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Hey all! I've got a quick and simple (I promise!) use case for the Analytics > Features > Adoption view.





Some team members noticed, through some monitoring systems we have, that our users broke previous usage records in July. This is especially surprising to us, since our user base primarily follows the academic calendar. We also had very few support tickets, so we really weren't expecting a high amount of usage that month.





Question: Which accounts were driving a spike in usage?





I was able to pull up a quick report in Analytics > Features > Adoption:











With this view, we were able to determine that 2 accounts in particular (blue and green above) were the main drivers of the usage spike.







  • Events Grouped by Accounts





  • Date Range: 7/1/19-7/31/19





  • Pie Chart Enabled




    • Note: It was very helpful to have the pie chart in this case. We weren't necessarily interested in the day-by-day events per account; we more wanted to see the frontrunners in total for the month. That said, it would be nice to make the pie chart the primary view, rather than being small and to the side.

You could probably stop there with some good data, but if you're adventurous/more interested in the product/support side, keep reading.











If you want to end here, you can skip down to the picture of my dog. 🙂





Then this got me thinking... we saw that







  1. These accounts had a high amount of usage AND





  2. Very few support tickets



Which made me ask the question:





Which features were those accounts using frequently without generating a high volume of support tickets?







  • Events Grouped by Features





  • Account filter set to OR




    • Account name is _____, _____, _____ (I filled these in with the top 3 identified in the report above)


  • Date Range: 7/1/19-7/31/19







I was able to find that all but 1 one of the top 10 features in this view were related to one area of our product. This is encouraging to us, since that area of our product is what our CS team is focused on driving our users to!





As promised... here's a picture of my dog 🙂









Amazing, thank you so much for this analysis recommendation.





P.S. Your dog deserves a treat for allowing you to publish that totally cute profile pic! :)



Sure thing!





P.S. I was definitely holding a treat next to the camera lens when taking this photo 🙂



I like how you slice and dice the data and tied two views of the same report together to deduce conclusions!



Fantastic!! While we recommend this as part of our recommneded analysis during onboarding, it's great to see some of you actually carry out this analysis!!!





Thanks for sharing @julie_pinto



Thanks! Whenever I see a dataset, I usually get curious and want more information. So I'm glad there's lots of ways to splice and dice the data in PX!



Definitely! It was cool to see that one of the more straightforward analysis views was able to answer our question today. The huge advantage for us was that it only took a few minutes to extract this information. It lets us just be curious about the data, rather than needing an established need to pull a complex report together.



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