Gaming analytics enable game developers to optimize business across player life-cycles by helping better understand how and why users behave. This analysis helps with the acquisition of higher value players and the prediction of lifetime value (LTV) and churn, while allowing developers to slice, segment and cohort players to the finest details. Gaming analytics allow a gaming company to create an engaging experience which retains players, drives monetization and keeps it several steps ahead of the competition. There are a number of different tools and analyses that can be used to gain behavioral insights, including cohort, path, funnel and KPI analysis.
Using Cohorts to Group Users with Similar Characteristics
Cohort analysis is rapidly becoming the standard for analyzing the retention of players across their lifespan. It isolates player groups based on a common characteristic such as signup date, country, or usage levels and then compares each group’s behavior over time. This comparison can reveal hidden insights which would have otherwise gotten lost when looking at users as one, homogeneous group.
An analyzed cohort may focus on a simple question like: “what is the daily retention for cohorts grouped by signup date?” Or a more complicated question like: “Do UK players on Android tend to lose interest faster than US players on iOS?” Comprehensive cohort analysis will allow a company to segment users into cohorts and then compare the results. So in case of US versus UK users, we can see that over time both cohorts lose interest, but users from the UK do so faster. With this insight, a company can re-tool its game specifically for UK users so that they lose interest at a slower pace, in-turn increasing overall retention and ROI. The company would not have been able to increase retention as effectively had it simply looked at all users as one big mass. Cohort analysis enables the necessary segmentation of users to draw actionable insights.
Tracking Users’ Paths Across a Game
Path analysis is a powerful tool that surfaces and indexes all routes players take between any predefined action points. For example, all the paths that users take between login and making an in-app purchase. While some users may follow path A (login → play levels 2 & 3 → make in-app purchase), other users will follow path B (login → edit profile → play levels 1 & 2 → see an ad). Tracking paths simultaneously exposes valuable insights about the way players move through a game in order to make more informed decisions.
Tracking all these paths at the same time helps eliminate the guesswork in defining specific paths and allow a company to simply ask questions and get answers. Questions like: “how do players typically reach my in-game store?” or “what do players usually do prior to purchasing a virtual item?” Armed with answers to these questions, gaming companies are able to offer coupon codes at drop-off points, or perks to loyal gamers.
Paths are also used to identify recurring patterns within a game session, and to discover subsequent behavioral segments. For example, path analysis is used to discover and define a segment of players who, based on their session activity, need guidance in order to monetize. They’re likely to be the ones who are spending lots of time in a game, but not making any in-app purchases. This knowledge enables companies to zoom into this segment and communicate with it to drive up conversion rates with customized offers and incentives.
Predict Dropoffs and Potential High Value Users
Predictive analytics will look for recurring behavioral patterns and player properties to point out players who are likely to monetize and to project their expected value. For example, it will highlight which users spend large amounts of time playing the game in addition to making in-app purchases, to optimize marketing spend by focusing efforts on potential paying players early in their lifecycle. Patterns like these can be discovered manually, but it is often very time consuming and expensive, so the use of a full service predictive analytics platform can save time and money. By using predictive analytics and a churn prediction algorithm to identify early signs of potential drop-offs, gaming companies can present targeted offers and retain players that are in risk of churning.
Using analytics to analyze user data in games allows for a better understanding not only of what users are doing, but of why they do it. Gaming analytics reveal seemingly invisible patterns and insights about users to allow the reduction of churn, increased monetization and better games that keep users coming back.