Today, gaming companies compete fiercely in red ocean spaces with free-2-play (F2P) games, where microtransactions bring revenue.
Microtransactions can be increased by providing personalized offers, yet most games tend to present general offers, as gaming companies find it difficult to calculate timely personalized offers, as they require implementing advanced logic that uses data sets that are normally outside the main game application and database, and require specialized skill, namely data scientists vs. game application developers.
Some gaming companies do present personalized offers, but they usually miss the tiny window of opportunity, as they are traditionally calculated outside of the main game application, using a data warehouse or data lake, done on a periodic basis (daily, weekly, etc.) and using offline batch processes and traditional ETL tools to move data around.
This causes loss of opportunity, given the lag from the time a user has taken an action, where a timely personalized offer could have been presented and had a higher chance of resulting in a purchase.
If gaming companies could react to player activity, in near-real time, and calculate and present personalized offers (using machine learning trained on player and purchasing behavior) in a matter of minutes or less (ideally as the gamer is engaged), we can increase the likelihood of microtransactions and increase revenue.
Watch the recording here. This demo was presented at Game Developer's Conference 2022