Category Archives: salesops

I’ve Got a Crazy Idea:  How About We Focus on Next-Quarter’s Pipeline?

I’m frankly shocked by how many startups treat pipeline as a monolith.

Sample CMO:  “we’re in great shape because we have a total pipeline of $32M covering a forward-four-quarter (F4Q) sales target of $10M, so 3.2x coverage.  Next slide, please.”

Regardless of your view on the appropriate magic pipeline coverage number (e.g., 2x, 3x, 4x), I’ve got a slew of serious problems with this.  What do I think when someone says this?

“Wait, hang on.  How is that pipeline distributed by quarter?  By stage?  By forecast category?  By salesrep?  You can’t just look at it as a giant lump and declare that you’re in great shape because you have 3x the F4Q coverage.  That’s lazy thinking.  And, by the way, you probably don’t even need 3x  the F4Q target, but you sure as hell need 3x this quarter’s coverage [1] and better be building to start next quarter with 3x as well.  You do understand that sales can starve to death and we can go out of business – the whole time with 3x pipeline coverage — if it’s all pipeline that’s 3 and 4 quarters, out?”

I’ve got a crazy idea.  How about as a first step, we stop looking at annual pipeline [2] and start looking at this-quarter pipeline and, most importantly, next-quarter pipeline?

What people tell me when I say this:  “No, no, Dave.  We can’t do that.  That’s myopic.  You need to look further out.  You can’t drive looking at the hood ornament.  Plus, with a 90-day average sales cycle (ASC) there’s nothing we can do anyway about the short term.  You need to think big picture.”

I then imagine the CMO talking to the head of demandgen:  “Yep, it’s week 1 and we only have 2.1x pipeline coverage.  But with a 90-day sales cycle, there’s nothing we can do.  Looks like we’re going to hit the iceberg.  At least we made our 3x coverage OKR on a rolling basis.  Hey, let’s go grab a flat white.”

I loathe this attitude for several reasons:

  • It’s parochial. The purpose of marketing OKRs is to enable sales to hit sales OKRs.  Who cares if marketing hit its pipeline OKR but sales is nevertheless flying off a cliff?  Marketing just had a poorly chosen OKR.
  • It’s defeatist. If “when the going gets tough, the tough get a flat white” is your motto, you shouldn’t work in startup marketing.
  • It’s wrong. The A in ASC stands for average.  Your average sales cycle.  It’s not your minimum sales cycle.  If your average sales cycle is 90 days [3] then you have lots of deals that close faster than 90 days, so instead of getting a flat white marketing should be focused on finding a bunch of those, pronto [4].

Here’s my crazy idea.  Never look at rolling F4Q pipeline again.  It doesn’t matter.  What you really need to do is start every quarter with 3.0x [5] pipeline.  After all, if you started every quarter with 3.0x pipeline coverage wouldn’t that mean you are teed up for success every quarter?  Instead of focusing on the long-term and hoping the short-term works out, let’s continually focus on the short-term and know the long-term will work out.

This brings to mind Kellogg’s fourth law of startups:  you have to survive short-term in order to exist long-term.

This-Quarter Pipeline
This process starts by looking at the this-quarter (aka, current-quarter) pipeline.  While it’s true that in many companies marketing will have a limited ability to impact the current-quarter pipeline — especially once you’re 5-6 weeks in — you should nevertheless always be looking at current-quarter pipeline and current-quarter pipeline coverage calculated on a to-go basis.  You don’t need 3x the plan number every single week; you need 3x coverage of the to-go number to get to plan.  To-go pipeline coverage provides an indicator of confidence in your forecast (think “just how lucky to do we have to get”) and over time the ratio can be used as an alternative forecasting mechanism [6].

this qtr togo

In the above example, we can see a few interesting patterns.

  • We start the quarter with high coverage, but it quickly becomes clear that’s because the pipeline has not yet been cleaned up. Because salespeople are usually “animals that think in 90-day increments” [7], next quarter is effectively eternity from the point of view of most salesreps, so they tend to dump troubled deals in next-quarter [8] regardless of whether they actually have a next-quarter natural close date.
  • Between weeks 1 and 3, we see $2,250K of current-quarter pipeline vaporize as part of sales’ cleanup. Note that $250K was closed – the best way for dollars to exit the pipeline!  I always do my snapshot pipeline analytics in week 3 to provide enough time for sales to clean up before trying to analyze the data.  (And if it’s not clean by week 3, then you have a different conversation with sales [9].)
  • Going forward, we burn off more pipeline to fall into the 2.6 to 2.8 coverage range but from weeks 5 to 9 we are generally closing and burning off pipeline [10] at the same rate – hence the coverage ratio is running in a stable, if somewhat tight, range.

Next-Quarter Pipeline
Let’s now look at next-quarter pipeline.  While I think sales needs to be focused on this-quarter pipeline and closing it, marketing needs to be primarily focused on next-quarter pipeline and generating it.  Let’s look at an example:

next qtr pipe

Now we can see that next-quarter plan is $3,250K and we start this quarter with $3,500K in next-quarter pipeline or 1.1x coverage.  The 1.1x is nominally scary but do recall we have 12 weeks to generate more next-quarter pipeline before we want to start next quarter with 3x coverage, or a total pipeline of $9,750K.  Once you start tracking this way and build some history, you’ll know what your company’s requirements are.  In my experience, 1.5x next-quarter coverage in week 3 is tight but works [11].

The primary point here is that given:

  • Your knowledge of history and your pipeline coverage requirements
  • Your marketing plans for the current quarter
  • The trends you’re seeing in the data
  • Normal spillover patterns

That marketing should be able to forecast next quarter’s starting pipeline coverage.  So, pipeline coverage isn’t just an iceberg that marketing thinks we’ll hit or miss.  It’s something can marketing can forecast.  And if you can forecast it, then you adjust your plans accordingly to do something about it.

Let’s stick with our example and make a forecast for next-quarter starting pipeline [12]

  • Note that we are generating about $250K of net next-quarter pipeline per week from weeks 4 to 9.
  • Assume that we are continuing at steady-state the programs generating that pipeline and ergo we can assume that over the next four weeks we’ll generate another $1M.
  • Assume we are doing a big webinar that we think will generate another $750K in next-quarter pipeline.
  • Assume that 35% of the surplus this-quarter pipeline slips to next-quarter [13]

If you do this in a spreadsheet, you get the following.  Note that in this example we are forecasting a shortfall of $93K in starting next-quarter pipeline coverage.  Were we forecasting a significant gap, we might divert marketing money into demand generation in order to close the gap.

fc next qtr

All-Quarters Pipeline
Finally, let’s close with how I think about all-quarters pipeline.

all qtr

While I don’t think it’s the primary pipeline metric, I do think it’s worth tracking for several reasons:

  • So you can see if pipeline is evaporating or sloshing. When a $1M forecast deal is lost, it comes out of both current-quarter and all-quarters pipeline.  When it slips, however, current-quarter goes down by $1M but all-quarters stays the same.  By looking at current-quarter, next-quarter, and all-quarters at the same time in a compact space you can get sense for what is happening overall to your pipeline.  There’s nowhere to hide when you’re looking at all-quarters pipeline.
  • So you can get a sense for the size of opportunities in your pipeline.  Note that if you create opportunities with a placeholder value then there’s not much  purpose in doing this (which is just one reason why I don’t recommend creating opportunities with a placeholder value) [14].
  • So you can get a sense of your salesreps’ capacity. The very first number I look at when a company is missing its numbers is opportunities/rep.  In my experience, a typical rep can handle 8-12 current-quarter and 15-20 all-quarters opportunities [15].  If your reps are carrying only 5 opportunities each, I don’t know how they can make their numbers.  If they’re carrying 50, I think either your definition of opportunity is wrong or you need to transfer some budget from marketing to sales and hire more reps.

The spreadsheet I used in this post is available for download here.

# # #

Notes

[1] Assuming you’re in the first few weeks of the quarter, for now.

[2] Which is usually done using forward four quarters.

[3] And ASC follows a normal distribution.

[4] Typically, they are smaller deals, or deals at smaller companies, or upsells to existing customers.  But they’re out there.

[5] Or, whatever your favorite coverage ratio is.  Debating that is not the point of this post.

[6] Once you build up some history you can use coverage ratios to predict sales as a way of triangulating on the forecast.

[7] As a former board member always told me — a quote that rivals “think of salespeople as single-celled organisms driven by their comp plan” in terms of pith.

[8] Or sometimes, fourth-quarter which is another popular pipeline dumping ground.  (As is first-quarter next year for the truly crafty.)

[9] That is, one about how they are going to get their shit together and manage the pipeline better, the first piece of which is getting it clean by week 3, often best accomplished by one or more pipeline scrub meetings in weeks 1 and 2.

[10] Burning off takes one of three forms:  closed/won, lost or no-decision, or slipping to a subsequent quarter.  It’s only really “burned off” from the perspective of the current-quarter in the last case.

[11] This depends massively on your specific business (and sales cycle length) so you really need to build up your own history.

[12] Technically speaking, I’m making a forecast for day-1 pipeline, not week-3 pipeline.  Once you get this down you can use any patterns you want to correct it for week 3, if desired.  In reality, I’d rather uplift from week 3 to get day-1 so I can keep marketing focused on generating pipeline for day-1, even though I know a lot will be burned off before I snapshot my analytics in week 3.

[13] Surplus in the sense that it’s leftover after we use what we need to get to plan.  Such surplus pipeline goes three places:  lost/no-decision, next-quarter, or some future quarter.  I often assume 1/3rd  goes to each as a rule of thumb.

[14] As a matter of principle I don’t think an opportunity should have a value associated with it until a salesrep has socialized a price point with the customer.  (Think:  “you do know it cost about $150K per year to subscribe to this software, right?”)  Perversely, some folks create opportunities in stage 1 with a placeholder value only to later exclude stage 1 opportunities in all pipeline analytics. Doing so gets the same result analytically but is an inferior sales process in my opinion.

[15] Once you’re looking at opportunities/rep, you need to not stop with the average but make a histogram.  An 80-opportunity world where 10 reps have 8 opportunities each is a very different world from one where 2 reps have 30 opportunities each and the other 8 have an average of 2.5.

Hiring Profiles: Step 0 of a Successful Onboarding Program

Happily, in the past several years startups are increasingly recognizing the value of strong sales enablement and sales productivity teams.  So it’s no surprise that I hear a lot about high-growth companies building onboarding programs to enable successfully scaling their sales organizations and sustain their growth.  What’s disappointing, however, is how little I hear about the hiring profiles of the people that we want to put into these programs.

Everyone loves to talk about onboarding, but everybody hates to talk about hiring profiles.  It doesn’t make sense.  It’s like talking about a machine — how it works and what it produces — without ever talking about what you feed into it.  Obviously, when you step back and think about it, the success of any onboarding program is going to be a function of both the program and people you feed into it.  So we are we so eager to talk about the former and so unwilling to talk about the latter?

Talking about the program is fairly easy.  It’s a constructive exercise in building something that many folks have built before — so it’s about content structuring, best practice sharing, and the like.  Talking about hiring profiles — i.e., the kind of people we want to feed into it — is harder because:

  • It’s constraining.  “Well, an ideal new hire might look like X, but we’re not always going to find that.  If that one profile was all I could hire, I could never build the sales team fast enough.”
  • It’s a matter of opinion.  “Success around here comes in many shapes and sizes.  There is not just one profile.”
  • It’s unscientific.  “I can just tell who has the sales gene and who doesn’t.  That’s the hardest thing to hire for.  And I just know when they have it.”
  • It’s controversial.  “Turns out none of my six first-line sales managers really agree on what it takes — e.g., we have an endless debate on whether domain-knowledge actually hurts or helps.”
  • It’s early days.  “Frankly, we just don’t know what the key success criteria are, and we’re working off a pretty small sample.”
  • You have conflicting data.  “Most of the ex-Oracle veterans we’ve hired have been fish out of water, but two of them did really well.”
  • There are invariably outliers.  “Look at Joe, we’d never hire him today — he looks nothing like the proposed profile — but he’s one of our top people.”

That’s why most sales managers would probably prefer discussing revenue recognition rules to hiring profiles.  “I’ll just hire great sales athletes and the rest will take care of itself.”  But will it?

In fact, the nonsensicality of the fairly typical approach to building a startup sales force becomes most clear when viewed through the onboarding lens.

Imagine you’re the VP of sales enablement:

“Wait a minute. I suppose it’s OK if you want to let every sales manager hire to their own criteria because we’re small and don’t really know for sure what the formula is.  But how am I supposed to build a training program that has a mix of people with completely different backgrounds:

  • Some have <5 years, some have 5-10 years, and some have 15+ years of enterprise sales experience?
  • Some know the domain cold and have sold in the category for years whereas others have never sold in our category before?
  • Some have experience selling platforms (which we do) but some have only sold applications?
  • Some are transactional closers, some are relationship builders, and some are challenger-type solution sellers?”

I understand that your company may have different sales roles (e.g., inside sales, enterprise sales) [1] and that you will have different hiring profiles per role.  But you if you want to scale your sales force — and a big part of scaling is onboarding — then you’re going to need to recruit cohorts that are sufficiently homogeneous that you can actually build an effective training program.   I’d argue there are many other great reasons to define and enforce hiring profiles [2], but the clearest and simplest one is:  if you’re going to hire a completely heterogeneous group of sales folks, how in the heck are you going to train them?

# # #

Notes

[1] Though I’d argue that many startups over-diversify these roles too early.  Concretely put, if you have less than 25 quota-carrying reps, you should have no more than two roles.

[2] Which can include conscious, deliberate experiments outside them.

 

 

Book Review: Enablement Mastery by Elay Cohen

I had the pleasure of working with Elay Cohen during my circa year at Salesforce.com and I reviewed SalesHood, his first book, over four years ago.  We were early and happy customers of the SalesHood application at Host Analytics.  I’m basically a big fan of Elay’s and what he does.  With the average enterprise SaaS startup spending somewhere between 40% to 80%+ of revenue on sales, doesn’t it make sense to carve off some portion of that money into a Sales Enablement team, to make sure the rest is well spent?  It sure does to me.

I was pleased to hear that Elay had written a second book, Enablement Mastery, and even more pleased to be invited to the book launch in San Francisco several weeks back.  Here’s a photo of Cloudwords CEO Michael Meinhardt and me at the event.

50023900_10157090392582028_3547117110700277760_o

I have to say I simply love salesops and sales productivity people.  They’re uniformly smart, positive, results-oriented, and — unlikely many salespeople — process-oriented.  A big part of the value of working with SalesHood, for a savvy customer, is to tap into the network of amazing sales enablement professionals Elay has built and whose stories are profiled in Enablement Mastery.

I read the book after the event and liked it.  I would call it a holistic primer on sales enablement which, since it’s a relatively new and somewhat misunderstood discipline, is greatly in need in the market.

Elay’s a great story-teller so the book is littered with stories and examples, from his own considerable experience building the impressive Salesforce.com sales productivity team, to the many stories of his friends and colleagues profiled in the book.

Some of the more interesting questions Elay examines in Enablement Mastery include:

  • Why sales enablement?
  • Where to plug it organizationally?  (With pros and cons of several choices.)
  • What to do in your first 90 days in a new sales enablement role?
  • What to look for when hiring sales enablement professionals?
  • How to get organizational (and ideally strong CEO) buy-in to the sales enablement program?
  • How sales enablement can work best with marketing?  (Hint:  there is often tension here.)
  • What is a holistic process map for the sales enablement function?
  • How to measure the sales enablement function?  (And it better be more than instructor ratings on the bootcamp.)
  • How to enable front-line managers to be accountable for their role enabling and developing their teams?  (Elay wrote a whole chapter on this topic.)
  • How to conduct a quarterly business review (QBR)?
  • How managers can use basic Selling through Curiosity principles to coach using curiosity as well?
  • How to build an on-boarding plan and program?
  • What core deliverables need to be produced by the marketing and sales productivity teams?

Elay, never one to forget to celebrate achievement and facilitate peer-level knowledge sharing, also offers tips on how to runs sales kickoffs and quota clubs.

Overall, I’d highly recommend Enablement Mastery as a quick read that provides a great, practical overview of an important subject.  If you’re going to scale your startup and your sales force, sales enablement is going to be an important part of the equation.

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.