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Why Gaming Analytics Is So Different from Other Types of Analytics

In the context of overcoming Big Data analytics challenges, SaaS offerings, Artificial Intelligence and Machine Learning have all been touted as magic solutions. In reality, however, there is no one-size-fits-all solution and a true investigation needs to look deeper into the specific needs and barriers encountered. Below, we will discuss some of the challenges encountered in Gaming Analytics that make it unique and the benefits of leveraging the right solution.

Big Data

Gaming Analytics teams face complex Big Data issues. The scale and the breadth of information sources is often staggering. They have to analyze multi-source traffic data for marketing teams, build ticket system data integrations for support teams, capture multi-platform deployment and usage metrics data for offering management teams, along with handling a multitude of impression and other tracking metrics to drive the monetization structure of the game. In terms of volume, successful games can easily produce hundreds of terabytes or even petabytes of data from all of these sources. Tracking sessions, multiple event types, transactions, and impressions will produce rapidly growing data, especially if games are being played for multiple hours a day and across multiple devices.

Custom Data Needs

Game developers, especially those creating games with microtransactions, are required to roll out monetization strategy features and engagement tracking features rapidly. This drives a critical requirement to have pipelines that are robust and reliable enough to process large volumes of data while remaining flexible enough to rapidly analyze newly introduced data structures. The ability to immediately produce insights about a new feature is crucial, especially if it is causing a drop in daily active users or other other player engagement metrics. Custom data structures used in games make leveraging traditional Artificial Intelligence and Machine Learning more difficult. For more structured and consistent features, the trade-off cost of investing in the modeling and data science process needed to produce results can be worth it.  However, with frequent releases of dynamically changing features, the time and effort spent in the churn of redeveloping the algorithms needed is rarely recovered in the value produced.

Cost Structures 

The usage pattern of certain games makes them incompatible to typical SaaS prices. For example, leveraging a SaaS solution for a microtransaction game with a significant amount of traffic, events, or users will trigger costs that are difficult to justify. Beyond the challenge arising from SaaS pricing structures, there is additional cost in not using more efficient technologies for data retention and analysis than the ones offered by the SaaS platform. A traditional database is one of the most expensive ways to store data. Many companies limit their retention period and employ archiving strategies to mitigate this expense. However, using a data lake and raw file stores is a more cost effective approach that also scales well with future needs.

While some of these challenges aren’t unique to the gaming industry, the combination of SaaS cost structures with the monetization strategies of certain games creates an especially taxing situation for the companies that create them. Many early stage companies often cannot obtain the scale and variety of data that they need without investing heavily in development teams to build custom solutions. They face the danger of locking themselves into a technology or platform that won’t scale cost effectively when the need arises.

 

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