Promoting Positive Post-Click Experience for Native Advertising

Since September 2013, I have been working on user engagement in the context of native advertising. This blog post describes our first paper on this work, published at the Industry Track of ACM Knowledge Discovery & Data Mining (KDD) conference in 2015 [1]. This is work in collaboration with Janette Lehmann, Guy Shaked, Fabrizio Silvestri and Gabriele Tolomei.

Feed-based layouts, or streams, are becoming an increasingly common layout in many applications, and a predominant interface in mobile applications. In-stream advertising has emerged as a popular online advertising because it offers a user experience that fits nicely with that of the stream, and is often referred to as native advertising. In-stream or native ads have an appearance similar to that of the items in the stream, but clearly marked with a “Sponsored” label or a currency symbol e.g. “$” to indicate that they are in fact adverts.

A user decides if he or she is interested in the ad content by looking at its creative. If the user clicks on the creative he or she is redirected to the ad landing page, which is either a web page specifically created for that ad, or the advertiser homepage. The way user experiences the landing page, the ad post-click experience, is particularly important in the context of native ads because the creatives have mostly the same look and feel, and what differs mostly is their landing pages. The quality of the landing page will affect the ad post-click experience.

A positive experience increases the probability of users “converting” (e.g., purchasing an item, registering to a mailing list, or simply spending time on the site building an affinity with the brand). A positive post-click experience does not necessarily mean a conversion, as there may be many reasons why conversion does not happen, independent of the quality of the ad landing page. A more appropriate proxy of the post-click experience is the time a user spends on the ad site before returning back to the publisher site:

“the longer the time, the more likely the experience was positive”

The two most common measures used to quantify time spent on a site are dwell time and bounce rate. Dwell time is the time between users clicking on an ad creative until returning to the stream; bounce rate is the percentage of “short clicks” (clicks with dwell time less than a given threshold). On a randomly sampled native ads served on a mobile stream, we showed that these measures were indeed good proxies of post-click experience.

We also saw that users clicking on ads promoting a positive post-click experience, i.e. small bounce rate, were more likely to click on ads in the future, and their long-term engagement was positively affected.

Focusing on mobile, we found that a positive ad post-click experience was not just about serving ads with mobile-optimised landing pages; other aspects of an landing page affect the post-click experience. We therefore put forward a learning approach that analyses ad landing pages, and showed how these can predict dwell time and bounce rate. We experimented with three types of landing page features, related to the actual content and organization of the ad landing page, the similarity between the creative and the landing page, and ad past performance. The later type were best at predicting dwell time and bounce rate, but content and organization features performed well, and have the advantages to be applicable for all ads, not only for those that have been served.

Finally, we deployed our prediction model for ad quality based on dwell time on Yahoo Gemini, an unified ad marketplace for mobile search and native advertising, and validated its performance on the mobile news stream app running on iOS. Analyzing one month data through A/B testing, returning high quality ads, as measured in terms of the ad post-click experience, not only increases click-through rates by 18%, it has a positive effect on users: an increase in dwell time (+30%) and a decrease in bounce rate (-6.7%).

This work has progressed in two ways. We have improved the prediction model using survival random forests and considered new landing page features, such as text readability and the page structure [2]. We are also working with advertisers to help improving the quality of their landing pages. More about this in the near future.

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