Hi all, here is the usual weekly look at our most important readership metrics (CCing the Analytics-l mailing list too this time).
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 continue to be welcome. Now to the usual data. (All numbers below are averages for October 26-November 1, 2015 unless otherwise noted.) Pageviews Total: 525 million/day (-1.5% from the previous week) Context (April 2015-October 2015): (see also the Vital Signs dashboard <https://vital-signs.wmflabs.org/#projects=all/metrics=Pageviews>) 1.5% is a somewhat noticeable drop, and as in the last report I ran a query for the countries with the largest changes from the previous week. Some interesting data, but not sufficient for attributing the overall drop to a particular area: - Ireland +40.2% - Romania -35.6% (previous week: +60%) - France +14.5% - Philippines -12.4% (previous week: -12.4%(!)) - Mexico -10.3% - Colombia -9.2% - Ecuador -8.9% - Israel -7.7% - Malaysia -7.6% - Sweden -7.5% Desktop: 57.7% Mobile web: 1.2% Apps: 41.1% (same as previous week) Global North ratio: 77.1% of total pageviews (previous week: 76.9%) Context (April 2015-November 2015): This week, instead of plotting the absolute numbers as usual <https://commons.wikimedia.org/wiki/File:Wikimedia_pageviews,_Global_South_vs._Global_North_(April_2015-).png>, let’s chart the percentage: It’s not a definite proof, but this chart shows a pretty clear rise (or conversely, decrease in the ratio of traffic from the Global South) during the time of the staggered HTTPS-only rollout in June. Unique app users Android: 1.161 million /day (-0.0% from the previous week) Context (August-November 2015): iOS: 278k / day (-0.7% from the previous week) Context (August-November 2015): As anticipated, the marked increase in new installations while the app was featured recently (see below) did not move the DAU needle much. New app installations Android: 37.2k/day (-1.2% from the previous week) Daily installs per device, from Google Play Context (August-November 2015): iOS: 3.96k/day (-35.0% from the previous week) Download numbers from App Annie Context (August-November 2015): A slightly conspicuous drop last Thursday. But most of the large week-over-week decrease came from the app having been featured in the App Store previously (see earlier weekly reports). And since you read this far, a little reward in form of a link <http://www.adweek.com/news/advertising-branding/ad-day-adobe-knows-what-your-marketing-doing-even-when-you-have-no-clue-152638> to a mildly amusing 1 minute video ad that mocks the data analytics mishaps of a fictitious but easily recognized large encyclopedia project ;) ---- 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 "2015-10-26" AND "2015-11-01"; 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 "2015-10-26" AND "2015-11-01" 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 "2015-10-26" AND "2015-11-01"; 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, changeratio, ROUND(milliondailyviewsthisweek,1) AS milliondailyviewsthisweek FROM (SELECT country_code, ROUND(100*SUM(IF((day>25 AND month=10) OR (day<2 AND month=11), view_count, null))/SUM(IF(day>18 AND day<26, view_count, null))-100,1) AS changeratio, SUM(IF((day>25 AND month=10) OR (day<2 AND month=11), view_count, null))/7000000 AS milliondailyviewsthisweek FROM wmf.projectview_hourly WHERE year = 2015 AND month > 9 AND agent_type = "user" GROUP BY country_code) AS countrylist WHERE milliondailyviewsthisweek > 1 GROUP BY country_code, changeratio, milliondailyviewsthisweek ORDER BY ABS(changeratio) DESC LIMIT 10; 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 20151026 AND 20151101; 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://console.developers.google.com/storage/browser/pubsite_prod_rev_02812522755211381933/stats/installs/ (“overview”) https://www.appannie.com/dashboard/252257/item/324715238/downloads/?breakdown=country&date=2015-08-04~2015-11-01&chart_type=downloads&countries=ALL (select “Total”) -- Tilman Bayer Senior Analyst Wikimedia Foundation IRC (Freenode): HaeB
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