Retention Over Clicks: A Surprising Lesson from Browser Game Analytics

Source: Dev.to

Introduction

In this series I discuss various aspects of developing my browser‑game portal Pausen Games.
For the portal it is crucial to find users, keep them engaged, and have them return regularly. I usually refer to these behaviours as acquisition, engagement time, and retention.

The hard lesson I learned: the way users are acquired determines their engagement time and retention. I need to attract users who are more likely to enjoy the site—even if that means higher acquisition effort and a smaller total audience.

In this post I’ll dive into the details of this mechanic.

Why paid traffic might not find the users you want

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

Paid‑traffic basics

• Choose an ad network (e.g., Google Ads).

• Set a daily budget and select target region and languages.

• Pick a bidding strategy:

  • Click‑based – optimise for the lowest cost‑per‑click (CPC).
  • Conversion‑value‑based – with extra implementation on my site, optimise for a conversion value (in my case, the number of games a visitor plays).

The ad network will then try to maximise the chosen goal within the budget:

• Click‑based: find as many users as possible at the lowest CPC.
• Conversion‑value‑based: still find many users at low CPC, but also weigh their conversion value.

I started with a conversion‑value strategy and random world regions, hoping the network would surface users who would enjoy my game portal.

Result: the budget drove a lot of traffic, but most visitors never returned. Weekly retention figures were discouraging. Was my portal really that bad?

How acquisition context shapes player behaviour

Using the analytics capabilities described in my previous post, I segmented users by properties such as region, language, and platform. Filtering these properties revealed three distinct groups (see the weekly retention charts below).

Retention charts

Group 2 – Repeated returns and progression‑oriented

Group 1 – Somewhat engaged and returning

Group 3 – Short‑term engagement and low multi‑day return

Interesting observation: the groups correlated with how much I paid for their acquisition. Users acquired at a higher cost were more likely to engage and return over days and weeks.

Armed with this insight, 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 users who are active and have fun on the portal.

Since retention varies by acquisition source and campaign cost (not by the people themselves), I can now target the audiences that show the best engagement figures. The approach is product‑specific but generally includes:

1. Optimise for intent, not volume – match ad‑copy expectations with the actual product (e.g., “free beer” may generate cheap clicks but high churn).
2. Combine target regions, platforms, and languages that have proven to work best for my audience.
3. Separate high‑ and low‑CPC audiences into different campaigns and budgets. This makes pattern identification easier and prevents optimisation against the wrong audience.

Conclusion

For someone with a marketing background this may not be new, but as a solo indie developer it was eye‑opening. Quantitative user analytics let me pinpoint the audience that truly enjoys my product. The way I configure my ad campaigns now directly influences who I acquire, and consequently, how well they retain.

Quantitative analytics + smart acquisition = better retention.

Which audience I attract?
Matching both yields makes 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.