Over the last several years, enterprises have accumulated massive amounts of data. Data volumes have increased at an unprecedented rate, exploding from terabytes to petabytes and sometimes exabyte’s of data. Increasingly, many enterprises are building highly scalable, available, secure, and flexible data lakes on AWS that can handle extremely large datasets. After data lakes are productionised, to measure the efficacy of the data lake and communicate the gaps or accomplishments to the business groups, enterprise data teams need tools to extract operational insights from the data lake.
Same thing goes into gaming data. Since Covid-19, the Gaming industry is booming in terms of Daily Active Users which accumulates high amount of data scaling from terabytes to petabytes. Games are generating more data than ever. Hence, it’s important to have access to the right data at the right time as you develop your games. This enables you to answer questions about how your games are performing and determine what changes you want to make to keep players engaged.
Some of the key benefits of using data analytics for gaming are as follows:
Player engagement: Analytics highlight areas where game design could be improved, helping you create more engaging games. Instrumenting your game to emit game events enables you to analyse the event data and reveal how your games are being played. Then, you can use that information to help enhance your design.
Monetization: The game industry is increasing adoption of games as a service operation model. With this model, recurring revenue is frequently generated through in-app purchases, subscriptions, advertising, and other techniques. To understand the features players are willing to pay for, it is helpful to know which elements of your game draw players in and keep them returning. With this information, you can encourage purchases, serve targeted ads, and offer rewarded videos.
While having access to analytics is important, there are some challenges unique to the gaming industry. Because games generate so much data, it’s important to understand what data to collect and how to collect it.
For one of our customers in the gaming industry, Flentas created an end-to-end gaming data analytics solution using AWS Services.
The customer already had the data analytics solution deployed on Google’s Firebase platform for all analytical requirements. Since the firebase platform is a standard product, there were obvious limitations with respect to customisations. For complex analytical requirements, some of which are not supported on Firebase platform, the Analytics team used to manually download the report, correlate it with other metrics/data points and fulfil their analytical requirements using Microsoft Excel based formulas. There was a need to build a custom analytics solution which can incorporate their ongoing custom requirements as well as can handle a huge amount of data, and can cater to multiple data sources, scale horizontally to handle multiple game data in future and is cost effective at same time.
Figure 1: Gaming Data analytics architecture diagram
Few of important KPI’s are listed as follows:
In this blog, we have covered how AWS services can be used to create a scalable data analytics solution. If you are looking to know more about how we can help you with data analytics solution on AWS for your game, reach out to us at email@example.com or simply fill in your details in the form below.