Behavioral Analytics: Understanding Player Behavior

July 13, 2017 — by Industry Contributions



Behavioral Analytics: Understanding Player Behavior

July 13, 2017 — by Industry Contributions

By Tsahi Levy, CMO, CoolaData

Today’s mobile gaming industry is thriving, with a rapidly growing number of players across the globe. According to Newzoo‘s Global Games Market Report, 2.2 billion gamers are expected to generate $108.9 billion in 2017, an increase of 7.8% compared to 2016. Mobile games are a marvel of code, challenging gaming operators at different levels: Dev teams must constantly deliver bigger and more complex updates, develop highly personalized systems, and keep up with trends like VR and synchronous multiplayer. Marketing teams are faced with high churn rates, and need to familiarize themselves with multiple user profiles, from whales through FTDs to annoying bonus abusers.

As a result, today’s gaming operators often become game analytics fanatics, measuring every possible KPI, but typically remaining at a one-dimensional perspective of player behavior. Flat measurements of daily or monthly active users (DAU/MAU) without a more complete picture of the player’s journey fail to answer more complex business questions, such as which players have the highest lifetime value (LTV) or what makes players finally deposit money into their account.

Behavioral analytics relies on querying a multitude of event data that is generated by all tracked player actions. With more complex analysis of players’ behavioral profiles, operators can optimize the next best offer to trigger in-app purchases for each segment of players, therefore increasing conversion rates and growing revenue.

Here are six ways to use behavioral analytics to understand the player’s journey:

#1: Focus on the right metrics

Measuring metrics and KPIs such as LTV, MAU, DAU, retention, and churn is still important. LTV is one of the most important KPIs, used for measuring the total revenue players bring to the company during their lifetime as paying customers. Using an overview dashboard to cover all of the main metrics provides a snapshot of the system’s performance status, including the number of current players in different segments and top traffic sources.

#2: Explore conversions from different angles

To continuously monitor your conversion, measure the length of conversion cycles. For example, the number of days from registration to first time deposit (FTD), or the global conversion rate. A global conversion measures conversions between the very first step in the upper funnel and the last step of the conversion. This type of analysis highlights different high and low points of conversions and allows drilldown into more advanced metrics.

#3: Use time-series and predictive analytics

Typical conversion channels analyze how players move through defined steps toward a specific goal. However, time-series analytics allow you to define a specific scope of the conversion funnel – both in terms of the steps taken to reach the conversion and the timeframe itself. For example, you can combine analysis of the steps a player takes within a specific conversion funnel or timeframe.

Another aspect of time-series analysis is the ability to perform sessionization – consolidating a user’s online session at different touchpoints and devices and view them as one unit. Examining a player’s path via a single session allows focused analysis of a specific player’s behavior and allow you to investigate what kind of routes players take, where they get stuck and how long specific steps take.
Time-series analytics relates to the broader concept of predictive analytics, a method which uses machine learning to detect patterns.

Predictive analytics uses a forecasting process based on large amounts of user data collected over a long period. With predictive analytics, you can define the parameters most likely to affect predicted events, and form predictive models to indicate possibilities of future events.

#4: Perform intelligent cohort analysis to fight churn

Customer churn is a major issue in mobile gaming – many users will arrive at a site once and leave without a trace. Cohort analysis allows you to gain a general understanding of your player attrition and retention, such as identifying cohorts for specific app install days.

Additionally, you can use reverse cohort analysis to examine player behavior in the past, starting with a particular event. For example, you can examine which players had a game event, such as a spin or claim reward, on the day of their first deposit. Investing in understanding the behavior of players that churned allows deeper insights into what may be causing the churn – whether it’s disinterest, a bad experience, or a wrong marketing campaign.

#5: Monitor what’s happening outside your system

We’ve established how analyzing what goes on inside your app can substantially improve your understanding of user behavior and potentially retain more users. But taking a step back, a solid network is key to happy users. As not to lose sight of what’s happening outside your game platform, make sure to monitor and analyze your gateways with network performance monitoring technologies. This will help you understand your users’ traffic patterns and habits.

Network performance is an important dimension of the gaming experience, which is commonly overlooked. If conversion rates are dropping, and regular analytics metrics cannot explain this trend , it could be explained by network congestion and performance parameters, because most modern games rely on the network.

Closing Thoughts

In this article we’ve identified five ways in which you can gain deeper insights of your players’ behavior, and use those insights to keep up with the competition for players’ attention and retention. But there are many more – for example, identifying behavioral profiles to spot top spenders and other types of players is crucial for monetization.

Using behavioral analytics requires a set of tools for performing such complex analytics and going beyond one-dimensional analysis of KPIs. CoolaData has developed a complete solution for behavioral analytics in the gaming industry, which performs time-series analytics and analyzes raw data to answer complex business questions.

Whether you’re planning to select a game analytics platform or are only performing initial research, becoming familiar with the player’s journey is crucial to driving business and gaining precedence in such a fierce market.

Tsahi is the CMO at Cooladata. He is a business executive with proven experience in global marketing, sales, business development and strategic planning in enterprise software, telecom and gaming industries. Prior to CoolaData, Tsahi co-founded Numgames, an end-to- end solution for companies that require e-commerce platforms. Tsahi also served as Microsoft’s business development manager for seven years.


Industry Contributions