Category Archives: Scaling

Using To-Go Coverage to Better Understand Pipeline and Improve Forecasting

This is the second in a three-part series focused on forecasting and pipeline.  In part I, we examined triangulation forecasts with a detailed example.  In this, part II, we’ll discuss to-go pipeline coverage, specifically using it in conjunction with what we covered in part I.  In part III, we’ll look at this/next/all-quarter pipeline analysis as a simple way to see what’s happening overall with your pipeline.

Pipeline coverage is a simple enough notion:  take the pipeline in play and divide it by the target and get a coverage ratio.  Most folks say it should be around 3.0, which isn’t a bad rule of thumb.

Before diving in further, let’s quickly remind ourselves of the definition of pipeline:

Pipeline for a period is the sum of the value of all opportunities with a close date in that period.

This begs questions around definitions for opportunity, value, and close date which I won’t review here, but you can find discussed here.  The most common mistakes I see thinking about the pipeline are:

  • Turning 3.0x into a self-fulfilling prophecy by bludgeoning reps until they have 3.0x coverage, instead of using coverage as an unmanaged indicator
  • Not periodically scrubbing the pipeline according to a defined process and rules, deluding yourself into thinking “we’re always scrubbing the pipeline” (which usually means you never are).
  • Applying hidden filters to the pipeline, such as “oh, sorry, when we say pipeline around here we mean stage-4+ pipeline.”  Thus executives often don’t even understand what they’re analyzing and upstream stages turn into pipeline landfills full of junk opportunities that are left unmanaged.
  • Pausing sales hiring until the pipeline builds, effectively confusing cause and effect in how the pipeline gets built [1].
  • Creating opportunities with placeholder values that pollute the pipeline with fake news [1A], instead of creating them with $0 value until a salesrep socializes price with the customer [2].
  • Conflating milestone-based and cohort-based conversion rates in analyzing the pipeline.
  • Doing analysis primarily on either an annual or rolling four-quarter pipeline, instead of focusing first on this-quarter and next-quarter pipeline.
  • Judging the size of the all-quarter pipeline by looking at dollar value instead of opportunity count and the distribution of oppties across reps [2A].

In this post, I’ll discuss another common mistake, which is not analyzing pipeline on a to-go basis within a quarter.

The idea is simple:

  • Many folks run around thinking, “we need 3.0x pipeline coverage at all times!”  This is ambiguous and begs the questions “of what?” and “when?” [3]
  • With a bit more rigor you can get people thinking, “we need to start the quarter with 3.0x pipeline coverage” which is not a bad rule of thumb.
  • With even a bit more rigor that you can get people thinking, “at all times during the quarter I’d like to have 3.0x coverage of what I have left to sell to hit plan.” [4]

And that is the concept of to-go pipeline coverage [5].  Let’s look at the spreadsheet in the prior post with a new to-go coverage block and see what else we can glean.

Looking at this, I observe:

  • We started this quarter with $12,500 in pipeline and a pretty healthy 3.2x coverage ratio.
  • We started last quarter in a tighter position at 2.8x and we are running behind plan on the year [6].
  • We have been bleeding off pipeline faster than we have been closing business.  To-go coverage has dropped from 3.2x to 2.2x during the first 9 weeks of the quarter.  Not good.  [7]
  • I can easily reverse engineer that we’ve sold only $750K in New ARR to date [8], which is also not good.
  • There was a big drop in the pipeline in week 3 which makes me start to wonder what the gray shading means.

The gray shading is there to remind us that sales management is supposed to scrub the pipeline in weeks 2, 5, and 8 so that the pipeline data presented in weeks 3, 6, and 9 is scrubbed.  The benefits of this are:

  • It eliminates the “always scrubbing means never scrubbing” problem.
  • It draws a deadline for how long sales has to clean up after the end of a quarter:  the end of week 2.  That’s enough time to close out the quarter, take a few days rest, and then get back at it.
  • It provides a basis for snapshotting analytics.  Because pipeline conversion rates vary by week things can get confusing fast.  Thus, to keep it simple I base a lot of my pipeline metrics on week 3 snapshots (e.g., week 3 pipeline conversion rate) [9]
  • It provides an easy way to see if the scrub was actually done.  If the pipeline is flat in weeks 3, 6, and 9, I’m wondering if anyone is scrubbing anything.
  • It lets you see how dirty things got.  In this example, things were pretty dirty:  we bled off $3,275K in pipeline during the week 2 scrub which I would not be happy about.

Thus far, while this quarter is not looking good for SaaSCo, I can’t tell what happened to all that pipeline and what that means for the future.  That’s the subject of the last post in this three-part series.

A link to the spreadsheet I used in the example is here.

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[1]  In enterprise SaaS at least, you should look at it the other way around:  you don’t build pipeline and then hire reps to sell it, you hire reps and then they build the pipeline, as the linked post discusses.

[1A]  The same is true of close dates.  For example, if you create opportunities with a close date that is 18+ months out, they can always be moved into the more current pipeline.  If you create them 9 months out and automatically assign a $150K value to each, you can end up with a lot air (or fake news/data) in your pipeline.

[2]  For benchmarking purposes, this creates the need for “implied pipeline” which replaces the $0 with a segment-appropriate average sales price (ASP) as most people tend to create oppties with placeholder values.  I’d rather see the “real” pipeline and then inflate it to “implied pipeline” — plus it’s hard to know if $150K is assigned to an oppty as a placeholder that hasn’t been changed or if that’s the real value assigned by the salesrep.

[2A] If you create oppties with a placeholder value then dollar pipeline is a proxy for the oppty count, but a far less intuitive one — e.g., how much dollar volume of pipeline can a rep handle?  Dunno.  How many oppties can they work on effectively at one time?  Maybe 15-20, tops.

[3] “Of what” meaning of what number?  If you’re looking at all-quarters pipeline you may have oppties that are 4, 6, or 8+ quarters out (depending on your rules) and you most certainly don’t have an operating plan number that you’re trying to cover, nor is coverage even meaningful so far in advance.  “When” means when in the quarter?  3.0x plan coverage makes sense on day 1; it makes no sense on day 50.

[4] As it turns out, 3.0x to-go coverage is likely an excessively high bar as you get further into the quarter.  For example, by week 12, the only deals still forecast within the quarter should be very high quality.  So the rule of thumb is always 3.0x, but you can and should watch how it evolves at your firm as you get close to quarter’s end.

[5]  In times when the forecast is materially different from the plan, separating the concepts of to-go to forecast and to-go to plan can be useful.  But, by default, to-go should mean to-go to plan.

[6] I know this from the extra columns presented in the screenshot from the same sheet in the previous post.  We started this quarter at 96% of the ARR plan and while the never explicitly lists our prior-quarter plan performance, it seems a safe guess.

[7]  If to-go coverage increases, we are closing business faster than we are losing it.  If to-go coverage decreases we are “losing” (broadly defined as slip, lost, no decision) business faster than we are closing it.  If the ratio remains constant we are closing business at the same ratio as we started the quarter at.

[8]  A good sheet will list this explicitly, but you can calculate it pretty fast.  If you have a pipeline of $7,000, a plan of $3,900, and coverage of 2.2x then:  7,000/2.2 (rounded) = 3,150 to go, with a plan of 3,900 means you have sold 750.

[9] An important metric that can be used as an additional triangulation forecast and is New ARR / Week 3 Pipeline.


Using Triangulation Forecasts For Improved Forecast Accuracy and Better Conversations

Ever been in this meeting?

CEO:  What’s the forecast?
CRO:  Same as before, $3,400K.
Director 1:  How do you feel about it?
CRO:  Good.
Director 2:  Where will we really land?
CRO:  $3,400K.  That’s why that’s the forecast.
Director 1:  But best case, where do we land?
CRO:  Best case, $3,800K.
Director 2:  How do you define best case?
CRO:  If the stars align.

Not very productive, is it?

I’ve already blogged about one way to solve this problem:  encouraging your CRO think probabilistically about the forecast.  But that’s a big ask.  It’s not easy to change how sales leaders think, and it’s not always the right time to ask.  So, somewhat independent of that, in this series I’ll introduce three concepts that help ensure that we have better conversations about the forecast and ultimately forecast better as a result:  triangulation forecasts, to-go pipeline coverage, and this/next/all-quarter pipeline analysis.  In this post, we’ll cover triangulation forecasts.

Triangulation Forecasts

The simplest way to have better conversations about the forecast is to have more than one forecast to discuss.  Towards that end, much as we might take three or four bearings to triangulate our position when we’re lost in the backcountry, let’s look at three or four bearings to triangulate our position on the new annual recurring revenue (ARR) forecast for the quarter.

In this example [1] we track the forecast and its evolution along with some important context such as the plan and our actuals from the previous and year-ago quarters.  We’ve placed the New ARR forecast in its leaky bucket context [2], in bold so it stands out.  Just scanning across the New ARR row, we can see a few things:

  • We sold $3,000K in New ARR last quarter, $2,850K last year, and the plan for this quarter is $3,900K.
  • The CRO is currently forecasting $3,400K, or 87% of the New ARR plan.  This is not great.
  • The CRO’s forecast has been on a steady decline since week 3, from its high of $3,800K.  This makes me nervous.
  • The CRO is likely pressuring the VP of Customer Success to cut the churn forecast to protect Net New ARR [3].
  • Our growth is well below planned growth of 37% and decelerating [4].

I’m always impressed with how much information you can extract from that top block alone if you’re used to looking at it.  But can we make it better?  Can we enable much more interesting conversations?  Yes.  Look at the second block, which includes four rows:

  • The sum of the sales reps’ forecasts [5]
  • The sum of the sales managers’ forecasts [6]
  • The stage-weighted expected value (EV) of the pipeline [7] [8]
  • The forecast category-weighted expected value of the pipeline [9]

Each of these tells you something different.

  • The rep-level forecast tells you what you’d sell if every rep hit their current forecast.  It tends to be optimistic, as reps tend to be optimistic.
  • The manager-level forecast tells you how much we’d sell if every CRO direct report hit their forecast.  This tends to be the most accurate [10] in my experience.
  • The stage-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each stage. A $1M pipeline consisting of 10 stage 2 $100K oppties has a much lower EV than a $1M pipeline with 10 stage 5 $100K oppties — even though they are both “$1M pipelines.”
  • The forecast category-weighted expected value tells you the value of pipeline when weighted by probabilities assigned to each forecast category, such as commit, forecast, or upside.

These triangulation forecasts provide different bearings that can help you understand your pipeline better, know where to focus your efforts, and improve the accuracy of predicting where you’ll land.

For example, if the rep- and manager-level forecasts are well below the CRO’s, it’s often because the CRO knows about some big deal they can pull forward to make up any gap.  Or, more sinisterly, because the CRO’s expense budget is automatically cut to preserve a target operating margin and thus they are choosing to be “upside down” rather face an immediate expense cut [11].

If the stage-weighted forecast is much lower than the others, it indicates that while we may have the right volume of pipeline that it’s not far enough along in its evolution, and ergo we should focus on velocity.

Now, looking at our sample data, let’s make some observations about the state of the quarter at SaaSCo.

  • The reps are calling $3,400K vs. a $3,900K plan and their aggregate forecast has been fairly consistently deteriorating.  Not good.
  • The managers, who we might notice called last quarter nearly perfectly ($2,975K vs. $3,000K) have pretty consistently been calling $3,000K, or $900K below plan.  Worrisome.
  • The stage-weighted EV was pessimistic last quarter ($2,500K vs. $3,000K) and may need updated probabilities.  That said, it’s been consistently predicting around $2,600K which, if it’s 20% low (like it was last quarter), it suggests a result of $3,240K [12].
  • The forecast category-weighted expected value, which was a perfect predictor last quarter, is calling $2,950K.  Note that it’s jumped up from earlier in the quarter, which we’ll get to later.

Just by these numbers, if I were running SaaSCo I’d be thinking that we’re going to land between $2,800K and $3,200K [13].  But remember our goal here:  to have better conversations about the forecast.  What questions might I ask the CRO looking at this data?

  • Why are you upside-down relative to your manager’s forecast?
  • In other quarters was the manager-level forecast the most accurate, and if so, why you are not heeding it better now?
  • Why is the stage-weighted forecast calling such a low number?
  • What’s happened since week 5 such that the reps have dropped their aggregate forecast by over $600K?
  • Why is the churn forecast going down?  Was it too high to begin with, are we getting positive information on deals, or are we pressuring Customer Success to help close the gap?
  • What big/lumpy deals are in these numbers that could lead to large positive or negative surprises?
  • Why has your forecast been moving so much across the quarter?  Just 5 weeks ago you were calling $3,800K and how you’re calling $3,400K and headed in the wrong direction?
  • Have you cut your forecast sufficiently to handle additional bad news, or should I expect it to go down again next week?
  • If so, why are you not following the fairly standard rule that when you must cut your forecast you cut it deeply enough so your next move is up?  You’ve broken that rule four times this quarter.

In our next post in the series we’ll discuss to-go pipeline coverage.  A link to the spreadsheet used to the example is here.

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[1] This is the top of the weekly sheet I recommend CEOs to start their weekly staff meeting.

[2] A SaaS company is conceptualized as a leaky bucket of ARR.

[3] I cheated and look one row down to see the churn forecast was $500K in weeks 1-6 and only started coming down (i.e., improving) as the CRO continued to cut their New ARR forecast.  This makes me suspicious, particularly if the VP of Customer Success reports to the CRO.

[4] I cheated and looked one row up to see starting ARR growing at 58% which is not going to sustain if New ARR is only growing at ~20%.  I also had to calculate planned growth (3900/2850 = 1.37) as it’s not done for me on the sheet.

[5] Assumes a world where managers do not forecast for their reps and/or otherwise cajole reps into forecasting what the manager thinks is appropriate, instead preferring for managers to make their own forecast, loosely coupling rep-level and the manager-level forecasts.

[6]  Typically, the sum of the forecasts from the CRO’s direct reports.  An equally, if perhaps not more, interesting measure would be the sum of the first-line managers’ forecasts.

[7] Expected value is math-speak for probability * value.  For example, if we had one $100K oppty with a 20% close probability, then its expected value would be $100K * 0.2 = $20K.

[8] A stage-weighted expected value of the (current quarter) pipeline is calculated by summing the expected value of each opportunity in the pipeline, using probabilities assigned to each stage.  For example, if we had only three stages (e.g., prospect, short-list, and vendor of choice) and assigned a probability to each (e.g., 10%, 30%, 70%) and then multiplied the new ARR value of each oppty by its corresponding probability and summed them, then we would have the stage-weighted expected value of the pipeline.  Note that in a more advanced world those probabilities are week-specific (and, due to quarterly seasonality, maybe week-within-quarter specific) but we’ll ignore that here for now.  Typically, one way I sidestep some of that hassle is to focus my quarterly analytics by snapshotting week 3, creating in effect week 3 conversion rates which I know will work better earlier in the quarter than later.  In the real world, these are often eyeballed initially and then calculated from regressions later on — i.e., in the last 8 quarters, what % of week 3, stage 2 oppties closed?

[9]  The forecast category-weighted expected value of the pipeline is the same the stage-weighted one, except instead of using stage we use forecast category as the basis for the calculation.  For example, if we have forecast categories of upside, forecast, commit we might assign probabilities of 0.3, 0.7, and 0.9 to each oppty in that respective category.

[10] Sometimes embarrassingly so for the CRO whose forecast thus ends up a mathematical negative value-add!

[11] This is not a great practice IMHO and thus CEOs should not inadvertently incent inflated forecasts by hard-coding expense cuts to the forecast.

[12] The point being there are two ways to fix this problem.  One is to revise the probabilities via regression.  The other is to apply a correction factor to the calculated result.  (Methods with consistent errors are good predictors that are just miscalibrated.)

[13]  In what I’d consider a 80% confidence interval — i.e., 10% chance we’re below $2,800K and 10% chance we’re above $3,200K.

Thoughts on Hiring Your First VP of Sales

There’s some great content out there on the subject of hiring your first VP of sales at a startup, so in this post I’m going to do some quick thoughts on the subject in an effort to complement the existing corpus.

In other words, this is not your classic TLDR Kelloggian essay, but some quick tips.

  • Hire them first.  That is, before hiring any salesreps.  The first VP of Sales should be your first salesrep.  Hire someone who wants to walk (and even discover) the path before leading others.  Hire someone who enjoys the fight.
  • Hire them hopelessly early.  Don’t wait for product availability.  Don’t wait until you’ve hired 3-4 reps and they need a manager.  Don’t wait until you have a bookings plan that needs hitting. Hire them as early as possible.
  • Glue yourselves together for 6-12 months.  You want to spend 6-12 months as Frick and Frack.  Why?  Most founders can sell their idea and their software.  The real question is:  can anyone else?  By gluing yourselves together you will transfer a huge amount of critical knowledge to the sales VP.  That, or you’ll drive each other crazy and discover you can’t work together.  Either way, it’s good to succeed or fail fast.  And the goal is total alignment.  [1]
  • Hire them before the VP of marketing.  I know some very smart people who disagree with me on this question, but as a three-time enterprise software CMO (and two-time CEO) I take no shame in saying that marketing is a support function.  We’re here to help.  Hire us after hiring sales.  Let the VP of Sales have a big vote in choosing who supports them [2].
  • Hire someone who is a first-line manager today.  Their title might be district manager or regional vice president, but you want someone close to the action, but who also is experienced in building and managing a team.  Why?  Because you want them to be successful as your first salesrep for 6-12 months and then build up a team that they can manage.  In a perfect world, they’d have prior experience managing up to 10 reps, but even 4-6 will do [3].  You want to avoid like the plague a big-company, second- or third-line manager who, while undoubtedly carrying a large number, likely spends more time in spreadsheets and internal reviews than in customer meetings.

# # #

[1] Hat tip to Bhavin Shah for this idea.

[2] A wise VP of Marketing often won’t join before of the VP of Sales anyway.

[3] On the theory that someone’s forward potential is not limited to their prior experience.  Someone who’s successfully managed 4-6 reps can likely manage 10-12 with one extra first-line manager.  Managing 36 through a full layer of first-line managers is a different story.  That’s not to say they can’t do it, but it is a different job.  In any case, the thing to absolutely avoid is the RVP who can only manage through a layer of managers and views the sales trenches as a distant and potentially unpleasant memory.

Should Your SDRs Look for Projects or Pain?

There’s a common debate out there, it goes something like this:

“Our sales development representatives (SDRs) need to look for pain: finding business owners with a problem and the ability to get budget to go fix it.”


“No, our SDRs need to look for projects: finding budgeted projects where our software is needed, and ideally an evaluation in the midst of being set up.”

Who’s right?

As once was once taught to me, the answer to every marketing question is “it depends” and the genius is knowing “on what.”  This question is no exception.  The answer is:  it depends.  And on:

  • Whether you’re in a hot or cold market.
  • Whether your SDR is working an inbound or outbound motion

I first encountered this problem decades ago rolling out Solution Selling (from which sprung the more modern Customer-Centric Selling).  Solution Selling was both visionary and controversial.  Visionary in that it forced sales to get beyond selling product (i.e., selling features, feeds, and speeds) instead focusing on the benefits of what the product did for the customer.  Controversial in that it uprooted traditional sales thinking — finding an existing evaluation was bad, argued Bosworth, because it meant that someone else had already created the customer’s vision for a solution and thus the buying agenda would be biased in their favor.

While I think Bosworth made an interesting point about the potential for wired evaluation processes and requests for proposal (RFPs), I never took him literally.  Then I met what I could only describe as “fundamentalist solution seller” in working on the rollout.

“OK, we we’re working on lead scoring, and here’s what we’re going to do:  10 points for target industry, 10 points for VP title or above, 10 points for business pain, -10 points for existing evaluation, and -10 points for assigned budget.”


I’d read the book so I knew what Bosworth said, but, but he was just making a point, right?  We weren’t actually going to bury existing evaluations in the lead pile, were we?  All because the customer knew they wanted to buy in our category and had the audacity to start an evaluation process and assign budget before talking to us?

That would be like living in the Upside Down.  We couldn’t possibly be serious?  Such is the depth of religion often associated with the rollout of a new sales methodology.

Then I remembered the subtitle of the book (which everyone seems to forget).

“Creating buyers in difficult selling markets.”  This was not a book written for sellers in Geoffrey Moore’s tornado, it was book for written for those in difficult markets, tough markets, markets without a lot of prospects, i.e., cold markets.  In a cold market, no one’s out shopping so you have no choice but find potential buyers in latent pain, inform them a solution exists, and try to sell it to them.

Example:  baldness remedies.  Sure, I’d rather not be bald, but I’m not out shopping for solutions because I don’t think they exist.  This is what solution sellers call latent pain.  Thus, if you’re going to sell me a baldness remedy, you’re going need to find me, get my attention, remind me that I don’t like being bald, then — and this is really hard part — convince me that you have a solution that isn’t snake oil.  Such is life in cold markets.  Go look for pain because if you look for buyers you aren’t going to find many.

However, in hot markets there are plenty of buyers, the market has already convinced buyers they need to buy a product, so the question sellers should focus on is not “why buy one” but instead, “why buy mine.”

I’m always amazed that people don’t first do this high-level situation assessment before deciding on sales and marketing messaging, process, and methodology.  I know it’s not always black & white, so the real question is:  to what extent are our buyers already shopping vs. need to be informed about potential benefits before considering buying?  But it’s hard to devise any strategy without having an answer to it.

So, back to SDRs.

Let’s quickly talk about motion.  While SDR teams may be structured in many ways (e.g., inbound, outbound, hybrid), regardless of team structure there are two fundamentally different SDR motions.

  • Inbound.  Following-up with people who have “raised their hand” and shown interest in the company and its offerings.  Inbound is largely a filtering and qualification exercise.
  • Outbound.  Targeting accounts (and people within them) to try and mutate them into someone interested in the company and its offerings.  In other words, stalking:  we’re your destiny (i.e., you need to be our customer) and you just haven’t figured it out, yet.

In hot markets, you can probably fully feed your salesforce with inbound.  That said, many would argue that, particularly as you scale, you need to be more strategic and start picking your customers by complementing inbound with a combination of named-account selling, account-based marketing, and outbound SDR motion.

In cold markets, the proverbial phone never rings.  You have no choice but to target buyers with power, target pains, and convince them your company can solve them.

Peak hype-cycle markets can be confusing because there’s plenty of inbound interest, but few inbound buyers (i.e., lots of tire-kickers) — so they’re actually cold markets disguised as hot ones.

Let’s finally answer the question:

  • SDRs in hot markets should look for projects.
  • SDRs in cold markets should look for pain.
  • SDRs in hot markets at companies complementing inbound with target-account selling should look for pain.


Hiring Profiles: Step 0 of a Successful Onboarding Program

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

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

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

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

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

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

Imagine you’re the VP of sales enablement:

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

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

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

# # #


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

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



Book Review: Enablement Mastery by Elay Cohen

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

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


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

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

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

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

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

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

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

Video of my SaaStr 2018 Presentation: Ten Non-Obvious Things About Scaling SaaS

While I’ve blogged about this presentation before, I only recently stumbled into this full-length video of this very popular session — a 30-minute blaze through some subtle SaaS basics.  Enjoy!

I look forward to seeing everyone again at SaaStr Annual 2019.