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 and eventually want to create dashboards for those which are not already available in that form already. Feedback on the format has continued to come in and remains welcome.
Besides going over the usual metrics, I’d like to highlight another piece of data this time: The app session metrics which we started to record in May and report in our quarterly review <https://commons.wikimedia.org/w/index.php?title=File:WMF_Reading_Quarterly_Review_Q1_2015-16.pdf&page=34>, but haven’t yet tracked systematically. (A session is defined as a sequence of pageviews by the same app user that are no longer than 30 minutes apart.) The median number of sessions per user has remained constant during this time, between 3 and 4 sessions in a 30-day period, as has the median number of pageviews per session (between 2 and 3). However, the median duration of a session has risen considerably: Anyone have thoughts about possible reasons? Also, I think this is a good opportunity to ponder what we would be maximizing if we were to maximize session length/time spent. (The data is for both Android and iOS, but - cf. the DAU numbers below - can be assumed to be dominated by Android. We’re going to see if we can obtain it separately by platform as well.) Now to the usual data. (All numbers below are averages for October 19-25, 2015 unless otherwise noted.) Pageviews Total: 533 million/day (+1.0% from the previous week) Context (April 2015-October 2015): (see also the Vital Signs dashboard <https://vital-signs.wmflabs.org/#projects=all/metrics=Pageviews>) Obviously this month is quieter than September (where we had a peak followed by drop). Desktop: 57.7% Mobile web: 41.1% Apps: 1.2% Global North ratio: 76.9% of total pageviews (previous week: 77.0%) Context (April 2015-October 2015): Out of curiosity whether there are more relevant small-scale changes happening beneath the surface of this tranquil weekly ebb and flow, I ran queries for the countries with the largest changes from the previous week (restricted to those with more than 1 million views per day). Largest increases from the previous week: - Romania +60.5% - Denmark +16.3% - Israel +13.4% - Brazil +12.2% - Vietnam +11.5% Largest decreases from the previous week: - Philippines -12.4% - unknown country (code “--”) -4.9% - Italy -4.9% - South Africa -4.9% - Austra -4.0% Something must have happened in Romania, which increased from 1.8 to 2.9 million pageviews per day last week. Unique app users Android: 1.161 million /day (+0.1% from the previous week) Context (July-October 2015): iOS: 280k / day (-0.2% from the previous week) Context (July-October 2015): New app installations Android: 37.6k/day (-0.8% from the previous week) (Daily installs per device, from Google Play) Context (July-October 2015): A small bump in the last few days. iOS: 6.09k/day (-9.0% from the previous week) (download numbers from App Annie) Context (July-October 2015): The app stopped being featured in the “Learn Your Fact” section of the App store as of last Thursday. ---- 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 "2015-10-19" AND "2015-10-25"; 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 "2015-10-19" AND "2015-10-25" 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 "2015-10-19" AND "2015-10-25"; 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; SELECT country_code, increaseratio, viewsthisweek FROM (SELECT country_code, ROUND(SUM(IF(day>18 AND day<26, view_count, null))/SUM(IF(day>11 AND day<19, view_count, null))-1,3) AS increaseratio, SUM(IF(day>18 AND day<26, view_count, null)) AS viewsthisweek FROM wmf.projectview_hourly WHERE year = 2015 AND month = 10 AND agent_type = "user" GROUP BY country_code) AS countrylist WHERE viewsthisweek > 7*1000000 GROUP BY country_code, increaseratio, viewsthisweek ORDER BY increaseratio DESC LIMIT 5; (and analogously for decreases) 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 20151019 AND 20151025; 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://play.google.com/apps/publish/?dev_acc=02812522755211381933#StatsPlace:p=org.wikipedia&statm=DAILY_DEVICE_INSTALLS&statd=OS_VERSION https://www.appannie.com/dashboard/252257/item/324715238/downloads/?breakdown=country&date=2015-07-28~2015-10-25&chart_type=downloads&countries=ALL (select “Total”) -- Tilman Bayer Senior Analyst Wikimedia Foundation IRC (Freenode): HaeB
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