Cross-Game Player Intelligence Dataset
Game Spective Cross-Game Player Intelligence Dataset
Behavioral visibility across the PC gaming ecosystem, from ownership and playtime to cross-title engagement, audience overlap, and player attention shifts.
Game Spective (GS) provides structured cross-game behavioral intelligence for data buyers, research teams, investors, and gaming companies. The GS Cross-Game Player Intelligence Dataset helps buyers analyze how player attention, ownership, engagement, and audience overlap move across the PC gaming ecosystem over time.
GS is a structured dataset for analyzing player behavior across games, not only within a single title. It combines ownership graphs, playtime engagement, session telemetry, and derived behavioral signals into one relational model that is queryable, joinable, and delivered as flat tables.
Context
Understand player behavior beyond single titles
- 01Most industry data is single-game, single-publisher, single-platform.
- 02Player behavior is not.
- 03Portfolios span dozens of titles, multiple genres, and multiple platforms.
- 04GS models the cross-game layer: how players allocate time, switch context, and move between titles across the ecosystem.
Pull quote
The full graph shows who plays what. The telemetry layer shows how they behave.
Methodology
Data methodology
GS data is built from longitudinal PC gaming profile records, including game ownership, playtime history, session observations, and derived behavioral signals.
The dataset is structured for B2B analysis and delivered through contract-governed access, with aggregation, anonymization, and sampling applied where required by the use case.
- 01Longitudinal PC gaming profile records
- 02Ownership and playtime history
- 03Session-level observations where available
- 04Derived behavioral signals for cross-title analysis
- 05Contract-governed access and use-case-specific delivery
Coverage
GS data coverage model
GS is structured in four layers. Ownership and playtime provide broad ecosystem coverage; session telemetry and derived behavioral signals provide smaller but deeper behavioral layers.
Schema
Structured data model for direct analysis
Fixed schema, warehouse-ready. Every table keyed on user_id and joinable across layers.
user_library_statsone row per player- user_id
- games_owned
- unique_genres
- unique_platforms
- total_playtime_minutes
- active_titles_30d
- portfolio_diversity_index
user_game_summaryone row per player × game- user_id
- game_id
- genre
- platform
- first_seen_at
- playtime_minutes
- sessions_count
- last_played_at
- engagement_tier
user_session_statsone row per player per observation window- user_id
- window_start
- sessions
- avg_session_minutes
- median_gap_minutes
- unique_titles_in_window
- context_switches
genre_overlap_matrixone row per genre pair- genre_a
- genre_b
- co_ownership_rate
- co_play_rate
- transition_rate
- cohort_size
Metrics
GS behavioral metrics
context_switch_rateFrequency a player changes titles between adjacent sessions.multi_game_engagementShare of playtime distributed across a player's top N titles.session_distributionDuration, cadence, and gap profile per player.genre_transitionsDirectional movement between genres in a session window.Formula
context_switch_rate = switches / (sessions - 1)
A switch is any adjacent session pair where the primary game_id differs.
Patterns
Observed patterns across the player base
Multi-game behavior is dominant
~99% of players own more than one title. Single-title players are a narrow minority of the dataset.
Portfolios are large and diverse
Average portfolio is ~480 title records per player, spanning multiple genres and platforms.
Switching is frequent
Mean context switch rate sits around 0.54. Players change titles roughly every other session.
Engagement varies widely
Session length and cadence distributions are long-tailed. A small cohort drives a disproportionate share of total playtime.
Genre overlap is structured
Co-play relationships follow consistent patterns, making transition matrices stable enough to model.
Use cases
Where GS data creates value
Portfolio analysis
Sizing, overlap, and engagement for publisher or platform catalogs.
Behavioral segmentation
Cohort definitions built on behavioral features, not demographics.
Cross-title audience overlap
Which audiences a title shares with its neighbors in the graph.
Early signal detection
Engagement shifts and transition-rate changes before they surface in revenue data.
Engagement modeling
Inputs for LTV, churn, and retention models that require cross-game features.
Buyer applications
Buyer applications
Investment research
Track title momentum, franchise health, engagement durability, and player attention shifts before they surface in public or financial indicators.
Market intelligence
Measure genre movement, cross-title overlap, audience adjacency, and competitive positioning across the PC gaming ecosystem.
Publisher strategy
Analyze portfolio overlap, cannibalization risk, dormant ownership, and audience expansion opportunities.
Data science
Enrich segmentation, churn, LTV, retention, and forecasting models with cross-game behavioral features.
Delivery
Data access and integration
Positioning
Dataset-first, analysis-ready
GS is built as a structured dataset for direct integration into analytics workflows, modeling pipelines, and portfolio research.
The core product is not a dashboard or closed reporting UI. Buyers can work with the data directly in their own warehouse, BI stack, or data-science environment.
Custom research briefs and sample insight packs are available for evaluation, onboarding, and partner-specific use cases.
- ·No closed dashboard dependency
- ·No forced reporting UI
- ·Fixed, versioned schema
- ·Direct integration into analyst and data-science environments
- ·Custom briefs available for evaluation and partner-specific questions
Access
Access the GS dataset
Sample access can include the full schema with a representative slice across all four layers, subject to buyer fit and data-use agreement.