Hi all,
here is the weekly look at our most important readership metrics.
As laid out earlier, 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 (e.g. to better take seasonality into account, in particular including year-over-year comparisons) and eventually want to create dashboards for those which are not already available in that form already. Feedback and discussion welcome.
The most interesting news this time is the effect of the iOS Wikipedia app getting featured in the App Store, see below. For those who haven’t been following the discussion on this list (Mobile-l), I’d also like to highlight that Jon Katz has recently posted (to quote his TLDR) “Directional data [that] suggests that the project-wide drop we see in pageviews is, in part, caused by shorter sessions on mobile web compared to desktop (and a migration from desktop to mobile web)”.
Now to the usual data. (All numbers below are averages for October 12-18, 2015 unless otherwise noted.)
Pageviews
Total: 528 million/day (+0.9% from the previous week)
Context (April 2015-October 2015):
See also the Vital Signs dashboard
Desktop: 57.3%
Mobile web: 41.5%
Apps: 1.2%
Global North ratio: 76.9% of total pageviews (previous week: 77.0%)
Context (April 2015-October 2015):
Unique app users
Android: 1.16 million /day (+-0.0% from the previous week)
Context (January 2015-October 2015):
Not much news here.
iOS: 280k / day (+0.9% from the previous week)
Context (January 2015-September 2015):
The overall DAU number don’t yet show a noticeable impact of the app getting featured (see below), we’ll see.
New app installations
Android: 37.9k/day (-4.0% from the previous week)
(Daily installs per device, from Google Play)
Context (July-October 2015):
The sustained rise in installs we’ve been seeing since around August 21 (see also the discussion in last week’s report about the possible connection with the “Back to School” recommendation in the Play store) is ebbing now, whereas the uninstall rate holds pretty much constant.
iOS: 6.69k/day (+48.0% from the previous week)
(download numbers from App Annie)
Context (July 24-Oct 21, 2015):
Last week, the Wikipedia app became featured on the iOS App Store homepage (below the fold, as second item in a list called "Learn Your Facts"). The effect on downloads is already clear - I’m including the last three days in the above chart too; we’ll see how the impact on user numbers and pageviews turns out. Josh from the IOS team is following this closely.
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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.
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 "2015-10-12" AND "2015-10-18";
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 "2015-10-12" AND "2015-10-18" 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') > 0, view_count, 0))/SUM(view_count) FROM wmf.projectview_hourly WHERE agent_type = 'user' AND CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0")) BETWEEN "2015-10-12" AND "2015-10-18";
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') > 0, view_count, 0)) AS Global_North_views 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 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 20151012 AND 20151018;
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';
https://www.appannie.com/dashboard/252257/item/324715238/downloads/?breakdown=country&date=2015-07-24~2015-10-21&chart_type=downloads&countries=ALL (select “Total”)