Reading/Web/Projects/Related pages

Read more is an experiment that will be carried out by the Reading team on desktop and mobile web, in order to further engage readers and increase their browsing time. The concept already exists on apps, where you can check the performance report

Read more aims to drive page views by engaging users by directing them to related content. We aim to show that at least 5% of page views that see read more engage with read more driving new eyes on our articles and more engagement in our project.

The Problem
If a reader has reached to the bottom of the article, they might be looking to read more about the topic and surfacing articles that are similar might be exactly what they are looking for.

This has been released on apps and saw a 16% click-through (For users who saw it). Additionally, 25% of the users who saw read more results clicked through at least more than once.

The How
Using the Extension:RelatedArticles extension we will show suggested articles at the bottom of pages encouraging the user to read another page. A user who gets to the bottom of an article on either mobile or desktop web is shown a list of other articles that are related to the one they just finished. The notion is that if the reader has read to the end of the article, they might be looking to read more about the topic and surfacing articles that are similar might be exactly what they are looking for. This has been release on apps and saw a 16% click-through (For users who saw it). Additionally, 25% of the users who saw read more results clicked through at least 1x in a __ period (https://docs.google.com/spreadsheets/d/1iyNqWgC8lNWk3ckSQJS_ACrfeW1UrBHOf58L97oeNqM/edit?ts=560ae622#gid=1184183009)

Rationale
If readers are offered suggestions that are similar to the topic they are reading about, this will further engage their reading session time, it will further educate them about the topic they are looking for, and supports a richer reading experience for those who are just randomly browsing topics.

Success criteria

 * %CTR (click through rate, clicks/views) is higher than 10%.
 * Ideally, we will be able to ensure that these clicks are not-cannabilistic (increasing overall clicks as opposed to taking clicks from blue links)

Prototyping
This will be tested in beta first.

MVP
A user reaching the bottom of an article is shown the title and lead image for 3 articles that relate to the article they just finished. We are able to measure the engagement clicks/impressions with this feature.

User Stories
A user reaching the bottom of an article is shown the title and lead image for 3 articles that relate to the article they just finished so that they can continue reading about that topic.Someone editing a wikipedia article can manually change the article suggestions using wikitext so that they can correct any erroneous or sub-optimal suggestions. An project stakeholder (such as PM or data analysit) is able to measure the engagement clicks/impressions with this feature so they can determine if it is adding user value.

Metrics Implementation
We will want to track:
 * Impressions of read more suggestions (the lump--not each item suggested)
 * Clicks to read a suggested article
 * The position of article (1,2,3). Ideally: whether or not article was edited manually
 * Ideally: a)overall referrals from a page with read more, b)overall referrals from same page without read more

Timeline Estimate

 * 1)     build read more according to mobile web specs ✅
 * 2)     launch on mobile web beta
 * 3)     measure impact using event logs CTR (referral data won't be helpful at these numbers)
 * 4)     discuss with community
 * 5)     launch on mobile
 * 6)     build for desktop
 * 7)     launch on desktop (open discussion about whether we launch in beta first and/or do progressive rollout)
 * 8)     Delivery Estimate: End of December on desktop?

FAQ
First and foremost editors whom can control the output with the #related magic word. When absent it will fall back to a more like query which will programmatically choose related pages based on similarity to the existing corpus of text.
 * Where do articles come from?

Not at the moment, if they haven't overridden the pages results manually then these will be automatic but it is one thing that could be developed according to editors'best convenience, when they test the feature in beta. Through the beta experiment, we will better asses user behavior in both cases.
 * Do editors have any control or are they given any preview of the suggested articles?
 * How is related articles different from see also?

Join the conversation
This task is being tracked on Phabricator and we'd love to hear your thoughts.