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.

# # #

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.

How To Sales Manage Upside and Unlikely Deals

If your sales organization is like most, you classify sales opportunities in about four categories, such as:

  • Commit, which are 90% likely to close
  • Forecast, which are 70% likely to close
  • Upside, which are 33% likely to close
  • Unlikely, which are 5% likely to close

And then, provided you have sufficient pipeline, your sales management team basically puts all of its effort into and attention on the commit and forecast deals.  They’re the ones that get deal reviews.  They’re the ones where the team does multiple dry runs before big demos and presentations.  They’re the deals that get discussed every week on the forecast call.

The others ones?  No such much.  Sure, the salesreps who own them will continue to toil away.  But they won’t get much, if any, management attention.  You’ll probably lose 75% of them and it won’t actually matter much, provided you have enough high-probability deals to make your forecast and plan.

But, what a waste.  Those opportunities probably each cost the company $2500 to $5000 to generate and many multiples of that to pursue.  But they’re basically ignored by most sales management teams.

The classical solution to this problem is to tell the sales managers to focus on everything.  But it doesn’t work.   A smart sales manager knows the only thing that really matters is making his/her number and doing that typically involves closing almost all the committed and most of the forecast deals.  So that is where their energy goes.

jumpballThe better way to handle these deals is to recognize they’re more likely to be lost than won (e.g., calling them jump-balls, 50/50 balls, or face-offs, depending on your favorite sport), find the most creative non-quota-carrying manager in the sales organization (e.g., VP of salesops) and have him/her manage these low-probability, high-risk deals in the last month of the quarter using non-traditional (i.e., Crazy Ivan) tactics.

This only works if you have happen to have a VP of salesops, enablement, alliances, etc., who has the experience, passion, and creativity to pull it off, but if you do it’s a simply fantastic way to allow core sales management to focus on the core deals that will make or break the quarter while still applying attention and creativity to the lower probability deals that can drive you well over your targets.

This is not as crazy as it might sound, because those in sales ops or productivity positions typically do have prior sales management experience.  Thus, this becomes a great way to keep their saw sharp and keep them close/relevant to the reality of the field in performing their regular job.  What could be better than a VP of sales productivity who works on closing deals 4 months/year?

If your VP of sales ops or sales enablement doesn’t have the background or interest to do this, maybe they should.  If not, and/or you are operating at bigger scale, why not promote a salesperson with management potential into jump-ball, overlay deal management as their first move into sales management?

Important Subtleties in Calculating Quarterly, Annual, and ATR-based Churn Rates

This post won’t save your life, or your company.  But it might save you a few precious hours at 2:00 AM if you’re working on your company’s SaaS metrics and can’t foot your quarterly and annual churn rates while preparing a board or investor deck.

The generic issue is a lot of SaaS metrics gurus define metrics in a generic way using “periods” without paying attention to some subtleties that can arise in calculating these metrics for a quarter vs. a year.  The specific issue is, if you do what many people do, that your quarterly and annual churn rates won’t foot — i.e., the sum of your quarterly churn rates won’t equal your annual churn rate.

Here’s an example to show why.

saas churn subtle

If I asked you to calculate the annual churn rate in the above example, virtually everyone would get it correct.  You’d look at the rightmost column, see that 2018 started with 10,000 in ARR, see that there were 1,250 dollars of churn on the year, divide 1,250 by 10,000 and get 12.5%.  Simple, huh?

However, if I hid the last column, and then asked you to calculate quarterly churn rates, you might come up with churn rate 1, thinking churn rate = period churn / starting period ARR.  You might then multiply by 4 to annualize the quarterly rates and make them more meaningful.  Then, if I asked you to add an annual column, you’d sum the quarterly (non-annualized) rates for the annual churn and either average the annualized quarterly rates or simply gray-out the box as I did because it’s redundant [1].

You’d then pause, swear, and double-check the sheet for errors because the sum of your quarterly rates (10.2%) doesn’t equal your annual rate (12.5%).

What’s going on?  The trap is thinking churn rate = period churn / starting period ARR.

That works in a world of one-year contracts when you look at churn on an annual basis (every contract in the starting ARR base of 10,000 faces renewal at some point during the year), but it breaks on a quarterly basis.  Why?  Because starting ARR is increasing every quarter due to new sales that aren’t in the renewal base for the year.  This depresses your churn rates relative to churn rate 2, which defines quarterly churn as churn in the quarter divided by starting-year ARR.  When you use churn rate 2, the sum of the quarterly rates equals the annual rate, so you can mail out that board deck and go back to bed [2].

Available to Renew (ATR-based) Churn Rates

While we’re warmed up, let’s have some more fun.  If you’ve worked in enterprise software for more than a year, you’ll know that the 10,000 dollars of starting ARR is most certainly not distributed evenly across quarters:  enterprise software sales are almost always backloaded, ergo enterprise software renewals follow the same pattern.

So if we want more accurate [3] quarterly churn rates, shouldn’t we do the extra work, figure out how much ARR we have available to renew (ATR) in each quarter, and then measure churn rates on an ATR basis?  Why not!

Let’s first look at an example, that shows available to renew (ATR) split in a realistic, backloaded way across quarters [4].

ATR churn 1

In some sense, ATR churn rates are cleaner because you’re making fewer implicit assumptions:  here’s what was up for renewal and here’s what we got (or lost).  While ATR rates get complicated fast in a world of multi-year deals, for today, we’ll stay in a world of purely one-year contracts.

Even in that world, however, a potential footing issue emerges.  If I calculate annual ATR churn by looking at annual churn vs. starting ARR, I get the correct answer of 12.5%.  However, if I try to average my quarterly rates, I get a different answer of 13.7%, which I put in red because it’s incorrect.

Quiz:  what’s going on?

Hint:  let me show the ATR distributed in a crazy way to demonstrate the problem more clearly.

atr churn 2

The issue is you can’t get the annual rate by averaging the quarterly ATR rates because the ATR is not evenly distributed.  By using the crazy distribution above, you can see this more clearly because the (unweighted) average of the four quarterly rates is 53.6%, pulled way up by the two quarters with 100% churn rates.  The correct way to foot this is to instead use a weighted average, weighting on an ATR basis.  When you do that (supporting calculations in grey), the average then foots to the correct annual number.

# # #

Notes:

[1] The sum of the quarterly rates (A, B, C, D) will always equal the average of the annualized quarterly rates because (4A+4B+4C+4D)/4 = A+B+C+D.

[2] I won’t go so far as to say that churn rate 1 is “incorrect” while churn rate 2 is “correct.”  Churn rate 1 is simple and gives you what you asked for “period churn / starting period ARR.”  (You just need to realize that the your quarterly rates will only sum to your annual rate if you have zero new sales and ergo you should calculate the annual rate off the yearly churn and starting ARR.)  Churn rate 2 is somewhat more complicated.  If you live in a world of purely one-year contracts, I’d recommend churn rate 2.  But in a world of mixed one- and multi-year contracts, then lots of contracts are in starting period ARR aren’t in the renewal base for the year, so why would I exclude only some of them (i.e,. those signed in the year) as opposed to others.

[3] Dividing by the whole ARR base basically assumes that the base renews evenly across quarters.  Showing churn rates based on available-to-renew (ATR) is more accurate but becomes complicated quickly in a world of mixed, multi-year contracts of different duration (where you will need to annualize the rates on multi-year contracts and then blend the average to get a single, meaningful, annualized rate).  In this post, we’ll assume a world of exclusively one-year contracts, which sidesteps that issue.

[4] ATR is normally backloaded because enterprise sales are normally backloaded.  Here the linearity is 15%, 17.5%, 25%, 42.5% or a 32.5/67.5 split across the first vs. second half of the year (which is pretty backloaded even for enterprise software).

[5] The spreadsheet I used is available here if you want to play with it.

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

There are only two archetypal marketing messages, exemplified by:

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

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

soup

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Customer: [dial tone]

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

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

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

Ah.  Much better.

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

messaging value vs diff

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

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

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

# # #

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

discover flavor

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

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

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

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

The Domo S-1: Does the Emperor Have Clothes?

I preferred Silicon Valley [1] back in the day when companies raised modest amounts of capital (e.g., $30M) prior to an IPO that took 4-6 years from inception, where burn rates of $10M/year looked high, and where $100M raise was the IPO, not one or more rounds prior to it.  When cap tables had 1x, non-participating preferred and that all converted to a single class of common stock in the IPO. [2]

How quaint!

These days, companies increasingly raise $200M to $300M prior to an IPO that takes 10-12 years from inception, the burn might look more like $10M/quarter than $10M/year, the cap table loaded up with “structure” (e.g., ratcheting, multiple liquidation preferences).  And at IPO time you might end up with two classes common stock, one for the founder with super-voting rights, and one for everybody else.

I think these changes are in general bad:

  • Employees get more diluted, can end up alternative minimum tax (AMT) prisoners unable to leave jobs they may be unhappy doing, have options they are restricted from selling entirely or are sold into opaque secondary markets with high legal and transaction fees, and/or even face option expiration at 10 years. (I paid a $2,500 “administrative fee” plus thousands in legal fees to sell shares in one startup in a private transaction.)
  • John Q. Public is unable to buy technology companies at $30M in revenue and with a commission of $20/trade. Instead they either have to wait until $100 to $200M in revenue or buy in opaque secondary markets with limited information and high fees.
  • Governance can be weak, particularly in cases where a founder exercises directly (or via a nuclear option) total control over a company.

Moreover, the Silicon Valley game changes from “who’s smartest and does the best job serving customers” on relatively equivalent funding to “who can raise the most capital, generate the most hype, and buy the most customers.”  In the old game, the customers decide the winners; in the new one, Sand Hill Road tries to, picking them in a somewhat self-fulfilling prophecy.

The Hype Factor
In terms of hype, one metric I use is what I call the hype ratio = VC / ARR.  On the theory that SaaS startups input venture capital (VC) and output two things — annual recurring revenue (ARR) and hype — by analogy, heat and light, this is a good way to measure how efficiently they generate ARR.

The higher the ratio, the more light and the less heat.  For example, Adaptive Insights raised $175M and did $106M in revenue [3] in the most recent fiscal year, for a ratio of 1.6.  Zuora raised $250M to get $138M in ARR, for a ratio of 1.8.  Avalara raised $340M to $213M in revenue, for a ratio of 1.6.

By comparison, Domo’s hype ratio is 6.4.  Put the other way, Domo converts VC into ARR at a 15% rate.  The other 85% is, per my theory, hype.  You give them $1 and you get $0.15 of heat, and $0.85 of light.  It’s one of the most hyped companies I’ve ever seen.

As I often say, behind every “marketing genius” is a giant budget, and Domo is no exception [4].

Sometimes things go awry despite the most blue-blooded of investors and the greenest of venture money.  Even with funding from the likes of NEA and Lightspeed, Tintri ended up a down-round IPO of last resort and now appears to be singing its swan song.  In the EPM space, Tidemark was the poster child for more light than heat and was sold in what was rumored to be fire sale [5] after raising over $100M in venture capital and having turned that into what was supposedly less than $10M in ARR, an implied hype ratio of over 10.

The Top-Level View on Domo
Let’s come back and look at the company.  Roughly speaking [6], Domo:

  • Has nearly $700M in VC invested (plus nearly $100M in long-term debt).
  • Created a circa $100M business, growing at 45% (and decelerating).
  • Burns about $150M per year in operating cash flow.
  • Will have a two-class common stock system where class A shares have 40x the voting rights of class B, with class A totally controlled by the founder. That is, weak governance.

Oh, and we’ve got a highly unprofitable, venture-backed startup using a private jet for a bit less than $1M year [7].  Did I mention that it’s leased back from the founder?  Or the $300K in catering from a company owned by the founder and his brother.  (Can’t you order lunch from a non-related party?)

As one friend put it, “the Domo S-1 is everything that’s wrong with Silicon Valley in one place:  huge losses, weak governance, and now modest growth.”

Personally, I view Domo as the Kardashians of business intelligence – famous for being famous.  While the S-1 says they have 85 issued patents (and 45 applications in process), does anyone know what they actually do or what their technology advantage is?  I’ve worked in and around BI for nearly two decades – and I have no idea.

Maybe this picture will help.

domosolutionupdated

Uh, not so much.

The company itself admits the current financial situation is unsustainable.

If other equity or debt financing is not available by August 2018, management will then begin to implement plans to significantly reduce operating expenses. These plans primarily consist of significant reductions to marketing costs, including reducing the size and scope of our annual user conference, lowering hiring goals and reducing or eliminating certain discretionary spending as necessary

A Top-to-Bottom Skim of the S-1
So, with that as an introduction, let’s do a quick dig through the S-1, starting with the income statement:

domo income

Of note:

  • 45% YoY revenue growth, slow for the burn rate.
  • 58% blended gross margins, 63% subscription gross margins, low.
  • S&M expense of 121% of revenue, massive.
  • R&D expense of 72% of revenue, huge.
  • G&A expense of 29% of revenue, not even efficient there.
  • Operating margin of -162%, huge.

Other highlights:

  • $803M accumulated deficit.  Stop, read that number again and then continue.
  • Decelerating revenue growth, 45% year over year, but only 32% Q1 over Q1.
  • Cashflow from operations around -$150M/year for the past two years.  Stunning.
  • 38% of customers did multi-year contracts during FY18.  Up from prior year.
  • Don’t see any classical SaaS unit economics, though they do a 2016 cohort analysis arguing contribution margin from that cohort of -196%, 52%, 56% over the past 3 years.  Seems to imply a CAC ratio of nearly 4, twice what is normally considered on the high side.
  • Cumulative R&D investment from inception of $333.9M in the platform.
  • 82% revenues from USA in FY18.
  • 1,500 customers, with 385 having revenues of $1B+.
  • Believe they are <4% penetrated into existing customers, based on Domo users / total headcount of top 20 penetrated customers.
  • 14% of revenue from top 20 customers.
  • Three-year retention rate of 186% in enterprise customers (see below).  Very good.
  • Three-year retention rate of 59% in non-enterprise customers.  Horrific.  Pay a huge CAC to buy a melting ice cube.  (Only the 1-year cohort is more than 100%.)

As of January 31, 2018, for the cohort of enterprise customers that licensed our product in the fiscal year ended January 31, 2015, the current ACV is 186% of the original license value, compared to 129% and 160% for the cohorts of enterprise customers that subscribed to our platform in the fiscal years ended January 31, 2016 and 2017, respectively. For the cohort of non-enterprise customers that licensed our product in the fiscal year ended January 31, 2015, the current ACV as of January 31, 2018 was 59% of the original license value, compared to 86% and 111% for the cohorts of non-enterprise customers that subscribed to our platform in the fiscal years ended January 31, 2016 and 2017, respectively.

  • $12.4M in churn ARR in FY18 which strikes me as quite high coming off subscription revenues of $58.6M in the prior year (21%).  See below.

Our gross subscription dollars churned is equal to the amount of subscription revenue we lost in the current period from the cohort of customers who generated subscription revenue in the prior year period. In the fiscal year ended January 31, 2018, we lost $12.4 million of subscription revenue generated by the cohort in the prior year period, $5.0 million of which was lost from our cohort of enterprise customers and $7.4 million of which was lost from our cohort of non-enterprise customers.

  • What appears to be reasonable revenue retention rates in the 105% to 110% range overall.  Doesn’t seem to foot to the churn figure about.  See below:

For our enterprise customers, our quarterly subscription net revenue retention rate was 108%, 122%, 116%, 122% and 115% for each of the quarters during the fiscal year ended January 31, 2018 and the three months ended April 30, 2018, respectively. For our non-enterprise customers, our quarterly subscription net revenue retention rate was 95%, 95%, 99%, 102% and 98% for each of the quarters during the fiscal year ended January 31, 2018 and the three months ended April 30, 2018, respectively. For all customers, our quarterly subscription net revenue retention rate was 101%, 107%, 107%, 111% and 105% for each of the quarters during the fiscal year ended January 31, 2018 and the three months ended April 30, 2018, respectively.

  • Another fun quote and, well, they did take about the cash it takes to build seven startups.

Historically, given building Domo was like building seven start-ups in one, we had to make significant investments in research and development to build a platform that powers a business and provides enterprises with features and functionality that they require.

  • Most customers invoiced on annual basis.
  • Quarterly income statements, below.

domo qtr

  • $72M in cash as of 4/30/18, about 6 months worth at current burn.
  • $71M in “backlog,” multi-year contractual commitments, not prepaid and ergo not in deferred revenue.  Of that $41M not expected to be invoiced in FY19.
  • Business description, below.  Everything a VC could want in one paragraph.

Domo is an operating system that powers a business, enabling all employees to access real-time data and insights and take action from their smartphone. We believe digitally connected companies will increasingly be best positioned to manage their business by leveraging artificial intelligence, machine learning, correlations, alerts and indices. We bring massive amounts of data from all departments of a business together to empower employees with real-time data insights, accessible on any device, that invite action. Accordingly, Domo enables CEOs to manage their entire company from their phone, including one Fortune 50 CEO who logs into Domo almost every day and over 10 times on some days.

  • Let’s see if a computer could read it any better than I could.  Not really.

readability

  • They even have Mr. Roboto to help with data analysis.

Through Mr. Roboto, which leverages machine learning algorithms, artificial intelligence and predictive analytics, Domo creates alerts, detects anomalies, optimizes queries, and suggests areas of interest to help people focus on what matters most. We are also developing additional artificial intelligence capabilities to enable users to develop benchmarks and indexes based on data in the Domo platform, as well as automatic write back to other systems.

  • 796 employees as of 4/30/18, of which 698 are in the USA.
  • Cash comp of $525K for CEO, $450K for CFO, and $800K for chief product officer
  • Pre-offering it looks like founder Josh James owns 48.9M shares of class A and 8.9M shares of class B, or about 30% of the shares.  With the 40x voting rights, he has 91.7% of the voting power.

Does the Emperor Have Any Clothes?
One thing is clear.  Domo is not “hot” because they have some huge business blossoming out from underneath them.  They are “hot” because they have raised and spent an enormous amount of money to get on your radar.

Will they pull off they IPO?  There’s a lot not to like:  the huge losses, the relatively slow growth, the non-enterprise retention rates, the presumably high CAC, the $12M in FY18 churn, and the 40x voting rights, just for starters.

However, on the flip side, they’ve got a proven charismatic entrepreneur / founder in Josh James, an argument about their enterprise customer success, growth, and penetration (which I’ve not had time to crunch the numbers on), and an overall story that has worked very well with investors thus far.

While the Emperor’s definitely not fully dressed, he’s not quite naked either.  I’d say the Domo Emperor’s donning a Speedo — and will somehow probably pull off the IPO parade.

###

Notes

[1] Yes, I know they’re in Utah, but this is still about Silicon Valley culture and investors.

[2] For definitions and frequency of use of various VC terms, go to the Fenwick and West VC survey.

[3] I’ll use revenue rather than trying to get implied ARR to keep the math simple.  In a more perfect world, I’d use ARR itself and/or impute it.  I’d also correct for debt and a cash, but I don’t have any MBAs working for me to do that, so we’ll keep it back of the envelope.

[4] You can argue that part of the “genius” is allocating the budget, and it probably is.  Sometimes that money is well spent cultivating a great image of a company people want to buy from and work at (e.g., Salesforce).  Sometimes, it all goes up in smoke.

[5] Always somewhat truth-challenged, Tidemark couldn’t admit they were sold.  Instead, they announced funding from a control-oriented private equity firm, Marlin Equity Partners, as a growth investment only a year later be merged into existing Marlin platform investment Longview Solutions.

[6] I am not a financial analyst, I do not give buy/sell guidance, and I do not have a staff working with me to ensure I don’t make transcription or other errors in quickly analyzing a long and complex document.  Readers are encouraged to go the S-1 directly.  Like my wife, I assume that my conclusions are not always correct; readers are encouraged to draw their own conclusions.  See my FAQ for complete disclaimer.

[7] $900K, $700K, and $800K run-rate for FY17, FY18, and 1Q19 respectively.

The Two Engines of SaaS: QCRs and DEVs

I remember one day, years ago, when I was a VP at $10M startup and Larry, the head of sales, came in one day handing out t-shirts that said:

“Code, sell, or get out of the way.”

Neither I, nor the rest of marketing team, took this particularly well because the shirt obviously devalued the contributions of F&A, HR, and marketing.  But, ever seeking objectivity, I did concede that the shirt had a certain commonsense appeal.  If you could only hire one person at a startup, it would be someone to write the product.  And if you could only hire one more, it would be someone to sell it.

This became yet another event that reconfirmed my belief in my “marketing exists to make sales easier” mantra.  After all, if you’re not coding or selling, at least you can help someone who is.

Over time, Larry’s t-shirt morphed in my mind into a new mantra:

“A SaaS company is a two-engine plane.  The left engine is DEVs.  The right is QCRs.”

QCR meaning quota-carrying (sales) representative and DEV meaning developer (or, for symmetry and emphasis, storypoint-burning developer).  People who sell with truly incremental quota, and people who write code and burndown storypoints in the process.

It’s a much nicer way of saying “code, sell, or get out of the way,” but it’s basically the same idea.  And it’s true.  While Larry was coming from a largely incorrect “protest overhead and process” viewpoint, I’m coming from a different one:  hiring.

The two hardest lines in a company headcount plan to keep at-plan are guess which two?  QCRs and DEVs.  Forget other departments for a minute — I’m saying is the the hardest line for the VP of Engineering to stay fully staffed on is DEVs, and the hardest line for the VP of Sales to stay fully staffed on is QCRs.

Why is this?

  • They are two, critical highly in-demand positions, so the market is inherently tight.
  • Given their importance, the hiring VPs can be gun-shy about making mistakes and lose candidates due to hesitation or indecision.
  • Both come with a short-term tax and mid-term payoff because on-boarding new hires slows down the rest of the team, a possible source of passive resistance.
  • Sales managers dislike splitting territories because it makes them unpopular, which could drive more foot-dragging.
  • It’s just plain easier to find the associated support functions — (e.g,. program managers, QA engineers, techops, salesops, sales productivity, overlays, CSMs, managers in general) than it is find the QCRs and DEVs.

Let me be clear:  this is not to say that all the supporting functions within sales and engineering do not add value, nor is this to say that supporting corporate functions beyond sales and engineering do not add value — it is to say, however, that far too often companies take their eye off the ball and staff the support functions before, not after, those they are supporting.  That’s a mistake.

What happens if you manage this poorly?  On the sales side, for example, you end up with an organization that has 1 SVP of Sales, 1 VP of sales consulting, 4 sales consultants, 1 director of sales ops, 1 director of sales productivity, 1 manager of sales development reps (SDRs), 4 SDRs, an executive assistant, and 4 quota-carrying salespeople.  So only 22% of the people in your sales organization actually carry a quota.

“Uh, other than QCRs, we’re doing great on sales hiring,”  says the sales VP.  “Other than that, Mrs. Lincoln, how did you find the play?” thinks the board.

Because I’ve seen this happen so often, and because I’ve seen companies accused of it both rightfully and unjustly, I’d decided to create two new metrics:

  • QCR density = number of QCRs / total sales headcount
  • DEV density = numbers of DEVs / total engineering headcount

The bad news is I don’t have a lot of benchmark data to share here.  In my experience, both numbers want to run in the 40% range.

The good news is that if you run a ratio-driven staffing model (which you should do for both sales and engineering), you should be able to calculate what these densities should be when you are fully staffed.

Let’s conclude with a simple model that does just that on the sales side, producing a result in the 38% to 46% range.

qcr dens

Finally, let me add that having such a model helps you understand whether, for example, your QCR density is low due to slow QCR hiring (and/or bad retention) against a good model, or on-pace hiring against a “fat” model.  The former is an execution problem, the latter is a problem with your model.

Talking Competition: Methinks Thou Doth Protest Too Much.

From time to time marketers and executives need to talk about the competition with those outside the company, including analysts, partners, and prospective investors.  In this post, we’ll cover my 4 rules for this type of communication.

Be Consistent. 
The biggest mistake people make is inconsistency, often because they’re trying to downplay a certain competitor.   Example:

“Oh, TechMo.  No, we never see them.  They’re like nowhere.  And you know their technology is really non-scaleable because it runs out of address space in the Java virtual machine.  And their list-based engine doesn’t scale because it didn’t scale when the same three founders, Mo, Larry, and Curly, did their last startup which used primarily the same idea.  And while I know they’re up to 150 employees, they must be in trouble because in the past 6 months they’ve lost their VP of Sales, Jon Smith, and their VP of Product Management, Paula Sands, and that new appexchange-like thing they launched last week, with 37 solutions, well it’s a not real either because 15 of the 37 solutions aren’t even built by partners, and they’re more prototypes than applications, and — another thing — I heard that TechMo World last week in Vegas had only 400 attendees and customers didn’t react well to the announcement they made about vertical strategy.  Yes, TechMo’s nobody to us.  We hardly ever see them.”

— Would-Be Dismissive Product Marketer.

What’s the one thing the listener is thinking on hearing all this?

“Holy Cow, these guys are tracking TechMo’s every move.  They sure know a lot about somebody they supposedly never see.”

Or, in other words, “the lady doth protest too much, methinks.”  (Hamlet.)

Don’t be this person.  Stay credible.  Be consistent.  If you’re going to be dismissive of someone, dismiss them.  But don’t try to dismiss them, then bleed fear and guilt all over the audience.  Line up your words, your attitude, and your behavior.

Cede, But Cede Carefully.
Some people say never cede anything at all, but I think that’s dangerous, particularly when dealing with sophisticated audiences like industry analysts, prospective investors, or channel partners (who work in the field every day).

I think ceding builds your credibility, but you need to be careful and precise in so doing so.  To take an old example, from BusinessObjects days:

  • Bad/sloppy:  Brio is doing pretty well.
  • Good/careful:  Brio is doing pretty well — in the USA, with companies where the end-users have a strong voice in the process, and they prioritize UI over security and administration.

It’s called positioning for a reason.  You’re supposed to be able to say what you do well, what your competitors do well, and what the difference is.  If you just go on singing “anything you can do I can do better, I can do anything better than you,” then you’re not going to build much credibility with your audience.

  • Bad/sloppy:  Competitor X seems to have some traction in the market.
  • Good/careful:  Competitor X is appearing in high-end deals, has a “fake cloud” offering, and competes well against entrenched Oracle product Y.

Don’t give competitor X an ounce more than they deserve and don’t forget to point out their limitations along the way.  When it comes to credit, give it where due, but be stingy — don’t give a drop more.

This will build your credibility in being reasonably objective.  More important, it also forces you to build some positioning.  As long you are claiming universal superiority — that no one will believe — you’re letting yourself off the hook for doing your job, in building credible positioning.

Keep Your Facts Straight
Be sure of what you say.  It’s far better to say less and be correct than to add just one more point you’re not sure of and get quickly contradicted.  Why?  Because your credibility is now in question as are all your other assertions — even the correct ones.

If you’re sure about something, then say it.  If you’re not sure but think it’s probable then weasel-word it — “we’re hearing,” “I heard from customers that,” “you can see several reviews on Glassdoor where former employees say,” or simply “we think.”  But don’t assert something as fact unless you are sure it is and you’re ready to defend it.

Read the Audience to Avoid the Blindside Hit
I warn every marketer and product manager I know about the blindside hit.  When you’re doing a briefing with hardened industry analyst on a market they’ve covered for 20 years, you’re as vulnerable to a blindside hit as an NFL quarterback.

You make some assertions, and you’re feeling good.  But you stop paying attention to the audience.  You don’t notice the body language showing that they’re not buying it anymore.  You don’t read the warning signs.  You miss the building tension in their voice.   You don’t know that the vendor you’re attacking is the analyst’s favorite and they just had a big steak dinner at the roadshow they did last week in Cleveland.

And then you make one too many false claims and then like a safety on a blitz, the analyst sees a hole in the offensive line, accelerates through it, and hits you in the back at full speed.  BOOM.  You awake a few minutes later and discover you’re strapped to a stretcher with a neck collar on and the CMO and the analyst relations director are carrying you out of the meeting.

“Sorry, Brian got a little ahead of himself, there.  Bob will take it from here.”

quarterback blindside hit

Product marketer carried out of industry analyst briefing. Don’t let this be you.