Once upon a time, a Sales Ops leader was informed of a big problem that one of his analysts had recently discovered…
On nearly every dimension, the salesperson responsible for the Southwest territory was significantly under-performing every other salesperson in the company. The data was crystal clear. This guy had been the worst performer in the bunch…by far…for multiple quarters in a row.
Frustrated by the obvious lack of management that had allowed a performance problem of this magnitude to continue for so long, the Sales Ops leader marched straight to the Sales VP’s office. And in dramatic fashion worthy of Perry Mason, he made his case and presented the data as the smoking-gun evidence…
Which the Sales VP utterly destroyed with a single question:
“Why would we expect this rep’s performance to be anything like the others?”
The Sales VP then pointed out how dramatically different this rep’s territory was from the others. He highlighted how this rep’s prospect pool didn’t look anything like what the other reps had available to them. And then the Sales VP ended his verbal dress-down by saying, “It seems to me that this rep is actually doing a pretty good job…all things considered.”
Beyond the sheer embarrassment this Sales Ops leader must have felt, he also damaged his team’s credibility. While everyone makes mistakes, the Sales VP will never forget that time when Sales Ops jumped to conclusions, based on a shallow analysis, and tried to get someone fired. And as a result, he’ll think twice…maybe even three, four, or five times…before accepting any of Sales Ops’ conclusions and recommendations in the future.
This is an extreme example, to be sure. And there’s a whole lot wrong with how the Sales Ops leader went about it in this case. But this cautionary tale does shine a spotlight on a couple of very important concepts:
- Underlying Segmentation — With proper segmentation to control for the variables and ensure apples-to-apples comparisons, the “problem” in this case would likely have disappeared entirely. And if it did happen to persist, the underlying segmentation would certainly make it much more difficult to brush away the conclusions by arguing that the comparison was wholly invalid to begin with.
- Root-Cause Diagnostics — Even without the segmentation to ensure a valid comparison, this Sales Ops leader would have saved himself a lot of trouble and embarrassment by asking “why?” a couple of times instead of just jumping to conclusions and blaming the rep. After all, the obvious “proximal cause” is rarely the actual “root cause”. And had this Sales Ops leader drilled even just one or two levels deeper before calling for heads to roll, he could have recognized how the makeup of the territory and prospect pool might account for the performance differences.
For Sales Ops groups, garnering enough credibility to be viewed as something more than a tactical support group is already a bit of an uphill battle. And as such, you really can’t risk eroding the credibility you’re building by making mistakes like those in the case above.
So learn the fundamentals of comparative sales analysis, root-cause diagnostics, change management, and so on. They’ll not only help you make progress and generate results; they’ll help protect your credibility along the way.
Diagnosing Sales Problems
Failing to identify the true root causes of sales performance problems often leads to a frustrating game of Whack-A-Mole. In this on-demand training webinar, learn how to identify and correct the real root causes behind sales performance issues.
The Fundamentals of Effective Sales Analysis
In this on-demand training webinar, we share the fundamental concepts and principles behind effective sales analysis, expose the critical building blocks that need to be in-place, and walk through a basic analysis example to pull everything together.