People are creatures of daily habits. Each individual has unique behavioural traits and routines backed up by emotions, historical background, and culture. And by knowing and understanding these habits, it is possible to create a better advertising strategy that will focus on the exact user needs and further lead to increased conversions and sales. That is why behavioural segmentation is a valuable tool for mobile marketing.
Such companies as Amazon, Spotify, and Netflix effectively use behavioural segmentation with their product recommendations. Most likely you also encountered it before. So let us find out what it is and discuss some tips on types and how you can implement behavioural segmentations in your mobile advertising.
What is behavioural segmentation?
Behavioural segmentation is basically the strategy of splitting your users into smaller groups based on their in-app actions and behaviour. For example, maybe some of your app users use it at a specific time, location or on particular days like holidays. This kind of information is a fundamental component of effective and engaging marketing. By leveraging behavioural user segmentation, it is possible to build a more personalized experience and optimize campaigns based on user needs.
The pros of segmenting users
There are millions of applications in App stores, and the number only rises with the days. No surprise, currently, it may be a challenge to drive users to an app and further keep them using it. Therefore, Плэйкор believes that it is necessary to tailor the user journey into segments because of the following benefits:
- You will know your user better, and you can create a Lookalike audience and target them.
- The ad targeting will be more personalized, and people will be more likely to interact with it. Hence, it will be more Cost-effective.
- While knowing the user timeframe, you can send ads, in-app messages, and push notifications at a time when they will have the best result.
- Increased brand loyalty is also guaranteed when brands offer personalized offers.
Some of the Behavioural segmentation models
Segmentation based on time is one of the simplest behavioural models. Basically, it is tracking app users based on time and day of the week. By knowing this information it is possible to calculate the most suitable time for communicating with the user.
For example, a food delivery app has higher app activity during weekends. However, there may be exceptions, such as when the friends gather up every week on the same day and order food for a movie evening. And it is possible to stay on top of these “traditions” with machine learning and send special offers.
In-app activity segmentation
By looking at user spending habits, interests, and in-app activity, it is possible to identify user cohorts and build behavioural patterns. And with trial and error, it is possible to find out methods that can influence user behaviour.
For example, Netflix machine learning calculates user watching habits and finds traits such as favourite genres, actors, countries, the time they spend watching, etc. This data further helps with recommending TV shows or movies.
A fantastic way to understand users is to monitor user outflow and purchase habits. This kind of data reveals valuable areas where user churn starts and helps prevent it by sending exclusive offers of incentives.
For example, a grocery app can use behavioural insights from users to enhance buyer experience within the app, improve searches, and even calculate at what stage of the funnel is user and alter it with discounts on concrete preferred items.
One more method of categorization is by using user location data ( geofencing). By knowing users’ location, and where they are going, it is possible to curate explicit notifications and prompts.
For example, such companies as Uber uses this kind of data to cut down on wait times. They analyze specific areas where users open the application the most, where they are going, fluctuate pricing based on traffic congestion, and offer special marketing prompts.
Loyal users are mainly the ones that generate the most of your application revenue and are closely united with in-app engagement and activity. And with the use of this data, it is possible to create an overview of how users become loyal and stimulate others.
For example, an app can reward LTV users with loyalty benefits so they would continue using the app. And those that are almost there to become loyal, a brand can stimulate with additional bonuses.
The overall purpose of behavioural segmentation is not to prioritize one user segment over the other but to find the most cost-effective methods of delivering personalized benefits to each user. Therefore, by gaining a better understanding of your users it is possible to identify the best value proposition and more efficiently reach KPIs.