In my experience, most sales or GTM teams I work with are obsessed with removing any barriers that might prevent customers from having the best buying experience possible. And the community at large has done an amazing job of identifying all of the metrics that should be tracked to ensure that their teams are firing on all cylinders. However, there is one metric that has remained elusive, mostly due to the fact it is not easy to track in a CRM system, but yet it's crucial to accurate forecasting.
It's called the Velocity Index.
It was first brought to my attention by Bob Huh, an experienced technology strategist and founder of OnCorps, a machine learning firm that enhances decision science. In his article for HBR, “Sales Teams Aren’t Great at Forecasting. Here’s How to Fix That,” he introduces the Velocity Index as a new way to identify, track and quantify all your deals to improve forecasting.
Track deals by ‘Time in Stage’
Simply put, Huh argues one of the most important metrics for forecasting is to track and understand the health of your deals based on how much time has been spent in a particular stage. Huh shares his reasoning:
"CRM systems automatically weight revenues by deal-stage (qualification, proposal, procurement) to forecast revenues. The theory behind this is sound, but the practice is spotty. As opportunities advance through a staged funnel, their odds of closing should increase. However, revenue drivers may use different criteria for a stage."
He gives the example:
"One salesperson may define a request for a price quote as a proposal, whereas another may have a more stringent criterion like the client identifying budget constraints. Both deals are marked as being in the 'the proposal' portion of deal-stage and thus ascribed to the same odds of success, though they may in fact differ considerably."
Unfortunately, most of us don't continuously and accurately track the actual outcomes of deals at any given stage. For example, if there were 100 deals in a stage that automatically assigns a 25% weighting, did 25 deals actually close? Sadly, most of us can’t answer this simple question because we fail to ask it.
Why does it matter so much?
Many executives (i.e. CFOs) cut or trim the forecast produced by their revenue leaders by as much as 20%. Fixing the forecast this way is crude and based on little more than gut feel, or perhaps a bitter experience.
As Huh suggests:
"Instead of using fixed, stage-based odds to forecast revenues, what if you were to continuously track deal progress and outcomes and use a continuously-fed bell curve to predict the odds of a given deal’s success based on its size and age. In other words, by simply counting the frequency of won deals as a percent of all deals, any new deal can be plotted with more accuracy."
Now, what about our favorite Sandbagging Reps? (Yup, I know we all have entered a conservative forecast in order to beat the system at one time or another.) Well, Bob continues to suggest that in order to establish accuracy, this (obviously) must be prevented. He says:
“To prevent it, create an algorithm that continually tracks the forecasting performance of each individual against the average for the entire group. Have it flag people who over time consistently beat significantly lower-than-average forecasts they have entered. Not only does sandbagging undermine forecasting accuracy, but it also deprives the company of growth that might have been achieved through more ambitious sales targets."
Perhaps this is all easier said than done, and if you do this already, bravo! But for many, it has been exceptionally difficult to do in your standard CRM. However, now there are plenty of solutions to help. (Feel free to email me if you need recommendations - email below.)
The bottom line is tracking deals in stages is a very powerful indicator of the real health of your deals, and thus your business. I can attest that the extra effort is worth its weight in gold, I’ve seen it make a tremendous difference in even just a small amount of time.
Curious if you do this today and what impact it has had on your forecasts. I'd love to hear from you: email@example.com.
Enjoying this article?
Sign up to gain access to our thought leadership and have future articles delivered directly to your email.