Regula Marti helps media organizations navigate transformation at the intersection of editorial, product, technology, and business strategy.
In this article, Regula discusses how engagement isn’t a single metric to maximize — it reflects different reader needs and moments, where the same behavior can signal success or friction depending on context. She recommends moving beyond averages and article-level metrics to analyze engagement by audience segments and sessions, focusing on whether readers get what they came for and return.
Most publishers have more engagement data than ever — and still struggle to agree on what engagement actually means. The tools aren’t the problem. The problem is the assumption behind them.
The assumption is this: engagement is one thing, and more of it is better. But engagement isn’t one thing. It’s the outcome of different reader behaviors, driven by different motivations.
Same reader, different moments
Think about how you consume news. Some mornings you want a quick overview. Some evenings you want to go deep. Sometimes you’re browsing. Sometimes you want distraction.
These aren’t different readers. They’re the same reader in different situations, with different needs.
A short session can mean the reader got exactly what they needed. A long one can mean depth — or friction. High article counts can mean curiosity, or confusion. The same metric means something different depending on what the reader was trying to do. Without that context, you’re pattern-matching on behavior you don’t fully understand.
Who you’re actually looking at
Here’s a pattern that shows up consistently across publisher data: a small group of heavy users (20+ visits per month) drives a disproportionate share of pageviews. A large flyby majority (one visit per month or fewer) makes up most of the audience but a tiny fraction of consumption. Frontpage clicks illustrate this well: they overrepresent heavy users. Optimizing for clicks risks building a homepage for the audience you already have, not the one you’re trying to grow.
In between sit the loyal readers, visiting 2 to 20 times a month. They have a relationship with you but haven’t yet built a habit. They’re the most likely to convert and retain — and the easiest to overlook, because heavy users dominate the averages. The real movement happens here: turning flybys into returning readers, and loyals into heavys.
What a summary feature taught us
Product decisions show what happens when you measure at the wrong level.
When we introduced article summaries across our portfolio at Tamedia — short digests at the top to help readers decide whether to go deeper — the concern was obvious: readers would skim and leave. That’s partly what happened. Scroll depth dropped for some users.
But at session level, something else emerged. Those readers went on to read more articles per visit, and overall session time increased. The summary helped them get oriented quickly and navigate to what mattered for them more in this moment.
Other readers used it differently. After reading the summary, they were more likely to finish the full article. It gave them confidence it was worth their time.
Two opposite behaviors at article level. Both positive at session level. Both pointing to the same outcome: a need met, and a higher likelihood to return.
Segment awareness can also shape product design from the start. We kept our fast news format intentionally compact. Heavy users quickly find what they need, while the freed-up space showcases the differentiated content that loyals and flybys need to keep coming back.
What this means for how you measure
The answer isn’t a more sophisticated metric. You need to be more deliberate about what you measure and why.
Look at your segments separately in your analytics tool. The same metric tells a different story across them — and the average hides most of it. Track them over time: a single snapshot tells you little; the direction of travel tells you a lot.
Article-level metrics will often mislead you. A reader can leave early and still have had a successful session. That’s why session-level metrics matter — but only when read together with segment data. A daily digest designed for quick orientation will show low time-on-page. That’s not underperformance — it’s the format working as intended. The right metric is whether those users come back the next day.
Try this in your next team meeting
Start in your next team meeting. Pick one feature or format you’ve recently launched or are evaluating. Review its impact at article level, then at session level. Then ask: for which segment was this actually useful — and in which moment? A multi-format feature is a good example: a reader who skipped the article but listened to the audio version on their commute — possibly in a podcast app, invisible to your analytics — may well return the next morning. Low article engagement, high likelihood to come back.
You’ll often find that what looks like low engagement in one view is exactly what success looks like in another.
The reader who got what they came for is more likely to return. Design your metrics to capture that.
