How to successfully use behavioral segmentation
We’re creatures of habit — we all have our own behaviors and routines influenced by our backgrounds and emotions. Behavioral segmentation is a valuable tool for marketers precisely for this reason. It allows them to understand user behavior and recognize trends to incorporate in unique advertising strategies.
Behavioral segmentation and product recommendations already account for 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix. In this article, we define behavioral segmentation and provide tips for implementing it as well as examples of behavioral segmentation marketing.
What is behavioral segmentation?
Behavioral segmentation is the process of dividing your users into groups based on their in-app behavior and actions. This information is a key component to marketing effectively and engaging your users with personalized experiences. For example, you may find some users engage with your app at specific times or on certain days.
By leveraging behavioral market segmentation, you can optimize campaigns for users depending on their needs. You may discover, for instance, that your most valuable users are more likely to use certain products more often than average users. You can try to change your onboarding process to ensure new users are streamlined to interact with that product, therefore increasing the likelihood of higher quality users.
The benefits of segmenting users
There are 4.83 million apps available across the Apple App Store and Google Play Store. To drive users to your app and keep them, you need to find ways to tailor the user journey for the specific user segments you identify. Behavioral segmentation offers the following benefits:
Improved ad targeting
Once you have discovered how and when your users interact with your app, you can drive traffic by delivering tailored push notifications, in-app messages, email communications and other prompts. A Mailchimp survey found that segmented campaigns had open rates 14.31% higher than non-segmented campaigns.
Increased brand loyalty
Your users expect personalized experiences — a survey by Epsilon and GBH Insights found that 80% of respondents are more likely to do business with brands that offer personalization.
In the long run, segmenting your users can save you precious time, effort and money — the success rate of selling to a customer you already have is 60-70%, while the success rate of selling to a new customer is just 5-20%. Retention campaigns for existing users are also 5-7% less costly to run than user acquisition campaigns.
Behavioral segmentation examples
Segmenting your audience provides multiple touchpoints that are attuned to each user's preferences. A SmarterHQ, Inc. report highlighted that 90% of consumers were willing to share behavioral data for a cheaper and easier brand experience. By tracking and collecting data, you have a greater opportunity to automate your marketing campaigns. This allows you to send specific customers an offer that’s relevant to them at the right time.
Time based segmentation
One of the simplest implementations of behavioral segmentation is tracking the time of day or day of week that your users are engaging with your app. With this data, you can see the best times to communicate with certain users or times where they may be more receptive to discounts, other in-app activities or influence the path to conversion.
For example, if you have a food delivery app you might normally see higher app activity from most users on weekends. However, there may be users who like to treat themselves on Wednesdays or order in to counteract the ‘Monday Blues’. With machine learning (ML), you can easily stay on top of these habits and adjust according to changes in user behavior. Rocket, a food delivery app, increased their ROI by 152% through behavioral segmentation including order times.
In-app activity segmentation
By looking at aspects such as user interests, spending habits and in-app activity, you can identify user cohorts and determine behavioral patterns. This type of behavioral segmentation lets you try out different actions and approaches to boost usage from existing users and find methods to influence behavior patterns.
Netflix relies on ML to learn its users in-app watching habits and curates recommended movies or TV shows from their favourite genres, actors etc. It’s able segment audience behavior based not only on what they watch, but how they watch — are users switching off after 15 minutes of watching or watching more films on weekends compared to weekdays? This type of personalization has a driving factor in Netflix’s success, alongside early flagship shows House of Cards and Orange is the New Black.
Behavioral segmentation can also be a fantastic way to understand users that have churned and retarget them. With this type of audience segmentation, marketers can also see the engagement statistics of users including time elapsed since prior purchases or in-app actions. This exposes valuable areas where churn starts and can help marketers develop solutions such as exclusive offers of incentives, depending on where users are in the funnel.
eBay has acquired decades of behavioral insights from users. In addition to highly personalized buyer experiences, enhanced searches and ad engines, eBay also tailors its offers based on predictions of how likely a customer is to churn.
You can also categorize users by geofencing (using user location data to establish an operation field). By knowing where users are, where they were and where they’re going, marketers can curate specific notifications and marketing prompts. Whether it be higher product relevance such as region/climate specific app features, products or even updates to transportation options.
This type of segmentation is wielded by ride sharing apps to forecast which areas have the most demand. Users can receive prompts depending on when and where they are. Ride sharing apps also use your location to apply dynamic pricing (prices that evolve and change).
Uber analyses historical location data, using it as a benchmark to predict future action and optimize supply. An Uber case study showed that they could bring supply and demand in line just from user activity. By analyzing a surge of app openings in one specific area, they drastically cut down on wait times.
Just over 5% of app users spend money on in-app purchases, accounting for 48.2% of mobile app earnings — your most loyal customers are the ones generating the majority of your app’s revenue. This type of behavioral segmentation ties in with in-app activity and engagement. However, the data it provides can give you an overview of what point in the user journey your user’s are more likely to become loyal. Additionally, it can provide clear distinctions between users in order to reward high lifetime value (LTV) users with loyalty programs or other benefits.
Strategies used by apparel and clothing retail apps are a prime example of loyalty segmentation in action - with ML, they can see who their frequent buyers are in comparison to other users. Segmenting these customers can be lucrative. A Bain & Company guide found that the average repeat customer spends 67% more in months 31-36 compared to months 0-6.
The key to using behavioral segmentation successfully in consumer markets
The intention of behavioral segmentation is not to prioritize one segment over the other. It’s discovering the most cost-effective way of providing benefit to each of your users through more accurate predictions. Tailored send times and personalized push notifications can improve app open rates by up to 9%. Gaining a better understanding of your users will help you identify the value proposition that you are able to offer them.
Getting to know what hooks a user is highly beneficial for the user themselves, too. They’re more likely to interact with creatives, products and services that they want rather than wading through irrelevant offers or notifications. Behavioral segmentation marketing can be a key differentiator in acquiring more high-value users or decreasing churn. 80% of companies report seeing an uplift in users since implementing personalization.
To learn more about user segmentation, read about our Audience Builder product, here