How to measure SaaS sales pipeline

Your sales forecast is only as good as your understanding of sales pipeline. And developing both can be hard. Here's how we do it at Equals.

We’ve written a lot about how freemium backfired at Equals. Given that, it’s probably not a surprise that our sales-led motion has made up a larger share of our total business over the last few quarters. As that transition has been underway, it’s required a deeper understanding of sales pipeline health. 

The Pipeline Review Meeting

Every company has a pipeline review meeting. Reps give a quick update on deals they’re working on with more time dedicated to deals with higher Annual Contract Values (ACVs) or a compelling event on the horizon.

The meeting is for sellers, by sellers, as it should be. Because of this, I found it difficult to move between the granular details of the deals we did cover and the high-level picture of how things were progressing. 

After some reflection, I realized there were three reasons why I was feeling this way:

1. There was never enough time to review every deal

2. Deals would “disappear”: we talked about them one week and never heard about them again

3. Each conversation focused on the pipeline on that particular day, but we had little understanding of how our overall pipeline had changed since the last update

To solve this, I started playing around with Opportunity History data in Salesforce and comparing snapshots of our pipeline across different weeks. I wanted to create a view that quickly summarized how our overall pipeline had changed since our last meeting. Eventually, I landed on the following framework:

Calculating how pipeline has changed week over week

By putting each deal into one of these six categories, I was able to write a quick SQL query that compares the week-on-week change in ARR, Close Date, and Stage of each deal in our pipeline.

The Pipeline Activity Report

The final product is the summary table below that starts with the prior week’s pipeline cohorted by Close Month and categorizes the movement of each deal this week:

Our Pipeline Activity Summary in Equals with dummy data

What I love about this view is it quickly summarizes the big picture of Net Pipeline Change while the underlying data powering the analysis gives me the deal-level detail necessary to understand what opportunities had the biggest impact on results.

Now, when I join our Pipeline Review Meeting, I have a solid understanding of:

1. Where we’re at today

2. How that changed vs. last week

3. What questions to ask to help explain movement in the numbers

Armed with a better grasp of pipeline status, next up was improving how we generated our sales forecast.

Forecasting future sales

To give us more confidence and predictability, we created an analysis focused on two data points:

1. How much of the pipeline we started the month with converted to closed won

2. New pipeline we would create and close in the current period not yet reflected

Breaking things down across these dimensions gave us a much clearer picture of historical win rates for existing pipeline. But the bigger breakthrough was gaining confidence in the predictability of in-month deals before we could see it in the data. 

Because we have shorter sales cycles, we found that in-month deals represented a meaningful portion of our total sales ARR in any given month.

Enter the Historical Win Rate Analysis

Similar to the pipeline activity reported, we leveraged Opportunity History data from Salesforce to build the Historical Win Rate analysis. Here’s a snapshot of how we track this in Equals:

Forecasting future sales with dummy data

Now, going into any month, we know that we'll close a certain percentage of the pipeline we enter with. And we know to expect a certain amount will close from the pipeline created in the same month.

Catering for “swing deals”

The last data point is the most obvious on the surface but requires the most scrutiny to do it well.

Swing deal reviews are deep dives into the largest opportunities in your pipeline. These conversations should be a tight collaboration between Finance and Sales where both teams can ask tough questions, present scepticism, and brainstorm ideas to get deals over the line.

A lot has been written on the best framework to use in these deal reviews. At Equals, we focus on SPIN selling in the discovery process and MEDDIC to assess the health of a deal, but my advice is not to overthink it. Pick one that helps you remove emotions from the process, focus on tangible facts and stick with it!

In the end, Finance should own their own forecast of the Sales business, which might differ from line-level sellers or Sales leaders. That’s normal, but you should always be able to bridge between the two, especially in identifying which swing deals you’re calling in vs. out.

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Hopefully, this helps you on your journey to measuring the health of your pipeline. I’d love to learn about your process or hear any questions you have.