[WikimediaMobile] Readership metrics for the week until November 22, 2015

Hi all, here is the weekly look at our most important readership metrics (apologies for the delay). Apart from the usual data, this time there is an additional chart to illuminate how our mobile readership ratio has developed since this spring, the iOS app retention stats are back after Apple fixed their data, and we conclude with some inspiring quotes about climate change awareness ;) As laid out earlier <https://lists.wikimedia.org/pipermail/mobile-l/2015-September/009773.html>, the main purpose of this report 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. Now to the usual data. (All numbers below are averages for November 16-22, 2015 unless otherwise noted.) Pageviews Total: 540 million/day (-0.0% from the previous week) Context (April 2015-November 2015): ( see also the Vital Signs dashboard <https://vital-signs.wmflabs.org/#projects=all/metrics=Pageviews>) The Analytics team improved web crawler detection further last week <https://meta.wikimedia.org/w/index.php?title=Dashiki%3APageviewsAnnotations&type=revision&diff=14776623&oldid=14617655>, meaning an “optical” (as opposed to real) drop in human pageviews from November 19 on - presumably smaller though than the one for September that we reported in the preceding report. Desktop: 57.2% ​(previous week: ​57.5%) Mobile web: 41.6% ​(previous week: 41.3%) Apps: 1.2% ​(previous week: 1.2%) Context (April 2015-November 2015): These percentages usually don’t change rapidly from week to week. For a wider perspective, I’m including a chart of the (aggregate) mobile percentage this time, too. Technically this information is already contained in the usual chart above. But here we can see even clearer indications for an impact of the HTTPS-only switchover during June (it appears to have taken out desktop traffic mainly), as well as the strong weekly periodicity (higher mobile ratio on weekends). It looks like mobile won’t overtake desktop anytime soon. Global North ratio: 77.3% of total pageviews (previous week: 77.6%) Context (April 2015-November 2015): New app installations Android: 30.9k/day (-44.2% from the previous week) Daily installs per device, from Google Play Context (last month): As described in the previous report, the Android Wikipedia app was featured in the "New and Updated Apps" section of the Google Play store from November 5-12, and while the huge positive impact overall on download numbers is obvious, they also decreased markedly afterwards. They seem to be coming back up a bit now, but we are still waiting for some more data before making a final estimate for the overall effect, and have also contacted Google to see if they can help us illuminate the mechanism behind this apparent effect. iOS: 4.69k/day (+2.2% from the previous week) Download numbers from App Annie Context (last three months): No news here. App user retention Android: 14.8% (previous week: 15.2%) (Ratio of app installs opened again 7 days after installation, among all installed during the previous week. 1:100 sample) Context (last three months): iOS: 12.0% (previous week: 11.9%) (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) Context (installation dates from October 18-November 15, 2015): This metric was left out of last week’s report because of inconsistencies. Indeed, Apple has since issued a correction notice <http://www.talkingnewmedia.com/2015/11/24/apple-issues-corrected-itunes-connect-reports/>. Unfortunately it looks like the data underlying the report for the week until November 8 was affected too, so please disregard the iOS retention figure given in that report. Unique app users Android: 1.190 million / day (-2.2% from the previous week) Context (last three months): This too will need another look. iOS: 281k / day (+0.1% from the previous week) Context (last three months): No news here. After publishing this report regularly for a bit over two months, we may be rethinking the weekly publication schedule a little - also to keep the balance between newsworthiness and keeping up general awareness for longterm developments. In that vein, some inspiring quotes about a weekly climate change newsletter <http://www.niemanlab.org/2015/11/climate-change-is-depressing-and-horrible-is-there-a-way-to-make-people-read-about-it/> that begins every issue by reciting the current CO2 ratio in the atmosphere as a KPI ;) Ultimately, Meyer said, the newsletter comes out of the idea that “if you’re worried about something, you should pay regular attention to it.” “By paying attention to it over time, and watching its texture change over time, you will come to have ideas about it,” he said. “You will come to understand it in a new way, and you will contribute in a very small way to how society addresses this big problem.” [...] So it seemed as if a newsletter might be a good way to cover the issue. [...] “You can get a continuity of storyline,” Meyer said. “You can’t cover all of everything that’s happening every week in the climate, but you can watch certain parts develop, and hopefully bring people in over time.” He leads off the “Macro Trends” section of each issue with the molecules per million of carbon dioxide in the atmosphere: The atmosphere is filling with greenhouse gases. The Mauna Loa Observatory measured an average of 398.51 CO2 molecules per million in the atmosphere this week. A year ago, it measured 395.84 ppm. Ten years ago, it measured 376.93 ppm. “What we’re doing now won’t show up in that number for a decade or so,” he said. “But by reminding myself of it every week, and thinking about its contours and its direction, that’s a way to stay focused on what matters.” ---- 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 "2015-11-16" AND "2015-11-22"; 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 "2015-11-16" AND "2015-11-22" 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 "2015-11-16" AND "2015-11-22"; 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 '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 20151116 AND 20151122; 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