Your Gaming Analytics are Misleading

Today’s gaming companies are metrics fanatics. They measure CPI, CTR, DAU/MAU, day-1/7/30 retention, churn, and many other KPIs. Having numbers, charts, and projections on a whiteboard (or on enormous LCD screens in the more successful gaming companies) is impressive, but do these metrics enable you to grow your business at a faster pace?

The jury is out on that one.

In our quest to be objective, scientific, and data-driven, we’ve lost our ability to be (or, at least, our focus on being) subjective, personal, and revenue-driven. We may have a firm grasp on our (ever-rising) CPI or our (ever-oscillating) DAU count, but what kind of business decisions can we make based on those?

Probably not the kind we need to make.

Instead, if we understand what causes our users to “go premium,” make in-app purchases (IAPs), invite friends, or churn, we increase value. By having a firm grasp on these relationships, we can make effective decisions that drive optimizations, growth, and revenue.

Knowing that CTR is low may lead us to rethink a CTA button to drive more users to click. Understanding why CTR is low by honing in on the user behavior that led (or didn’t) to an action helps us optimize the user journey toward the desired click.

Which do you think is more effective for long-term growth: the fact that CTR is low or the reason it is low?

A new tool stack

To gain the visibility you need for behavioral analysis, start with a tool stack that provides transparency of event data. This will help you understand who does what, and add to that a layer of time to understand when actions were taken. This way, you gain the ability to analyze effects of a sequence of actions. Consider these: Path analysis, reverse cohort analysis and behavioral funnel analysis.

Get answers to the important questions

What was the user’s journey in your app in the days leading up to the conversion to premium?

Behavioral Path Analysis will reveal answers to questions like these in a way that “vanity metrics” you’re tracking now ever could. For example, if you could track that 57.7% of users who upgraded followed a particular path that included collecting a daily bonus every day for 3 days, what optimizations could you make to encourage more users to take that path? Perhaps you could push notifications about the daily bonus or offer a bonus that increases each day to encourage them to come back?



Behavioral Path Analysis for Gaming


Behavioral Path Analysis – First steps of users who pressed “Buy” at least N times in the same session but didn’t purchase

A DAU KPI will tell you how many total users you have, but if you aren’t satisfied by that number, it will never result in such actionable metrics. Behavioral path analysis will.

Analytics-based growth hacking

Did users who installed your app as a result of a certain campaign convert at a higher rate than those from another campaign? Of users who made IAPs, did completing a certain level impact sales? Of users who churned, looking back at their activities, did a certain level over-frustrate them?

Looking at CTR and churn numbers won’t answer these important questions, but reverse cohort analysis will.

When we take a winning event, like an upgrade to a premium account and then analyze backwards all of the events the preceded it, we uncover behavior patterns that lead to wins. If, for example, we notice that of those who completed IAPs during a particular week, 63.4% failed level 27, we may make the earlier levels easier to conquer with the goal of expediting users to level 27 so that they make their purchases sooner.

This is purely intelligent and actionable data analysis.

Multi-device reality check

Can you send personalized offers to users based on where they dropped out of your funnel? Do you have a way to help them return and continue to the next step? Do you send different offers to those who never made a purchase than to those who have already converted?

A behavior funnel analyzes the actions a user takes in one session: from app install to game open and all the way through conversion.

To properly analyze a behavioral funnel, you first need clarity into server-side sessionization and how it plays out in a multi-device reality. Once you are able to collect activates from different sources and group them into a single session, you are ready to understand your user behavior funnel. Here is an example: 12,628 users downloaded a game, 96.24% of them opened the game within the first session, and 1.34% of them made a purchase:


Behavioral Funnel Analysis for Gaming

Behavioral funnel analysis: Act on the users who did not convert.
Once you understand the user behavior of your funnels, you can do more than present those numbers on a white board – you can act on the insights. For example, you can target different offers to the 1.34% who already made a first time deposit (FTD) than to the vast majority who did not.

Converting users from free to paid

While getting more downloads of your free game may be fun to track and increasing the lifetime value of your paying users may give you a sense of accomplishment, growth-focused gaming companies must focus a significant portion of their resources on growing the conversion rate from free to paid users.

By using the new tool stack described above, you will be empowered to understand the behavioral path of your paying users. Did a disproportionately high rate come from a particular campaign (that you should begin funding more)? Are conversions preceded by a pattern of game successes or frustrations (which you could push more)? Do paying users typically play on multiple devices (which you could promote or encourage)?

An advanced tool stack enables you to answer these important questions and act on the insights you derive.

The real benefits of real time analysis

You are building the successor to Angry Birds, Candy Crush, or Clash of the Titans and don’t have time to create all of the SQL reports you need to get the information needed to be successful. Creating those takes time away from your core activities.

The key to growth and success lies in the ability to analyze deeper than “vanity KPIs” and truly understand user behavior in order to predict the next move, and optimize towards higher conversion from free to paying.

You need a quick solution that will include everything from tracking to visualization in real-time. The gaming business is dynamic – user behavior changes, competitors rise and fall, waiting for analysis of yesterday’s data is no longer sufficient.  Accessing real-time information from multiple data sources is what is necessary to stay compensative in today’s marketplace.

That is the only way you’ll get the insight needed to make the decisions that will drive your business. You need immediate time to insight. That is what you get when boosting your BI agility with CoolaData to analyze, visualize, predict and act on big data, without dedicated resources.

Schedule your demo to learn how behavioral analysis can improve to your game.

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