Tightening up a sales forecast

Let’s dive into the world of forecasting. Most articles you see out there focus on how to do a forecast. And what you’ll find is that there’s a million and one ways to do a forecast. Face it. A forecast is a guess. Some guesses are better than others. Some companies are fantastic at it while others are not. In the absence of a large data set with a probabilistic forecast layered on top you might find yourself in one or both of the following situations:

1. Bottoms up sales rep submission

2. Stage weighted forecasting

These are perfectly reasonable. Let’s talk about each one for a second real quick. Let’s make it quick. Most of you already know how to do this.

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Bottoms up sales rep submission

In a bottoms up the sales reps faithfully (or not?) fill out the CRM on time before the forecast is locked. As they say in computer programming, garbage in garbage out. If your pipeline is junk, then your forecast is a guess layered on top of junk. I call that a sh*t sandwich. Yuck!

Say for example you have a few forecast categories in your CRM:

• Commit

• Most Likely

• Best Case

• Omit

Three of the four above are out-of-the-box options for the forecast category field in Salesforce. Ask three sales reps to define what each of those mean. If you get three different answers to each one of these then you’re getting a 3 (reps) x 4 (categories) factorial of confusion. Take that to the Nth degree within your business.

What a recipe for confusion and muddling!

Here’s what Gemini (Google search’s AI engine) gave me. Pretty darn generic if you ask me.

Here’s an example from my Sales Ops Masterclass course. The trick is to be prescriptive. Commits should ALMOST never slip.

A sales rep’s bottoms up submission includes the following:

1. Updating Next Steps (pro tip: make sure you have a timestamp field that tracks the last time the Next Steps field was updated)

2. Opportunity hygiene (see below article to put a hygiene score together)

3. Forecast category update (see above)

4. Commentary prepped for the deal review call

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Stage Weighted Forecast

Assuming you have historical conversion rates by stage and assumed conversion rates for forecast categories. Operations can then slap on a weighted forecast.

Pipeline by Stage X Stage Weight X Pipeline Amount = Forecast A

Pipeline by Forecast Category X Category Weight X Pipeline Amount = Forecast B

These two are likely to give you two very different numbers. They might be close, they might be far apart. At least you have two points of view to talk about with leadership.

And that’s a good thing! Single point estimates make it really difficult to examine a business. If you’re predicting orbital trajectory for a space mission then you absolutely better have single point accuracy! But we’re talking about sales forecasting here. Totally different stakes. You don’t have to be as accurate but you better be prepared to discuss the different possible outcomes.

Here’s our worst case.

Here’s our most likely range.

Here’s our best case.

Predicting a hurricane is a bit more accurate. Hurricane Milton recently hit Florida. Also, if you were affected by this storm (and Hurricane Helene) I am terribly sorry! Please stay safe.

But hurricanes can change on you. So can sales pipelines.

How good are you at forecasting?

Well. Really. How good are you at forecasting? Go back in time and take a look. Fill out this scorecard below.

I dare you. Fill it out.