Guest Personalization
“What amenities should we offer this guest?”
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A real-world example
What amenities should we offer this guest?
Hotels spend $2-5 per guest on amenity and upgrade offers with a 3-5% take rate on generic campaigns. Personalized offers (right amenity, right guest, right price) convert at 15-25%, generating $30-80 in incremental revenue per stay. For a chain with 10M annual stays, moving from generic to personalized amenity offers generates $200-500M in additional ancillary revenue annually.
How KumoRFM solves this
Graph-powered intelligence for travel and hospitality
Kumo connects guests, stays, preferences, feedback, and loyalty into a hospitality graph. The GNN learns individual preference patterns across the guest network: how stay history, trip purpose (business vs. leisure), companion type, and feedback sentiment predict amenity uptake. PQL ranks amenities per guest per stay, maximizing incremental revenue while maintaining guest satisfaction scores.
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
GUESTS
| guest_id | loyalty_tier | total_stays | avg_spend_per_stay |
|---|---|---|---|
| GST001 | Platinum | 45 | $520 |
| GST002 | Gold | 12 | $340 |
| GST003 | None | 2 | $280 |
STAYS
| stay_id | guest_id | property | purpose | companions |
|---|---|---|---|---|
| STY501 | GST001 | Beach Resort | Leisure | Family |
| STY502 | GST002 | City Hotel | Business | Solo |
| STY503 | GST003 | Beach Resort | Leisure | Couple |
PREFERENCES
| guest_id | room_pref | dining_pref | activity_pref |
|---|---|---|---|
| GST001 | High floor, ocean view | Fine dining | Spa, Golf |
| GST002 | Quiet floor, workspace | Room service | Gym |
| GST003 | Unknown | Unknown | Unknown |
FEEDBACK
| stay_id | overall_score | highlights | complaints |
|---|---|---|---|
| STY501 | 9.2 | Spa, pool area | Slow room service |
| STY502 | 8.5 | WiFi, gym | Noise from hallway |
LOYALTY
| guest_id | points_balance | nights_ytd | spend_ytd |
|---|---|---|---|
| GST001 | 125,000 | 12 | $6,240 |
| GST002 | 28,000 | 4 | $1,360 |
| GST003 | 0 | 0 | $0 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(STAYS.amenity_purchased, 0, 3, days) FOR EACH GUESTS.guest_id, AMENITIES.amenity_id RANK TOP 3
Prediction output
Every entity gets a score, updated continuously
| GUEST_ID | AMENITY | UPTAKE_PROB | OPTIMAL_PRICE | RANK |
|---|---|---|---|---|
| GST001 | Spa Package | 0.72 | $180 | 1 |
| GST001 | Ocean-view Upgrade | 0.65 | $85 | 2 |
| GST002 | Late Checkout | 0.58 | $45 | 1 |
| GST003 | Couples Dinner | 0.41 | $120 | 1 |
Understand why
Every prediction includes feature attributions — no black boxes
Guest GST001 -- Platinum, family leisure stay at Beach Resort
Predicted: 72% uptake for Spa Package at $180 (Rank #1)
Top contributing features
Spa highlighted in past feedback
9.2 score, Spa mentioned
30% attribution
Activity preference: Spa, Golf
Known preference
25% attribution
Family trip context (spa while kids at pool)
Leisure + Family
19% attribution
Spend capacity (Platinum, $520 avg)
High
15% attribution
Similar Platinum guests' Spa uptake
68% at this property
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 10M annual stays generates $200-500M in additional ancillary revenue by personalizing amenity offers. Kumo's guest graph matches amenities to individual preferences, trip context, and spend capacity, lifting take rates from 3-5% to 15-25%.
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.
