Just like any business, the gaming industry has many metrics that can be tracked based on business, functions of business, gamers, customers, games and on many other categories. Like any business, the standard management level KPIs are sales, revenues, profit,
As gaming is a B2C business, and the revenue flows through both the channels – customers purchasing items inside the game and corporates advertising in the games. On the gamer’s side, we can track metrics like
On the corporate and advertising side, there are metrics like
Then there are a host of web analytics metrics for any web-based games. Typical metrics for any website are Visitors, Ad Revenue, Acquisition rates, Churn rate, Engagement time etc.
For analysing game data, predominantly visualisation tools are used in the existing setup. There is more to analytics besides visualisation in the Gaming world when gaming companies want to dig deeper.
Typically visualisation tools like QlikView, Tableau, PowerBI can be used to visualise the raw data from the games. The rich in-game data can be loaded in big data frameworks and can be aggregated and visualised using these tools.
But as many games are web-based, tools like Google Analytics, Adobe’s Omniture can be used to measure web metrics like the pattern of visitors, traffic, sources, and marketing channels.
Then there is the third level of predictive analytics suites like R, Python, Matlab to solve advanced analytics problems to know the reasons, forecasts, associations, clusters, classes etc. Easy to use machine learning suites like Azure ML studio, Alteryx are used to fit standard models quickly.
Gaming company dashboards are a mixture of business, gamers and in-gaming reports. Similar to web-based business which creates reports with Google Analytics, dashboards give insights on
- Upward or downward trend in sales/ revenue/ expenses
- Month on Month comparison of user addition/ churn It helps in finding if the sources are value for money acquisition sources
- Ad revenue increase/decrease, and which are the sources that are in green and red.
- Gamers’ engagement time which can be drilled further to find and show the heat maps of highly engaged and ignored game phases
These dashboards can be viewed using Google Analytics or Adobe’s Omniture which tracks web behaviour.
Otherwise, there are tools like Tableau, Power BI, Qlik and the likes which connect with the database which stores the raw data about users, in-game movements, ads etc.
To collect this raw data, the gaming company has to carefully decide the events that it wants to track. Every mouse click and movement tracking can result in Game latency issues, and in turn the gamer churning out.
As a huge amount of data is collected in tracking the in-game data, deciding on a suitable infrastructure is of utmost importance. When many events are tracked in a game session, it generated a good amount of data for a single user session alone. This might be equivalent to IOT data that is produced every microsecond, in the gaming case it might be in seconds. Architecture on clouds like AWS, Azure can be used to store data. IOT hub, Time Series Insights provide such capability in the Azure cloud.
There is always an alternate set of databases when the data size is low and when the data from the events are in a manageable size of lesser than 10 GB per day. The servers can be on-premise or can be on the cloud where there is
Reporting in Game Analytics is rich with ad reporting and user level reporting. These are the two common reporting areas utilised by most of the gaming companies. It is the first level of knowing the business and is mandatory before we jump on to any other reporting areas.
Ad reporting consists of the performance of advertisers, the performance of different types of ads like interstitial ads or banner ads etc., the average revenue per ad for different advertisers,
Then there are the user level reports such as user-level engagement by the time of the day, by day of the month, active user reports. All can be drilled further by different regions and time periods.
Gamers’ cohort reports which give an idea of how the user has moved from free to premium models. The premium models can be in the range of what is the average amount spent by a gamer in-game over a time period. This helps the companies find the movement of gamers towards the monetisation behaviour.