Feel free to. Are you planning an annual event of sorts? ;) On Thu, Feb 18, 2016 at 4:00 PM, Volker Eckl <vol...@wikimedia.org> wrote:
> Tilman, is it ok to cite "December 20, 2015 will forever be engraved in > Wikimedia history as the first day where mobile pageviews surpassed desktop > pageviews (51% vs. 49%)" > on the Internetz? > > On Thu, Feb 18, 2016 at 2:46 PM, Oliver Keyes <oke...@wikimedia.org> > wrote: > >> Hey Tilman, >> >> I would really like to thank you for producing these reports. They're >> wonderfully informative, great for transparency, and something more teams >> should be doing. I'm personally gonna be stealing a leaf from your book and >> doing something similar :). >> >> On 18 February 2016 at 17:37, Tilman Bayer <tba...@wikimedia.org> wrote: >> >>> Hi all, >>> >>> this resumes the usual look at our most important readership metrics. >>> Among other things, this time we observe the annual Christmas slump in >>> pageviews, hail the advent of the mobile singularity (December saw the >>> first ever day with >50% mobile pageviews), and resolve the mystery of the >>> Android app’s installation drop since mid November. >>> >>> 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; feedback and discussion welcome. >>> >>> After switching away from the weekly schedule (week-over-week and >>> month-over-month changes are now being recorded on the Product page >>> <https://www.mediawiki.org/wiki/Wikimedia_Product#Reading> at >>> MediaWiki.org) we also skipped an issue and added one week, so this edition >>> of the report covers a timespan of nine weeks. >>> >>> See also the slides from the Reading team’s quarter review meeting >>> <https://meta.wikimedia.org/wiki/WMF_Metrics_and_activities_meetings/Quarterly_reviews/Reading_and_Community_Tech,_January_2016> >>> on January 20 for an in-depth look at various metrics. >>> >>> Now to the usual data. (All numbers below are averages for December 7, >>> 2015 to February 7, 2016 unless otherwise noted.) >>> >>> Pageviews >>> >>> Total: 529 million/day >>> >>> Context (April 2015-February 2016): >>> >>> >>> >>> (see also the Vital Signs dashboard >>> <https://vital-signs.wmflabs.org/#projects=all/metrics=Pageviews>) >>> >>> Total pageviews saw a sharp drop about a week before Christmas (almost >>> entirely on desktop), coinciding with a drop >>> <http://discovery.wmflabs.org/external/#traffic_by_engine> in Google >>> referrals. But both recovered in early January, even rising above earlier >>> levels, thanks to mobile (cf. below). Historical data from one and two >>> years ago shows a very similar slump in total and desktop pageviews in the >>> second half of December (with a lasting increase in mobile around >>> Christmas, too), so I’m interpreting this as seasonal even though it >>> preceded the actual holidays by a few days. In January 2014, traffic did >>> not fully recover to the levels of November/early December. In January >>> 2015, it rose slightly above them, similar to now. >>> >>> Desktop: 54.3% >>> >>> Mobile web: 44.4% >>> >>> Apps: 1.3% >>> >>> Context (April 2015-February 2016): >>> >>> >>> December 20, 2015 will forever be engraved in Wikimedia history as the >>> first day where mobile pageviews surpassed desktop pageviews (51% vs. 49%). >>> It was a Sunday - as we have known for a long time, mobile usage is higher >>> on weekends. In general, the mobile percentage over a whole week still >>> remains well below 50%. But even as desktop pageviews recovered from the >>> Christmas slump at the beginning of January, the mobile percentage remains >>> roughly 2% higher than before mid-December - quite likely due to a lot of >>> new phones entering usage as Christmas presents. >>> >>> Global North ratio: 78.3% of total pageviews >>> >>> Context (April 2015-February 2016): >>> >>> New app installations >>> >>> Android: 38.4k/day >>> >>> Daily installs per device, from Google Play >>> >>> Context (January 2015-February 2016): >>> >>> As reported earlier, the app was featured twice on Google Play in recent >>> months, and we can now estimate how many additional installs each may have >>> caused. November’s placement in the “new and updated” section appears to >>> have brought in more than 200k installs, and from early December to early >>> January, the apps was featured as one of the “Best Apps of 2015” in many >>> countries, resulting in around 350k additional installs. >>> >>> The preceding reports also described how we noticed a sharp drop in the >>> new install rate around November 12, and started investigating the cause >>> with our contact at Google. After some detective work, it turned out that >>> the app had been benefitting (since April) from a new feature >>> <https://googlewebmastercentral.blogspot.com/2015/04/drive-app-installs-through-app-indexing.html> >>> in Google Search showing install buttons for a website’s app next to search >>> results from that site for general search terms. Google ended this >>> experiment in November, causing the app’s baseline install rate to drop >>> significantly (coincidentally around the end of the “new and updated” >>> promotion). Going forward, this means that the ongoing growth rate >>> (installs minus uninstalls) remains much lower than it was during most of >>> 2015. >>> >>> >>> iOS: 4.94k/day >>> >>> Download numbers from App Annie >>> >>> Context (last three months): >>> >>> Like for the Android app, there was a notable bump around Christmas, but >>> also an even larger spike on January 14 - the reason is not clear to us. >>> >>> App user retention >>> >>> Android: 16.8% >>> >>> (Ratio of app installs opened again 7 days after installation, among all >>> installed during the previous week. 1:100 sample) >>> >>> Context (last four months): >>> >>> Retention for installations since around Christmas appears to be higher; >>> but the data is quite noisy. >>> >>> 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) >>> >>> As reported earlier, we encountered data quality issues with this >>> metric. We eventually received an explanation from Apple last week, but it >>> doesn’t look very satisfactory for our purposes. We are now working >>> <https://phabricator.wikimedia.org/T126693> to measure retention >>> ourselves via EventLogging, like we do on Android. >>> Unique app users >>> >>> Android: 1.206 million / day >>> >>> Context (last four months): >>> >>> A clearly visible and lasting increase around Christmas, which again >>> indicates that the retention rates for the install peak around that time >>> were higher than during the install peaks caused by the Google Play >>> promotions (even though they resulted in more installs initially). >>> >>> iOS: 303 k / day >>> >>> Context (last four months): >>> >>> Similar to Android, a clearly visible and lasting increase around >>> Christmas. >>> >>> ---- >>> >>> For reference, the queries and source links used are listed below >>> (access is needed for each). Unless otherwise noted, all content of this >>> report is © Wikimedia Foundation and released under the CC BY-SA 3.0 >>> <https://creativecommons.org/licenses/by-sa/3.0/> license. 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*9) 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 >>> "2015-12-07" AND "2016-02-07"; >>> >>> 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 > 0 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 "2015-12-07" >>> AND "2016-02-07" 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 "2015-12-07" >>> AND "2016-02-07"; >>> >>> 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 > 0 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_02812522755211381933/stats/installs/ >>> (“overview”) >>> >>> >>> https://www.appannie.com/dashboard/252257/item/324715238/downloads/?breakdown=country&date=2015-12-07~2016-02-07&chart_type=downloads&countries=ALL >>> (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 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 >>> 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 >>> >>> _______________________________________________ >>> Analytics mailing list >>> analyt...@lists.wikimedia.org >>> https://lists.wikimedia.org/mailman/listinfo/analytics >>> >>> >> >> >> -- >> Oliver Keyes >> Count Logula >> Wikimedia Foundation >> >> _______________________________________________ >> Mobile-l mailing list >> Mobile-l@lists.wikimedia.org >> https://lists.wikimedia.org/mailman/listinfo/mobile-l >> >> > -- Tilman Bayer Senior Analyst Wikimedia Foundation IRC (Freenode): HaeB
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