In-App Purchase Prediction
“Which players will make an in-app purchase?”
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A real-world example
Which players will make an in-app purchase?
Only 2-5% of free-to-play players ever make a purchase. Showing the wrong offer at the wrong time trains players to ignore your store entirely. A game generating $30M in annual IAP revenue that improves conversion from 3% to 4% adds $10M. The signal is not in demographics alone; it is in the sequence of gameplay behaviors, social influences, and store browsing patterns that precede a first purchase.
How KumoRFM solves this
Graph-learned player intelligence across your entire game ecosystem
Kumo models the journey from install to first purchase as a relational graph connecting sessions, store views, level progress, and social connections. It learns that players who view a specific item category 3+ times after failing a hard level, while their guild mates have recently purchased, convert at 8x the base rate. The model distinguishes between curiosity browsing and purchase intent by analyzing temporal patterns across the player network.
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
PLAYERS
| player_id | install_date | platform | total_spend |
|---|---|---|---|
| PLR101 | 2025-02-01 | iOS | $0.00 |
| PLR102 | 2025-01-15 | Android | $0.00 |
| PLR103 | 2025-02-20 | iOS | $4.99 |
SESSIONS
| session_id | player_id | timestamp | duration_min | store_visits |
|---|---|---|---|---|
| S101 | PLR101 | 2025-03-02 | 38 | 3 |
| S102 | PLR102 | 2025-03-01 | 12 | 0 |
| S103 | PLR103 | 2025-03-02 | 55 | 1 |
STORE_VIEWS
| view_id | player_id | item_id | category | timestamp |
|---|---|---|---|---|
| SV01 | PLR101 | ITM_GEM500 | Currency | 2025-03-02 |
| SV02 | PLR101 | ITM_SKIN_DRAGON | Cosmetic | 2025-03-02 |
| SV03 | PLR102 | ITM_GEM100 | Currency | 2025-02-28 |
PURCHASES
| purchase_id | player_id | item_id | amount_usd | timestamp |
|---|---|---|---|---|
| PUR101 | PLR103 | ITM_BATTLEPASS | 9.99 | 2025-02-25 |
LEVEL_PROGRESS
| progress_id | player_id | level | attempts | completed |
|---|---|---|---|---|
| LP01 | PLR101 | 28 | 7 | N |
| LP02 | PLR102 | 12 | 2 | Y |
| LP03 | PLR103 | 35 | 1 | Y |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(PURCHASES.*, 0, 7, days) FOR EACH PLAYERS.PLAYER_ID WHERE PLAYERS.TOTAL_SPEND = 0
Prediction output
Every entity gets a score, updated continuously
| PLAYER_ID | PLATFORM | DAYS_SINCE_INSTALL | IAP_PROB_7D |
|---|---|---|---|
| PLR101 | iOS | 29 | 0.68 |
| PLR102 | Android | 45 | 0.09 |
Understand why
Every prediction includes feature attributions — no black boxes
Player PLR101 -- iOS, Day 29, $0 spend
Predicted: 68% IAP probability within 7 days
Top contributing features
Store views (last 3d)
8 views, 3 categories
30% attribution
Level fail-retry pattern
7 attempts on L28
25% attribution
Guild member purchase rate
4 of 6 purchased
19% attribution
Session duration trend
+15% last 7d
14% attribution
Cosmetic store dwell time
4.2 min avg
12% 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 free-to-play game with 2M DAU that converts 1% more free players to payers adds $10M in annual IAP revenue. Kumo detects purchase intent signals across store behavior, progression frustration, and social influence that propensity models on flat feature tables cannot learn.
Related use cases
Explore more gaming 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.
