When Numbers Lie
I was conducting a series of reviews with a team of sales executives. They were struggling to meet their numbers, asked me to spend some time with them. As usual, one of the first things I looked at was their pipeline. I asked them a few questions about win rates, sales cycles, average deal value—they had some answers, but not the kind that make one feel really comfortable.
After a quick review, I said, “You need to dramatically increase the number of opportunities in your pipeline, you aren’t chasing enough high quality deals to achieve your goals, based on your current performance.”
The sales manager replied, “We want to manage to a 3 times coverage model, we know it’s far better to have a 3X coverage model — or less.”
Confused, I asked, “Why? Clearly with that model, you aren’t achieving your goals, so something’s wrong here.”
The manager responded by pulling out a slide from a “Consultant Report.” (I hate it when they do that–file this under “beware of consultants/market researchers spouting numbers.” They are always true, but there are a lot of “but’s” and conditions that accompany the numbers.)
The chart said, “Companies that manage a pipeline of 3X or less had a 32% better close rate!*”
The manager went on to say, “That’s why we believe the pipeline coverage should be 3 times and we are striving for 2 times, it means we won’ t have to generate more leads, we can focus on fewer high quality deals.”
I bet you think I’m making this up.
For me, it was an “aha” moment with this group.
Any number we have, any data we believe, we can’t accept at face value, we have to understand what that data means. If we don’t we can do absolutely disastrous things.
So let me go back to the numbers in that consultant report.
The number of opportunities required in a “healthy pipeline” is determined by a few key pieces of information–pipeline integrity, win rate (Ideally stage to stage conversion rate), sales cycle, average deal size, and velocity.
Just taking a simple model:
- If I have a 20% win rate, I require a pipeline that has roughly 5 times the number I need to make my goal. So if my goal is $1m, I need a pipeline of $5M.
- If I have a win rate of 25%, I need a pipeline of $4M—this is actually a 25% improvement in close rate over the 5X pipeline.
- If I have a win rate of 33%, I need a pipeline of $3M or 3X coverage. This is 33% improvement in close rate than the 4X pipeline, and 66% better close rate than the 5X pipeline.
- If I have a win rate of 50%, I need a pipeline of $2m or 2X coverage. Again this is a 52.5% improvement in close rate over the 3X model, a 100% improvement over the 4X model, and 150% improvement over the %x model.
Clearly, the consultant report was reporting one of those “Duuuggh Observations.” The higher your win rate, the lower the coverage requirement. Math works, always!
But if you don’t understand what you were looking at, you can make disastrous conclusions. This sales management team had taken that consultant data point completely out of context (in fairness, it was a data point presented to my client by another consultant, which didn’t show the rest of the report.)
The conclusion this team leapt to, mistakenly, was “A 3 times coverage model is much better than a higher coverage model.” They didn’t understand the coverage is determined by win rate and the other factors I outlined. So they were managing to a 3X coverage model when their win rate was significantly less than 33%.
Numbers and data can be our friends, but only if we don’t treat them blindly. We have to understand what drives the numbers and how we leverage them to achieve our goals. The coverage number is an outcome number, it is the result of a number of inputs. Those inputs drive the determination of coverage, not the reverse.
In the case of this company, they had a number of choices to drive performance and reach their goals. They could have improved the skills, capabilities, and execution of the sales team—driving win rates to 33%. Then the 3 times model would have worked for them.
They might improve the quality of the pipeline, by better focus and disqualification–our win rates are always higher when we focus on our sweet spot. In this case, the quality of the deals in the pipeline was pretty good.
They could have abandoned the 3X coverage model, gone to a 4X coverage model, which would have more closely aligned with their win rates. This would mean a step up in both marketing programs (lead gen) and prospecting by the sales people.
There are lot’s of choices, but you have to understand the data, and what drives the data. Companies facing the same dilemma may choose different paths. Basically, we need to assess, “How quickly can we improve the skills/execution of our people? What’s the risk? What’s the cost? How quickly can we drive more into the pipeline, do we have the resource to cover that increase in deals? Do we have the marketing programs, lead gen, prospecting programs that can drive the pipeline volume?”
I suppose I mistitled this post. Numbers are numbers–nothing more. What screws us up is our misinterpretation of the meaning of those numbers.
*In fairness to the research, this data point was taken out of a single slide in a very large research presentation. As happens, this slide has made the rounds of blogs, articles, tweets, and other things–so it was separated from the explanations of what the number means, and what drives that number. The consultant/researcher who developed this is actually a friend who is wickedly smart and is would be horribly distressed by how that single chart is being used.
I do stand by my “beware of consultants bearing numbers” comment, always challenge them, always understand the assumptions, and the caveats. It they are good, they will always be able to support the numbers.
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