Category Archives: Sales

The Two Archetypal Marketing Messages: “Bags Fly Free” and “Soup is Good Food.”

There are only two archetypal marketing messages, exemplified by:

  • Bags Fly Free, a current advertising slogan used by Southwest airlines.
  • Soup is Good Food, a 1970s campaign slogan used by Campbell’s soup [1].

Screen-Shot-2014-12-29-at-11.26.14-PM

soup

Quick, what’s the difference between these two messages?

Soup is Good Food answers the question “why buy one (at all)?” while Bags Fly Free answers the question “why buy mine?”  Soup is Good Food markets the category while Bags Fly Free markets one vendor’s product/service within it.  In short, Soup is Good Food is about value.  Bags Fly Free is about differentiation.

Once you see things through his lens, you will be shocked how many marketers confuse one with the other.  Some never get the difference sorted out in the first place.  Others mix up value and differentiation messages, because they are bowing to adages or dictums [2] (e.g., “always sell value” or “benefits, not features”), instead acting based on the company’s business situation.

The simple fact is that some situations call for messaging value and others call for messaging differentiation. Somewhat perversely, the hotter your market, the less you need to message around value.  The cooler your market, the less you need to message around differentiation.

Why?  Hot markets definitionally have lots of buyers.  Those buyers already understand the value of the category and are trying to figure out which product to buy within it.  That’s why in hot markets you need a strong differentiation message.

During our hypergrowth phase at BusinessObjects nobody called up saying “why should I buy a BI tool?”   Everybody called up saying, “I’m going to buy a BI tool, my boss said to evaluate three, and Gartner said to look at BusinessObjects, Cognos, and Brio.”

When that buyer asks “why should I buy BusinessObjects?” think about how stupid you’ll look if you answer like this (thinking you need to sell value):

“Whoa, slow down there.  First, let’s talk about the business benefits of using BI in general.  We’ve found that compared to writing your own SQL queries and doing centralized report generation that you can lower IT support costs, reduce the backlog of requested reports, and empower end users to do their query and reporting.  This is why someone should buy an BI solution.”

The whole time you’re blabbering, the customer is wondering if Cognos or Brio can do a better job of answering their question.  In a hot category, you better be darn good at answering “why buy mine?” in a clear and compelling way.

Similarly, in hot categories, people don’t typically ask about return on investment (ROI) [3]:  they already know they want to buy one.  Ironically — and this surprises some — when you have a lot of people asking about ROI, you are probably in a cold category, not a hot one [4].

This is why some salespeople have such a hard time when they move from hypergrowth market leaders to early-stage startups.  In their prior job, all they had to sell was differentiation — “let me explain why mine’s better.”  In the new job, they can’t survive without selling value — “wait, before you hang up, please give me a second to explain why to buy one at all.”

If you’re not sure whether you’re in a hot or a cold category, I will refer you to Kellblog official Simple, Definitive, One-Step Hot Category Test:

If you have to ask whether you’re not a hot category, you’re not in one.

If you were, you’d be too busy to ask.  You’d be growing too fast.  In too many deals.  Running around with your hair on fire.  If you have time to sit around in meetings debating whether you’re in the hot category, I can assure you that you’re not in one.

Let’s look at cold markets for a bit.  I’ll pick the early days at MarkLogic when we were selling an XML database system.  There were two not-so-subtle indicators that it was not a hot market:  first, we had the time to ask and second, Gartner had literally published a note declaring that it wasn’t (“XML Database:  The Market That Never Was“).

The value of our system (to the information industry) was that we could help companies build new, powerful information products faster.  The differentiation was that we used a unique termlist-based indexing mechanism that allowed us to process essentially any XQuery statement with constraints on both structure and text at extremely high performance.

Imagine calling the SVP of Digital Strategy at McGraw-Hill and delivering the differentiation, instead of the value, message.

Sales:  Hi, I’m from MarkLogic and we have the world’s best XML database system.

Customer (if they didn’t hang up already):  I thought XML databases were, like Snake Plissken, dead.  Gartner said so.  Nobody’s using them, I need to —

Sales:  — Wait, don’t worry about that.  Let me explain for a minute why we have the best XML database because how we use termlists instead of traditional b-tree indices to process queries.

Customer: [dial tone]

You’re telling the customer why something she doesn’t want to buy is different from something else she doesn’t want to buy.  Instead, imagine delivering the value message, telling her why she should want to buy one:

Sales:  Hi, I’m from MarkLogic and we help media companies quickly build powerful information products.

Customer:  I’m in charge of our strategy for doing that.  Who uses you and what are they doing?

Ah.  Much better.

Another way to look at this is from a Geoffrey Moore lifecycle perspective:

messaging value vs diff

Early on, you need to message value — why do you want to buy one?  Once you cross the chasm into the high-growth “tornado,” you need to message differentiation — why buy from me. Once the market cools down, you need to start working to expand it by once again messaging value.  In three phases, Soup is Good Food, then My Soup’s Better, then Soup is Good Food.

All marketers should be able to answer both questions (e.g., why buy yours, why buy one at all) [5] about their product.  But which one you develop most deeply and push most in the market should be a function of your business situation.

Think value:  Soup is Good Food
Think differentiation:  Bags Fly Free

# # #

Notes
[1] And in my humble opinion much better than current messaging:  “Discover Flavor.  Convenient tasty solutions for everyone and every occasion.  Campell’s soups are made for real, real life (TM).”  First, let me save Campell’s $50K in legal fees — don’t bother registering that trademark — nobody’s ever going to steal it.  Presumably Discover Flavor is an attempt at differentiation, but … do the other guys’ soups really lack flavor?  I thought Campbell’s was getting hit at the high-end by tasty premium soups, not at the low-end with cheap, flavorless ones.  Seen in that light, Discover Flavor seems more a defensive message than either a differentiation or value message.  (“I know you may not think it, but our soups have flavor, too!”)  Finally, I can’t even classify “made for real, real life” as a message (other than as puffery) because it doesn’t mean anything.  Are other soups made for “fake, real life” or “real, fake life”?  Drivel, but I’m sure somehow it “tested well” in focus groups.

discover flavor

[2] Apologies to my high school Latin teacher, Mr. Maddaloni, for not using the more proper, dicta.

[3] As I often said when I lived in France, “ROI is King” (in cold categories, at least).

[4] The exception would be in a hot category where the ROI is quite different among competing solutions.  Usually, this is not the case — the return is generally more a property of the category than any given product.  When there is a difference, it’s typically due not to return, but investment — i.e., the total cost of ownership (TCO) can often vary significantly among different systems.

[5] We’ll leave the next logical question (“why buy now?”) for another post.

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.

How to Walk From a Deal

Like it or not, once in a while it’s appropriate for a vendor to walk away from a prospective deal.  Why might you want to do that?

  • You think your product is a poor fit with the customer’s needs.
  • You believe there is insufficient budget to achieve success on the project.
  • You feel like the deal is wired for another vendor, i.e., you think you are column fodder in the evaluation process.
  • You (and all your fellow reps) are fully booked with other more qualified opportunities.

One day I should probably write a post on how to make the critical stay vs. walk decision.  But today, I want to focus on something downstream of that — I want to focus on how to successfully walk from a deal once you’ve decided that it’s necessary to do so.

A good walk-away process should pass three tests in the mind of the customer.

  1. The customer should feel like they were treated respectfully.
  2. In the future, the customer should remain interested in buying from both you individually and your company, should circumstances be different.  (Ideally, they will be more interested in buying from you because you walked.)
  3. The customer should feel like the decision was not unilateral.

Given these three tests, here a few ways NOT to walk away from an opportunity.

  • Calling five minutes before a meeting to say you’re too busy to work on the opportunity because you don’t think it’s qualified anyway.
  • Leaving a voicemail in the middle of the night saying that you’ve decided to stop pursuing the opportunity.
  • Telling the customer their problem is too simple and/or their people are not sufficiently sophisticated to use your software.
  • Emailing to say that they are running a rigged process in which you can no longer, in good conscience, compete.

And there are lots more.  In short, there are a lot of WRONG ways to walk from an opportunity.  The right way involves doing the following things:

  • Bring it up quickly.  Once you realize there’s good reason to walk, you immediately get in touch with the customer.
  • Get the key contact on the phone and saying you’re considering dropping out and would welcome the chance to explain why.
  • Have a meeting or call to discuss the reasons you believe you should no longer participate in the sales cycle.
  • Ask for their feedback on those reasons.
  • Unless you hear otherwise in their feedback, thank them for their time.
  • Check back in later (e.g., in a few months) to ask how things turned out.

Amazingly, a lot of salespeople are afraid to walk away correctly.  So they procrastinate and then, suddenly, at the 11th hour, burst out saying “we’re not coming.”  This leaves a terrible impression on the customer and denies them the chance to correct potential misunderstandings in the logic that led to the walk-away decision.

My company has won deals by walking away in the right fashion.  To be clear, I am not advocating bluffing; when you say you’re walking you need to be prepared to do so.  But I have seen cases where the walk-away attempt revealed either a misunderstanding of the problem or the fact that no other vendor was willing to tell the customer what they didn’t want to hear.

I’ve seen cases where we get invited back six to eighteen months later and then win the deal.

I’ve also seen cases where the rep mangles the walk-away process, the customer goes ballistic and I, as CEO, need to jump in, eat a large piece of humble pie, figure out what’s going on, and assign a new rep to the deal.  We’ve won a few of these as well.

A fair number of salespeople like to brag about walking from deals, yet relatively few are mindful in how they do it.  Those who are mindful, and who follow the rules and steps above, will sell more in both the short- and long-term than those who are not.

Quota Over-assignment and Culture

Here’s a great slide from the CFO Summit at Zuora’s 2017 annual flagship Subscribed event.

underassign

Since they talk about this as under-assignment, since people aren’t great at flipping fractions in their head, and since I think of this more intuitively as over-assignment, I’m going to invert this and turn it into a pie chart.

quota over

So, here you can  see that 22% of companies have 0-11% over-assignment of quota, 44% have 11-25% over-assignment, 23% have 25-43%, 5% have 43-100% over-assignment, and 7% have more than 100% over-assignment of quota.

Since this is a pretty broad distribution — and since this has a real impact on culture, I thought examine this on two different angles:  the amount of total cushion and where that cushion lives.

The 0-11% crowd either has a very predictable business model or likes to live dangerously.  Since there’s not that much cushion to go around, it’s not that interesting to discuss who has it.  I hope these companies have adequately modeled sales turnover and its effects on quota capacity.

The 11-25% crowd strikes me as reasonable.  In my experience, most enterprise software companies run in the 20% range, so they assign 120 units of quota at the salesrep level for an operating plan that requires 100 units of sales.  Then the question is who has the cushion?  Let’s look at three companies.

cushion

In company 1, the CEO and VP of Sales are both tied to the same number (i.e., the CEO has no cushion if the VP of Sales misses) and the VP of Sales takes all of the cushion, giving the sales managers none.  In company 2, the CEO takes the entire 20% cushion for him/herself, leaving none for either the VP of Sales or the sales managers.  In company 3, the cushion is shared with the CEO and VP of Sales each taking a slice, leaving nearly half for the sales managers.

While many might be drawn to company 3, personally, I think the best answer is yet another scenario where the CEO and VP of Sales are both tied to 100, the sales managers to 110, and the aggregate salesrep quota to 120.  Unless the CEO has multiple quota-carrying direct reports, it’s hard to give the VP of Sales a higher quota than him/herself, so they should tie themselves together and share the 10% cushion from the sales managers who in turn have ~10% cushion relative to their teams.

I think this level of cushion works well if you’re building it atop a productivity model that assumes a normal degree of sales turnover (and ramp resets) and are thus using over-assignment simply to handle non-attainment, and not also sales turnover.  If you are using over-assignment to handle both, then a higher level of cushion may be needed, which is probably why 22% of companies have 25-43% over-assignment in their sales model.

The shock is the 12% that together have more than 43% over-assignment.  Let’s ponder for a minute what that might look like in an example with 60% over-assignment.

company4

So think about this for a minute.  The VP of Sales can be at 83% of quota, the sales managers on average can be at 71% of quota, and the salesreps can be at 63% of their quota — and the CEO will still be on plan.  The only people hitting their number, making their on-target earnings (OTE), and drinking champagne at the end of the quarter are the CEO and CFO.  (And they better drink it in a closet.)

That’s why I believe cushion isn’t just a math problem.  It’s a cultural issue.  Do you want a “let them eat cake” or a “we’re all in this together” culture.  The answer to that question should help determine how much cushion you have and where it lives.

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.