Category Archives: Sales

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.

Using Time-Based Close Rates to Align Marketing Budgets with Sales Targets

This post builds on my prior post, Win Rates, Close Rates, and Milestone vs. Flow Analysis.  In it, I will take the ideas in that post, expand on them a bit, and then apply them to difficult problem of ensuring you have enough marketing demand generation budget to hit your sales targets.

Let’s pretend it’s 4Q17 and that we need to model 2018 sales based solely on marketing-generated SALs (sales accepted leads).  To do that, we need to decompose our close rate over time because knowing we eventually close 40% of SALs is less useful than knowing the typical timing in how they close over time.

decompose closed

In a perfect world, we’d have 6-8 cohorts, not two.  The goal is to produce the last line, the average of the in-quarter, first-quarter, second-quarter, and so on close rates for a SAL.

Using these time-based average close rates, we can build a waterfall that takes historical, forecast (for the current quarter), and planned 2018 SALs and converts them into deals.

waterfall

This analysis suggests that with the currently planned SALs you can support an ARR number of $16.35M.  If sales needs more than that, you either need to assume an improvement in close rates or an increase in SAL generation.

Once you’ve established the required number of SALs, you can then back into a total demand-generation budget by knowing your cost/SAL, and then building out a marketing mix of programs (each with their own cost/SAL) that generates the requisite SALs at the targeted overall cost.

Win Rates, Close Rates and Milestone vs. Flow Analysis

Hey, what’s your win rate?

It’s another seemingly simple question.  But, like most SaaS metrics, when you dig deeper you find it’s not.  In this post we’ll take a look at how to calculate win rates and use win rates to introduce the broader concept of milestone vs. flow analysis that applies to conversion rates across the entire sales funnel.

Let’s start with some assumptions.  Once an opportunity is accepted by sales (known as a sales-accepted opportunity, or SAL), it eventually will end up in one of three terminal states:

  • Won
  • Lost
  • Other (derailed, no decision)

Some people don’t like “other” and insist that opportunities should be exclusively either won or lost and that other is an unnecessary form of lost which should be tracked with a lost reason code as opposed to its own state.  I prefer to keep other, and call it derailed, because a competitive loss is conceptually different from a project cancellation, major delay, loss of sponsor, or a company acquisition that halts the project.  Whether you want to call it other, no decision, or derailed, I think having a third terminal state is warranted from first principles.  However, it can make things complicated.

For example, you’ll need to calculate win rates two ways:

  • Win rate, narrow = wins / (wins + losses)
  • Win rate, broad = wins / (wins + losses + derails)

Your narrow win rate tells you how good you are at beating the competition.  Your broad rates tells you how good you are at closing deals (that come to a terminal state).

Narrow win rate alone can be misleading.  If I told you a company had a 66% win rate, you might be tempted to say “time to add more salespeople and scale this thing up.”  If I told you they got the 66% win rate by derailing 94 out of every 100 opportunities it generated, won 4, and lost the other 2, then you’d say “not so fast.”  This, of course, would show up in the broad win rate of 4%.

This brings up the important question of timing.  Both these win rate calculations ignore deals that push out of a quarter.  So another degenerate case is a situation where you win 4, lose 2, derail 4, and push 90 opportunities.  In this case, narrow win rate = 66% and broad win rate = 40%.  Neither is shining a light on the problem (which, if it happens continuously, I call a rolling hairball problem.)

The issue here is thus far we’ve been performing what I call a milestone analysis.  In effect, we put observers by the side of the road at various milestones (created, won, lost, derailed) and ask them to count the number opportunities that pass by each quarter.  The issue, especially with companies that have long sales cycles, is that you have no idea of progression.  You don’t know if the opportunities that passed “win” this quarter came from the opportunities that passed “created” this quarter, or if they came from last quarter, the quarter before that, or even earlier.

Milestone analysis has two key advantages

  • It’s easy — you just need to count opportunities passing milestones
  • It’s instant — you don’t have to wait to see how things play out to generate answers

The big disadvantage is it can be misleading, because the opportunities hitting a terminal state this quarter were generated in many different time periods.  For a company with an average 9 month sales cycle, the opportunities hitting a terminal state in quarter N, were generated primarily in quarter N-3, but with some coming in quarters N-2 and N-1 and some coming in quarters N-4 and N-5.  Across that period very little was constant, for example, marketing programs and messages changed.  So a marketing effectiveness analysis would be very difficult when approached this way.

For those sorts of questions, I think it’s far better to do a cohort-based analysis, which I call a flow analysis.  Instead of looking at all the opportunities that hit a terminal state in a given time period, you go back in time, grab a cohort of opportunities (e.g., all those generated in 4Q16) and then see how they play out over time.  You go with the flow.

For marketing programs effectiveness, this is the only way to do it.  Instead of a time-based cohort, you’d take a programs-based cohort (e.g., all the opportunities generated by marketing program X), see how they play out, and then compare various programs in terms of effectiveness.

The big downside of flow analysis is you end up analyzing ancient history.  For example, if you have a 9 month average sales cycle with a wide distribution around the mean, you may need to wait 15-18 months before the vast majority of the opportunities hit a terminal state.  If you analyze too early, too many opportunities are still open.  But if you put off analysis then you may get important information, but too late.

You can compress the time window by analyzing programs effectiveness not to sales outcomes but to important steps along the funnel.  That way you could compare two programs on the basis of their ability to generate MQLs or SALs, but you still wouldn’t know whether and at what relative rate they generate actual customers.  So you could end up doubling down on a program that generates a lot of interest, but not a lot of deals.

Back to our original topic, the same concept comes up in analyzing win rates.  Regardless of which win rate you’re calculating, at most companies you’re calculating it on a milestone basis.  I find milestone-based win rates more volatile and less accurate that a flow-based SAL-to-close rate.  For example, if I were building a marketing funnel to determine how many deals I need to hit next year’s number, I’d want to use a SAL-to-close rate, not a win rate, to do so.  Why?  SAL-to-close rates:

  • Are less volatile because they’re damped by using long periods of time.
  • Are more accurate because they actually tracking what you care about — if I get 100 opportunities, how many close within a given time period.
  • Automatically factor in derails and slips (the former are ignored in the narrow win rate and the latter ignored in both the narrow and broad win rates).

Let’s look at an example.  Here’s a chart that tracks 20 opportunities, 10 generated in 1Q17 and 10 generated in 2Q17, through their entire lifetime to a terminal stage.

oppty tracking

In reality things are a lot more complicated than this picture because you have opportunities still being generated in 3Q17 through 4Q18 and you’ll have opportunities that are still in play generated in numerous quarters before 1Q17.  But to keep things simple, let’s just analyze this little slice of the world.  Let’s do a milestone-based win/loss analysis.

win-loss

First, you can see the milestone-based win/loss rates bounce around a lot.  Here it’s due in part due to law of small numbers, but I do see similar volatility in real life — in my experience win rates bounce within a fairly broad zone — so I think it’s a real issue.  Regardless of that, what’s indisputable is that in this example, this is how things will look to the milestone-based win/loss analyzer.  Not a very clear picture — and a lot to panic about in 4Q17.

Let’s look at what a flow-based cohort analysis produces.

cohort1

In this case, we analyze the cohort of opportunities generated in the year-ago quarter.  Since we only generate opportunities in two quarters, 1Q17 and 2Q17, we only have two cohorts to analyze, and we get only two sets of numbers.  The thin blue box shows in opportunity tracking chart shows the data summarized in the 1Q18 column and the thin orange box shows the data for the 2Q18 column.  Both boxes depict how 3 opportunities in each cohort are still open at the end of the analysis period (imagine you did the 1Q18 analysis in 1Q18) and haven’t come to final resolution.  The cohorts both produce a 50% narrow win rate, a 43% vs. 29% broad win rate, and a 30% vs. 20% close rate.  How good are these numbers?

Well, in our example, we have the luxury of finding the true rates by letting the six open opportunities close out over time.  By doing a flow-based analysis in 4Q18 of the 1H17 cohort, we can see that our true narrow win rate is 57%, our true broad win rate is 40%, and our close rate is also 40% (which, once everything has arrived at a terminal state, is definitionally identical to the broad win rate).

cohort7

Hopefully this post has helped you think about your funnel differently by introducing the concept of milestone- vs. flow-based analysis and by demonstrating how the same business situation results in a very different rates depending on both the choice of win rate and analysis type.

Please note that the math in this example backed me into a 40% close rate which is about double what I believe is the benchmark in enterprise software — I think 20 to 25% is a more normal range. 

 

Just Effing Demo

I remember one time reading a win/loss report that went something like this.

“We were interested in buying Host and it made our short list.  When we invited you in for a demo with our team and the CFO, things went wrong.  After 20 minutes, your sales team was still talking about the product so the CFO left the meeting and didn’t want to evaluate your solution anymore.”

Huh?  What!  We spend a few hundred dollars to get a lead, maybe a few thousand to get it converted to a sales opportunity, we give it to our sales team and then they ‘show up and throw up’ on a prospect, talking for so long that the key decision maker leaves?

Yes, salespeople love to talk, but this can’t happen.  I remember another time a prospect called me.

“Look, I’ve been using EPM systems for 25 years.  I’ve used Hyperion, Essbase, TM1, and BPC.  I’ve been in FP&A my entire career.  I have an MBA from Columbia.  I am fully capable of determining my own needs and don’t want to play Twenty Questions with some 20-something SDR and then play it again with some sales consultant before I can get a live demo of your software.  Can we make that happen or not?”

Ouch.  In this case, our well defined and valued sales process (which required “qualification” and then “discovery”) was getting in the way of what the eminently qualified prospect wanted.

In today’s world, prospects both have and want more control over the sales process than ever before.  Yes, we might want to understand your requirements so we can put proper emphasis on different parts of the demonstration, but when a prospect — who clearly knows both what they’re doing and what they want — asks us for a demo, what should we do?  One thing:

Just effing demo  —  and then ask about requirements along the way

Look, I’m not trying to undo all the wisdom of learning how to do deep discovery and give customized demos, espoused by world-class sales trainers like Barry Rhein or in books like Just F*ing Demo (from whose title I derived the title of this post [1]).  These are all great ideas.  They should be your standard procedure.

But you need to remember to be flexible.  I always say don’t be a slave to metrics.  Don’t be a slave to process, either.

Here’s what I’ve learned from these situations:

Avoid triple-qualifying prospects with an SDR, then a rep, then an SC. Make SDR qualification quick and light.  Combine rep and SC qualification/discovery whenever possible. Don’t make the prospect jump through hoops just to get things started.

Intelligently adapt your process. If the prospect says they’re an expert, wants to judge for themselves, and just wants a quick look at your standard demo, don’t try to force a deep discovery call so you can customize – even if that’s your standard process.  Recognize that you’re in a non-standard situation, and just show up and do what they want.

Set expectations appropriately. There is a difference between a “Product Overview” and “Demonstration.”  If you think the right meeting is 30 minutes of slides to frame things and then a 30-minute demo, tell the prospect that, get their feedback, and if everyone agrees, then write “Product Overview” (not “Demonstration”) on the agenda.

Don’t make them wait. If you say the presentation is a one-hour demo, you should be demoing software within the first 5-7 minutes.  While brief personnel introductions are fine, anything else you do up-front should tee-up the demo.  This is not the time to talk about your corporate values, venture investors, or where the founder went to school.  Do that later, if indeed at all.

# # #

[1] A great book, by the way.  My favorite quote:  “in short, I stopped trying to deliver the perfect demo for my product and starting trying to deliver the perfect demo for my audience.”

Can You Solution Sell without Selling Solutions?

Yes.  And for those who get the distinction, I’d might add, somewhat obviously.

But too many people don’t get it.  Too many folks equate “solution selling” with “selling solutions.”  In fact, they’re quite different.  So, in this post, we’ll try to make the world a better place by explaining the difference between selling solutions and solution selling [1].

What is Solution Selling?

First and foremost, Solution Selling is a book [2].  And it’s a book written by a guy, Michael Bosworth, who, if memory serves, was trying to sell Knowledge Management Software in the 1980s.  Never forget this.  Solution Selling wasn’t written by a guy selling easy-to-sell products in a hot category, such as (at the time) Oracle database or PeopleSoft applications.  Solution Selling was written by a guy trying to sell in a tough category. Look at the subtitle of the book:  “Creating Buyers in Difficult Selling Markets.”

Necessity, as they say, is the mother of invention.

When you’re selling in a hot category [3], this is what you hear from the market.

“Yes, we’re going to buy a business intelligence tool and Gartner tells us it should be one of Cognos, Brio, and you — so you’re going to need explain why we should pick you over the other two.”

Nothing about value.  Nothing about problems.  Nothing about ROI.  We’ve already decided we’re going to buy one and you need to convince us why to buy yours.  [4]

When you’re selling in a cold category, the conversation goes something like this:

“A what?  An XML database system?  Wait, didn’t Gartner call that ‘the market that never was’ about two years ago — why in the world would anyone ever buy one of those.” [5]

In the first case, the sales cycle is all about differentiation.  In the second case, it’s all about value.  In the first case, it’s why buy one from me.  In the second, it’s why buy one at all.

Solution selling is the process of identifying a business problem that the product solves, finding the business owner of that problem, and selling them on the value of solving that problem and your ability to do so.

To use my favorite marketing analogy [6], solution selling is the process of selling the value of a ¼” hole.  Product selling is talking all about the wonderful titanium that’s in the ¼” drill bit.

For example, at MarkLogic we sold the world’s finest XML database system and XQuery processing engine.  In terms of market interest, that plus $3 will get you a tall latte.  That is, no one cared.  You could call up IT people and database architects and database administrators all day and tell them you had the world’s finest XQuery engine and no would care.  They weren’t interested in the category.

Certain businesspeople, however, were quite interested in what you could do with it.

  • If you called the SVP of K-12 Education at Pearson and talked about solving the tricky problem of customizing textbooks to meet many and varied state regulations, you’d get a call back.
  • If you called an intelligence officer at your favorite three-letter agency and talked about gathering, enriching, and querying open source content to build next-generation OSINT systems, you’d get a call back.
  • If you called the SVP of Digital Strategy at McGraw-Hill and talked overall about how the industry needed to separate content from the container in building next-generation products in response to the massive threat to media caused by Google, you’d get a call back.

Simply put, if you called a person about an important problem that they needed to solve, they’d call you back.  Whether they’d buy from you would come down to the extent they believed you can solve the problem based on several factors including a technology assessment, conversations with reference customers for whom you’ve solved the problem before, the cost/benefit associated with the project, and whether they wanted to work with you. [7]

What is Selling Solutions?

Geoffrey Moore refers to an important concept called “whole product” in Crossing the Chasm.  And it’s the idea that you’re not just selling technology platform to your beachhead market, you’re selling the fact that you know how to solve problems with it. Solving those problems might require hundreds of hours of consulting services, integration with complementary third-party software packages, and data integration with existing core systems.

But nobody said the “whole product” had to be packaged up, for example, as a set of templates that you customize that help accelerate the process of solving the problem.  This is the zone of “solutions.”

Many companies, early in their lifecycle for focus reasons or late in their lifecycle to increase the size of a saturating market [8], decide they want to package up a solution after repeatedly solving a problem in a certain area.  This often starts out as leftover consulting-ware and over time can evolve into a set of full-blown applications.

At most software companies, particularly bigger ones, when you start talking about packaged solutions, this is what you mean:  the combination of know-how and leftover intellectual property (IP) from prior engagements not licensed as software product but nevertheless used to both accelerate the time it takes to build the solution and reduce the risk of failure in so doing.

For example, during my time at MarkLogic, we often debated whether and to what extent we should create a packaged custom publishing solution or simply think of custom publishing as a focus area, something that we had a lot of know-how in, and re-use whatever leftover IP we could from prior gigs without glorifying it as a packaged solution.  Because the assignments were so different (publishers used as the the platform to build their products) we never opted to do so.  Had we been selling a business-support application as opposed to do product development platform, we probably would have.

The Difference Between Solution Selling and Selling Solutions

Solution selling is an approach to (and a complete methodology for) the sales process.  Selling solutions means selling packaged, typically application-layer, know-how typically built into a series of templates and frameworks that help accelerate the process of solving a given problem.

They are different ideas.

You can solution sell without a single packaged solution in your product line.  To again answer the question posed by the title of this post:  Yes, you can solution sell without selling solutions.

Solution selling is simply an approach to how you sell your product.  Certainly it can be easier to solution sell when you are selling solutions.  But it is not required and one is not tantamount to the other.

# # #

Notes

[1] In fact, rather perversely, you can sell solutions without solution selling.  If your company built a custom-tailored solution to solve a specific business problem and if you sold it emphasizing the features of the solutions (i.e., “feeds and speeds”) without trying to understand the customer’s specific business problem and its impacts, then you’d be guilty of product-selling a solution.  See end of the post.

[2] Which has largely been replaced by the author’s next book, Customer Centric Selling, but which – like many classics – was better before it was “improved” in my humble opinion.

[3] Which leads to one of my favorite sayings:  “if you have to ask if you’re working in a hot category, you’re not.”  If you were, two things would be different:  first, you’d know and second, you’d be too busy to ask.  QED.

[4] Which results in what I call an “axe battle” sales process, reminiscent of knights in heavy armor swinging axes at each other where each is blow can be thought of as feature.  “We have aggregate awareness, boom.”  “We have dynamic microcubes, boom.”  And so on.

[5] Gartner did, in fact, say precisely this about this XML database market, but that didn’t stop us from building MarkLogic from $0 to an $80M revenue run-rate during my six years there.  It did, however, provide a huge clue that we needed to adopt a solution-selling methodology (and bowling-alley strategy) in so doing.

[6] “Purchasing agents buy ¼” holes, not ¼” bits.”  Theodore Levitt.

[7] Because a startup can only develop this fluency and experience in a small number of solutions, you should cross the chasm by focusing on an initial beachhead and then build out into other markets through adjacencies (aka, bowling alley strategy) as described in Inside the Tornado.  In many ways, the solution selling sales methodology goes hand in hand with these strategy books by Geoffrey Moore.

[8] Geoffrey Moore calls these +1 additions that help grow the market as the once-hot core technology market saturates and you need to switch back to a solution focus if you wish to increase the market size.

The SaaS Rule of 40

After the SaaSacre of early 2016, investors generally backed off a growth-at-all-costs mindset and started to value SaaS companies using an “appropriate” balance of growth and profitability.  The question then became, what’s appropriate?  The answer was:  the rule of 40 [1].

What’s the rule of 40?  Growth rate + profit should be greater than or equal to 40%.

There are a number of options for deciding what to use to represent growth (e.g., ARR) and profit (e.g., EBITDA, operating margin). For public companies it usually translates to revenue growth rate and free cash flow margin.

It’s important to understand that such “rules” are not black and white.  As we’ll see in a minute, lots of companies deviate from the rule of 40.  The right way to think about these rules of thumb is as predictors.  Back in the day, what best predicted the value of a SaaS company?  Revenue growth — without regard for margin.  (In fact, often inversely correlated to margin.)  When that started to break down, people started looking for a better independent variable.  The answer to that search was the rule of 40 score.

Let’s examine a few charts courtesy of the folks at Pacific Crest and as presented at the recent, stellar Zuora CFO Forum, a CFO gathering run alongside their Subscribed conference.

rule-of-40

This scatter chart plots the two drivers of the rule of 40 score against each other, colors each dot with the company’s rule of 40 score, and adds a line that indicates the rule of 40 boundary.  42% of public SaaS companies, and 77% of public SaaS market cap, is above the rule of 40 line.

As a quick demonstration of the exception-to-every-rule principle, Tintri recently went public off 45% growth with -81% operating margins, [2] reflecting a rule of 40 score of -36%, and a placement that would be off the chart (in the underneath sense) even if corrected for non-cash expenses.

For those interested in company valuations, the more interesting chart is this one.

rule of 40 valuation.PNG

This chart plots rule of 40 score on the X axis, valuation multiple on the Y axis, and produces a pretty good regression line the shows the relationship between the two.  In short, the rule of 40 alone explains nearly 50% of SaaS company valuation.  I believe that outliers fall into one of two categories:

  • Companies in a strategic situation that explains the premium or discount relative to the model — e.g., the premium for Cloudera’s strong market position in the Hadoop space.
  • Companies whose valuations go non-linear at the high end due to scarcity — e.g., Veeva.

Executives and employees at startups should understand [3] the rule of 40 as it explains the general tendency of SaaS companies to focus on a balance of growth and profitability as opposed to a growth at all costs strategy that was more popular several years back.  Ignore the rule of 40 at your peril.

Notes

[1] While the Rule of 40 concept preceded the SaaSacre, I do believe that the SaaSacre was the wake-up call that made more investors and companies pay attention to.

[2] Using operating margin here somewhat lazily as I don’t want to go find unlevered free cash flow margin, but I don’t think it materially changes the point.

[3] Other good rule of 40 posts are available from:  Tomasz Tungaz, Sundeep Peechu, and Jeff Epstein and Josh Harder.

Detecting and Eliminating the Rolling Hairballs in your Sales Pipeline

Quick:  what’s the biggest deal in this quarter’s sales pipeline?  Was that the biggest deal in last quarter’s pipeline?  How about the quarter before?  Do you have deals in your pipeline older than your children?

If you’re answering yes to these questions, then you’re probably dealing with “rolling hairballs” in your pipeline.  Rolling hairballs are bad:

  • They exaggerate the size of the pipeline.
  • They distort coverage and conversion ratios.
  • They mess up expected-value forecasts, like a forecast-category or stage-weighted sales forecast.

Maybe they’re real deals; maybe they’re figments of a rep’s imagination.  But, if you’re not careful, they pollute your pipeline and your metrics.

Let’s define a rolling hairball

A rolling hairball is a typically large opportunity that sits in your current-quarter pipeline every quarter, with a close date that slips every quarter.  At 2 quarters it’s a suspected rolling hairball; at 3 or more quarters it’s a confirmed one.

Rolling Hairball Detection

The first thing you need to do is find rolling hairballs.  They’re tricky because salesreps always swear they’re real deals that are supposed to finally close this quarter.  What makes rolling hairballs obvious is their ever-sliding close dates.  What makes them dangerous is their size (including an accumulation of them that aggregate to a material fraction of the pipeline).

If you want to find rolling hairballs, look for opportunities in the current-quarter pipeline that were also in last-quarter’s pipeline.  That will find numerous bona fide slipped deals, but it will also light-up potential rolling hairballs.  To determine if an opportunity is  a rolling hairball, for sure, you can do one of two things:

  • See if it also appeared in the current-quarter pipeline in any quarters prior to the previous one.
  • Look at its stage or forecast category.  If either of those suggest it won’t be closing this quarter, it’s another big hairball indicator.

The more sophisticated way to find them is to examine “stuck opportunity” reports that light-up deals that are moving through pipeline stages too slowly compared to your norms.

But typically, the hairball is a big opportunity hiding in plain sight.  You know it was in last quarter’s pipeline and the quarter before that.  You’ve just been deluded into believing it’s not a hairball.

Fixing Rolling Hairballs

There are two ways to fix rolling hairballs:

  • Fix the close date.  Reps are subtly incented to put deals in the current quarter (e.g., to show they’re working on something, to show they might bring in some big sales this quarter). The manager needs to get on the phone with the customer and, after having verified it’s a real opportunity, get the real timeframe in which it might close.  Assigning a realistic close date to the opportunity makes your pipeline more real and reminds the rep that they need to be working on other shorter-term opportunities as well.  (There is no mid-term if you fail enough in the short term.)  The deal will still remain in the all-quarters pipeline, but it won’t always be in the current-quarter pipeline, ever-sliding, and distorting metrics and ratios.

 

  • Fix the size. While a realistic close date is the best solution, what makes rolling hairballs dangerous is their size.  So, if the salesrep really believes it’s a current-quarter opportunity, you can either reduce its size or split it into two opportunities (particularly if that’s a possible outcome), a small one in the current quarter along with an upsell in the future.  Note that this approach can be dangerous, with lots of little hairball-lets flying below radar, so you should only try if it you’re sure your salesops team can produce the reports to find them and if you believe it reflects real customer buying patterns.

Don’t let rolling hairballs pollute your pipeline metrics and ratios.  Admit they exist, find them, and fix them.  Your sales and sales forecasting will be more consistent as a result.