Category Archives: salesops

The Two Engines of SaaS: QCRs and DEVs

I remember one day, years ago, when I was a VP at $10M startup and Larry, the head of sales, came in one day handing out t-shirts that said:

“Code, sell, or get out of the way.”

Neither I, nor the rest of marketing team, took this particularly well because the shirt obviously devalued the contributions of F&A, HR, and marketing.  But, ever seeking objectivity, I did concede that the shirt had a certain commonsense appeal.  If you could only hire one person at a startup, it would be someone to write the product.  And if you could only hire one more, it would be someone to sell it.

This became yet another event that reconfirmed my belief in my “marketing exists to make sales easier” mantra.  After all, if you’re not coding or selling, at least you can help someone who is.

Over time, Larry’s t-shirt morphed in my mind into a new mantra:

“A SaaS company is a two-engine plane.  The left engine is DEVs.  The right is QCRs.”

QCR meaning quota-carrying (sales) representative and DEV meaning developer (or, for symmetry and emphasis, storypoint-burning developer).  People who sell with truly incremental quota, and people who write code and burndown storypoints in the process.

It’s a much nicer way of saying “code, sell, or get out of the way,” but it’s basically the same idea.  And it’s true.  While Larry was coming from a largely incorrect “protest overhead and process” viewpoint, I’m coming from a different one:  hiring.

The two hardest lines in a company headcount plan to keep at-plan are guess which two?  QCRs and DEVs.  Forget other departments for a minute — I’m saying is the the hardest line for the VP of Engineering to stay fully staffed on is DEVs, and the hardest line for the VP of Sales to stay fully staffed on is QCRs.

Why is this?

  • They are two, critical highly in-demand positions, so the market is inherently tight.
  • Given their importance, the hiring VPs can be gun-shy about making mistakes and lose candidates due to hesitation or indecision.
  • Both come with a short-term tax and mid-term payoff because on-boarding new hires slows down the rest of the team, a possible source of passive resistance.
  • Sales managers dislike splitting territories because it makes them unpopular, which could drive more foot-dragging.
  • It’s just plain easier to find the associated support functions — (e.g,. program managers, QA engineers, techops, salesops, sales productivity, overlays, CSMs, managers in general) than it is find the QCRs and DEVs.

Let me be clear:  this is not to say that all the supporting functions within sales and engineering do not add value, nor is this to say that supporting corporate functions beyond sales and engineering do not add value — it is to say, however, that far too often companies take their eye off the ball and staff the support functions before, not after, those they are supporting.  That’s a mistake.

What happens if you manage this poorly?  On the sales side, for example, you end up with an organization that has 1 SVP of Sales, 1 VP of sales consulting, 4 sales consultants, 1 director of sales ops, 1 director of sales productivity, 1 manager of sales development reps (SDRs), 4 SDRs, an executive assistant, and 4 quota-carrying salespeople.  So only 22% of the people in your sales organization actually carry a quota.

“Uh, other than QCRs, we’re doing great on sales hiring,”  says the sales VP.  “Other than that, Mrs. Lincoln, how did you find the play?” thinks the board.

Because I’ve seen this happen so often, and because I’ve seen companies accused of it both rightfully and unjustly, I’d decided to create two new metrics:

  • QCR density = number of QCRs / total sales headcount
  • DEV density = numbers of DEVs / total engineering headcount

The bad news is I don’t have a lot of benchmark data to share here.  In my experience, both numbers want to run in the 40% range.

The good news is that if you run a ratio-driven staffing model (which you should do for both sales and engineering), you should be able to calculate what these densities should be when you are fully staffed.

Let’s conclude with a simple model that does just that on the sales side, producing a result in the 38% to 46% range.

qcr dens

Finally, let me add that having such a model helps you understand whether, for example, your QCR density is low due to slow QCR hiring (and/or bad retention) against a good model, or on-pace hiring against a “fat” model.  The former is an execution problem, the latter is a problem with your model.

“Always Scrubbing the Pipeline” Means “Never Scrubbing the Pipeline.”

Perhaps you’ve seen this movie:

CEO:  “Wow the quarterly pipeline dropped 20% this week.  What’s going on sales VP?”

Sales VP:  “Well, that’s because we cleaned it up this week.”

CEO:  “That sounds great, but you said that last week.”

VP of Sales: “Well, that’s because we scrubbed it then, too.”

CEO:  “So shouldn’t it have been clean after last week’s cleaning?  Why did it require so much more cleaning that it dropped another 20% this week.”

VP of Sales:  “Well, you know it’s a big job and you can’t clean up the whole pipeline in a week.”

CEO:  “Should I expect it to drop another 20% next week?”

VP of Sales:  “Uh.”

CEO:  “Soon you’re going to say that we don’t have enough to make our numbers.”

VP of Sales:  “Well, I did mean to mention that I’ve been thinking of cutting the forecast because we just don’t have enough opportunities to work on.”

CEO:  “But we started the quarter with 3.2x pipeline coverage, shouldn’t that be enough?”

VP of Sales:  “Normally, yes.  But the pipeline wasn’t really clean.  Some of those opportunities weren’t real opportunities.” [1]

CEO:  “What does ‘clean’ mean?  When does it get clean?  Once clean, how long does it stay clean.”

VP of Sales:  “Well, look our view here is that we should always be scrubbing, so we’re constantly scrubbing the pipeline, always finding new things.”

What’s wrong with this conversation?  A lot. This Sales VP:

  • Has no clear definition of a scrubbed pipeline.
  • Has no process for scrubbing the pipeline.
  • Takes no accountability for the pipeline and its quality.

In my experience, the statement “we always scrub the pipeline” means precisely one thing:  “we never scrub the pipeline.”

Should that matter?  Well, using some quick assumptions [2], the average first-line enterprise sales manager is managing pipeline that cost $50,000 to generate per rep, so if they’re managing 6-8 reps they are managing pipeline that cost the company $300,000 – $400,000.  Sales managers need to manage that pipeline.  The way to manage it is through periodic, disciplined scrubs [3].

Now some managers don’t play the “always scrubbing” card.  Instead, they say “we scrub the pipeline every week on my sales forecast call.”  But once understand what a pipeline scrub looks like and remember the purpose of a forecast call [4], you realize that it’s impossible to do both at once.

How to Properly Scrub the Pipeline

While everyone will want to take their own unique angle on how to approach this, the core of a pipeline scrub is to review all the opportunities (this quarter and out quarters) in every sales rep’s pipeline to ensure that they are classified correctly with respect to:

  • Close date (which determines what quarter pipeline it’s in)
  • Stage (along a series of well defined and verifiable stages)
  • Forecast category (e.g., forecast, commit, upside)
  • Value (following specific rules about how and when to value opportunities)

These rules should be documented in a living document called something like Pipeline Management Rules (PMR) to which managers should refer during the pipeline scrub (e.g., “Jimmy, tell me what’s the rule for picking a close date in the PMR document”).

The other important thing about pipeline scrubs is timing, because pipeline scrubs will affect your sales analytics (e.g., pipeline coverage ratios, pipeline conversion rates, stage- and forecast-category weighted expected values).  Ergo, I picked a few fixed weeks per quarter (weeks 3, 6, and 9) to present scrubbed pipeline and then we typically use the week 3 snapshot for most of our early-quarter pipeline analytics [5].

The goal of the pipeline scrub is to ensure that the entire pipeline is fairly represented with respect to those rules.  By following this disciplined procedure you can ensure that your sales forecasting and analytics are not a castle built on a sand foundation, but an edifice built on bedrock.

Notes

[1] If you haven’t gone insane yet, this one should push you over.  Wait, whose job it is to accept opportunities into the pipeline?  Sales!  Once an opportunity gets into what’s known as either “stage 2” or “sales accepted lead” status, sales doesn’t get to play that card.  This represents a total failure to accept accountability.

[2] 10 this-quarter and 10 out-quarter opportunities per rep * $2,500 mean cost per opportunity = $50,000.

[3]  I am not arguing that you can’t also clean up opportunities along the way, but that needs to be a supplement to, not a substitute for, a proper pipeline scrubbing process.

[4] A forecast call is usually focused on the current quarter and on the opportunities that are expected to close in order to make the forecast.  Thus, low-probability and out-quarter opportunities are easily overlooked.

[5] Implying of course that sales perform the scrubs during weeks 2, 5, and 8 so the resulted can be presented on Monday morning of weeks 3, 6, and 9.

Using Pipeline Conversion Rates as Triangulation Forecasts

In this post we’ll examine how we to use pipeline conversion rates as early indicators of your business performance.

I call such indicators triangulation forecasts because they help the CEO and CFO get data points, in addition to the official VP of Sales forecast, that help triangulate where the company is going to land.  Here are some additional triangulation forecasts you can use.

  • Salesrep-level forecast (aggregate of every salesperson’s forecast)
  • Manager-level forecast (aggregate of the every sales manager’s forecast)
  • Stage-weighted expected value of the pipeline, which takes each opportunity and multiplies it by a stage- and ideally time-specific weight (e.g., week 6 stage 4 conversion rate)
  • Forecast-category-weighted expected value of the pipeline, which does the same thing relying on forecast category rather than stage (e.g., week 7 upside category conversion rate)

With these triangulation forecasts you can, as the old Russian proverb goes, trust but verify what the VP of sales is telling you.  (A good VP of sales uses them as part of making his/her forecast as well.)

Before looking at pipeline conversion rates, let me remind you that pipeline analysis is a castle built on a quicksand foundation if your pipeline is not built up from:

  • A consistent, documented, enforced set of rules for how opportunities are entered into the pipeline including, e.g., stage definitions and valuation rules.
  • A consistent, documented, enforced process for how that pipeline is periodically scrubbed to ensure its cleanliness. [1]

Once you have such a pipeline, the first thing you should do is to analyze how much of it you convert each quarter.

w3 tq

This helps you not only determine your ideal pipeline coverage ratio (the inverse of the conversion rate, or about 4.0x in this case), but also helps you get a triangulation forecast on the current quarter.  If we’re in 4Q17 and we had $25,000K in new ARR pipeline at week 3, then using our trailing seven quarter (T7Q) average conversion rate of 25%, we can forecast landing at $6,305K in new ARR.

Some folks use different conversion rates for forecasting — e.g., those in seasonal businesses with a lot of history might use the average of the last three year’s fourth-quarter conversion rate.  A company that brought in a new sales VP five quarters ago might use an average conversion rate, but only from the five quarters in her era.

This technique isn’t restricted to this quarter’s pipeline.  One great way to get sales focus on cleaning next quarter’s pipeline is to do the same analysis on next-quarter pipeline conversion as well.

w3 nq

This analysis suggests we’re teed up to do $6,818K in 1Q18, useful to know as an early indicator at week 3 of 4Q17 (i.e., mid/late October).

At most companies the $6,305K prediction for 4Q17 new ARR will be pretty accurate.  However, a strange thing happens at some companies:  while you end up closing around $6,300K in new ARR, a fairly large chunk of the closed deals can’t be found in the week 3 pipeline.  While some sales managers view this as normal, better ones view this as a sign of potentially large problem.  To understand the extent to which this is happening, you need perform this analysis:

cq pipe

In this example, you can see a pretty disturbing fact — while the company “converted” the week 3 ARR pipeline at the average rate, more than half of the opportunities that closed during the quarter (30 out of 56) were not present in the week 3 pipeline [2].  Of those, 5 were created after week 3 and closed during the quarter, which is presumably good.  However, 25 were pulled in from next quarter, or the quarter after that, which suggests that close dates are being sandbagged in the system.

Notes

[1] I am not a big believer in the some sales managers “always be scrubbing” philosophy for two reasons:  “always scrubbing” all too often translates to “never scrubbing” and “always scrubbing” can also translate to “randomly scrubbing” which makes it very hard to do analytics.  I believe sales should formally scrub the pipeline prior to weeks 3, 6, and 9.  This gives them enough time to clean up after the end of a quarter and provides three solid anchor points on which we can do analytics.

[2] Technically the first category, “closed already by week 3” won’t appear in the week 3 pipeline so there is an argument, particularly in companies where week 1-2 sales are highly volatile, to do the analysis on a to-go basis.