How to Boost Your App with Elite User Segmentation

How to Boost Your App with Elite User Segmentation

Intro to App User Segmentation

We have all experienced misplaced, irrelevant mobile ads. Inapt ads are not only annoying but one of the primary reasons for app uninstalls. Whether you’re a mobile app developer or advertiser, you want to deliver an immersive, engaging experience to your users. But users are diverse, and the irrelevant ads that disrupt their experiences are caused by a failure to segment them accurately. If you’re looking to enhance your mobile app marketing campaign ROAS and deliver the best possible experience to your users, then understanding user segmentation is key. 

What is user segmentation?

User segmentation is the marketing technique of dividing up (segmenting) your user base into segments based on diverse parameters, and then delivering the most relevant ads to each segment for more accurate retargeting. One could say, segments make up the secret sauce for successful mobile app retargeting activity.

app user segmentation

Examples of segments could include nationality, app version, player’s age, level reached in-app, paying, non-paying, and much more.

How do we segment users?

At Bigabid, each app user is segmentized and added to a certain audience with other users that share similar characteristics and quantifiable metrics within the app. It’s the first and most important step of the user evaluation process.

 To allocate users in an accurate way, we try to answer big questions as if we were able to ask a user in person like:

user segmentation

To represent these kinds of questions, Bigabid creates an enormous amount of features to develop multi-label machine learning that ultimately matches users to a specific segment. In cooperation with the developer, each of the segments gets a different treatment.

A treatment in retargeting can be related either to the ad a user sees or a gift/promotion they get after returning to the app. For example, a user without jams (or chips) will get jams upon returning to the app. A user with lots of coins will get a special promotion for getting more of these.

Or maybe the users should get exactly the opposite treatment! That’s a challenging question that should be constantly tested as even within segments we can find different feedback at times.

Read more in The 4 Pillars for Successful Retargeting.

Tips and Definitions of Useful User Segments

  • Paying Users – Paying users are those who make in-app purchases. Since you are already monetizing them, it is important to not also inundate them with ads. Though that doesn’t mean not showing them anything. You could show them the same number of ads but with different rewards. We also suggest creating sub-segments based on different levels of spending e.g., Highest LTV Users, High LTV Users, and Medium LTV Users. This way, you can serve different ads, frequencies, and pacing based on how much revenue a given user is devoting to your app.

Learn more about Targeting High LTV Users.

  • Non-Paying Users  – Yes, even non-paying users who don’t generate revenue with in-app purchases (or are predicted not to) deserve a certain segment and treatment. It makes the most sense to show non-paying users system-initiated ads like banners or interstitials, as they don’t require users to opt-in and can be worth the lighter spend.

Check out 5 Tips for Successful Retargeting Ads in Gaming.

  • Geographic – It can be helpful to segment your users by the country, city, state, etc. that they reside in or use a certain app in. For example, the spending routines of players in Japan versus those in Mexico can be very different. This can be due to overall higher incomes in certain geographies, cultural differences, availability of competitive apps, etc. You can’t assume it’s just about spending, so delineating segments by geography can improve targeting and your user’s experience.
  • Dynamic Segmentation Dynamic Segmentation is when certain segments can be updated during a user’s session. Let’s say a user was segmented as a non-paying user prior to their session, but during the session, they make an in-app purchase. With real-time ML technologies, the paying user segment will be automatically updated to include that user during their session. Dynamic Segmentation ensures more accurate targeting and a better overall user experience.

Read more about Real-Time Data Analysis.

  • Demographic Segmentation – Demographic segmentation divides your user base into things like gender, age, income, marital status, etc.. Demographic segmentation is a great starting point, but not very useful on its own as it naturally creates very broad segments.
  • Psychological Segmentation – It isn’t enough to know deterministic facts about your users like geography or demographic. Psychological Segmentation helps you segment your users based on values, personal attitudes, interests, and other personality traits.

Check out Creative Personalization for more on the phycological factors in creatives.

  • Behavioral Segmentation – Behavioral segmentation takes Psychographic Segmentation one step further by not asking who your users are but by asking what your users are doing. Behavioral segmentation is based on actions or inactions, spending or usage habits, session frequency, shared search history, etc. Another type of Behavioral segmentation is User Status Segmentation.
  • User Status Segmentation – User Status Segmentation is the process of dividing user segments based on their relationship to a mobile app e.g., segmenting users as prospective users, active users, engaged users, ex-users, etc.
  • Motivational Segmentation – Motivational Segmentation is the “why” users act a certain way and segment them accordingly. By segmenting based on motivations you can align a deeper level of creative strategy with ads that motivate your users and ensure a smooth ad experience.
  • Technological Segmentation – Technological Segmentation divides users into segments based on their preferences for mobile phones, tablets, computers, software, etc. For example, even if you know a user is on an iPhone it might be useful that they happen to use Windows/PC as their primary CPU as it affects their exposure to different apps, games, etc.
How to Boost Your AppAsset

Conclusion to App User Segmentation 

App user segmentation is the ultimate organizer for accurately targeting High LTV Users. We have discussed many ways to segment your users in this article, but with ever-evolving ML (Machine Learning) technologies, the actual process is much more complex. Bigabid’s AI combines the above-mentioned types of segmentation with a myriad of other variables in real-time to dynamically make millions of ad recommendations to the right users at the right time, all to ensure a great ROAS. Beyond that, we study and share these insights to help you understand your constantly evolving audience so you can provide them with the best possible user experience and maintain lasting loyalty. If you’re ready to scale your app with ML-powered user segmentation, we’d love to show you it in action!

Results based advertising starts here.

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