How to build a pacing model that tells you where the month will land
A pacing model tells you where the month will land by comparing your results so far to how past months actually played out, business day by business day. It beats a flat daily target because deals don't close evenly.
The target at the last company I worked for was simple. Close 12 new customers every business day. Hit 12 every day and you hit the month.
Nobody ever hit 12 every day.
Why doesn't a flat daily target work?
Closes don't show up evenly. Early in the month you get a spike. Mid-month it flattens out and looks slow. Then the last few business days close strong. Plot a month and you get a U, not a line.
So on day 10, a flat target says you're behind. The pattern might say you're exactly where you should be. That's the difference between a panic meeting and a useful one.
Think of a golfer who plays a back-nine charge. The card looks ugly at the turn. The round still comes together.
What do you need to build one?
Three things. Closed-won records with a close date for the prior 13 months. A business day calendar, so every close maps to business day 1, 2, 3 and not a date. And a CRM your team actually uses.
That last one matters more than it sounds. For a long time I worked from a flat average, because that's all the data would support. As the company used the CRM more, the data got good enough to show the real shape of the month.
How do you build it?
- Pull every close from the last 13 full months, with the close date.
- Convert each date to a business day number. Skip weekends and holidays.
- For each month, work out the cumulative share of that month's total closed by each business day.
- Average those curves across the 13 months. That's your expected curve.
- Each day, divide what you've closed so far by the expected share for that business day. That's your projected month-end.
Say you're on business day 10 with 60 closes, and the curve says about half of a typical month is done by now. Sixty divided by 0.52 is roughly 115. That's where you're trending. (Made-up numbers, but you get the idea.)
Then add a range. Use your best and worst past months at that same business day. Early in the month the range is wide. By the last week it tightens.
It's the same idea as the rolling email benchmark. Thirteen months of your own history beats a gut feel.
What did it change?
The conversation. Instead of "we need 12 a day," we could say "at this point in the month we're usually about halfway, and we're a little ahead." Different meeting.
It also gave leadership something to trust. When numbers dipped mid-month, we could show that dips like that were normal, and whether we were truly on pace based on prior performance and timing. Sometimes the answer was yes. Sometimes it was "no, and here's by how much," which is just as useful.
What went wrong along the way?
Two things.
First, we ignored weekends and holidays. Our first version spread closes across calendar days, so the per-day average looked off every month. A month with a long weekend looked like a bad month when it was just a short one. Counting business days fixed it.
Second, we didn't see the U shape. We assumed a steady pace until the data told us otherwise. We only caught it as the team used the CRM more consistently and the history got clean enough to show the spike, the dip, and the strong finish.
Neither fix was clever. Both came from better data and a team that logged its work in the same system.
What should you do this week?
Pull your closed-won data for the last 13 months and convert the close dates to business days. That's it. Plot one month's cumulative curve and look at the shape. If it isn't a straight line, and it won't be, your flat target is wrong.