I am working to identify customers device sharing behavior, where user can login with two different credential on the same device. We have concept of "customer id" which is used in identity-map on top of ECID and wondering if we can create segment: where "customer id" > 1 for same ECID. Any though or idea will be helpful.
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Shared device scenario is not handled properly in AEP as of now. Last time I looked into this problem, we were not able to leverage IdentifyMap object in AEP Segments which contains mapping of ECIDs to customer ids.
if you are just doing the analysis, maybe you can try to find shared device scenarios through query service by running queries on Analytics, AAM and other datasets which are feeding RTCDP.
@amarjeetsingh You can try something like this...
I am assuming you have a situation where same customer ID's exists for multiple ECID's and you have an event for logins.
1. Create an audience in the form of a segment for 'Known customers'
2. Create a segment & select 'Event>Environment>Type' = Browser, App etc.
3. Go to Audiences and select the segment / audience you created in Step 1.
4. Select to aggregate the events of login, events of login > 2
5. The new segment will contain people who have logged in twice with different logins but with same browser for example.
Hopefully, this helps.
Thanks for the response!
Unfortunately we don't have events for Login but i got what you mean!
Basically i was wondering if we can create look back segment where we can create a segment not just at event level but instead at visitor level as well (Something we have in AA) that will resolve my problem.
Thanks
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Shared device scenario is not handled properly in AEP as of now. Last time I looked into this problem, we were not able to leverage IdentifyMap object in AEP Segments which contains mapping of ECIDs to customer ids.
if you are just doing the analysis, maybe you can try to find shared device scenarios through query service by running queries on Analytics, AAM and other datasets which are feeding RTCDP.
Views
Likes
Replies