Hi Pine,
I think for those who are not used to working with it, the day 7 retention
rate can look low, but it's important to remember that this day 7 retention
metric is not the number of users who continue to use the app after 7
days.  It is the number of people who actually visit on the 7th (not the
6th, not the 8th, not the 100th) day after using it for the first time.  It
is a standard app metric and we fit well within the benchmark here.  We are
working on a similar metric for the web, and early results suggest the
number for both apps is much higher than the web.

-J


On Tue, Feb 14, 2017 at 10:54 PM, Tilman Bayer <[email protected]> wrote:

> Hi Pine,
>
> out of curiosity, what is the "rather low" assessment based on? Does this
> refer to an industry standard (links welcome), or is it more a subjective,
> personal impression?
>
> In any case, thanks for reading the report and sharing your thoughts -
> glad to see that it stimulates metrics-based thinking.
>
> On Tue, Feb 14, 2017 at 8:33 PM, Pine W <[email protected]> wrote:
>
>> Thanks Zareen. I'm particularly interested in the mobile app retention
>> percentages, which seem rather low. I wonder if it would make more sense to
>> take all the money and employee hours that are currently being invested in
>> mobile apps, and redirect those resources to mobile web.
>>
>> Pine
>>
>>
>> On Tue, Feb 14, 2017 at 4:45 PM, Zareen Farooqui <[email protected]>
>> wrote:
>>
>>> Link to PDF of report in Commons
>>> <https://commons.wikimedia.org/wiki/File:Wikimedia_Readership_metrics_for_the_timespan_until_February_5,_2017.pdf>
>>>
>>> Hi all,
>>>
>>> Here is the usual look
>>> <https://www.mediawiki.org/wiki/Reading/Readership_metrics_reports> at
>>> our most important readership metrics. This time we see an overall rise in
>>> pageviews following the seasonal winter slump, examine the recent
>>> year-over-year growth in pageviews more closely, and introduce a new day-7
>>> retention metric for the Wikipedia iOS app.
>>>
>>> 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, and facilitate thinking about core
>>> metrics in general. As always; feedback and discussion welcome.
>>> Week-over-week and month-over-month changes are being recorded on the
>>> Product page <https://www.mediawiki.org/wiki/Wikimedia_Product#Reading>
>>> at MediaWiki.org. This edition of the report covers a timespan of five
>>> weeks.
>>>
>>> You can also find lots of other traffic and usage data in the quarterly
>>> metrics presentation
>>> <https://commons.wikimedia.org/wiki/File:Wikimedia_Foundation_Reading_metrics_Q2_2016-17_(Oct-Dec_2016).pdf>
>>> for Q2 2016-2017 (October - December) that was just published by the WMF
>>> Reading team.
>>>
>>>
>>> All numbers below are averages for January 2 - February 5, 2017 unless
>>> otherwise noted.
>>> Pageviews
>>>
>>> Total: 582 million/day (+10.14% from the previous report)
>>>
>>>
>>> Context (April 2015-February 2017):
>>>
>>>
>>> See also the Vital Signs dashboard
>>> <https://analytics.wikimedia.org/dashboards/vital-signs/#projects=all/metrics=Pageviews>
>>>
>>> After the seasonal winter slump, we see a rise in desktop pageviews, as
>>> expected. Mobile pageviews continue to remain at higher levels than before
>>> christmas. The previously mentioned iOS app’s pageview increase is still
>>> under investigation, and may turn out to be an anomaly
>>> <https://phabricator.wikimedia.org/T154735> inflating mobile pageviews
>>> by roughly 5 million views per day.
>>>
>>> This chart looks at long-term traffic trends from May 2013 - January
>>> 2017. This shows that over this timespan, the annual change in overall
>>> pageviews was -2%, desktop has been down 15%, and mobile (web + apps) has
>>> been trending upwards at a rate of 23% per year. However, the past few
>>> months have seen total pageviews increasing year-over-year (chart further
>>> below).
>>>
>>> To facilitate our understanding of which traffic movements are seasonal
>>> and which may indicate lasting changes, here is a chart overlaying the
>>> total pageview numbers back to May 2013 (the earliest time for which we
>>> have data according to the current pageview definition):
>>>
>>>
>>> Total pageviews have continued rising and are now higher than before the
>>> winter holidays. The blue line shows that the increase in overall pageviews
>>> year-over-year remains (January 2017 is up 5% from January 2016). It is
>>> possible that a smaller part of this is due to unidentified bot traffic
>>> (e.g. we just updated <https://phabricator.wikimedia.org/T157528> the
>>> pageview definition to exclude a recently discovered bot that had been
>>> causing up to 0.9% of total pageviews). But overall it is starting to look
>>> like a small but sustained rise in real human pageviews.
>>>
>>> Here we see that the changed trend in recent months can be attributed to
>>> desktop pageviews, which have mostly stopped declining year-over-year. This
>>> chart also shows that the seasonal christmas dip in pageviews comes
>>> predominantly from desktop views. Most recently, January 2017 desktop
>>> pageviews are again down 1.8% from January 2016, but that is still much
>>> less than the aforementioned -15% yearly trend since 2013.
>>>
>>> Year-over-year mobile pageviews are still increasing at a notable rate,
>>> but slower than in 2013-15. January 2017 mobile pageviews are up 13% from
>>> January 2016, less than the yearly mobile growth of 23% in the overall
>>> 2013-17 timespan. We can see how mobile pageviews always increase around
>>> the winter holidays and maintain higher levels afterwards.
>>>
>>> Desktop: 50.9% ​(previous report: ​51.5%)
>>>
>>> Mobile web: 46.7% ​(previous report: 46.8%)
>>>
>>> Apps: 2.4% ​(previous report: ​1.7%)
>>>
>>> Mobile percentage dropped a bit following the christmas spike, but
>>> remains high at an average of 49% mobile views. The previously mentioned
>>> iOS app’s pageview increase is still under investigation, and may turn out
>>> to be an anomaly <https://phabricator.wikimedia.org/T154735> inflating
>>> this metric by about 1%.
>>>
>>> Global North ratio: 77.3% of total pageviews (previous report: 76.8%)
>>>
>>> Context (January 2016-February 2017):
>>>
>>> Although Global North pageview percentage fell following the winter
>>> spike, but overall this metric grew since the last report. Levels remain in
>>> the high 70 percentile range.
>>>
>>> NB: We are currently rethinking this metric and might replace it with a
>>> different country selection constructed as part of the work on the New
>>> Readers project
>>> Unique devices
>>>
>>> See the announcement blog post
>>> <https://blog.wikimedia.org/2016/03/30/unique-devices-dataset/> from
>>> March 2016 for background and details on this metric. These estimated
>>> numbers are provided for all Wikimedia language projects (separately for
>>> the desktop and mobile web version). Because of the instrumentation method,
>>> there is no global metric for all projects and all languages, but it is
>>> currently being extended <https://phabricator.wikimedia.org/T138027> to
>>> a cross-language global metric per project at least. For now, we track the
>>> daily numbers of English Wikipedia in this report.
>>>
>>> Daily unique devices estimate for English Wikipedia:
>>>
>>>    -
>>>
>>>    Mobile web: 36.3 million
>>>    -
>>>
>>>    Desktop: 24.3 million
>>>    -
>>>
>>>    Total: 60.6 million
>>>    -
>>>
>>>    Average mobile web ratio: 60.0%
>>>
>>>
>>>
>>> Context (January 2016- February 2017):
>>>
>>> Since the last report, both daily unique desktop devices and mobile
>>> devices grew. Mobile web daily uniques mirrors the daily pageviews chart -
>>> mobile web unique levels have remained higher than before the winter bump.
>>>
>>> January 2017 was the first month we could compare year-over-year changes
>>> in unique devices. While pageviews on English Wikipedia are up from January
>>> 2016, the number of monthly unique devices is down 16%. For this report
>>> timespan (Jan 2 - Feb 5), daily mobile web uniques are up 2.6%, while daily
>>> desktop uniques are down 8.2%. This is currently being investigated.
>>>
>>>
>>> While the number of daily unique desktop devices has grown overall, the
>>> ratio of pageviews to devices on desktop has steadily declined back to pre
>>> christmas levels. The small, but notable increase in daily views per device
>>> for mobile web which happened in November has held up.
>>>
>>> New app installations
>>>
>>> Android: 18.0k/day (-5.9% from the previous report)
>>>
>>> Daily installs per device, from Google Play
>>>
>>> Context (last nine months):
>>>
>>> The number of daily installs dropped after the christmas spike, but
>>> remains higher than the number of uninstalls (except for January 26).
>>> Overall, the number of daily installs has dropped nearly 6% and daily
>>> uninstalls dropped about 8% since the last report.
>>>
>>> iOS: 5.67k/day (+6.5% from the previous report)
>>>
>>> Download numbers from App Annie
>>>
>>> Context (last two months):
>>>
>>> [image: Wikipedia iOS app daily downloads by country, Dec 6, 2016 - Feb
>>> 5, 2017 (App Annie).png]
>>>
>>> Similar to other mobile metrics measured in this report, the iOS app
>>> download metric has remained higher than usual following the winter bump.
>>> During the timespan of this report, iOS app downloads remain fairly steady
>>> besides two large, unusual spikes at the end of January in the United Arab
>>> Emirates and the United States (which follow the spike from the Netherlands
>>> in December) which are likely anomalies. We are looking into filing a bug
>>> report with App Annie to investigate this.
>>> App user retention
>>>
>>> Android: 15.6% (previous report: 16.8%)
>>>
>>> (Ratio of app installs opened again 7 days after installation for all
>>> dates that fall within this report. 1:100 sample)
>>>
>>> Context (last six months):
>>>
>>>
>>> As remarked in earlier reports, this data is a bit too noisy for drawing
>>> conclusions about whether retention changed significantly between different
>>> releases. However, we can at least rule out the existence of major shifts
>>> during this timespan. There was a small spike in day 7 retention for
>>> installs on January 22nd.
>>>
>>> iOS: 19.2% (previous report: N/A
>>> <https://phabricator.wikimedia.org/T126693>)
>>>
>>> (Ratio of app installs opened again 7 days after installation for all
>>> dates that fall within this report. 100:100 sample since iOS app only sends
>>> data when users opt-in so there is a small user base)
>>>
>>> Context (last two months):
>>>
>>>
>>> After a long break where we refrained from reporting iOS retention here
>>> due to data quality issues with the number provided by Apple, we are happy
>>> to report iOS app 7 day retention again, based on a new EventLogging
>>> instrumentation that parallels the one on Android. There doesn’t seem to
>>> have been any significant changes in retention levels during the timespan
>>> of this report, even after new releases of the app. Looking back at
>>> December though, it appears that there was a period of lower retention
>>> which roughly corresponds with stability issues (crashes) the app was
>>> having at this time, and which have since been fixed.
>>> Unique app users
>>>
>>> Android: 1.211 million / day  (+2.7% from the previous report)
>>>
>>> Context (last eight months):
>>>
>>> Android daily average levels have remained higher following the
>>> christmas and new year’s bump. This matches the mobile pageviews and unique
>>> mobile devices metrics.
>>>
>>> iOS: N/A <https://phabricator.wikimedia.org/T130432>
>>>
>>> Zareen Farooqui, Data Analyst Intern, Wikimedia Foundation
>>>
>>> Tilman Bayer, Senior Analyst, Wikimedia Foundation
>>> Data sources
>>>
>>> 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/wiki/Category:Wikimedia_readership_metrics_reports>
>>>
>>>
>>> SELECT year, month, CONCAT(year,"-",LPAD(month,2,"0")) as yearmonth,
>>> SUM(view_count) AS allhuman, sum(IF(access_method = 'desktop', view_count,
>>> null)) AS desktophuman  FROM wmf.projectview_hourly WHERE year = 2017 AND
>>> agent_type = 'user' GROUP BY year, month ORDER BY year, month LIMIT 1000;
>>>
>>> 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 = 2017 AND agent_type = 'user' GROUP BY year, month, day ORDER BY
>>> year, month, day LIMIT 1000;
>>>
>>> SELECT access_method, SUM(view_count)/(7*5) FROM wmf.projectview_hourly
>>> WHERE agent_type = 'user' AND 
>>> CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0"))
>>> BETWEEN "2017-01-02" AND "2017-02-05" GROUP BY access_method;
>>>
>>> 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,I
>>> S,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 = 2017 AND agent_type='user' GROUP BY
>>> year, month, day ORDER BY year, month, day LIMIT 1000;
>>>
>>> SELECT year, month, day, 
>>> CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0"))
>>> as date, SUM(IF(uri_host LIKE 'en.m.wikipedia%', uniques_estimate, 0)) AS
>>> enwiki_mobile_web FROM wmf.last_access_uniques_daily WHERE year=2017 GROUP
>>> BY year, month, day ORDER BY year, month, day LIMIT 1000;
>>>
>>> SELECT year, month, day, 
>>> CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0"))
>>> as date, SUM(IF(uri_host LIKE 'en.wikipedia%', uniques_estimate, 0)) AS
>>> enwiki_desktop FROM wmf.last_access_uniques_daily WHERE year=2017 GROUP BY
>>> year, month, day ORDER BY year, month, day LIMIT 1000;
>>>
>>> SELECT year, month, day, 
>>> CONCAT(year,"-",LPAD(month,2,"0"),"-",LPAD(day,2,"0"))
>>> AS date, SUM(IF(access_method = 'mobile web', view_count, null)) AS
>>> mobilewebviews, SUM(IF(access_method = 'desktop', view_count, null)) AS
>>> desktopviews FROM wmf.projectview_hourly WHERE year=2017 AND agent_type =
>>> 'user' AND project = 'en.wikipedia' GROUP BY year, month, day ORDER BY
>>> year, month, day LIMIT 1000;
>>>
>>> https://console.cloud.google.com/storage/browser/pubsite_pro
>>> d_rev_02812522755211381933/stats/installs/
>>>
>>> https://www.appannie.com/dashboard/252257/item/324715238/downloads/
>>>
>>>
>>> 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 '2017%' AND userAgent LIKE '%-r-%' AND userAgent NOT LIKE
>>> '%Googlebot%' GROUP BY date ORDER BY DATE;
>>>
>>> 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 '%iPhone%' OR userAgent LIKE '%iOS%' GROUP BY date ORDER BY DATE;
>>>
>>> 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 year =
>>> 2017 AND platform = 'Android';
>>>
>>> Zareen Farooqui
>>>
>>>
>>> _______________________________________________
>>> Mobile-l mailing list
>>> [email protected]
>>> https://lists.wikimedia.org/mailman/listinfo/mobile-l
>>>
>>>
>>
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>>
>>
>
>
> --
> Tilman Bayer
> Senior Analyst
> Wikimedia Foundation
> IRC (Freenode): HaeB
>
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>
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