Stage-based Sales Forecasting – Why Won’t this Concept die?

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A popular post I wrote after the launch of the SalesWays Hub was called “Stage-Based Sales Forecasting – It Doesn’t Work.” In it I discussed specifically why it’s a terrible idea to tie probability directly to a linear sales stage. Probability isn’t necessarily, or even generally, tied to where you are in the sales process. Milestones in the sales cycle do not govern your chances of winning. Your product and your performance at those milestones do. I’d like to expand on this, taking on a new model called “High-Velocity Selling.”

High-Velocity Selling

There is a popular (within the entrepreneurial software world) new sales process for enterprise called “High-Velocity Selling.” The concept was first described in a post by a VC, Lars Leckie, back in 2010.  In it, he outlined a new model of thinking for enterprise sales and marketing which is inside sales driven and takes advantage of consumer internet technologies (i.e. marketing automation).

Sounds great – and I love it.  I think it makes a ton of sense, and I tend to agree that the days of the world travelling software sales organization are numbered. Companies such as and Zendesk have adopted this model with great success.

I do think face-to-face interactions are important and shouldn’t be discounted – but the entire sales organization doesn’t need to be out on the road anymore. The other ideas behind High-Velocity Selling include the use of consumer marketing concepts, aggressive inside sales teams, strong metrics measurement and analysis, and an accessible product (users can start trials with no interaction directly with vendor). All of this makes sense.

So why am I bringing up High-Velocity Selling? Well, a critical concept in the model is metrics, identifying every measureable event in the sales and adoption process and analyzing them every step of the way to improve results. And they’re right – these days, software companies can track metrics like never before.  Companies using High-Velocity Selling track a ton of information – monthly, weekly, or even daily. Metrics include web site traffic, conversion statistics, number of calls, lead generation (by quality), demos, trials, and much more.

Sounds great still, right? But now let’s look at how they forecast.  Here is an example of how probability is actually being calculated from all of these metrics:

Step Probability Description
Step 1 10% Qualified Lead
Step 2 20% Discovery Process
Step 3 50% Demo
Step 4 65% Trial in Process
Step 5 80% Selected, Quote waiting on Final Approval
Step 6 90% Received paperwork
Step 7 100% Received Payment

Well, that looks familiar, doesn’t it?  Sigh.

Let’s revisit why this doesn’t work – and why the new thinking sometimes just can’t let go of the old.  Once again, probability isn’t tied to where you are in the sales cycle!

Let’s take CRM as an example.  A customer starts a CRM selection process via a evaluation and purchasing committee.  They search Google for ‘CRM’, and find SugarCRM,, and Microsoft Dynamics.  They’ve spoken with a sales rep at each of the companies, have seen demos, and are now evaluating a trial.

So, using the above model, Microsoft, Salesforce, and Sugar all have a 65% chance of winning. So the total probability of a win is 195%. Someone (two) is going to be surprised and disappointed.

What if the customer only discovered a single CRM vendor?  Would the probability still be 65%?

A lot more matters when calculating probability – and we’ll keep banging our heads against this particular wall until someone maybe hears us.  It’s a tragedy really – so many companies copy this stage based probability method blindly. Instead, it’s time to understand that probability is independent from where you are in the sales process.

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