Visitor Retention Analysis using Calculated Metric Function
#AdobeAnalyticsProfessionalAndRecruiters

Visitor Retention Analysis using Calculated Metric Function

After getting inspired by Adam Greco blog & Piermarco Burrafato article, I decided to play around and try to recreate the result using powerful calculated metric function in Adobe Analytics.

I would consider visitor retention cycle 6 month of time range instead of 24 month, which can be extended to any number of month as desired by the business requirement.

Before we start, lets understand the user metric definitions. New user are the acquired unique users this month. Repeat user are returned user from last six month. Retained user are the users this month which were also users last month. Resurrected user are repeat users this month that are not retained from last month but from some month prior to it.

Monthly Active Users = New users + Retained users + Resurrected users

Repeat Users = Retained users + Resurrected users

Step 1: Create date ranges for ‘monthly’ and ‘6 month' time ranges for each month

Here is an example of time range builder for ‘Two months ago’ and ‘6 months prior to 4 months ago’

Step 2: Create segments for ‘monthly user’, ‘monthly new user’ for each month

Here is an example of segment builder for ‘Monthly user (3 months ago)’, ‘New user (Last month)’

Step 3: Create segments for ‘monthly repeat user’, ‘monthly retained user’ for each month

Here is an example of segment builder for ‘Repeat user (4 months ago)’ and ‘Retained user (2 months ago)’

There are two ways to create these segments.

Option 1 : Segment definition without 'month selector'.

When two date range are having inclusive AND operation, the visitor counts are carried over to both of the month ranges.

Option 2 : Segment definition with 'month selector' time range as HIT container.

To exclude the visitor counts for earlier months, select 'month selector' time range as HIT container.

This option is better, if you want to represent visitor count for each month in separate column.

Step 4: Create ‘New user’ metric

Use nested If function in metric builder, and put 'Month number' criteria in each logical condition. Every false container would have IF-True-False logic for next month.

Step 5: Create ‘Repeat user’ & ‘Retained user’ metric,

Just replace the yellow highlighted ‘New user’ monthly segments with ‘Repeat user’ & ‘Retain user’ monthly segments created in step 3. Segment definition in option 1 is having less number of components and operational logic, so it would be faster to process. And good news is both of the segment definition returns the same result from calculated metric function.

I used 'Save as' method instead of rebuilding the entire metric.

Step 6: Create ‘Resurrected user’ metric

Resurrected users (metric)= Repeat users (metric) - Retained users (metric)

Optionally I have created additional metric which is aggregation of ‘New user’ & ‘Repeat user’ metric, the total count should match with Monthly Unique visitor column.

Step 7: Create metric table

These numbers should match with segments table created in step 3.

Note these custom component definition is focused for monthly based analysis. Therefore if you plan to use, smaller scale for your retention cycle such as a week, a day. For that, you would have to replace monthly time ranges with weekly or day wise time ranges.

I will be happy to help, if you have any question…


PS : ?? Join our group #AdobeAnalyticsProfessionalAndRecruiters if you are passionate about Adobe Analytics

Rishabh Motani

Product Head, Tata | Product Growth Leader at Airtel, Yatra | Building & scaling revenue across B2B, B2C business

7 年

Thanks for sharing this. However, what is the way of calculating % retained user (Retained users this month / Monthly user last month) and % Resurrected user (Ressurected user this month/Monthly user 5 months prior to last month)

Randy F.

Analytics Pro seeking new opportunities! Adobe Certified | Google Certified... Adobe Analytics/CJA | AEP/Launch | GA4 | GTM | Target | Tealium |

7 年

Great stuff, Pradeep Jaiswal! Can't wait to give it a try.

Pradeep Jaiswal

Solution Architect | Adobe AEP, RT-CDP, CJA, AJO, Analytics, Target

7 年

Tony Ferreira You need to first create custom time ranges(using component builder) for the desirable months per your use case, and then create segment using those custom time ranges. one can not create dimension in AA user interface, as they are basically variables from the data captured by AA server. I will b happy to assist you and walk you through each step, if you want to connect for a session. feel free to pm me.

回复
Tony Ferreira

Marketing Technology & Automation | B2C & B2B Customer Experience | Driving Business Growth & Operational Excellence | Strategic Data Driven Leader

7 年

Pradeep Jaiswal after following your steps I am unable to see the Months as a dimension for only the months chosen in my Time Range. No matter what I choose I see back to December. I used the same settings as you for the time ranges above, do you know why this may be happening?

Pradeep Jaiswal

Solution Architect | Adobe AEP, RT-CDP, CJA, AJO, Analytics, Target

7 年

Ben Gaines is there any plan to have date/time range component available in calculated metric builder? I would love to get your feedback as well..

要查看或添加评论,请登录

Pradeep Jaiswal的更多文章

  • OneTrust Implementation in Adobe Launch

    OneTrust Implementation in Adobe Launch

    OneTrust offers a comprehensive platform to help businesses achieve and maintain compliance with the California…

    11 条评论
  • Adobe AEP-CDP-WebSDK Implementation WhatsApp Group

    Adobe AEP-CDP-WebSDK Implementation WhatsApp Group

    Knowledge is like money, to be of value it must circulate, and in circulating it can increase in quantity and…

    47 条评论
  • ADOBE ANALYTICS ARTICLE/POST INDEX

    ADOBE ANALYTICS ARTICLE/POST INDEX

    Over the years, I have read a few articles/posts related to Adobe Analytics to upgrade my knowledge. I consistently…

    26 条评论
  • Anomaly detection - Non programming way

    Anomaly detection - Non programming way

    I was once asked, how to determine anomaly/outlier in data set without using any analytical tool or programming…

    11 条评论
  • Adobe Analytics Professional & Recruiters

    Adobe Analytics Professional & Recruiters

    Knowledge is like money, to be of value it must circulate, and in circulating it can increase in quantity and…

    287 条评论
  • Abuse of 'Reply to All' ..!!

    Abuse of 'Reply to All' ..!!

    Either people don’t know what reply all is, or social networks have convinced them that broadcasting is the only…

    2 条评论

社区洞察

其他会员也浏览了