Player Churn Prediction
“Which players will stop playing within 7 days?”
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A real-world example
Which players will stop playing within 7 days?
Mobile games lose 75% of players within the first 3 days. Acquiring each player costs $2-$8 via paid UA, and a game with 5M MAU losing 20% of monetizing players monthly leaves $18M in annual revenue on the table. Generic retention campaigns treat every player the same, wasting live-ops resources on players who were never going to stay while missing the ones teetering on the edge.
How KumoRFM solves this
Graph-learned player intelligence across your entire game ecosystem
Kumo connects players, sessions, purchases, achievements, and social connections into a single relational graph. It learns that players whose guild members have gone inactive, who have hit a specific level-difficulty wall, and whose session lengths have dropped 40% over 3 days are 6x more likely to churn. The social graph signal is critical: when a player's friends leave, that player follows within days. Traditional cohort models miss these network effects entirely.
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 | country | level |
|---|---|---|---|---|
| PLR001 | 2025-01-05 | iOS | US | 42 |
| PLR002 | 2025-02-18 | Android | JP | 15 |
| PLR003 | 2025-01-22 | iOS | US | 67 |
SESSIONS
| session_id | player_id | start_time | duration_min | levels_played |
|---|---|---|---|---|
| S001 | PLR001 | 2025-03-01 18:30 | 45 | 3 |
| S002 | PLR002 | 2025-03-02 09:15 | 8 | 1 |
| S003 | PLR003 | 2025-03-01 21:00 | 62 | 5 |
PURCHASES
| purchase_id | player_id | item | amount_usd | timestamp |
|---|---|---|---|---|
| PUR01 | PLR001 | Gem Pack 500 | 4.99 | 2025-02-20 |
| PUR02 | PLR003 | Battle Pass | 9.99 | 2025-02-15 |
ACHIEVEMENTS
| achievement_id | player_id | name | unlocked_date |
|---|---|---|---|
| ACH01 | PLR001 | Boss Slayer III | 2025-02-28 |
| ACH02 | PLR003 | Guild Leader | 2025-02-10 |
SOCIAL_CONNECTIONS
| connection_id | player_id | friend_id | type |
|---|---|---|---|
| SC01 | PLR001 | PLR003 | Guild |
| SC02 | PLR002 | PLR001 | Friend |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(SESSIONS.*, 0, 7, days) = 0 FOR EACH PLAYERS.PLAYER_ID WHERE COUNT(SESSIONS.*, -7, 0, days) > 0
Prediction output
Every entity gets a score, updated continuously
| PLAYER_ID | PLATFORM | LEVEL | CHURN_7D_PROB |
|---|---|---|---|
| PLR001 | iOS | 42 | 0.14 |
| PLR002 | Android | 15 | 0.83 |
| PLR003 | iOS | 67 | 0.06 |
Understand why
Every prediction includes feature attributions — no black boxes
Player PLR002 -- Android, Level 15, Japan
Predicted: 83% churn probability within 7 days
Top contributing features
Session duration trend (7d)
-72% decline
31% attribution
Level progression stall
Stuck at L15 for 5d
24% attribution
Friend activity (active friends)
0 of 3 active
19% attribution
Days since last purchase
Never purchased
14% attribution
Tutorial completion rate
60%
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 mobile game with 5M MAU that retains just 10% more of its at-risk monetizing players saves $18M annually. Kumo captures friend-graph churn contagion and progression-wall patterns that cohort analytics miss, letting live-ops target the players who can still be saved.
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.
