#oddlysatisfying data diving

Have you ever checked out the r/oddlysatisfying stream on Reddit?  My teenagers showed it to me a couple years back and I've become a huge fan.  Here is an #oddlysatisfying for all of you fellow #CRMnerds out there.

I'm working through some reports in our own Sugar system right now as I prepare our quarterly scorecard that we share across the company and with the board.  In that effort, I was coming up with a different MQL count for the quarter than what our marketing operations team had.  

I'm a bit of a data junky.  I admit it.  I love digging through data, finding trends, discovering hidden insights.  I've become a huge fan of our two new CRM data tools, Sugar Discover and Sugar Predict.  Finding data discrepancies actually gets me excited to go solve the puzzle.

In reviewing our MQL counts for last quarter, I was surprised to find a discrepancy of a couple hundred leads in my report versus what marketing ops was publishing.  We get thousands of leads a quarter, so a difference in a couple hundred leads isn't huge.  But at the same time, a couple hundred leads is a couple hundred leads.  Must. Find. Answer.

So I started spelunking through the data, looking at leads by campaign, by geo, by assigned to, by created by.  Soon enough, we found that leads coming from some of our newly deployed lead tools (chat, Exceed.ai and SalesLoft) were getting categorized in a way that wasn't getting captured in our reports in an expected way.

Most importantly, we had an "oh sh*t" moment of worrying that these leads weren't being followed up on.  Quick enough, we determined that the leads were being pursued as expected.  It was just a report filter issue.

Report filters were updated to properly capture these new lead records and all of our scorecards matched up again.  Big smile, and that #oddlysatisfying feeling, of reports tying up properly and data discrepancies resolved.

What a #CRMnerd!  And proud of it.