Player Lifetime Value Prediction
“What is each player's 90-day lifetime value?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
What is each player's 90-day lifetime value?
UA teams spend $50M+ annually acquiring players with 7-day LTV estimates that miss long-tail spenders by 40%. A game spending $5 per install that misattributes high-LTV channels wastes $12M per year on the wrong ad networks. The 90-day LTV is shaped not just by individual behavior but by the referral chain quality, guild spending norms, and content engagement depth that simple regression on D7 revenue cannot capture.
How KumoRFM solves this
Graph-learned player intelligence across your entire game ecosystem
Kumo connects players, purchases, sessions, and referral chains into a graph that captures spending contagion patterns. It learns that players referred by high-spenders who join active guilds within 48 hours of install have 3.5x higher 90-day LTV. The model tracks temporal spending trajectories and social spending norms across the network, producing accurate LTV estimates by Day 3 that traditional models cannot match until Day 30.
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 | source | country |
|---|---|---|---|
| PLR201 | 2025-02-01 | Facebook Ads | US |
| PLR202 | 2025-02-10 | Organic | UK |
| PLR203 | 2025-02-05 | Google UAC | DE |
PURCHASES
| purchase_id | player_id | amount_usd | item_type | timestamp |
|---|---|---|---|---|
| PUR201 | PLR201 | 4.99 | Currency | 2025-02-08 |
| PUR202 | PLR201 | 19.99 | Bundle | 2025-02-20 |
| PUR203 | PLR202 | 9.99 | Battle Pass | 2025-02-15 |
SESSIONS
| session_id | player_id | date | duration_min | events |
|---|---|---|---|---|
| S201 | PLR201 | 2025-03-01 | 55 | 142 |
| S202 | PLR202 | 2025-03-01 | 22 | 38 |
| S203 | PLR203 | 2025-02-28 | 8 | 12 |
REFERRALS
| referral_id | referrer_id | referred_id | timestamp |
|---|---|---|---|
| REF01 | PLR201 | PLR203 | 2025-02-05 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(PURCHASES.AMOUNT_USD, 0, 90, days) FOR EACH PLAYERS.PLAYER_ID
Prediction output
Every entity gets a score, updated continuously
| PLAYER_ID | SOURCE | D7_ACTUAL | PREDICTED_D90_LTV |
|---|---|---|---|
| PLR201 | Facebook Ads | $4.99 | $82.40 |
| PLR202 | Organic | $9.99 | $31.20 |
| PLR203 | Google UAC | $0.00 | $3.10 |
Understand why
Every prediction includes feature attributions — no black boxes
Player PLR201 -- Facebook Ads, US, Day 28
Predicted: $82.40 predicted 90-day LTV
Top contributing features
Purchase velocity (first 14d)
2 purchases
28% attribution
Session engagement depth
142 events/session
23% attribution
Referral network spending
$45 avg in referral chain
20% attribution
Guild spending norm
$8.50 ARPPU
17% attribution
Content completion rate
78% of available
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 game studio spending $50M on UA that improves Day-3 LTV prediction accuracy by 35% reallocates $12M from underperforming channels to high-LTV sources. Kumo captures referral chain quality and social spending norms that D7 regression models miss, delivering accurate LTV estimates 27 days earlier.
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.
