What is the Player Warehouse?
The Player Warehouse provides player and event-level granularity of the data collected from devices. Player Warehouse is designed to provide a better understanding of your players and events, hence providing a more detailed look into specific aspects of your game - such as progression through levels, player behaviour and segmentation, or game economy.
Using the Player Warehouse removes the need for setting up your own SDK, processing pipeline, storage and data warehouse solution. It saves months of development time & enables your team to focus on developing differentiating capabilities that increase competitiveness.
Player Warehouse Use Cases
The Player Warehouse incorporates all essential building blocks of a data warehouse, leaving only the most exciting and unique part to your team: the analysis itself. With the data collected, processed, and formatted so that it’s instantly ready to query, your data team can immediately focus on developing unique models and queries rather than piping data between databases.
Device-level ROI analysis
Combining device-level ad revenue and IAP revenue data from the Player Warehouse with device-level or estimated CPI per user from attribution or ad campaigns, it will be possible to obtain a device-level ROI figure. ROI-positive players can then be retained with live ops campaigns or funneled to new games, where they can continue to drive revenue.
Player-level information allows for validating revenue data with other data sources. For example, ad spend data from Player Warehouse can be used to match with your revenue data downloaded from various ad networks you use. Or take campaign IDs from attribution data to correlate with ad network attribution stored elsewhere.
With information on player’s attributes like playtime, spend, progression, and virtual currency use, the Player Warehouse delivers all the data points needed to create highly relevant audiences for any initiative – from cross-promotion to re-engagement / retargeting or in-app offer targeting.
Advanced behaviour analysis
Event-level tables can be used to analyse patterns in user interaction, such as identifying connections between specific events and ad views or IAP conversions. For example, if your levels are randomised, you could find that a particular sequence of levels leads to higher yields per user or higher retention rates.
Programmatic user profiling
Data from Player Warehouse can be used with machine learning and AI models to develop lookalike user profiles. These profiles can then be used in programmatic bidding strategies to acquire high-value users off affordable advertising inventory and increase player-level margins.
Cross game player analysis
With device-level information, it is possible to identify users playing across multiple games within your studio. Using Player Warehouse for insights into player behaviour along with deeper player-level analysis out of A/B Testing and retention funnels unlocks the potential for better and specific targeting for acquisition, conversion or upselling of in-game products.
Custom A/B Testing analysis
Our tool offers a list of A/B Testing goal metric; however, with Player Warehouse, you have the possibility of deep-diving into the behaviour of players in different variants based on any metric important for you with the given data points.