Measuring Ramped and Steady-State Sales Productivity: The Rep Ramp Chart

In prior posts I have discussed how to make a proper sales bookings productivity model and how to use the concept of ramped rep equivalents (RREs) in sales analytics and modeling. When it comes to setting drivers for both, corporate leaders tend to lean towards benchmarks and industry norms for the values.  For example, two such common norms are:

• Setting steady-state (or terminal) productivity at $1,200K of new ARR per rep in enterprise SaaS businesses
• Using a {0%, 25%, 50%, 100%} productivity ramp for new salesreps in their {1st, 2nd, 3rd, 4th} quarters with the company (and 100% thereafter)

In this post, I’ll discuss how you can determine if either of those assumptions are reasonable at your company, given its history.

To do so, I’m introducing one of my favorite charts, the Rep Ramp Chart.  Unlike most sales analytics, which align sales along fiscal quarters, this chart aligns sales relative to a rep’s tenure with the company.

You start by listing every rep your company has ever hired [1] in order by hire date.  You then record their sales productivity (typically measured in new ARR bookings [2]) for their series of quarters with the company [3], up to and including their current-quarter forecast (which you shade in green).  Reps who leave the company are shaded black.  Reps who get promoted out of quota-carrying roles (e.g., sales management) are shaded blue.  Future periods are shaded grey.  Add a 4+ quarter average productivity column for each row, and average each of the figures in the columns [4].

Here’s what you get:

Despite having only a relatively small amount of data [5], we can still interpret this a little.

• The relative absence of black lines means we’re pretty good at sales hiring.   I’ve seen real charts with 5 black lines in a row, usually down to a single bad management hire.
• The absence of black lines that “start late”  — for example {0, 25, 75, 25, 55, black} — is also good.  Our reps are either “failing fast” or succeeding, but things are not dragging on forever when they’re not working.
• Our average 4Q+ productivity is $308K per quarter, almost exactly $1,200K per year so it does seem valid to use that figure in our modeling.
• Entering $300K as target productivity then shows the empirical rep ramp as a percent of steady-state productivity, exactly how sales leaders think of it.  In this case, we see a {10%, 38%, 76%, 85%, 98%} empirical ramp across the first five quarters.  If our bookings model assumed {0%, 25%, 50%, 100%, 100%}, you’d say our model is somewhat pessimistic in the first three quarters, a little optimistic in the 4th, and pretty much on-target (a tiny bit optimistic) in the 5th.  If we had more data, we might adjust it a bit based on that.

I love this chart because it presents unadulterated history and lets you examine the validity of two hugely important drivers in your sales bookings capacity model — drivers, by the way, that are often completely unquestioned [6].  For that reason, I encourage everyone to make this a standard slide in your Sales ops review (aka, QBR) template.  Note that since different types of rep ramp differently and hit different steady-state productivity levels, you should create one rep ramp per major type of rep in your company.  For example, corporate (or inside) sales reps will typically ramp more quickly to lower productivity levels than field reps who will ramp more slowly to higher productivity.  Channels reps will ramp differently from direct reps.  International reps may need their own chart as well.

You can download the spreadsheet I used here.

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Notes

[1] Sales management may want to omit those no longer with the company, but that also omits their data, and might omit important patterns of hiring failure, so don’t omit anyone.  You can always exclude certain rows from the analysis without removing them from the chart (i.e., hiding them).

[2] New ARR bookings typically includes new ARR to both new and existing customers.

[3] You’ll need as many columns to do this as your longest tenured rep has been with the company, so it can get wide.  Let it.  There’s data in there.

[4] Ensuring empty cells are not confused with cells whose value is zero.  Excel ignores empty cells in calculating averages but will average your 0’s in when you probably don’t want them.

[5] In order to keep it easily and quickly grasped

[6] Particularly the ramp.