Tag Archives: pipeline analysis

The Pipeline Chicken or Egg Problem

The other day I heard a startup executive say, “we will start to accelerate sales hiring — hiring reps beyond the current staffing levels and the current plan — once we start to see the pipeline to support it.”

To mix metaphors, what comes first: the pipeline or the egg?  To un-mix them, what comes first:  the pipeline or the reps to prosecute it?  Unlike the chicken or the egg problem, I think this one has a clear answer: the reps.

My answer comes part from experience and part from math.

First, the experience part:  long ago I noticed that the number of opportunities in the pipeline of a software company tends to be a linear function of the number of reps, with a slope in the 12-18 range as a function of business model [1].  That is, in my 12 years of being a startup CEO, my all-quarters, scrubbed [2] pipeline usually had somewhere between 12 and 18 opportunities per rep and the primary way it went up was not by doing more marketing, but by hiring more reps.

Put differently, I see pipeline as a lagging indicator driven by your capacity and not a leading indicator driven by opportunity creation in your marketing funnel.

Why?  Because of the human factor:  whether they realize it or not, reps and their managers tend to apply a floating bar on opportunity acceptance that keeps them operating around their opportunity-handling capacity.  Why’s that?  It’s partially due to the self-fulfilling 3x pipeline prophecy:  if you’re not carrying enough pipeline, someone’s going to yell at you until you do, which will tend to drop your bar on opportunity acceptance.  On the flip side, if you’re carrying more opportunities than your capacity — and anyone is paying attention — your manager might take opportunities away from you, or worse yet hire another rep and split your territory.  These factors tends to raise the bar, so reps cherry pick the best opportunities and reject lesser ones that they’d might otherwise accept in a tougher environment.

So unless you’re running a real machine with air-tight definitions and little/no discretion (which I wouldn’t advise), the number of opportunities in your pipeline is going to be some constant times the number of reps.

Second, the math part.  If you’re running a reasonably tight ship, you have a financial model and an inverted funnel model that goes along with it.  You’re using historical costs and conversion rates along with future ARR targets to say, roughly, “if we need $4.0M in New ARR in 3 quarters, and we insert a bunch of math, then we’re going to need to generate 400 SALs this quarter and $X of marketing budget to do it.”  So unless there’s some discontinuity in your business, your pipeline generation doesn’t reflect market demand; it reflects your financial and demandgen funnel models.

To paraphrase Chester Karrass, you don’t get the pipeline you deserve, you get the one you plan for.  Sure, if your execution is bad you might fall significantly short on achieving your pipeline generation goal.  But it’s quite rare to come in way over it.

So what should be your trigger for hiring more reps?  That’s probably the subject of another post, but I’d look first externally at market share (are you gaining or losing, and how fast) and then internally at the CAC ratio.

CAC is the ultimate measure of your sales & marketing efficiency and looking at it should eliminate the need to look more deeply at quota attainment percentages, close rates, opportunity cost generation, etc.  If one or more of those things are badly out of whack, it will show up in your CAC.

So I’d say my quick rule is if your CAC is normal (1.5 or less in enterprise), your churn is normal (<10% gross), and your net dollar expansion rate is good enough (105%+), then you should probably hire more reps.  But we’ll dive more into that in another post.

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Notes

[1]  It’s a broad range, but it gets tighter when you break it down by business model.  In my experience, roughly speaking in:

  • Classic enterprise on-premises ($350K ASP with elephants over $1M), it runs closer to 8-10
  • Medium ARR SaaS ($75K ASP), it runs from 12-15
  • Corporate ARR SaaS ($25K ASP) where it ran 16-20

[2] The scrubbed part is super important.  I’ve seen companies with 100x pipeline coverage and 1% conversation rates. That just means a total lack of pipeline discipline and ergo meaningless metrics.  You should have written definitions of how to manage pipeline and enforce them through periodic scrubs.  Otherwise you’re building analytic castles in the sand.

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.