Category Archives: Sales

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.

How to Train Your VP of Sales to Think About the Forecast

Imagine a board meeting.

Director:  What’s the forecast for new ARR this quarter?

Sales VP:  $4.3M, with a best case of $5.0M.

Director:  So what’s the most likely outcome?

Sales VP:  $4.3M.

Director:  What are you really going to do?  (The classic newb trap question.)

Sales VP:  I think we can come in North of that.

Director:  What’s the worst case?

Sales VP:  $3.5M.

Director:  What are the odds of coming in at or above the forecast? 

Sales VP:  I always make my forecast.

Director:   What do you mean by worst case?

Sales VP:  You know, well, if the stars align in a bad way – a lot of stuff would have to go wrong – but if that happened, then we could end up at $3.5M.

Director:  So, let’s say a 10% chance of being at/below the worst case?

Sales VP:  I’d say more like 5%.

Director:  What do you mean by best case?

Sales VP:  Well, if we really struck it rich and everything lined up just the way I wanted, that would be best case.

Director:  You mean if all the deals came in — so best case basically equals pipeline?

Sales VP:  No, that never happens, I’ve made about 10 scenarios of different deal closing combinations and in 2 of them I can get to the best case.

You see the problem?  Does it sound familiar?  Do you realize how much time we spend talking in board meetings about “forecast,” “best case,” and “worst case” without every discussing what we mean by those terms?

Do you see how this is compounded by the sales VP’s natural, intuitive view of the outcomes?  Do you see the obvious mathematical contradictions?  “I always make my forecast” says it’s a 100% number, but then the VP says it’s the “most likely” number which implies 50%.  Then the VP says there’s a 5% chance of coming in at/less than worst case (which is much lower) and then kind of implies that there’s a 20% chance of beating best case – but the 2 out of 10 is meaningless because it’s not a probability, it’s just a count of scenarios.  Nothing adds up.

The result is, if you’re not careful, the board ends up counting angels on pinheads.  What can we do to fix this?  It’s simple:  teach (and if need be, force) your sales VP to think probabilistically.  Ask him/her how often:

  • It is reasonable to miss the forecast.  A typical answer might be 10%.
  • It is likely to come in at/below the worst case? Typical answer, 5%.
  • It is likely to meet/beat the best case? Typical answer, 20%.

So, with those three questions, we’ve now established that we want the sales VP to give us:

  • A 90% number on being at/above the forecast
  • A 20% number on being at/above the best case
  • A 5% number on being at/below the worst case

Put differently, when the sales VP decides what number to forecast that they should be thinking:

  • I should come in under my forecast once every 2.5 years (10 quarters).
  • I should hit/beat the best case about once every 5 quarters (a bit less than once a year).
  • I should come in/under the worst case once every 20 quarters (once every 5 years, or for most minds, basically never).

The beauty here is that when you work at a company a long time you can get enough quarters under your belt, to start really seeing how you’re doing relative to these frequencies.  What’s more, by converting the probabilities into frequencies (e.g., once every 10 quarters) you make it more intuitive for the sales VP and the organization to think this way.

In addition, you have a basis for conversations like this one which, among other things, is about overconfidence:

CEO:  You need to work on your forecasting.

Sales VP:  You know it’s hard out there, very competitive, and we don’t have much deal flow.  Back when I was at { Salesforce | Oracle | SAP }, I was much better at forecasting because we had more volume.

CEO:  But we agreed your forecast should be a 90% number and you’ve missed it 2 out of the past 4 quarters.

Sales VP:  Yes, but as I’ve said it’s tough to forecast in this market.

CEO:  Then forecast a lower number so you can beat it 90% of the time.  I’m asking you for a 90% number and empirically you’re giving me a 50% number. 

Sales VP:  OK.

CEO:  Plus, when those two big deals slipped last quarter you didn’t drop your forecast, why?

Sales VP:  Because where I grew up, you don’t cut the forecast.  You try like crazy to hold it.  Do you know the morale problems it causes when I cut the forecast – especially if it’s below plan? So, yes, when those two deals slipped it added more risk to the forecast – and I told you and the board that — but I didn’t cut forecast, no. 

CEO:  But “adding risk” here is meaningless.  In reality, “adding risk” means it’s not a 90% number anymore.  You’ve taken what was a 90% number and it’s now more like a 60% or 70% number.  So I want you to forget what they taught you growing up in sales and always – every week – give me a number that based on all available information you are 90% sure you can beat.  If that means dropping the forecast so be it.

sales forecast

This also helps with the board and the inevitable sandbagger issue.  In my experience (and with a bit of exaggeration) you always seem to be in one of two situations:  (1) intermittently missing plan and in trouble or (2) consistently making plan and a “sandbagger” – it feels like there’s nothing in between.

Well, if you establish with the board that your company forecast is a 90% number it means you are supposed to beat it 9 times out of 10 so you can only really be labelled a sandbagger when you’re 15 for 15 or 20 for 20.  It also reminds them that you’re supposed to arrive at the forecast so that you miss once every 10 quarters so they shouldn’t freak out if once every 2.5 years if that happens — it’s supposed to happen in this system.  (Just don’t let a once-in-ten-quarter event happen twice in a row.)

I like this quantitative basis for sales forecasting and I carry it down to the salesrep and pipeline level.  I believe that each “forecast category” should have a probability associated with it.  For example, at the opportunity level, you should link probabilities to categories, such as:

  • Commit = 90%
  • Forecast = 70%
  • Upside = 30%

This, in turn, means that over time, a given salesrep should close 90% of their committed deals, 70% of their forecast deals, and 30% of their upside.  Deviations from this over time indicate that the rep is mis-categorizing the deals because the probability should be the basis for the forecast category assignment [1].

Finally, I do believe that salesreps should give quarterly forecasts [2] that reflect their sense for how things will come in given all the odd things that can happen to deals (e.g., size changes, acceleration, slippage).  I believe those forecasts should be a 70% number because the sales manager will be managing across a  portfolio of them and while there is little room for a company to miss at the VP of Sales level, there is more room for and more variance in performance across salesreps.

While I know this will not necessarily come naturally to all sales VPs — and some may push-back hard — this is a simple, practical, and rigorous way to think about the forecast.

# # #

[1] Some people do this through an independent (orthogonal) field in the CRM system called probability.  I think that’s unnecessary because in my mind forecast category should effectively equal probability and your options for picking a probability should be bucketed.  No one can say a deal is 43% vs. 52% and forecast category doesn’t indicate some probability of closing, then … what use is it and on what basis should you classify something as forecast vs. upside?

[2] Some people believe that only managers should make forecasts, but I believe both reps and managers should forecast for two reasons:  (1) provided it’s left independent and not “managed” by the managers, the aggregated salesrep-level forecast provides another, Wisdom of Crowds-y, view into the sales forecast and (2) it’s never too early to teach salesreps how to forecast which is best learned through the experience of trial and error over many quarters.

Aligned to Achieve: A B2B Marketing Classic

Tracy Eiler and Andrea Austin’s Aligned to Achieve came out today and it’s a great book on an important and all too often overlooked topic:  how to align sales and marketing.

I’m adding it to my modern SaaS executive must-read book list, which is now:

So, what do I like about Aligned to Achieve?

The book puts a dead moose issue squarely on the table:  sales and marketing are not aligned in too many organizations.  The book does a great job of showing some examples of what misalignment looks like.  My favorites were the one where the sales VP wouldn’t shake the new CMO’s hand (“you’ll be gone soon, no need to get to know you”) and the one where sales waived off marketing from touching any opportunities once they got in the pipeline.  Ouch.  #TrustFail.

Aligned to Achieve makes great statements like this one:  “We believe that pipeline is absolutely the most important metric for sales and marketing alignment, and that’s a major cultural shift for most companies.”  Boom, nothing more to say about that.

The book includes fun charts like the one below.  I’ve always loved tension-surveys where you ask two sides for a view on the same issue and show the gap – and this gap’s a doozy.

sm gap

Aligned to Achieve includes the word “transparency” twenty times.  Transparency is required in the culture, in collaboration, in definitions, in planning, in the reasons for plans, in process and metrics, in data, in assessing results, in engaging customers, and in objectives and performance against them.  Communication is the lubricant in the sales/marketing relationship and transparency the key ingredient.

The book includes a nice chapter on the leadership traits required to work in the aligned environment:  collaborative, transparent, analytical, tech savvy, customer focused, and inspirational.  Having been a CMO fifteen years ago, I’d say that transparent, analytical, and tech savvy and now more important than ever before.

Aligned to Achieve includes a derivative of my favorite mantra (marketing exists to make sales easier) in the form of:

Sales can’t do it alone and marketing exists to make sales easier

The back half of that mantra (which I borrowed from CTP co-founder Chris Greendale) served me well in my combined 12 years as a CMO.  I love the insertion of the front half, which is now more true than ever:  sales has never been more codependent with marketing.

The book includes a fun, practical suggestion to have a bi-monthly “smarketing” meeting which brings sales and marketing together to discuss:

  • The rolling six-week marketing campaign calendar
  • Detailed review of the most recently completed campaigns
  • Update on immediately pending campaigns
  • Bigger picture items (e.g., upcoming events that impact sales and/or marketing)
  • Open discussion and brainstorming to cover challenges and process hiccups

Such meetings are a great idea.

Back in the day when Tracy and I worked together at Business Objects, I always loved Tracy’s habit of “crashing” meetings.  She was so committed to sales and marketing alignment – even back then – that if sales were having an important meeting, invited or not, she’d just show up.  (It always reminded me of the Woody Allen quote, 80% of success is showing up.)  In her aligned organization today, the CEO makes sure she doesn’t have to do that, but by hook or by crook the sales/marketing discussion must happen.

Aligned to Achieve has a nice discussion of the good old sales velocity model which, like my Four Levers of SaaS, is a good way to think about and simplify a business and the levers that drive it.

Unsurprisingly, for a book co-authored by the CMO of a company that sells market data and insights, Aligned to Achieve includes a healthy chapter on the importance of data, including a marketing-adapted version of the DIKW pyramid featuring data, insights, and connections as the three layers.  The nice part is that the chapter remains objective and factual – it doesn’t devolve into an infomercial by any means.

The book moves on to discuss the CIO’s role in a sales/marketing-aligned organization and provides a chapter reviewing the results of a survey of 1000 sales and marketing professionals on alignment, uncovering common sources of misalignment and some of the practices used by sales/marketing alignment leaders.

Aligned to Achieve ends with a series of 7 alignment-related predictions which I won’t scoop here.  I will say that #4 (“academia catches up”) and #6 (“account-based everything is a top priority”) are my two favorites.

Congratulations to my long-time friend and colleague Tracy Eiler on co-authoring the book and to her colleague Andrea Austin.