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Highlights from the Fenwick & West 3Q18 Venture Capital Survey

It’s been a few years since I wrote about this survey, which I post about less to communicate recent highlights and more to generate awareness of its existence.  The Fenwick & West Venture Capital Survey is must-read material for any entrepreneur or startup CEO because it not only makes you aware of trends in financing, but also provides an excellent overview of venture capital terminology as well as answering the important question of “what’s normal” in today’s venture funding environment (also known as “what’s market.”)

So if you’re not yet subscribed to it, you can sign up here.

3Q18 F&W Venture Capital Survey Highlights

  • 215 VC financing rounds were closed by companies with headquarters in Silicon Valley
  • Up rounds beat down rounds 78% to 9%
  • The average price increase (from the prior round) was 71%
  • 24% of rounds had a senior liquidation preference
  • 8% of rounds had multiple liquidation preferences
  • 11% of rounds has participation
  • 6% of rounds had cumulative dividends
  • 98% of rounds had weighted-average anti-dilution provisions
  • 2% of rounds had pay-to-play provisions
  • 6% of rounds had redemption rights

In summary, terms remained pretty friendly and valuations high.  Below is Fenwick’s venture capital barometer which focuses on price changes from the prior financing round.  It’s a little tricky to interpret because the amount of time between rounds varies by company, but it does show in any given quarter what the price difference is, on average, across all the financings closed in that quarter.  In 3Q18, it was 71%, slightly down from the prior quarters, but well above the average of 57%.

vc barometer

 

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Lost and Founder: A Painful Yet Valuable Read

Some books are almost too honest.  Some books give you too much information (TMI).  Some books can be hard to read at times.  Lost and Founder is all three.  But it’s one of the best books I’ve seen when it comes to giving the reader a realistic look at the inside of Silicon Valley startups.NeueHouse_Programming_LostandFound

In an industry obsessed with the 1 in 10,000 decacorns and the stories of high-flying startups and their larger-than-life founders, Lost and Founder takes a real look at what it’s like to found, fund, work at, and build a quite successful but not media- and Sand-Hill-Road-worshiped startup.

Rand Fishkin, the founder of Moz, tells the story of his company from its founding as a mother/son website consultancy in 2001 until his handing over the reins, in the midst of battling depression, to a new CEO in February 2014.  But you don’t read Lost and Founder to learn about Moz.  You read it to learn about Rand and the lessons he learned along the way.

Excerpt:

In 2001, I started working with my mom, Gillian, designing websites for small businesses in the shadow of Microsoft’s suburban Seattle-area campus. […] The dot-com bust and my sorely lacking business acumen meant we struggled for years, but eventually, after trial and error, missteps and heartache, tragedy and triumph, I found myself CEO of a burgeoning software company, complete with investors, employees, customers, and write-ups in TechCrunch.

By 2017, my company, Moz, was a $45 million/ year venture-backed B2B software provider, creating products for professionals who help their clients or teams with search engine optimization (SEO). In layman’s terms, we make software for marketers. They use our tools to help websites rank well in Google’s search engine, and as Google became one of the world’s richest, most influential companies, our software rose to high demand.

Moz is neither an overnight, billion-dollar success story nor a tragic tale of failure. The technology and business press tend to cover companies on one side or the other of this pendulum, but it’s my belief that, for the majority of entrepreneurs and teams, there’s a great deal to be learned from the highs and lows of a more middle-of-the-road startup life cycle.

Fishkin’s style is transparent and humble.  While the book tells a personal tale, it is laden with important lessons.  In particular, I love his views on:

  • Pivots (chapter 4).  While it’s a hip word, the reality is that pivoting — while sometimes required and which sometimes results in an amazing second efforts — means that you have failed at your primary strategy.  While I’m a big believer in emergent strategy, few people discuss pivots as honestly as Fishkin.
  • Fund-raising (chapters 6 and 7).  He does a great job explaining venture capital from the VC perspective which then makes his conclusions both logical and clear.  His advice here is invaluable.  Every founder who’s unfamiliar with VC 101 should read this section.
  • Making money (chapter 8) and the economics of founding or working at a startup.
  • His somewhat contrarian thoughts on the Minimum Viable Product (MVP) concept (chapter 12).  I think in brand new markets MVPs are fine — if you’ve never seen a car then you’re not going to look for windows, leather seats, or cup-holders.  But in more established markets, Fishkin has a point — the Exceptional Viable Product (EVP) is probably a better concept.
  • His very honest thoughts on when to sell a startup (chapter 13) which reveal the inherent interest conflicts between founders, VCs, and employees.
  • His cheat codes for next itme (Afterword).

Finally, in a Silicon Valley where failure is supposedly a red badge of courage, but one only worn after your next big success, Fishkin has an unique take on vulnerability (chapter 15) and his battles with depression, detailed in this long, painful blog post which he wrote the night before this story from the book about a Foundry CEO summit:

Near the start of the session, Brad asked all the CEOs in the room to raise their hand if they had experienced severe anxiety, depression, or other emotional or mental disorders during their tenure as CEO. Every hand in the room went up, save two. At that moment, a sense of relief washed over me, so powerful I almost cried in my chair. I thought I was alone, a frail, former CEO who’d lost his job because he couldn’t handle the stress and pressure and caved in to depression. But those hands in the air made me realize I was far from alone— I was, in fact, part of an overwhelming majority, at least among this group. That mental transition from loneliness and shame to a peer among equals forever changed the way I thought about depression and the stigma around mental disorders.

Overall, in a world of business books that are often pretty much the same, Lost and Founder is both quite different and worth reading.  TMI?  At times, yes.  TLDR?  No way.

Thanks, Rand, for sharing.

How To Present a Quarterly Sales Forecast to Your SaaS Company Board

While most companies put real thought into how they present numbers in their post-quarter board decks and other management reports, one area in which you’ll find a lack of discipline is in how they present quarterly sales forecasts to the board.

They’re typically done as a quick update email to the board.  They’ll usually mention the forecast number this quarter (but only usually) and only sometimes include the plan and almost never include the prior-sequential or year-over-year quarter.  Sometimes, they’ll be long, rambling updates about deals with no quarterly number at all — only ARR per deal on an list of deals with no idea which permutations are likely to close.  Sometimes, they’ll confuse “commit” (a forecast category status) with “booked QTD” — a major confusion as “commit” is only “done” in the eyes of an optimistic sales VP — I have little interest in the former (unless it’s part of a general, proven stage-weighted expected value) and a lot of interest in the latter (what actually has been sold thus far).  They’ll often use terms like “forecast,” “commit,” “upside,” “worst case,” and “best case” without defining them (and questions about their definitions are too often met with blank stares or squishy replies).

In this post, I’ll discuss how to present these forecasts better.  If you follow this advice, your board will love you.  Well, they’ll love your communication at least.  (They’ll only love you if the numbers you’re presenting are great to boot.)

The Driving Principles
I think CEOs write these hastily dashed-off forecast emails because they forget some basics.  So always remember:

  • Your board members have day jobs.  They’re not necessarily going to remember your plan number, let alone what you did last year or last quarter.  So help them — provide this context.  (And do the percent math for them.)
  • Your board members care about deals, but only at a summary level and only after they’ve been given the numbers.  They typically care about deals for two reasons:  because they might be able to help if they know an executive at the target company and because they like to see if the deals that close are the same ones management said were “key deals” all quarter.
  • Communication with your board members will be more effective if you have standard definitions for “forecast” or “best case.”  I like to define “forecast” (at the VP of sales level) to be 90% confidence in beating and “best case” to mean 20% confidence in beating.  This means you get to miss your forecast once every 2.5 years and you should beat your best case once every 5 quarters.  See How to Train Your VP of Sales to Think About the Forecast for more.
  • After hearing a forecast the next question most board members will have is about pipeline coverage.  Ergo, why not answer that up front and provide them with the current quarterly pipeline and a to-go coverage ratio to get to plan.  To-go coverage = (current quarterly pipeline) / (new ARR bookings needed to get to plan).

How to Present a SaaS Company Quarterly Forecast
So, now that we’ve covered the logic behind this, let’s show you the spreadsheet that I’d embed or attach in a short email to the board about the current quarter forecast.

how to present forecast 2

Using “Win Themes” to Improve Your Sales Management and Increase Win Rates

At most sales review meetings what do you hear sales management asking the reps?  Questions like these:

  • What stage is this opportunity in?
  • What value do you have it at in the pipeline?
  • Is there upside to that value?
  • What forecast category is it in?
  • In what quarter will it close?
  • What competitors are in the deal?
  • What products will they be buying?
  • Do they have budget for the purchase?
  • How do we meet their primary requirements for a solution?
  • How have we demonstrated that we can meet those requirements?
  • What are the impacts of not solving those problems?
  • How did they attempt to solve those problems before?
  • Who is impacted by the consequences of those impacts?
  • Who is the primary decision maker?
  • What is the decision-making process?
  • Who else is involved in the decision and in what roles?
  • Who have you developed relationships with in the account?
  • What risk is there of a goal-post move?

And on and on.

Some of these questions are about systems and process.  Some are about forecasting.  Ideally, most are about the problem the customer is trying to solve, the impacts of not solving it, how they tried to solve it before, the ideal solution to the problem, and the benefits of solving it.  But in our collective hurry to be process-oriented, methodology-driven, systems-compliant, and solutions-oriented, all too often something critical gets lost:

Why are we going to win?

What?  Oh shoot.  Yep, forgot to ask that one.  And, of course, that’s the most important one.  As I sometimes need to remind sales managers, while the process is great, let’s not forget the purpose of the process is to win.

(I’ve even met a few sales managers so wedded to process and discipline that I’ve wondered if they’d rather crash while flying in perfect formation than win flying out of it.)

Process is great.  I love process.  But let’s not forget the point.  How can we do that?  With win themes — two to three simple, short, plain-English reasons why you are going to win the deal.  Here’s an example.  We are going to win because:

  • Joe the CFO saw first-hand how Adaptive didn’t scale in his last job and is committed to purchasing a system he can grow with.
  • Our partner, CFO Experts, has worked with Joe in the past, has a great relationship with him, and firmly believes that Host is the best fit with the requirements.

Build win themes into your systems and process.  Don’t add win themes to the bottom of your Salesforce opportunity screen; put them right up top so the first conversation about any deal — before you dive into the rabbit hole — is “why are we going to win?”   Two to three win themes should provide a proposed answer and a healthy platform for strategic discussion.

(And, as my friend Kate pointed out, in case it didn’t come up in the win theme conversation, don’t forget to ask “why might we lose?”)

The Three Types of Customer Success Manager

Since we’re now officially in 2019 planning season, I’ve been thinking about — among other things — our Customer Success model for next year and talking with friends in my network about that.  Since Customer Success is (sadly, perhaps) still a relatively new discipline in enterprise software companies, I’d say the whole field is evolving quickly, so it’s important to keep up with the changes.

In this post, I won’t approach things from a Customer Success Model perspective and how Customer Success interfaces to Sales (e.g., hunter/farmer, hunter-in-zoo, farmer-with-shotgun, account manager) [1].  Instead, I’m going to look bottom-up at the three basic types of customer success manager (CSM).  While they may share a common job title, CSMs are often cut from very different cloth.  Regardless of which model you implement, I believe you’ll be working with individuals who fall into one of three basic types to staff it.

  • Product-oriented
  • Process-oriented
  • Sales-oriented

In order to characterize the three types clearly and concretely, I’m going to use a template — first, I’ll show how each type introduces themselves to a customer, then I’ll present fragments of typical conversations they like to have with customers.

Note here that I’m talking about people, not roles.  In defining Customer Success Models you map people to roles and roles to duties [3].  In this post I’m really writing about the nature of the CSMs themselves because — all other complexity aside — I think the people pretty naturally drift to one of three types.

The art of setting up the right Customer Success Model is to clearly map out the sales and CSM roles (who does what), define the appropriate frequencies (how often do they do it), and then put the right people in the right roles — both to maximize job satisfaction as well as performance [4] [5].

The Product-Oriented CSM
Introduction: “Hi, I’m Jane, and my job is to ensure you get best use of our products. I’ll be here to keep an eye on your implementation process and to answer any technical questions that go beyond normal technical support. I’ll also perform periodic, proactive ‘health checks’ to ensure that you are using the system properly and making best use of new features.  I’m an expert in our products and previously worked at a consulting shop helping people implement it.  I’m here if you need me.”

Conversations they like to have:

  • “How’s that report working that I helped you build?”
  • “Yes, there are two ways of solving that problem in the product, let me help you pick the right one.”
  • “So you’re having some issues with performance, let me get in a take a look.”

The Process-Oriented CSM
Introduction: “Hi, I’m Joe, and my job is to make sure you are happy with our service and renewing your contract every year.  I’ll drive the renewal process (which, you should note, starts about 4-6 months before the subscription end date), monitor your adoption of the service, ask you to complete our ongoing customer satisfaction surveys, inform you about local user community events, and proactively call you about once a month to check-in.  Should we hit a rough patch, I’ll also serve as your escalation manager and pull together the right resources across the company to get you successfully through it.  I’m a very organized person — I was a project manager in my prior job — and I can manage 10,000 things at once, so don’t hesitate to call — I’m here if you need me.”

Conversations they like to have:

  • “Have you guys budgeted for next year’s renewal — and by the way don’t forget to leave room for the annual price increase?”
  • “I see you hired a new CFO, do you think that’s going to have an impact on our renewal process and can we setup a time to meet her?”
  • “We’re having a training class on new features in the November release and wanted to make sure you knew about it.”
  • “You’ve got two tickets stuck in technical support?  Let me swing over there and find out what’s going on.”

The Sales-Oriented CSM
Introduction: “Hi, I’m Kelly, and my job is to make sure your company gets maximum benefit from, and makes maximum use of, our software.  I’ll be managing your account from here forward, taking care of the renewal, and working to find other areas of your company that can benefit from our solutions [6].  Of course, I know you won’t be expanding usage if you’re not successful, so a big part of my job is to keep you happy as well — towards that end I’ll be keeping an eye on your implementation and your ongoing satisfaction surveys, and setting up periodic health checks with our ace technical team.  For routine technical or services questions, you should call those departments, but if you find yourself getting stuck do not hesitate to call me.  And, well, not to get ahead of myself, but I was wondering if you could introduce me to the CFO of the XYZ division, because I’d love to see if we could get in there and help them experience the same benefits that you’re going to be getting.  In my prior job, I worked as as sales development rep (SDR) and was promoted into this position 2 years ago.’

Conversations they like to have:

  • “Do you see any reason why you wouldn’t be renewing the subscription in February?”
  • “I’d love to come in next week and demonstrate our new Modeling product; I think it could help you with the inventory problem your CFO told me about.”
  • “I see you hired a new finance VP; can the three of us get together next week to discuss her goals for the team and our history working with you as a supplier?

Conclusion
I’ve exaggerated the types to make them clear.  What kind of CSM are you?  What other types have you seen? I’d love to hear.

In the end, it’s all about getting the right Sales and Customer Success Models working side by side, with the duties clearly mapped, and with the right people in the right roles.  I think the best way to do that is a mix of top-down planning and bottom-up assessment.  How do we want to break up these duties?  And who do we have on our team.

# # #

Notes

[1] The way to define your Customer Success model is to define which duties (e.g., adoption, upsell, renewal) are mapped to which Customer Success and Sales roles in your company.  I won’t dive into Customer Success models in this post (because I can think of 3-5 pretty quickly) and each of those models will have a different duty mapping; so the post would get long fast.  Instead, I’m focusing on people because in many ways it’s simpler — I think CSMs come with different, built-in orientations and its important that you put the right CSM into the right role.

[2] I *love* characterizing jobs in this way.  It’s so much more concrete than long job descriptions or formal mission statements.  Think:  if a customer asks you what your job is, what do you say?

[3] As well as map duties to frequencies — e.g., a tier-one CSM may perform monthly outreach calls and setup quarterly health checks, whereas a tier-three CSM may perform quarterly outreach calls and setup annual health checks.

[4] You can make a product-oriented CSM responsible for renewals, but they probably won’t like it.  You could even make them responsible for upsell — but you won’t get much.

[5] To keep things simple here, I’m omitting the Customer Success Architect (CSA) role from the discussion.  Many companies, particularly as they grow, break product-oriented CSMs out of the CSM team, and move them into more of an advanced technical support and consulting role (CSA).  While I think this is generally a good idea, once broken out, they are no longer technically CSMs and out of scope for this post.

[6] One of my favorite quotes from a sales VP I know:  “I always put ‘sales’ on my business card — and not account executive or such — because I don’t want anyone to be surprised when I ask for money.”  This introduction preserves that spirit.

The Big Mistake You Might Be Making In Calculating Churn: Failing to Annualize Multi-Year ATR Churn Rates

Most of the thinking, definitions, and formulas regarding SaaS unit economics is based on assumptions that no longer reflect the reality of the enterprise SaaS environment.  For example, thinking in terms of MRR (monthly recurring revenue) is outdated because most enterprise SaaS companies run on annual contracts and thus we should think in terms of ARR (annual recurring revenue) instead.

Most enterprise SaaS companies today do a minimum one-year contract and many do either prepaid or non-prepaid multi-year contracts beyond that. In the case of prepaid multi-year contracts, metrics like the CAC payback period break (or at the very least, get difficult to interpret).  In the case of multi-year contacts, calculating churn correctly gets a lot more complicated – and most people aren’t even aware of the issue, let alone analyze it correctly.

If your company does multi-year contracts and you are not either sidestepping this issue (by using only ARR-pool-based rates) or correcting for it in your available-to-renew (ATR) churn calculations, keep reading.  You are possibly making a mistake and overstating your churn rate.

A Multi-Year Churn Example
Let’s demonstrate my point with an example where Company A does 100% one-year deals and Company B does 100% three-year deals.  For simplicity’s sake, we are going to ignore price increases and upsell [1].  We’re also not going to argue the merits of one- vs. three-year contracts; our focus is simply how to calculate churn in a world of them.

In the example below, you can see that Company A has an available-to-renew-based (ATR-based) [2] churn rate of 10%.  Company B has a 27% ATR-based churn rate.  So we can quickly conclude that Company A’s a winner, and Company B is a loser, right?

Capture

Not so fast.

At the start of year 4, a cohort of Company A customers is worth 72.9 units, the exact same as a cohort of Company B customers.  In fact, if you look at lifetime value (LTV), the Company B cohort is worth nearly 10% more than the Company A cohort [3].

my churn1

Wait a minute!  How can a company with 27% churn rate be “better” than a company with 10% churn rate?

It’s All About Exposure:  How Often are Deals Exposed to the Churn Rate?
One big benefit of multi-year deals is that they are exposed to the churn rate less frequently than one-year deals.  When you exclude the noise (e.g., upsell, discounts, and price increases), and look at churn solely as a decay function, you see that the N-year retention rate [4] is (1-churn rate)^N.  With 10% churn, your 2-year retention rate is (1-0.1)^2 = 0.9^2 = 0.81.  Your 3-year retention rate is (1-0.1)^3 = 0.9^3 = 0.729, or a retention rate of 73%, equivalent to a churn rate of 27%.

Simply put, churn compounds so exposing a contract to the churn rate less often is a good thing:  multi-year deals do this by excluding contracts from the ATR pool, typically for one or two years, before they come up for renewal [5].  This also means that you cannot validly compare churn rates on contracts with different duration.

This is huge.  As we have just shown, a 10% churn rate on one-year deals is equivalent to a 27% churn rate on three-year deals, but few people I know recognize this fact.

I can imagine two VCs talking:

“Yo, Trey.”

“Yes.”

“You’re not going to believe it, I saw a company today with a 27% churn rate.”

“No way.”

“Yep, and it crushed their LTV/CAC — it was only 1.6.”

“Melting ice cube.  Run away.”

“I did.”

Quite sad, in fact, because with a correct (annualized) churn rate of 10% and holding the other assumptions constant [6], the LTV/CAC jumps to healthy 4.4.  But any attempt to explain a 27% churn rate is as likely to be seen as a lame excuse for a bad number as it is to be seen as valid analysis.

Best Alternative Option:  Calculate Churn Rates off the Entire ARR Pool
I’m going to define the 27% figure as the nominal ATR-based churn rate.  It’s what you get when you take churn ARR / ATR in any given period.  I call it a nominal rate because it’s not annualized and it doesn’t reflect the varying distribution of 1Y, 2Y, and 3Y deals that are mixed in the ATR pool in any given quarter.  I call it nominal because you can’t validly compare it to anything [7].

Because correcting this to a more meaningful rate is going to involve a lot of brute force math, I’ll first advise you to do two things:

  • Banish any notion from your mind that ATR rates are somehow “more real” than churn rates calculated against the entire ARR pool [8].
  • Then use churn rates calculated against the entire ARR pool and sidestep the mess we’re about to enter in the next section [9] where we correct ATR-based churn rates.

In a world of mixed-duration contracts calculating churn rates off the entire ARR pool effectively auto-corrects for the inability of some contracts to churn.  I have always believed that if you were going to use the churn rate in a math function (e.g., as the discount rate in an NPV calculation) that you should only use churn rates calculated against the entire ARR pool because, in a mixed multi-year contract world, only some of the contracts come up for renewal in any given period.  In one sense you can think of some contracts as “excluded from the available-to-churn (ATC) pool.”  In another, you can think of them as auto-renewing.  Either way, it doesn’t make sense in a mixed pool to apply the churn rate of those contracts up for renewal against the entire pool which includes contracts that are not.

If you want to persist in using ATR-based churn rates, then we must correct for two problems:  we need to annualize the multi-year rates, and we then need to calculate ATR churn using an ATR-weighted average of the annualized churn rates by contract duration.

Turning Nominal ATR Churn into Effective, Annualized ATR Churn
Here’s how to turn nominal ATR churn into an effective, annualized ATR churn rate [10] [11]:

Step 1:  categorize your ATR and churn ARR by contract duration.  Calculate a 1Y churn rate and nominal 2Y and 3Y ATR churn rates.

Step 2:  annualize the nominal multi-year (N-year) churn rates by flipping to retention rates and taking the Nth root of the retention rate.  For example, our 27% 3-year churn rate is equivalent to a 73% 3-year retention rate, so take the cube root of 0.73 to get 0.9.  Then flip back to churn rates and get 10%.

Step 3:  do an ATR-weighted average of the 1Y and annualized 2Y and 3Y churn rates.  Say your ATR was 50% 1Y, 25% 2Y, and 25% 3Y contracts and your annualized churn rates were 10%, 12%, and 9%.  Then the weighted average would be (0.5*0.10) + (0.25*0.12) + (0.25*0.09) = 10.25%, as your annualized, effective ATR churn rate.

That’s it.  You’ve now produced an ATR churn rate that is comparable to a one in a company that does only 1-year contracts.

Conclusion
If nothing else, I hope I have convinced that you it is invalid to compare churn rates on contracts of different duration and ergo that is simpler to generally calculate churn rates off the entire ARR pool.  If, however, you still want to see ATR-based churn rates, then I hope I’ve convinced you that you must do the math and calculate ATR churn as a weighted average of annualized one-, two-, and three-year ATR churn rates.

# # #

Notes
[1] In a world of zero upsell there is no difference between gross and net churn rates, thus I will simply say “churn rate” in this post.

[2] As soon as you start doing multi-year contracts then the entire ARR base is no longer up for renewal each year.  You therefore need a new concept, available to renew (ATR), which reflects only that ARR up for renewal in a given period.

[3] Thanks to its relatively flatter step-wise decay compared to Company A’s more linear decay.

[4] Retention rate = 1 – churn rate.

[5] If it helps, you can think of the ATR pool in a glass half-empty way as the available-to-churn pool.

[6] Assuming CAC ratio of 1.8 and subscription gross margins of 80%.

[7] Unless your company has a fixed distribution of deals by contract duration – e.g., a degenerate case being 100% 3Y deals.  For most companies the average contract duration in the inbound ATR pool is going to vary each quarter.  Ergo, you can’t even validly compare this rate to itself over time without factoring in the blending.

[8] Most people I meet seem to think ATR rates are more real than rates based on the entire ARR pool.  Sample conversation  — “what’s your churn rate?”  “6%.”  “Gross or net?  “Gross.”  “No, I mean your real churn rate – what gets churned divided only by what was up for renewal.”    The mistake here is in thinking that using ATR makes it comparable to a pure one-year churn rate – and it doesn’t.

[9] Gross churn = churn / starting period ARR.  Net churn = (gross churn – upsell) / starting period ARR.

[10] I thought about trying a less brute-force way using average contract duration (ACD) of the ATR pool, but decided against it because this method, while less elegant, is more systematic.

[11] Note that this method will still understate the LTV advantage of the more step-wise multi-year contract decay because it’s not integrating the area under the curve, but instead intersecting what’s left of the cohort after N years.  In our first example, the 1Y and 3Y cohorts both had 73 units of ARR, but because the multi-year cohort decayed more slowly it’s LTV to that point was about 10% higher.

The Use of Ramped Rep Equivalents (RREs) in Sales Analytics and Modeling

[Editor’s note:  revised 7/18, 6:00 PM to fix spreadsheet error and change numbers to make example easier to follow, if less realistic in terms of hiring patterns.]

How many times have you heard this conversation?

VC:  how many sales reps do you have? 

CEO:  Uh, 25.  But not really.

VC:  What do you mean, not really?

CEO:  Well, some of them are new and not fully productive yet.

VC:  How long does it take for them to fully ramp?

CEO:  Well, to full productivity, four quarters.

VC:  So how many fully-ramped reps do you have?

CEO:  9 fully ramped, but we have 15 in various stages of ramping, and 1 who’s brand new …

There’s a better way to have this conversion, to perform your sales analytics, and to build your bookings capacity waterfall model.  That better way involves creating a new metric called ramped rep equivalents (RREs). Let’s build up to talking about RREs by first looking at a classical sales bookings waterfall model.

ramped rep equivalents, picture 1, revised

I love building these models and they’re a lot of fun to play with, doing what-if analysis, varying the drivers (which are in the orange cells) and looking at the results.  This is a simplified version of what most sales VPs look at when trying to decide next year’s hiring, next year’s quotas [1], and next year’s targets.  This model assumes one type of salesrep [2]; a distribution of existing reps by tenure as 1 first-quarter, 3 second-quarter, 5 third-quarter, 7 fourth-quarter, and 9 steady-state reps; a hiring pattern of 1, 2, 4, 6 reps across the four quarters of 2019; and a salesrep productivity ramp whereby reps are expected to sell 0% of steady-state productivity in their first quarter with the company, and then 25%, 50%, 75% in quarters 2 through 4 and then become fully productive at quarter 5, selling at the steady-state productivity level of $1,000K in new ARR per year [3].

Using this model, a typical sales VP — provided they believed the productivity assumptions [4] and that they could realistically set quotas about 20% above the target productivity — would typically sign up for around a $22M new ARR bookings target for the coming year.

While these models work just fine, I have always felt like the second block (bookings capacity by tenure), while needed for intermediate calculations, is not terribly meaningful by itself.  The lost opportunity here is that we’re not creating any concept to more easily think about, discuss, and analyze the productivity we get from reps as they ramp.

Enter the Ramped Rep Equivalent (RRE)
Rather than thinking about the partial productivity of whole reps, we can think about partial reps against whole productivity — and build the model that way, instead.  This has the by-product of creating a very useful number, the RRE.  Then, to get bookings capacity just multiply the number of RREs times the steady-state productivity.  Let’s see an example below:

ramped rep equivalents, picture 2, revised

This provides a far more intuitive way of thinking about salesrep ramping.  In 1Q19, the company has 25 reps, only 9 of whom are fully ramped, and rest combine to give the productivity of 8.5 additional reps, resulting in an RRE total of 17.5.

“We have 25 reps on board, but thanks to ramping, we only have the capacity equivalent to 17.5 fully-ramped reps at this time.”

This also spits out three interesting metrics:

  • RRE/QCR ratio:  an effective vs. nominal capacity ratio — in 1Q19, nominally we have 25 reps, but we have only the effective capacity of 17.5 reps.  17.5/25 = 70%.
  • Capacity lost to ramping (dollars):  to make the prior figure more visceral, think of the sales capacity lost due to ramping (i.e., the delta between your nominal and effective capacity) expressed in dollars.  In this case, in 1Q19 we’re losing $1,875K of our bookings capacity due to ramping.
  • Capacity lost to ramping (percent):  the same concept as the prior metric, simply expressed in percentage terms.  In this case, in 1Q19 we’re losing 30% of our bookings capacity due to ramping.

Impacts and Cautions
If you want to move to an RRE mindset, here are a few tips:

  • RREs are useful for analytics, like sales productivity.  When looking at actuals you can measure sales productivity not just by starting-period or average-period reps, but by RRE.  It will provide a much more meaningful metric.
  • You can use RREs to measure sales effectiveness.  At the start of each quarter recalculate your theoretical capacity based on your actual staffing.  Then divide your actuals by that start-of-quarter theoretical capacity and you will get a measure of how well you are performing, i.e., the utilization of the quarterly starting capacity in your sales force.  When you’re missing sales targets it is typically for one of two reasons:  you don’t have enough capacity or you’re not making use of the capacity you have.  This helps you determine which.
  • Beware that if you have multiple types of reps (e.g., corporate and field), you be tempted to blend them in the same way you do whole reps today –i.e., when asked “how many reps do you have?” most people say “15” and not “9 enterprise plus 6 corporate.”  You have the same problem with RREs.  While it’s OK to present a blended RRE figure, just remember that it’s blended and if you want to calculate capacity from it, you should calculate RREs by rep type and then get capacity by multiplying the RRE for each rep type by their respective steady-state productivity.

I recommend moving to an RRE mindset for modeling and analyzing sales capacity.  If you want to play with the spreadsheet I made for this post, you can find it here.

Thanks to my friend Paul Albright for being the first person to introduce me to this idea.

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Notes
[1] This is actually a productivity model, based on actual sales productivity — how much people have historically sold (and ergo should require little/no cushion before sales signs up for it).  Most people I know work with a productivity model and then uplift the desired productivity by 15 to 25% to set quotas.

[2] Most companies have two or three types (e.g., corporate vs. field), so you typically need to build a waterfall for each type of rep.

[3] To build this model, you also need to know the aging of your existing salesreps — i.e., how many second-, third-, fourth-, and steady-state-quarter reps you have at the start of the year.

[4] The glaring omission from this model is sales turnover.  In order to keep it simple, it’s not factored in here. While some people try to factor in sales turnover by using reduced sales productivity figures, I greatly prefer to model realistic sales productivity and explicitly model sales turnover in creating a sales bookings capacity model.

[5] This is one reason it’s so expensive to build an enterprise software sales force.  For several quarters you often get 100% of the cost and 50% of the sales capacity.

[6] Which should be an weighted average productivity by type of rep weighted by number of reps of each type.