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 1-day period (data).

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-cannibalistic (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. A project stakeholder (such as PM or data analysis) 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)     discuss with community
 * 2)     build read more according to mobile web specs ✅
 * 3)     launch on mobile web beta
 * 4)     measure impact using event logs CTR (referral data won't be helpful at these numbers)
 * 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
A more like query which will programmatically choose related pages based on similarity to the existing corpus of text. User:Jkatz (WMF) thinks that making them editable is a problem because: They have pretty pictures. Which increases click-through rate. Which is the metric of success for this feature.
 * Where do articles come from?
 * Do editors have any control or are they given any preview of the suggested articles?
 * the results are not automatically updated
 * it means that improvements we make to the algorithm/selection will be lost to pages where an editor has overridden the automated selection.
 * right now the manual selection option only applies to the web (not the apps), which is misleading
 * some editors have requested that we do automated refreshes every time you load the page--this would not be possible if it was manually derived.
 * How is related articles different from the See also section, navigation boxes and the category system?

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

Event logging
As measured for the first two weeks of 2016 on a sampled basis, related articles items were frequently followed on mobile web beta (the skin "minerva-beta" in the table below) - 19.7% of the time when seen. The relatively high click rate on the mobile web is believed to be attributable to the automatically collapsed sections in its phone form factor layout yielding a higher impact visually to the related articles at the end of the article.

Interestingly, while users on the desktop web (see "vector" in data below) were likely to see the related articles panel (35% ready-to-seen event ratio versus mobile web's 27.3% ratio), desktop users evidently were far less likely to click on a related article - they apparently clicked only 3.4% of the time when seeing the related articles panel.

event_skin	event_eventName	count(*) cologneblue	ready	3 minerva-beta	clicked	217 minerva-beta	ready	4025 minerva-beta	seen	1099 modern	ready	114 modern	seen	62 monobook	clicked	1 monobook	ready	270 monobook	seen	82 vector	clicked	48 vector	ready	4053 vector	seen	1429

The relatively lower click rate on the desktop web is believed to be attributable to the richly laid out expanded sections yielding a relatively lower visual impact for the related articles. It may also be related to the mechanics of beta opt-in: on the mobile web beta opt-in enables all beta features, whereas on the desktop web users are able to opt-in to specific beta features. It should be noted that some users on desktop web also enable the feature to auto-enroll on all beta features.

Tablet devices by default are presented the mobile website, which commonly expands sections, but still has a relatively lighter weight layout. But users have the ability to change to desktop mode, where again sections are expanded but there's a relatively heavier weight layout.

Note that for a confined set of tablet devices, although it's an imperfect analysis and there are a variety of potentially impacting factors that could muddle the analysis, the data suggest that we can at least say with some level of confidence that


 * mobile web beta treatment dramatically outperformed the desktop treatment
 * although users with a confined set of tablets on mobile web beta had not insignificant engagement with related articles, their rate of engagement was somewhat lower than the fuller mobile web beta population (confined tablet population's engagement at 16.7% versus full mobile web beta population's engagement of 19.7% for a two week period and 13.2% versus 18.4% for the full period since it was introduced on mobile web beta)

minerva-beta	clicked	11 minerva-beta	ready	262 minerva-beta	seen	66 monobook	ready	1 monobook	seen	1 vector	ready	37 vector	seen	9

All events and skins since related articles available in beta for confined set of tablets...

minerva-beta	clicked	39 minerva-beta	ready	1485 minerva-beta	seen	295 monobook	ready	2 monobook	seen	1 vector	ready	70 vector	seen	21

All events and skins since related articles available in beta for all UAs...

cologneblue	ready	9 cologneblue	seen	2 minerva-beta	clicked	768 minerva-beta	ready	19009 minerva-beta	seen	4170 modern	ready	185 modern	seen	105 monobook	clicked	4 monobook	ready	535 monobook	seen	171 vector	clicked	110 vector	ready	8005 vector	seen	2848

Do note that a CTA experiment to heighten the general enrollment in mobile web beta was running in the latter part of 2015 but was disabled, hence relatively lower event counts for the two week period observed at the start of January 2016. Engagement with the related articles was relatively high with both the CTA in force and has continued to be relatively high (albeit slightly lower) since the CTA was removed. See the data above. Note also that a small experiment with collapsed sections has been running, but should have a negligible impact.

HTTP Referer header analysis
One additional signal in the environment suggesting the efficacy of the mobile web beta related articles feature is the share of internally referred pageviews as a percentage of total pagevews. Again, caveats could apply, but it appears that prior to the related articles feature, mobile web beta internally referred pageviews were in the low 50s percentagewise, whereas with the feature they are now in the high 60s percentagewise. However, this is only a signal; partial rollout in the stable channel of the mobile web would be more telling.

What follows are relative internally referred pageviews rates on the mobile web for a set of Thursdays - two Thursdays in the first two weeks of January, and two Thursdays prior to the related articles feature being released in beta on the mobile web and desktop web. Dates coinciding with holidays or known high rate fundraising campaign banners are not included, as they would complicate the analysis.

Thursday, January 14
Feature implemented.

beta: 48450/(48450+14958+8465) = 67.4% stable: 58400028/(58400028+56362895+113239899) = 25.6%

external	NULL	113239899 external	b	8465 unknown	NULL	56362895 unknown	b	14958 internal	NULL	58400028 internal	b	48450

Thursday, January 7
Feature implemented.

beta: 51732/(51732+14540+8137) = 69.5% stable: 59881357/(59881357+55286651+109964317) = 26.6%

external	NULL	109964317 external	b	8137 unknown	NULL	55286651 unknown	b	14540 internal	NULL	59881357 internal	b	51732

Thursday, December 3, 2015
Feature not yet implemented.

beta: 184067/(184067+55814+119778) = 51.1% stable: 50273872/(50273872+51372508+102363512) = 24.6%

external	NULL	102363512 external	b	119778 unknown	NULL	51372508 unknown	b	55814 internal	NULL	50273872 internal	b	184067

Thursday, November 19
Feature not yet implemented.

beta = 189359/(189359+58549+121871) = 51.2% stable = 51119529/(51119529+54209700+105205731) = 24.3%

external	NULL	105205731 external	b	121871 unknown	NULL	54209700 unknown	b	58549 internal	NULL	51119529 internal	b	189359