In-Game Purchase Data
Buy and sell in-game purchase data data. Microtransaction patterns, loot box outcomes, and virtual currency spending. The $60B mobile gaming economy in raw data.
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Find Me This Data →Overview
What Is In-Game Purchase Data?
In-game purchase data captures the monetization behavior of mobile and digital game players, including microtransaction patterns, virtual currency spending, and item purchases. This dataset reflects real-world spending decisions across millions of players in free-to-play games, where players opt to buy cosmetics, power-ups, boosters, and other in-game advantages. The data reveals how players engage with monetization mechanics and which purchase triggers drive conversion, making it valuable for game designers optimizing revenue while balancing gameplay experience. Free-to-play games commonly implement monetization through purchasable help items, boosters, and cosmetics, ensuring gameplay remains accessible without spending while capturing revenue from engaged users willing to pay.
Market Data
759,382 players
Players in Large-Scale Game Study
Source: ACM Research Paper
6 months
Data Collection Period
Source: ACM Research Paper
Free-to-play with purchasable boosters and power pieces
Monetization Model Focus
Source: ACM Research Paper
Who Uses This Data
What AI models do with it.do with it.
Game Designers & Monetization Teams
Analyze purchase patterns to optimize difficulty curves, personalize offers, and balance monetization without compromising retention. Data on which boosters and power pieces drive conversion informs in-game economy design.
Player Retention & Churn Analysis
Use spending behavior combined with progression data to identify at-risk players and predict churn. Purchase timing and product selection reveal engagement signals before players quit.
Dynamic Difficulty Adjustment Systems
Combine in-game purchase data with player progression to personalize difficulty and monetization offers. Systems learn which price points and item combinations maximize both engagement and revenue for individual player segments.
App Store Optimization & Marketing
Developers benchmark their monetization performance against category trends and A/B test pricing, offer timing, and product bundling to improve conversion rates and user acquisition efficiency.
What Can You Earn?
What it's worth.worth.
Small Dataset (Thousands of Players)
Varies
Limited historical transactions; useful for niche game genres or emerging titles
Medium Dataset (Hundreds of Thousands)
Varies
Sufficient for cohort analysis, purchase funnel studies, and product-level monetization insights
Large Dataset (Millions+ Players)
Varies
Industry-scale data covering multiple games and genres; premium for competitive benchmarking and predictive modeling
What Buyers Expect
What makes it valuable.valuable.
Complete Player History
Datasets should include full progression and purchase history from first login, not partial snapshots. This ensures accurate cohort analysis and churn prediction without survivorship bias.
High-Cardinality Player & Item Data
Rich attributes describing individual players (playtime, attempt history, skill level) and purchasable items (type, price, rarity, effect) enable sophisticated segmentation and predictive modeling.
Temporal Consistency & Standardization
Purchase timestamps, currency conversions, and level metadata must be clean and standardized across all records. Data quality issues affect difficulty prediction and monetization models.
Minimum Engagement Threshold
Exclude low-engagement players (churn before meaningful interaction) to ensure meaningful monetization and retention signals. Datasets should represent players with sustained game interaction.
Companies Active Here
Who's buying.buying.
Optimize in-game economies, balance monetization with retention, and personalize offers. Use purchase data to fine-tune difficulty curves and identify churn risk.
Aggregate purchase data across games to provide benchmarking, cohort analysis, and monetization advisory to development teams.
Study spending patterns, loot box engagement, virtual currency adoption, and behavioral economics in free-to-play games at scale.
FAQ
Common questions.questions.
What makes in-game purchase data valuable for game design?
Purchase data combined with player progression reveals which monetization mechanics drive conversion and which discourage spending. Game designers use this to balance difficulty, personalize offers, and ensure profitability without compromising the free-to-play experience. Data on booster and power piece sales directly informs in-game economy adjustments.
How is this data collected and what privacy considerations apply?
In-game purchase data is generated naturally through game telemetry systems that log transactions, player progression, and engagement metrics. Datasets should be anonymized and aggregated to respect player privacy while retaining the cohort-level and behavioral signals buyers need for analysis and modeling.
What's the difference between in-game purchase data and app download volume?
App download volume measures how many times an app is installed, reflecting discoverability and initial interest. In-game purchase data tracks monetization behavior after installation—which players spend, what they buy, and when. Purchase data is far more valuable for understanding revenue potential and player lifetime value.
Can purchase data predict player churn?
Yes. Combined with progression and engagement metrics, purchase behavior—particularly sudden drops or changes in spending patterns—signals churn risk. Players who stop buying boosters before progression slows are often early indicators of pending account abandonment.
Sell yourin-game purchasedata.
If your company generates in-game purchase data, AI companies are actively looking for it. We handle pricing, compliance, and buyer matching.
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