In-Game Content Recommendation
“Which in-game items should we show this player?”
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A real-world example
Which in-game items should we show this player?
The average game store shows 200+ items but only 3% get meaningful engagement. A game earning $40M in IAP where 80% of revenue comes from 5% of the catalog has massive untapped potential. Generic featured-item rotations ignore that a player who just unlocked a new character class wants complementary gear, not random skins. Personalized stores lift conversion 30-50% but require understanding the intersection of player progression, inventory gaps, and social trends.
How KumoRFM solves this
Graph-learned player intelligence across your entire game ecosystem
Kumo connects players, inventories, the item catalog, and purchase histories into a graph where item affinity propagates through ownership patterns and social influence. It learns that players who own specific item combinations and just reached a new progression tier purchase complementary items at 5x the base rate. The model captures trending items within guilds, seasonal preference shifts, and inventory-gap signals that collaborative filtering alone cannot detect.
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 | level | class | total_spend |
|---|---|---|---|
| PLR401 | 45 | Warrior | $34.99 |
| PLR402 | 22 | Mage | $0.00 |
| PLR403 | 60 | Archer | $129.50 |
INVENTORY
| inv_id | player_id | item_id | acquired_date | source |
|---|---|---|---|---|
| INV01 | PLR401 | SWORD_EPIC_3 | 2025-02-20 | Purchase |
| INV02 | PLR401 | SHIELD_RARE_7 | 2025-02-25 | Drop |
| INV03 | PLR402 | STAFF_COMMON_1 | 2025-02-18 | Starter |
STORE_CATALOG
| item_id | name | category | price_usd | rarity |
|---|---|---|---|---|
| HELM_EPIC_2 | Dragonbone Helm | Armor | $4.99 | Epic |
| STAFF_EPIC_1 | Arcane Focus | Weapon | $7.99 | Epic |
| SKIN_LEG_5 | Phoenix Wings | Cosmetic | $14.99 | Legendary |
PURCHASE_HISTORY
| purchase_id | player_id | item_id | timestamp |
|---|---|---|---|
| PH01 | PLR401 | SWORD_EPIC_3 | 2025-02-20 |
| PH02 | PLR403 | HELM_EPIC_2 | 2025-02-15 |
| PH03 | PLR403 | SKIN_LEG_5 | 2025-02-22 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
RANK TOP 5 STORE_CATALOG.ITEM_ID FOR EACH PLAYERS.PLAYER_ID PREDICT BOOL(PURCHASE_HISTORY.*, 0, 7, days)
Prediction output
Every entity gets a score, updated continuously
| PLAYER_ID | RANK | ITEM_ID | ITEM_NAME | PURCHASE_PROB |
|---|---|---|---|---|
| PLR401 | 1 | HELM_EPIC_2 | Dragonbone Helm | 0.72 |
| PLR401 | 2 | SKIN_LEG_5 | Phoenix Wings | 0.41 |
| PLR402 | 1 | STAFF_EPIC_1 | Arcane Focus | 0.38 |
Understand why
Every prediction includes feature attributions — no black boxes
Player PLR401 -- Warrior, Level 45, $34.99 spent
Predicted: 72% purchase probability for Dragonbone Helm
Top contributing features
Inventory gap (armor set completion)
Missing helm slot
33% attribution
Guild trending purchase
4 guild members bought
22% attribution
Complementary item ownership
Has matching sword
19% attribution
Price vs avg purchase
Within range ($4.99)
14% attribution
Time since last purchase
10 days
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 earning $40M in IAP that personalizes its store to each player lifts conversion by 30%, adding $12M in annual revenue. Kumo learns inventory-gap signals, guild purchase trends, and item affinity patterns that static featured rotations and basic collaborative filtering miss.
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.
