In-game shop segmentation tactic to increase average check

To increase your game LTV, you need to treat each of your users differently. Of course, in most cases, it’s impossible to track each user individually and make so many unique offers.

That’s where segmentation comes in handy. It’s easier to group users into segments based on some criteria and then target each segment with individual offers.

For example, if some of your players pay very often with the minimum payment of $10, it doesn’t make sense to show them store tiers in the $1$-$5 price range, right?

A shop display for non-paying players

The best practice is to rearrange the offers for such users:

  • Remove cheap tiers.
  • Add more expensive tiers.
  • Add labels such as ‘most popular’, ‘best deal’, etc. to the tiers slightly more expensive than the user is used to purchasing.
A shop display for players who pay at least $10 per transaction

Expected outcomes:

  • Players won’t be scrolling through cheap tiers they are not going to buy anyway.
  • Players instantly see the tier they usually buy along with several more attractive and more expensive tiers.
  • You are motivating players to increase their usual check size.

That was just one of the many examples of how you can segment your players and make value for your company and your audience. There are many more examples we are going to learn soon. But before we go any further, make sure you know how to implement this simple segmentation in your game.

We’ve prepared an example project with open-source code. Follow the instructions below to try it out:

  1. Register at
  2. When you land on an empty dashboard, click on the Examples button at the top.
  3. Find the example called Balancy Example:
    1. Clone the example to get a copy of the game to your dashboard.
    2. There is also a link to the git repository, where you can download Unity Project.
    3. Watch the video explaining how to work with the demo.

In the next article, we’ll be talking about how else players can be segmented and which type of segmentation balancy supports out-of-the-box.

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