In wake of app revolution for a while I felt Google Analytics has missed the App bus and will never be able to catch up with other analytics vendors who specialize in app tracking.
But recent updates have made me change my opinion and if GA continues on this path it may turn out to be as useful for mobile apps as it is for websites.
What made me change my opinion?
2 reports which are important for any platform but are imperative for analyzing mobile app were released by GA during last couple of months. These reports are “Lifetime Value” and “Cohort Analysis”.
I am very eager to write a detailed post about these reports and will do so as soon as I get some breather. But in the meanwhile lets discuss these 2 reports briefly:
There is a very fundamental difference between these 2 reports and any other GA report. This difference is reporting period. As we are all aware primary use case for analytics is, understanding cause and effect relationship. But marketing teams all over the world started realizing cause and effect is not a one to one relationship and one cause may(I am tempted to say does) actually lead to multiple effects over period of what I would like to call is ripple time.
What is a ripple time?
Consider a pebble hitting calm waters as cause which will lead to creation
of multiple ripples (effects) over next few seconds and then eventually ripples will fade away. I would like to define ripple time as timestamp of last ripple fading away – timestamp of pebble hitting the water.
Like pebble hitting water as a cause, effects in to multiple ripples in finite period of time every marketing event leads to several effects in user interaction with your data. (I am using word data here because this data can be served via website, mobile app, wearable device, smart tv and god knows what else smart is coming up). This is especially important in case of mobile app because after installing a mobile app user interaction may lead to multiple micro / macro conversions.
When you run any normal GA report it gives you cause and effects which happened during a finite reporting period but what it fails to do is to show how ripples are weakening over period of time and when do you need to drop another pebble to continue enjoying ripple effect. Also a normal GA report will let you chose only one reporting period so you cannot study cause effect relationship where cause and effects are in 2 different time frames. E. g. Cause is an advertisement in newspaperX which has led to acquisition of 100K new users for your app. In this case you can report that you have acquired 100k users because of Newspaper advertisement but you cant report how those users behaved over next 2 months and how many macro and micro conversions they effected. Now this might come in really handy if you want to compare newspaperX vs newspaperY.
This is where these to report differ from other report and you can chose a time period for causes and then study data of causes over period of time.
Which in marketing means ,these reports are able to show you how long you can enjoy micro and macro conversions from your marketing activity. There are several other use cases for these reports but for me this is the most important use case because this fundamentally changes the way we think about analytics, reporting and the time itself.
Some of my friends argue that this was always possible with use of custom variables / dimensions but I personally believe the granularity that we get with these report was practically impossible to achieve with this work around.
Looking forward to write about both of these reports along with “Monthly Active Users” Report.
Digital Marketing / Web &amp; App Analytics expert with experience ranging from small businesses to very large portals. Across varied verticals like banking, travel, ecommerce, insurance. Google Adwords top contributor since 2013 and currently working with a leading Indian ecommerce portal. Aspiring writer, wants to be lighter!
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