Hi all,

here is the weekly look at our most important readership metrics, a bit belatedly 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 welcome.

For readers of this report who haven’t already seen it, I’d like to mention the exciting announcement https://lists.wikimedia.org/pipermail/analytics/2015-November/004529.html of the new pageview API for per-article readership metrics.

Now to the usual data. (All numbers below are averages for November 9-15, 2015 unless otherwise noted.)

Pageviews

Total: 540 million/day (+0.7% from the previous week)

Context (April 2015-November 2015):

(see also the Vital Signs dashboard https://vital-signs.wmflabs.org/#projects=all/metrics=Pageviews)

Some may remember that back in September, this weekly report called https://lists.wikimedia.org/pipermail/mobile-l/2015-September/009794.html out https://lists.wikimedia.org/pipermail/mobile-l/2015-September/009785.html a “conspicuous 4.3% drop” in total pageviews during the week until Sept 20 (followed by another 0.7% decrease the following week). Well, last week the Analytics team solved that mystery https://phabricator.wikimedia.org/T114379#1802927: An improvement in detection of web crawlers had caused much more pageviews to be classified as non-human, from Sept 16 on (e.g. for Commons, estimated human traffic dropped https://vital-signs.wmflabs.org/#projects=commonswiki/metrics=Pageviews from about 12 million to about 4 million per day).

Desktop: 57.5% (previous week: ​57.5%)

Mobile web: 41.3% (previous week: ​41.3%)

Apps: 1.2% (previous week: ​1.2%)

Global North ratio: 77.6% of total pageviews (previous week: 77.5%)

Context (April 2015-November 2015):

New app installations

Android: 55.3k/day (-8.8% from the previous week)

Daily installs per device, from Google Play

Context (last month):

As already mentioned in last week’s report, the Android Wikipedia app got featured in the "New and Updated Apps" section of the Google Play store on November 5, enabled by the Android team’s recent update work and facilitated by the Partnerships team. The promotion lasted one week and we can now see that it was a huge success (with the effect on download numbers much more clearly discernible than in the case of the “Back to School” feature we discussed last month https://lists.wikimedia.org/pipermail/mobile-l/2015-October/009835.html). Predictably, uninstalls went up slightly too, but most of the new users kept the app on their phone. What is a little concerning though is that after the promotion, install numbers fell below the previous baseline, with the install base even shrinking a tiny bit right afterwards. (One possibility is that we are seeing some sort of depletion effect, due to people who would have installed the app anyway around this time, but saw it earlier due to the promotion.) For that reason, we will wait a bit longer before estimating the overall impact of this promotion.

iOS: 4.59k/day (+4.3% from the previous week)

Download numbers from App Annie

Context (last 12 months):

No big news here - things are back to normal after the App Store feature last month.

App user retention

Android: 15.2% (previous week: 13.9%)

(Ratio of app installs opened again 7 days after installation, among all installed during the previous week. 1:100 sample)

Context (last three months):

Recall that this metric lags one week behind, so to speak. I.e. the effects of the Play Store promotion are not fully visible yet above (spoiler though, having looked at a few more days of data already: retention for installs who had come in during the promotion does not appear to have been lower than usual, which is good news).

In general, this data is quite noisy due to the low (1:100) sample rate.

iOS: N/A

(Ratio of app installs opened again 7 days after installation, among all installed during the previous week. From iTunes Connect, opt-in only = ca. 20-30% of all users) Unfortunately I encountered some data quality issues with this metric this week. Will investigate, and report iOS retention again once this is sorted out. (The numbers and charts provided in the iTunes Connect App Analytics appears to have changed quite a bit retroactively.) Unique app users

Android: 1.217 million / day (+2.7% from the previous week)

Context (last three months):

A somewhat noticeable rise that could well be connected with the aforementioned Play Store promotion, but still needs a closer look once more data is in.

iOS: 281k / day (+0.2% from the previous week)

Context (last three months):

No news here.

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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 https://commons.wikimedia.org/w/index.php?title=Special:ListFiles&offset=20150923010000&user=Tbayer+%28WMF%29&ilshowall=1&limit=4 .

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 "2015-11-09" AND "2015-11-15";

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 "2015-11-09" AND "2015-11-15" 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 "2015-11-09" AND "2015-11-15";

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;

https://console.developers.google.com/storage/browser/pubsite_prod_rev_02812... (“overview”)

https://www.appannie.com/dashboard/252257/item/324715238/downloads/?breakdow... (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 '201511%' 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 20151109 AND 20151115;

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';