Category Archives: Scaling

My Perspectives on Growth (Presentation)

In my new capacity as an EIR at Balderton Capital, I recently gave a presentation to a leadership meeting at a high-growth, Balderton-backed startup offering my perspectives on growth and the challenges that come with it.

I discussed these five challenges:

  1. Next-levelitis, an obsessive focus on scaling everything to the next level.  (Which is great if not overdone.)
  2. Absorbing new leaders, (aka, “FBI guys”) and the challenges that come when hiring the wrong next-level people and they blow themselves up at the start.
  3. Conflation of regional culture and opinion, a common problem in international expansion.  (What’s a bona fide regional difference vs. a difference of opinion masked as one?)
  4. Missing an opportunity that you want (aka, getting “passed over” for a promotion) and what to do about it.
  5. Getting things wrong to get other things right.  Startups are 100% about getting what matters right.  Which begs the question, what matters?

The slide deck is below.

 

By the way, you have to watch the referenced Die Hard videos; they do a superb job of portraying what it feels like in these situations:

“I’m Dwayne Robinson … and I’m in charge here.”

“Not any more.”

Structuring the Organization and Duties of Product Marketing and Competitive Analysis

I sometimes get asked about how to structure an enterprise software marketing organization and the relative roles of product marketing vs. competitive analysis.  In this post, I’ll share my (somewhat contrarian) thoughts on this topic.

My first job in marketing, which served as my bridge from a technical to a sales-and-marketing career, was as a competitive analyst.  Specifically, I was the dedicated Sybase competitive analyst at Ingres in the late 1980s, in a corporate job, but working out of the New York City sales office.  Because, at the time, Sybase was a strong new entrant with a beachhead strategy in financial services, this was rough equivalent of working for the Wehrmacht on Omaha Beach on D-Day.  I learned not only by watching Sybase’s market invasion, but more importantly by watching how the local reps [1] and corporate [2] responded to it.

I’m a huge believer in competitive analysis, which probably started when I first heard this quote watching Patton as an adolescent:

“Rommel, you magnificent bastard, I read your book!” [3]

My other formative experience came from watching yet another movie, Wall Street, where antagonist Gordon Gekko refers to Sun Tzu’s The Art of War.

While Gekko doesn’t use my favorite quote for these purposes [4], his reference to the book was very much in vogue at the time, and probably why I first read it.  My favorite quote from The Art of War is this one:

“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”

Regular readers know I believe the mission of marketing is to make sales easier.  So the question becomes:  in enterprise software, how do we structure product marketing and competitive in the best way to do just that?

First, let’s review some common mistakes:

  • Not specializing competitive, instead declaring that each product marketing manager (PMM) will cover their respective competitors.  Too much scope, too little focus.
  • Understaffing competitive.  Even in organizations where competitive exists as its own team, it’s not uncommon to see a ratio of 5-10 PMMs per competitive analyst in terms of staffing.  This is too unbalanced.
  • Chartering competitive as strategic.  While I often euphemize the competitive team as “strategic marketing” or “market intelligence,” that’s not supposed to actually change their mission into some think tank.  They exist to help sales win deals.  Don’t let your competitive team get so lofty that they view deal support as pedestrian.
  • Putting competitive under product marketing.  This both blurs the focus and, more importantly, eliminates a healthy tension [5].  If your messaging doesn’t work in the field, the CMO should want to hear about it early (e.g., in their own staff meeting) and have a chance to fix it before it escalates to the corporate QBR and a potential sales attack on marketing in front of the CEO.
  • Putting competitive in the field.  This happens when marketing abdicates responsibility for producing sales-ready competitive materials and someone else picks up the ball, usually the sales productivity team, but sometimes field marketing [6].   This disconnects corporate product marketing from the realities of the field, which is not healthy.

Now, let’s tell you how I think structuring these departments.

  • Product marketing exists to build messaging and content [7] that describe the features and benefits of the product [8].  The job is to articulate.  They are experts in products.
  • Competitive analysis exists to research competitors, devise plays, and build tools to help sales win deals.  The job is to win.  They are experts in the competitors.

As long as we’re in movie quote mode, here’s one of my favorite quotes from James Mason’s character in The Verdict [9]:

I’d prepared a case and old man White said to me, “How did you do?” And, uh, I said, “Did my best.” And he said, “You’re not paid to do your best. You’re paid to win.”

While he was speaking to about lawyers, he might as well have been speaking to competitive:  you’re paid to win.

That’s why I believe competitive needs to be holistic and play-oriented.  Simply put, take everything you know about a competitor  — e.g., products, leadership, history, tactics — and devise plays that will help you win against them.  Then train sales on how to run those plays and supp0rt them in so doing.

If you adopt this mindset you end up with an organization where:

  • Product marketing and competitive are separate functions, both reporting directly to the CMO
  • Product marketing is product-oriented, focused on articulation of features and benefits
  • Competitive is competitor-oriented, focused on using all available information to create plays that win deals and support sales in executing them
  • Product marketing staffing is driven by the number of products you’re covering
  • Competitive staffing is driven by the number of competitors you’re covering (and at what depth level or tier).
  • You end up with a ratio of more like 3:1 than 10:1 when it comes to the relative staffing of product marketing and competitive

You think of these organizations as a matrix:

# # #

Notes

[1]  In the case of the reps, their response was to walk away from financial services deals because they knew they were likely to lose.  This, of course, had the effect of making it easier for Sybase to enter the market.  The smart reps went to Westchester and Long Island and sold in other verticals.  The dumb ones battled Sybase on Wall Street, lost deals, missed mortgage payments, broke marriages, and got fired — all for doing what the c0mpany strategically should have wanted them to do:  to slow down the invasion.   A classic case of micro and macro non-alignment of interests.

[2] The corporate response was to blame sales management.  Rather than seeing the situation as a strategic problem where an enemy was breaking through lines with an integrated strategy (e.g., partners), they chose to see it as an operational or execution problem.  Think:  we’re hiring bad reps in NYC and losing a lot deals — fire the sales manager and get some new talent in there.

[3]  Good Strategy, Bad Strategy tells the presumably more common inverse tale, where during the Gulf War in 1991 General Schwarzkopf was widely credited with a left-hook strategy described as “surprise,” “secret,” and “brilliant,” that was clearly published in the US Army Field Manual 100-5 saying the following, complete with an illustration of a left hook.

Envelopment avoids the enemy’s front, where its forces are most protected and his fires most easily concentrated. Instead, while fixing the defender’s attention forward by supporting or diversionary attacks, the attacker maneuvers his main effort around or over the enemy’s defenses to strike at his flanks and rear.

[4] Gekko refers to:  “Every battle is won before it’s ever fought.”

[5] Organization design is all about creating and managing healthy tensions.  Such tensions are a key reason why I like marketing reporting to the CEO (and not sales), customer success reporting to the CEO (and not the CRO/sales), and engineering reporting to the CEO (and not product), for a few examples.

[6] At one point, way back, Oracle had a huge market intelligence organization, but housed within Americas Marketing, a field marketing organization.

[7] Content being collateral (e.g., web content, white papers, e-books), presentations (internal and external), and demonstrations — all built around communicating the key messages in their messaging blueprint.

[8] Often, but not always, with a primary emphasis on differentiation.

[9] It’s not lost on me that the character was morally bankrupt and was implicitly saying to win at any and all costs.  But I nevertheless still love the quote.  (And yes, win within normal legal and societal constraints!  But win.)

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.

# # #

Notes

[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.

# # #

Notes

[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.

# # #

Notes
[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.