Sales Operations teams can spend quite a bit of time and energy trying to make our analyses as accurate and precise as possible.
We improve the segmentation by adding a seventeenth attribute to the model. We clean the data again…and again. We refine the conversion and yield estimates to yet another decimal place. We run another scenario…or four…or ten. And we debate…at length…whether the appropriate recommendation is 14.57% or 15.12%.
Of course, none of this is inherently bad. After all, a better analysis is just…well…better.
But at some point, we need to ask an uncomfortable question…
Is analytical accuracy and precision really the thing that’s holding back our improvement efforts? Really?
Very often, the Sales Ops team already has a reasonably good analysis. Now, maybe it isn’t perfect. Maybe the segmentation could be a little more granular, the trade-off estimates could be tighter, and the recommended action steps could be refined even more.
But the analysis is probably sound enough to generate meaningful improvement without any adverse impacts.
In many cases, the bigger problem is that no one is acting on the analysis…no matter how accurate or precise it may be.
Reps are ignoring the guidance whenever they want. Managers are approving exceptions according to their mood. Business units are picking and choosing when to apply the recommended procedures. The top brass agrees with the initiative in principle…but then loses interest once the implementation creates the tiniest bit of internal friction.
Meanwhile, Sales Ops is working to fine-tune the analysis even more.
There’s something a bit crazy about spending more time improving the analytical accuracy of recommendations that only get followed 50% of the time.
Think about the basic math…
Suppose your current analysis gets you 80% of the way to the theoretically perfect answer, but the organization only executes that answer half the time. Would you generate more value and impact by spending the next six months getting the analysis from 80% accuracy to 90% accuracy?
Or by getting adoption and compliance from 50% to 75%?
While these numbers are hypothetical, the underlying point is not.
Once our analysis reaches some reasonable level of accuracy, the limiting factor shifts from analytical sophistication to organizational execution. And once that happens, continuing to pour a bunch of resources into further analytical refinement is a recipe for diminishing returns.
This can be an uncomfortable realization for data-driven sales operations practitioners. Analytical work feels productive. It’s tangible. We can build models, run analyses, identify patterns, and make things mathematically better.
On the other hand, influencing and persuading commercial teams to change their behavior? That’s much harder to model. Organizational dynamics don’t fit neatly into a spreadsheet. And there’s no regression analysis that will make a regional sales VP stop approving every oddball exception request that lands in his inbox.
But unfortunately, inside that pile of messy organizational stuff is where some of the biggest improvement opportunities will be found.
Now…none of this means Sales Ops should stop caring about analytical accuracy. Obviously, garbage analysis executed perfectly is still garbage. The point is simply that accuracy is only one component of sales improvement.
The bottom line is that a reasonably good analysis that is understood, accepted, implemented, and consistently executed will almost always outperform a theoretically brilliant analysis that sits in a PowerPoint deck while everyone just continues to do things the way they always have.
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