In the pre­vi­ous post Cohort Analy­sis was intro­duced and we set up Clas­si­fi­ca­tions to be able to do ana­lyze App Install Cohorts in Site­Cat­a­lyst. This post will con­tinue from that point and focus on using Adobe Report Builder for robust Mobile Cohort Analysis.

If your App Install date eVar expi­ra­tion has not been set to “never” use the fol­low­ing; oth­er­wise, skip to sec­tion B.

Sec­tion A, using Seg­ments for Cohort App Install Month Analysis

If the eVar cap­tur­ing App Install Month is not set to expire “never”, then you will need to use Site­Cat­a­lyst seg­ments to get the data for each cohort. Cre­ate a Vis­i­tor seg­ment for each monthly cohort where “App Install Month equals (some month)”. Each seg­ment should look like the exam­ple below.

Cohort Visitor SegmentThen cre­ate a data block in Excel using the Report Builder tool and the Life­Cy­cle met­rics avail­able from the App­Mea­sure­ment libraries as well as any engage­ment met­rics used for the app. Be sure to use the vis­i­tor seg­ment for the App Install Month and cre­ate a data block for each month’s cohort.

Example Report Builder Data Block 1

Use Excel for­mu­las to reor­ga­nize the data to appear like the data blocks below that high­light one met­ric per data block, and where each cohort is on a dif­fer­ent row.

Sec­tion B, using App Install eVar with “never” expire for Cohort App Install Month Analysis

In Report Builder cre­ate a data block of 12 Month Cohorts trended over 12 months. In the exam­ple below App Install cohorts from 2012 are trended across all of 2012 by month. The data block starts in cell D10 with cohorts run­ning down col­umn D, while month data is trended across columns E through P. Use the Life­Cy­cle met­rics from the App­Mea­sure­ment library or any event you are track­ing in the app.

Data Block Example 2

Arrange the data using Excel for­mu­las so that the columns con­tain met­rics for “1 month after install”, “2 months after install”, etc. by copy­ing the Cohort names from D27 through D38. And label “Month 1″, “Month 2″, etc. across row 27. Next, reor­ga­nize your data to pull in the met­ric accord­ing to the length of time it occurred after Install. If you have fol­lowed the Excel loca­tions used in the exam­ples above, you may use the for­mula: =IFERROR(INDEX(E12:P23,ROW($D27)-ROW(D26),ROW($D27)-ROW(D26)+COLUMN(E$26)-COLUMN(E26)),”-”). This for­mula can be copied/pasted or dragged across from E27:P38 and will dynam­i­cally pop­u­late all the met­ric data for  your cohorts across the entire time period.

Excel Cohort Formula Example

Add Excel’s Con­di­tional For­mat­ting Color Scales to the chart, along with any other for­mat­ting changes you pre­fer and you have Cohort data you can start to ana­lyze! The met­ric for App Launches was used in the exam­ples above, but any event can be used in Cohort Analy­sis and may pro­vide dif­fer­ent clues for your Mobile App’s user experience.

cohort analysis 1

For exam­ple, when you cre­ate cohort data for launches per month, you can start to see how many months after down­load the user engage­ment with app begins to decline. In the exam­ple you can see that around 6–8 months after install users start to launch the app less often. Con­se­quently, the 6–8 month time period could sig­nify an oppor­tune time to start to re-market app fea­tures or upgrades to users to keep them engaged. It could also sig­nify the life span of the app and allow mar­keters to adjust spend accord­ingly depend­ing on the app itself.

Cohort Analysis 2

Look­ing at churn rate by Cohort allows mar­keters to keep an eye on app loy­alty by seg­ment. From the data we can see that our Jan­u­ary cohort (which in the exam­ple is the first group of App Installers) does not reach a churn rate of 65–70% until 10–12 months out. While our July and August cohorts are reach­ing that same churn rate at only 4–5 months out. This is show­ing that our early adopters are not only installing the app ear­lier but also stick­ing around longer. You may be famil­iar with the inno­va­tion adop­tion curve with “Early Adopters” engag­ing read­ily, fol­lowed by “Major­ity Con­ser­v­a­tive” and finally “Skep­tics”. Cohort analy­sis may help you make deci­sions on whether this applies to your app and how to mar­ket to the dif­fer­ent cohorts.

Cohort Analy­sis was also a high­light of the Advanced Mobile Ana­lyt­ics ses­sion at Sum­mit 2013, you can click through to view the ses­sion and more!

 

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