This won't be a separate KR anymore (rather a hypothesis under one of the IR2 KRs)

Amazing, thank you @Khantstop !

Resolving this as the dashboard now works! Thanks @Snwachukwu for the work!

Thank you Sandra, it times out for me unfortunately!

I think setting this to zero (no intervention) would be fine for now unless @Khantstop has pilot data to share here?

Amazing, thank you so much!
@Khantstop you can add this to the documentation for IR2. Thanks so much everyone!

Thank you @Snwachukwu just double checking, will this sanitization make the wprov data clean enough for metrics calculation purposes?

@Khantstop agree. We discussed on Wednesday to expand this visualization to track data real time. I am hearing from senior leadership there is no need for that at the moment, so I'll close this task. Thank you everyone!

Some updates on the Target discussion.
TL;DR: we are landing around a target of 100M pageviews as marginal gain obtained via non-search interventions.

@Maryana @MaCollins-WMF declining this task as this KR has been merged with IR1.1

@Marostegui @GWeld this is approved on my end, thank you!

@mforns checking if you have more clarity around timelines here? Thank you!

Thank you @dr0ptp4kt @Khantstop @DTotten-WMF and @Snwachukwu for the very productive meeting yesterday.

Updates here:

After discussing with @YLiou_WMF, let's exclude the survey-based impact measurement for this one. The data would be too sparse and measuring the effect through surveys impossible. Surveys can be used if we want to set realistic targets for interventions (answering questions like: how likely are people to click on a Wikipedia link after seeing attribution?).

Changing status to stalled until we can proceed on the implementation.

Some updates:

Some updates

• Implementation. We made a lot of progress on metric implementation and we now know how exactly we are going to measure intervention effects. Sources of data are clear and we will document the process.
• Dashboarding. This is an intervention-based metric and only after each intervention we will be able to calculate the marginal gain on expected pageviews on the specific pages affected by it. However, we do want to show an interactive example to explore pageview gains on specific pages affected by an intervention, using data from a small study @Khantstop made this month. We've asked @Ahoelzl for DPE support for this.
• Target. @Maryana suggested a target that we are verifying with existing data and will be finalized by 2026-05-05.

@Khantstop and I had a conversation today about how to implement the metric. Confirming that the definition is Marginal gain in non-search-referred pageviews on segments affected by interventions, there are a few issues with measuring effects of interventions:

1. We often are not able to distinguish traffic coming smaller non-search sites in our traffic (so we can't isolate pageviews coming from specific partners unless they are large media sites)
2. Even if we look globally at the whole non-search traffic, the data is very noisy and varies a lot month-by-month (so we can't measure any effect on this data)
3. Only some partners might activate the provenance parameter wpprov

So we came up with three proposals that we need to further check with our collaborators

1. Get a visibility metric through regular surveys to be discussed with @YLiou_WMF
2. Using the wprov parameter, tag "treatment" pages as the pages that have been included into the experiment, and define a "control" set of similar pages, then compare pageview gain
3. Using the wprov parameter, simply count the Lift in Pageviews with Provenance from partnered websites.