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The 4 most powerful user segmentations for in-app purchase pricing

You probably know the saying: "Don't compare apples with oranges." You have other desires and value different things than (probably) your neighbor. You may pay a subscription fee for a cute productivity app, but your dear neighbor won't. One price simply does not fit all!

If you already published a mobile app, you already came across at least one customer complaining about your pricing. An excellent strategy to tackle that dilemma is a concept called "dynamic pricing", which we covered in a previous blog post: Dynamic Pricing for mobile Apps. Dynamic pricing in a mobile app context is a pricing strategy where you set multiple price points of your in-app purchase products for different customer types. This way, you can charge different amounts of money depending on the person you are selling to! The problem you need to solve is how to group your customers for different prices. This process is called customer segmentation.

In this blog post, we want to cover the four most effective customer segmentation methods for mobile app users.

Customer segmentation by demographics

In the non-mobile app environment, we sometimes see pricing based on demographics. A good example is special discounts for the young or elderly. Seeing that kind of discrimination in action in a mobile app is probably hard to witness. It may happen without the user noticing anything.

Since we are in the mobile app environment, app developers can collect all kinds of data about app users. Age, gender, language, or other characteristics might come in handy to group people of different purchasing powers together. Using demographics is probably the most effective kind of user segmentation, but it also has several drawbacks.

First of all, using that kind of data always needs to be in the user's consent. In any case, sensitive information should be communicated clearly, and legal advice should always be taken before implementing any customer data gathering. This goes especially when you want to offer your app to customers in the EU.

Second of all, practice pricing discrimination on these kinds of sensible characteristics might be recognized as unethical. In some cases, it could also be illegal! Keep that in mind.

Customer segmentation by mobile app user behavior

Like using a user's demographic characteristics, app developers might segment users by their behavior and actions inside an app. This practice may also be a more privacy-friendly solution, as long as the usage data cannot be used to identify the user uniquely.

An example would be the usage time or login frequency. In the case of a dating app, it could be the number of swipes or within a gaming app, the number of achieved levels. There are thousands of possibilities to use this kind of information. The drawback is that it is sometimes hard to quantify if an attribute makes sense for segmentation purposes. Especially combinations of behavior attributes may result in thousands of customer groups. These groups may somehow need to be categorized together to prevent enormous complexity and too many price points. Hence a data science team may be required to accomplish such a segmentation.

App user segmentation based on device type

Welcome to an ancient and often useless discussion. Do Android users have a lower purchasing power than Apple users? Sure, you can segment your app's users by the app store platform. But which users of which platform pay more? Studies found that apps may be more expensive in the Android environment due to less app competition. Others found that Apple users have a higher purchasing power. Putting all that aside, it will likely vary from app to app. Hence research is needed. A general answer might be as meaningful as the current position of the moon relative to the earth.

But a segmentation based on the app store platform is just one way of segmenting a device. You can go even further. You could segment based on the device manufacturer, device age, os version, or even network provider. But again, massive research is needed to put this kind of segmentation to work.

In-App purchase power segmentation by country

The most simple yet powerful segmentation possible is by the app users' location. It's well known that developed countries have a higher purchasing power than an emerging economy. So why would you charge the same price for your in-app purchase in both regions? You will miss a lot of sales in low-income countries if the price is as high as in wealthier countries. The other way around, you would probably have high opportunity costs! In both cases, you would "lose" money. The question that remains is, how do we know what to charge for in-app purchases in each country? The answer again is excessive research in all app store countries. Which means running hundreds of price sensitivity tests around the globe.


All strategies for a mobile customer segmentation have their advantages and disadvantages. In the end, you need to know how many resources you are able to spend on price optimization. All strategies do require a lot of time and work. You probably need at least one person in a full-time job position who manages the whole research, implementation, and testing process. What you also have to keep in mind is that markets always change. Once you completed your first market research, your knowledge will not be true forever. It is a continuous process of learning and adapting. Like rolling out mobile app updates, you always want to update your research and implement your latest findings, aka in-app purchase prices.

The good thing is that there is a cost-effective alternative for doing in-app purchase price optimization based on the country and device segmentation strategy: Mage.

With Mage, you do not need to change your app's logic or hire a data science team. You just need to configure your app and its product in our dashboard. Once you have done that, you are going to add new in-app purchases to Google Play Console or iTunes Connect. After completion, you implement our SDK for iOS, Android, or React-Native. The whole process may take up two to eight hours, depending on the complexity of your app. After that, you have an automatic price research and optimization process for your in-app purchases in 170+ countries. Mage generates new price recommendations for you on a continuous basis. You can always accept or deny price changes. Once you accept a price change, the new price goes live to your app users without the need to update your app.

With Mage, you get price optimization through a user segmentation based on country and device. Our future versions probably offer other strategies as well. So stay tuned!

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