Behavioral Segmentation Basics for App Developers

Posted on October 17, 2012
By Arun Pattabhiraman

Great Insights lead to great actions and when it comes to managing an app business, knowing what insights to look for matters the most as its easy to get overwhelmed by the abundance of datthat can be tracked or measured.

In my previous post, I introduced the notion of LTV, what it means for an app and how one can measure it. The obvious next point of discussion is how and what developers should do to maximize the LTV of app users. Before we dig into this deeper, it is imperative to realize that LTV maximization is not just one-time exercise for app developers, but often needs to be continuous effort. App users are an extremely dynamic group and their behavior constantly evolves over their lifecycles. This necessitates constant and varied actions at different points in time to maximize users contribution to the apps LTV.

Why segmentation is important

Traditionally, marketers have used segmentation as an important technique to divide their customers into manageable homogenized buckets, on which they can take targeted actions to extract the maximum marketing value. While demographic and geographic segmentation serve this purpose for most businesses, managing more complex customer segments such as app-users calls for fairly more sophisticated techniques. Given that the primary objective for app developers is to maximize the lifetime value of their users through higher engagement and monetization, it makes most sense to start monitoring, segmenting and measuring app users on precisely these demonstrated behaviors. Clever segmentation also allows developers to ensure that they can allocate their marketing efforts to segments that matter the most and in designing unique marketing programs that meet the segments idiosyncratic needs.

Behavioral Segmentation of app users.

In the context of mobile app, behavioral segmentation could translate to grouping users based on how they interact within the app- such as the time they spend using the app, the number of levels they manage to crack, or even the quantum of in-app purchases they make. While there may be infinite ways to segment users on behavioral attributes, the trick lies in identifying those that affect your overall objective of LTV maximization the most. The simplest way to effectively capture all attributes that affect LTV is to lay out the two broad axes along which user LTV varies: Engagement and Monetization.

Engagement attributes:

These are largely attributes that describe the depth, frequency and the duration of users engagement inside the app. For example, attributes that measure Depth -How many levels have users crossed in your app/game? Frequency How many times in day do users open your app? How many days in month do users use the app? Duration - how much time do users spend inside an app? In which level of the app do users spend the most time? These are some engagement attributes that you could use to segment app users.

Monetization attributes:

These largely describe the recency, frequency and monetary value of the revenues you earn from your apps users. How recently did the user buy virtual good? What was the dollar value of the purchase?How many purchases did the user make in the last one month? These are fantastic insights to have about users and can help in designing bespoke incentive programs to enhance the monetary value of each user.

Developing simple segmentation map:

Once you have defined and measured the above behavioral attributes, you could craft simple segmentation map such as the one given below, depending on the attribute you choose.

Do bear in mind that the definition of high and low (or even medium, if you may will) varies from one app to another and developers need to define these based on the unique nature of their app. Also be sure to include the number of users that fall into each of the above segments when you prepare this map. This would allow you to understand which bucket needs the maximum attention and which one the least. For instance, if you realize that most of the app users fall in the high engagement, low monetization bucket or in other words, the green bucket, you could devise strategies to enhance monetization from this segment by selling virtual good at discounted price. On the other hand, if most users fall in the orange bucket read low engagement, high monetization bucket, you could focus your efforts on extending the time they spend in the app by offering them free trial for paid level inside your game. Sky is the limit when it comes to designing clever actions that can influence user behavior - and understanding what works best for you often happens by trial and error.

Conclusion:

Effective segmentation is the first step towards crafting robust LTV maximization strategy. With the right insights about your users and meaningful segmentation framework in place, app developers can begin implementing powerful action strategy to influence user engagement or monetization. Depending on the nature of the app, these actions can vary but we will take deeper look at some of the possibilities in my next post in this series. If you have any specific queries on LTV maximization or thoughts on this topic, feel free to share them with me at arun@inmobi.com  



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