Problem: Often times you share some use-cases around
- Absence of Data - Companies without Risk CTA, Companies without timeline update this qtr, Companies without manual health score updates in a qtr, etc
- Latest X - Latest timeline entry for a company, Latest update in a CTA, etc.
- Common Customer Success Analytics - Functions to calculate CS metrics, Ranks, Window Functions
While technically all the above are possible using Data Designer, its not very straightforward for someone who is not very much familiar with the product. Also for those who are familiar, its not as easy a task to accomplish.
After our current enhancements to Data Prep (Union, Pivot), I am thinking more about some out of the box data prep / analytics functions in Data Designer. I would love your thoughts and inputs on this. More specifically:
- Anecdotes where you wish things are more OOTB
- Any usecases that you were tasked with or wanted to solve in your CS orgs (Also why would help here)
- What analytical capabilities (in Explore and in Reporting) can help you provide that visibility from your data?
Vote this if you like this direction.
This sounds great Rakesh. One common use case I see a lot of customers run into is being able to calculate simple percentages without having to create a whole bunch of transformations. For example, if I’m reporting on cases, I’d like to be able to create a widget that - for example - can calculate the percentage of COUNT of tickets closed in past 90 days divided by COUNT of total cases opened in past 90 days. a.k.a. let us be able to aggregate fields and use them in formulas all in one dataset. Let me know if that makes sense.
This sounds great Rakesh. One common use case I see a lot of customers run into is being able to calculate simple percentages without having to create a whole bunch of transformations. For example, if I’m reporting on cases, I’d like to be able to create a widget that - for example - can calculate the percentage of COUNT of tickets closed in past 90 days divided by COUNT of total cases opened in past 90 days. a.k.a. let us be able to aggregate fields and use them in formulas all in one dataset. Let me know if that makes sense.
This is a good example of Window function Spencer!