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Creating CTA based off of low healthscores

  • 14 December 2018
  • 4 replies
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Hi!



I could really use some help on creating the logic to trigger this CTA.



Goal: To fire CTA if the account has had 3 months in a row of 'risky' healthscores. For our instance, I am just trying to look at the overall score instead of each individual measure. So, if the overall score for the account was under '40' for 3 consecutive months, I would like to fire a CTA for the CSM.



I'm working off of the Account Scorecard History MDA object. The current problem I am having is that the logic will pull in accounts that have had a 'risk' healthscore at least once in the previous 3 months but otherwise have been above that. So, I don't want to fire the CTA for these accounts.



I think I need to use a few more steps in the rule but unsure on how to proceed. Is there a Transformation task I could leverage which would look for that healthscore to be risky for 3 straight months?



Any help would be appreciated!!


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Best answer by dan_ahrens 14 December 2018, 16:40

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Userlevel 7
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Hi Manmeet,



You could do this by first fetching using an aggregation to count the number of weeks where the score was below 40 in the past 90 days, like this:







This will give you one row per customer with a count of how many weeks they had a risky (below 40) overall score.



Then filter this list in a transform task to show accounts where the number of weeks is 12 or greater (3 straight months of risky scores).


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Hi Dan,



Thank you so much for your help again!



I think the part I am struggling with is the transform task part. I am not quite sure how to filter the initial fetch task in this second task.



Do I use the Snapshot Date field in the "Filters" section? Is it simple as Snapshot Date = Subtract N Days From Rule Date = 90 days?



Sorry if I am completely off base here! I really feel like I don't understand the Transform task functionality that well and struggle here.


Userlevel 7
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In your transform task, pull in the data from the fetch task and then apply a filter as shown below:






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Thank you for the quick response! Really appreciate the help here.


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