[Analytics] Readership metrics for the week until November 8, 2015

Hi all, here is the weekly look at our most important readership metrics (CCing Wikitech-l too this time). As laid out earlier <https://lists.wikimedia.org/pipermail/mobile-l/2015-September/009773.html>, the main purpose is to raise awareness about how these are developing, call out the impact of any unusual events in the preceding week, and facilitate thinking about core metrics in general. We are still iterating on the presentation and eventually want to create dashboards for those which are not already available in that form already. Feedback and discussion continue to be welcome. As it might be of interest for readers of this report who haven’t already seen the news on Analytics-l or Wikitech-l, I’d like to mention the exciting news that the monthly pageview data on Wikistats <https://stats.wikimedia.org/EN/TablesPageViewsMonthlyCombined.htm> has been transitioned to the new pageview definition <https://lists.wikimedia.org/pipermail/analytics/2015-November/004502.html>. Now to the usual data, while introducing one new metric as well this time. (All numbers below are averages for November 2-8, 2015 unless otherwise noted.) Pageviews Total: 536 million/day (+2.2% from the previous week) Context (April 2015-November 2015): We more than reversed the -1.5% drop from last week, yay! (See also the Vital Signs dashboard <https://vital-signs.wmflabs.org/#projects=all/metrics=Pageviews>) Desktop: 57.5% Mobile web: 41.3% Apps: 1.2% Global North ratio: 77.5% of total pageviews (previous week: 77.1%) Context (April 2015-November 2015): New app installations Android: 60.6k/day (+63.2% from the previous week) Daily installs per device, from Google Play Context (last three months): On November 5, the app started to get featured in the "New and Updated Apps" section of the Google Play store, enabled by the Android team’s recent update work. The effect is already clearly visible here; we’ll have a fuller view of the impact in the next report after the placement ends today. iOS: 4.41k/day (+11.2% from the previous week) Download numbers from App Annie Context (September 2014-September 2015): Things are back to normal after the iOS app had been featured in the App Store in mid-October. (Much of the 11.2% rise over the preceding week can be tied to the - still unexplained - drop on Oct 30.) App user retention With this issue of the report, we’re adding a new metric that should be more directly tied to how new users perceive the quality and usefulness of the apps. Day-7 retention (D7) is defined as the proportion of users who used the app again on the seventh day after they first opened it. The iOS team has set themselves a quarterly goal to bring this “stickiness” metric to at least 15% with their 5.0 update <https://commons.wikimedia.org/wiki/File:IOS_Wikipedia_App_5.0_Update.pdf> (p.5 of that doc contains some further context on this metric and links on how it is perceived elsewhere in the industry; the following post is also useful for perspective: “losing 80% of mobile users is normal, and why the best apps do better” <http://andrewchen.co/new-data-shows-why-losing-80-of-your-mobile-users-is-normal-and-that-the-best-apps-do-much-better/>). Android: 13.9% (previous week: 11.5%) (1:100 sample) Context (last three months): iOS: 13.1% (previous week: 10.6%) (from iTunes Connect, opt-in only = ca. 20-30% of all users) Context (October 11-November 8, 2015): Unique app users Android: 1.185 million / day (+2.0% from the previous week) Context (last three months): There are already signs of a small but discernible rise in active users due to the app being featured, but we’ll need to wait until later to fully assess this. iOS: 280k / day (+1.0% from the previous week) Context (last three months): No news here ---- For reference, the queries and source links used are listed below (access is needed for each). Most of the above charts are available on Commons, too <https://commons.wikimedia.org/wiki/Category:Wikimedia_readership_metrics_reports> . hive (wmf)> SELECT SUM(view_count)/7000000 AS avg_daily_views_millions FROM wmf.projectview_hourly WHERE agent_type = 'user' AND CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) BETWEEN "2015-11-02" AND "2015-11-08"; hive (wmf)> SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, sum(IF(access_method <> 'desktop', view_count, null)) AS mobileviews, SUM(view_count) AS allviews FROM wmf.projectview_hourly WHERE year=2015 AND agent_type = 'user' GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; hive (wmf)> SELECT access_method, SUM(view_count)/7 FROM wmf.projectview_hourly WHERE agent_type = 'user' AND CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) BETWEEN "2015-11-02" AND "2015-11-08" GROUP BY access_method; hive (wmf)> SELECT SUM(IF (FIND_IN_SET(country_code, 'AD,AL,AT,AX,BA,BE,BG,CH,CY,CZ,DE,DK,EE,ES,FI,FO,FR,FX,GB,GG,GI,GL,GR,HR,HU,IE,IL,IM,IS,IT,JE,LI,LU,LV,MC,MD,ME,MK,MT,NL,NO,PL,PT,RO,RS,RU,SE,SI,SJ,SK,SM,TR,VA,AU,CA,HK,MO,NZ,JP,SG,KR,TW,US') agent_type = 'user' AND CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) BETWEEN "2015-11-02" AND "2015-11-08"; hive (wmf)> SELECT year, month, day, CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")), SUM(view_count) AS all, SUM(IF (FIND_IN_SET(country_code, 'AD,AL,AT,AX,BA,BE,BG,CH,CY,CZ,DE,DK,EE,ES,FI,FO,FR,FX,GB,GG,GI,GL,GR,HR,HU,IE,IL,IM,IS,IT,JE,LI,LU,LV,MC,MD,ME,MK,MT,NL,NO,PL,PT,RO,RS,RU,SE,SI,SJ,SK,SM,TR,VA,AU,CA,HK,MO,NZ,JP,SG,KR,TW,US') WHERE year = 2015 AND agent_type='user' GROUP BY year, month, day ORDER BY year, month, day LIMIT 1000; https://console.developers.google.com/storage/browser/pubsite_prod_rev_0281… (“overview”) https://www.appannie.com/dashboard/252257/item/324715238/downloads/?breakdo… (select “Total”) SELECT LEFT(timestamp, 8) AS date, SUM(IF(event_appInstallAgeDays = 0, 1, 0)) AS day0_active, SUM(IF(event_appInstallAgeDays = 7, 1, 0)) AS day7_active FROM log.MobileWikiAppDailyStats_12637385 WHERE timestamp LIKE '201510%' AND userAgent LIKE '%-r-%' AND userAgent NOT LIKE '%Googlebot%' GROUP BY date ORDER BY DATE; (with the retention rate calculated as day7_active divided by day0_active from seven days earlier, of course) https://analytics.itunes.apple.com/#/retention?app=324715238 hive (wmf)> SELECT SUM(IF(platform = 'Android',unique_count,0))/7 AS avg_Android_DAU_last_week, SUM(IF(platform = 'iOS',unique_count,0))/7 AS avg_iOS_DAU_last_week FROM wmf.mobile_apps_uniques_daily WHERE CONCAT(year,LPAD(month,2,"0"),LPAD(day,2,"0")) BETWEEN 20151102 AND 20151108; hive (wmf)> SELECT CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, unique_count AS Android_DAU FROM wmf.mobile_apps_uniques_daily WHERE platform = 'Android'; hive (wmf)> SELECT CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) as date, unique_count AS iOS_DAU FROM wmf.mobile_apps_uniques_daily WHERE platform = 'iOS'; -- Tilman Bayer Senior Analyst Wikimedia Foundation IRC (Freenode): HaeB