Retention Over Clicks: A Surprising Lesson from Browser Game Analytics

Retention Matters More Than Traffic

In this series, I discuss various aspects of developing my browser game portal Pausen Games.

For this portal, it is crucial to find users, to keep them engaged, and for them to come back regularly. I usually use the terms acquisition, engagement time and retention to describe their behavior.

The hard lesson I learned: The way the users are acquired determines their engagement time and retention. I need to find those users who are more likely to enjoy using my website, even if that means higher efforts during acquisition and fewer total number of users.

In this post I will dive into the details of this mechanic.

Why paid traffic might not find the users you want

For acquisition, I am combining organic search (SEO) and paid traffic. I am still learning and experimenting and trying out different ideas.

For paid traffic, I can use an ad network like Google Ads, assign a daily budget and select the region and languages my ads should be targeting.

In addition, for the bidding strategy's incentive, I either aim for clicks, or, with additional implementation effort within my website, define and optimize for a conversion value which in my case would be determined by how many games a visitor plays.

Then naturally, the ad network will try to maximise the conversion goal with the given budget:

• For click-based strategy, find as many users with the lowest cost-per-click as possible
• For conversion-value-based strategy, still find the most users possible with the lowest cost-per-click, but also consider their conversion value.

To get me started, I selected random regions in the world and chose the conversion value strategy, hoping that the ad network would find me many users who would enjoy using my game portal.

Unfortunately, this didn't quite happen. Over a longer period of time, my ad budget was used to direct many users to my website, but most of them would never come back a second time.

The average weekly retention figures were discouraging. Was my game portal really so bad?

How Acquisition Context Shapes Player Behavior

Using the user analytics capabilities I discussed in my last post, I could segment my users along different properties such as region, language, used platform etc.

By filtering these properties I could identify three different groups as illustrated in the weekly retention charts below:

• Group 1 shows short-term engagement and low multi-day return. This is the biggest group
• Group 2 show repeated return and is progression-oriented. We see retention rates of whopping 60%! By digging into detailed user data, I could even find a few individual users who come back on a daily basis for weeks and play the same game over and over again (yay! someone seems to enjoy my stuff!)
• Group 3 is somewhere between the other two groups

Group 1: Short-term engagement and low multi-day return

Group 2: Repeated returns and progression-oriented

Group 3: Somewhat engaged and returning

Now comes the really interesting part: These groups seemed to correlate with how much I was paying for their acquisition!

If I paid a lot for acquiring a user, they were more likely to engage with my game portal and come back over days and weeks.

If I attracted users with low cost-per-click, they were more likely to engage less with my game portal and they didn't come back much over days and weeks.

This made it clear that optimizing for low acquisition costs would jeopardize my engagement numbers.

Applying what I just learned, I adjusted my acquisition strategy.

Using Retention Insights to Guide Strategy

For me, weekly retention and multi-session engagement matter far more than total visits. I prefer my users to be active and have fun on my gaming portal.

Now that I learned that retention varies by acquisition source and campaign cost (not by the people themselves), I could adjust my strategy to find users.

In my ads network, I need to only target those users that show the best engagement figures. This is probably very specific to the kind of product offered, but it is a mix of the following:

• Optimize for intent not volume. I need to match expectations created by ad assets with actual product (advertising free beer might create many cheep clicks but high-churn users)
• Combine targeted regions, platforms, languages according to what I find to be the best working audience for my product
• Separate high and low cost-per-click audiences by setting up different campaigns and budget. This will make it easier to identify useful patterns and to avoid optimizing for the wrong audience.

Conclusion

I am aware that for someone with a marketing background, this might not be super new. But for me as solo indie dev, this was quite relevant, surprising and new.

Quantitative user analytics enables me to identify the audience which enjoys my product most. The way I configure my ad campaign determines which audience I attract. Match both yields in making me happy when I look at the statistics, because happy users are what drives me.

I'd be interested to know if you have similar or contrary experiences - feel free to leave a comment.