Pipeline Coverage Isn’t Enough

How do most B2B software teams predict whether they’re going to hit their bookings number? They use pipeline coverage. And that’s a good idea.

I prefer using to-go pipeline coverage, for several reasons:

It’s more precise. I’m constantly surprised by how many sales leaders skip the step of defining exactly what they mean by “pipeline” and what its coverage means. To-go pipeline coverage is a formula with specific inputs. Until you define the inputs, the output doesn’t mean much. Getting specific helps you avoid the garbage in / garbage out metric phenomenon.

The formula for To-Go Pipeline Coverage — The numerator is how much open pipeline you have w/ close dates in this quarter, and the denominator is your “to-go” bookings needed to hit plan

It accounts for what you’ve already won and how much you have left to hit your goal. As the quarter bumps along, to-go pipe coverage naturally adjusts to how much you have “to-go” to hit your number. Here’s what that looks like.

In this example, you have pretty good pipeline coverage (3.0X+) at the beginning of the quarter, all the way up through week 8 (4.0X)

It creates useful conversations about early-in-the-quarter growth problems (are we giving ourselves a chance to hit the quarter and starting with sufficient pipeline coverage of 3.0x plus?) vs. late-in-quarter growth problems (are we winning what we should, converting our pipeline to bookings, and actually hitting our number?).

But even using if you use to-go coverage appropriately (i.e., updating it every week, sharing it with your team, and using it to inform your forecast), you can still find yourself in a weird spot.

Imagine yourself in Week 8 of the quarter in the example above. On the one hand, there are plenty of winnable deals to go close. Lots of pipeline coverage — that’s good! On the other hand, you’re close to 70% through the quarter, and you’ve only booked 50% of your plan. Not so good.

Imagine yourself in that situation.

How would you square that tension?

Where do you expect to land for the quarter?

How should you feel?

This example — and the forecasting challenges this creates — illustrates the critical variable that pipeline coverage ignores:

Time.

Sure, you’ve got a big pipeline. But here’s the more important fact: You still have half of your planned bookings to go get, and only a third of a quarter to do it in. All of a sudden, hitting your plan is less about abundance (the size of your pipeline) and more about speed (your ability to convert that pipeline quickly into ARR).

So, in this situation… how worried should you be?

How behind are you?

What is Linearity Analysis?

This common pipeline head-fake is why you need another data point (beyond pipeline coverage) to tell you how you should feel about your quarter and what you should do next.

Basically, you need a time-based pipeline metric.

You need something called “linearity analysis.”

Linearity analysis measures “how far along you are this quarter” by converting closed-won bookings dollars into a percentage attainment figure vs. your plan. It also allows you to benchmark your weekly progress against a comparable benchmark period (e.g., the same quarter last year) to get a sense of “whether you’re ahead or behind.”

Here’s what it looks like in practice. First, you divide the quarter into weeks. Then you pull historical data on “what you did” in the prior year period (in this case, Q1'22) in bookings to establish your baseline and build a three-part table which includes:

How much you booked that week
Your running total bookings for the quarter
What % of the eventual total you had booked by the end of that week

Then you do the same thing for this year. Record your bookings each week, your running total, and the % of your plan/target bookings for the quarter.

When you plot that out in table form, it looks like this.

Last year Q1 bookings attainment vs. this year Q1 bookings attainment (we’ll make it easier to look at in a minute)

That’s hard to look at . I did that on purpose. It’s difficult to derive meaning from this data when it’s still in table form. You gotta make it visual.

Let’s do that by plotting this year’s weekly booking attainment against plan in blue bars, and last year’s bookings attainment using a pink line. Here’s what you get.

Q1 this year (blue bars) vs. Q1 last year (pink line)

Uh oh.

When you plot it this way — normalizing between last year (where we booked $425K ARR) and this year (where our plan is $500K ARR), you can see very quickly we’ve fallen behind the bookings pace we set last year. In other words, even though we’re planning to book more this year (15% growth in ARR YOY), by expressing things in percentage attainment vs. dollars, we can compare this quarter to the same quarter last year and see very quickly that we have some ground to make up. All in all, we’re about 15% behind last year’s booking pace at this point in the quarter (or about $75K in ARR).

Even though we’ve got lots of pipeline coverage, we’re a little behind. We have some catching up to do.

And knowing that as early as possible in the quarter is a good thing.

What Do I Do With This?

Well first, show it to your PE friends. We love this stuff. And we love it when people sanity-check their own forecasts.

Second, add this to your weekly reporting pack. That way, you’ll be able to recognize a pipeline head-fake earlier so you can…

Adjust your forecast (if needed)
Inspect the deals that matter to make sure you have a sense of where the risk is (and increase our win probability)
If necessary, get creative to pull forward deals and get late-stage pipeline moving so you can hit the plan

And if you want to use my pre-built linearity analysis spreadsheet, you can steal it here.

(If you end up using it, I’d love to hear about it.)

Final Thoughts + What Else to Read

As my friend and mentor Dave Kellogg says,

“The simplest way to have better conversations about the forecast is to have more than one forecast to discuss.”

If you’re only using pipeline coverage to predict where you expect to land this quarter, you need another way to look at how things are going.

And if you have the historical data you need to build it (hint: if your company is more than 1-2 years old, you probably do), linearity analysis is a great option.

Thanks to Jeremy Curley and Edward Kenna for sharing their thinking on linearity analysis and for enthusiastically nerding out on sales + marketing analytics with me every month. Friends don’t let friends build crappy reporting.

Are You Ahead Or Are You Behind?