HTL001 | 180 | 8 |
| VW602 | SRC401 | HTL002 | 45 | 2 |
| VW603 | SRC402 | HTL003 | 22 | 1 |
BOOKINGS
| booking_id | user_id | property_id | total | timestamp |
|---|---|---|---|---|
| BK6001 | USR001 | HTL001 | $1,020 | 2025-03-01 10:25 |
PROPERTIES
| property_id | name | star_rating | avg_rate | review_score |
|---|---|---|---|---|
| HTL001 | Ocean Breeze Resort | 4-star | $295 | 4.6 |
| HTL002 | City Center Hotel | 3-star | $185 | 4.2 |
| HTL003 | Manhattan Suites | 4-star | $380 | 4.4 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(BOOKINGS.booking_id, 0, 1, hours) FOR EACH SEARCHES.search_id
Prediction output
Every entity gets a score, updated continuously
| SEARCH_ID | USER_ID | DESTINATION | BOOKING_PROB | RECOMMENDED_ACTION |
|---|---|---|---|---|
| SRC401 | USR001 | Miami | 0.82 | Show best match |
| SRC402 | USR002 | NYC | 0.09 | Offer discount |
| SRC403 | USR003 | Miami | 0.44 | Show urgency |
Understand why
Every prediction includes feature attributions — no black boxes
Search SRC401 -- User USR001 searching Miami hotels
Predicted: 82% booking probability
Top contributing features
Detailed property review (180s + 8 photos)
High engagement
30% attribution
Loyalty tier and booking history
Gold, 12 past bookings
24% attribution
Date proximity (13 days out = committed)
Mar 14-17
19% attribution
Price alignment with avg booking value
$295 vs $340 avg
16% attribution
Search refinement pattern (narrowing)
2 destination searches
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: An OTA with $5B in gross bookings generates $83M in incremental revenue by improving conversion 0.5 percentage points. Kumo's travel graph detects high-intent sessions from engagement depth, search refinement patterns, and user-property affinity signals.
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.
