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Rethinking Audience Segmentation by Behavior

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The media landscape has been experiencing constant shifts and changes over the past decade. Publishers are required to be agile and adapt to different consumption changes, significant platform algorithms changes, and privacy regulations. But through them all, one thing remains constant, their need to engage and retain their audience to stabilize their business and enable its growth. 

We’ve deemed audience engagement, and ultimately audience loyalty, as one of the most crucial challenges for publishers. After all, without an audience, your content, advertising and growth will be shut down before you can even get started. But understanding what audience engagement means and how to segment audiences can also be a challenge, and may not be a one-size-fits-all approach.

Many publishers begin their segmentation by utilizing simple, dry metrics like geography, age and gender. While this method of persona mapping has worked for years, brands have begun to realize that these static metrics may not be the most effective in predicting the actions of their users. Therefore, brands that look at the bigger picture are using more fluid metrics to track and target relevant users. To better understand audience consumption habits and how to drive brand loyalty, we’ve had success in segmenting audiences by behavior and taking a hard look at the data driving each behavior. 

Based on this, we have divided our audiences into three categories: Fly-by, Engaged, and Brand Lovers as recommended by Google News Initiative. These three groups were determined by the user's average number of sessions and pageviews per month. With a new focus on these two metrics, users can move between buckets as their commitment to your brand becomes more concrete. 

Google analytics data in three categories
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However, after working within these metrics for some time, we came up with a new approach that better reflects user stickiness, which can lead to becoming a Brand Lover. First, we replaced the number of page views with session duration. Then, we created a single metric that represents user loyalty based on the monthly time spent on site per user. The ability to sort using a single metric makes it easier to digest large amounts of data, and provides a clearer bigger picture of the audience.

When tracking users by their behavior, publishers may have more opportunities to improve the experience for the audience who most frequently visits their site. In addition to optimizing for the most loyal users, brands can also identify different trends across each bucket and make an informed effort to convert 'Fly-By' users into 'Engaged' or 'Brand Lovers'.

After users are grouped by loyalty metrics that fit your brand, it's possible to segment your audience further, depending on the traffic acquisition medium, geographic location, device type, or URL. The final step is to look at how each segment performs in terms of your brand's KPIs (i.e., average session duration, visits per month, etc.) and start to interpret the different trends you see across the data. 

With your audience segmented based on several different behavioral metrics and some key trends emerging through the numbers, now you can focus on maximizing revenue. For example, if referrals drive a large percentage of your overall traffic, but most of these users are bucketed in the 'Fly-By' section with a short session duration, it may be wise to rework your brand's referral partnerships. 

However, it is important not to be overly dependent on average metrics. We’ve found that the lowest hanging fruits can be identified by looking into the worst and best consumption patterns that could manipulate your overall data into higher or lower scores. For example, the session duration could be higher than average, but when looking at the traffic distribution graphs, there may be a lower percentage of traffic that is pushing the average up. So publishers will need to dig deep to get the fullest understanding. 

Team members collaborating with user data
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Changing a publishers’ segmentation strategy may come with unexpected challenges the first time around, but these hurdles shouldn't discourage from continuing to refine segments over time. For behavior-based segmentation to truly work, brands need to reevaluate their segments constantly, especially when new features or experiences are added to a publisher. 

By keeping a proactive mindset to the segmentation process, teams can quickly identify emerging trends amongst audiences and have the tools to make the necessary adjustments.  The shift from demographic to behavioral metrics symbolizes a brand's willingness to move away from the typical solution that neglects the user's actual actions.