How content labels increase clicks

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📝 Context

Topics: Website/App | Messaging & Copy
For: B2C
Research date: April 2025
Universities: University of Georgia, New York University

You’re a content marketer at a SaaS tool that helps brands collect and showcase customer reviews.

Your blog is packed with useful resources on how to manage reviews well. There’s one article in particular that keeps getting clicks. It’s helpful, practical, and it nudges readers closer to trying your product.

You want to further increase traffic to it, and are wondering how.

Science Says, a simple label will have a big impact, but make sure it's the right label

P.S.: If you’re working on growing a SaaS, you may find our Science-based Playbook of SaaS Optimization to be very useful.

📈 Recommendation

Use labels to increase clicks on your content. Use “most read” for informative content (e.g. a how-to guide, a case study) and “most shared” for entertaining content (e.g. a personal story, memes, food recipes).

People will be more likely to click on it.

🎓 Findings

• When reading content, people looking for information click more on content labeled as “most read”. Those looking for entertaining content, click more on “most shared”.

• Across 5 experiments, 2 surveys, and an analysis of 36,679 ads clicks, researchers found that:

  • People perceived "most read” labels as 18.4% more informative (vs “most shared”), and “most shared” labels as 32.5% more entertaining (vs “most read”)

  • When they looked for informative content (e.g. article on the benefits of roundabouts), people clicked 23.9% more on an article labeled as “most read” and said they were 67.9% more willing to read it (vs “most shared”) 

  • On the other hand, when they looked for entertaining content (e.g. creamy macaroni recipe), people chose articles labeled “most shared” more

🧠 Why it works

• When we’re looking to learn, we look for content that teaches us something.

• When we want to be entertained, we look for something fun or emotional.

• We rely on signals to decide what kind of content we're about to click.

• One of those signals is how other people interact with it.

• If a piece of content is shared often, we assume it must be because people send it to friends and family, which indicates the article is highly entertaining.

• On the other hand when an article is labeled as “most read”, we assume it’s because it’s highly informative.

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✋ Limitations

• The study only measured content clicks, it did not measure how labels influence people’s reading of the article.

• The focus of the research was news articles, it’s likely though not studied, that the effect is the same for other types of content (e.g. social media).

• The research did not look at how labels influence people’s perceptions of the publisher, though it’s likely that publishers using predominantly “most read” are perceived as more informative and those using “most shared” are perceived as more entertaining.

👀 Real-life example

HubSpot is rich in content and accurately categorises it. Some articles are more useful, while others are more entertaining, but content is only divided by topic (e.g. service, marketing etc.).

❌ Issue: There is a featured and latest article sections but no real social signal or label to nudge people to read specific articles. This is underoptimizing for clicks, and leads (to read the articles, visitors need to leave their email).

✅ Solution: HubSpot could improve its click-through rate and generate more leads by using labels, both on its website and in content-focused ads:

• On their website, they could use “most read labels” for informational content (e.g. how-to guides and reports). Occasionally, if they have entertaining content (e.g. a podcast episode with a strong personality) they can use “most shared” labels.

• The same labels could be used for ads. For example “Download the AI prompt guide, one of the most read guides we’ve ever created”. This would likely increase the ad’s effectiveness. 

• As an experiment, HubSpot could even test the expectation of a piece becoming popular (e.g. “we expect this to become one of our most read”). Research has found that expectation-based social proof can work to drive sign-ups (e.g. we expect 15,000 users to join this year).

🔍 Study type

Lab and online experiments, field experiments (Facebook ads A/B Testing), and market observation (audit of top 120 media websites).

📖 Research

Most Read Versus Most Shared: How Less (vs. More) Social Popularity Labels Influence News Media Consumption. Journal of Consumer Research (April 2025)

🏫 Researchers

• Sokiente W. Dagogo-Jack, University of Georgia

• Jared Watson, New York University

Remember: This is a new scientific discovery. In the future it will probably be better understood and could even be proven wrong (that’s how science works). It may also not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.

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