I was playing around on the enterprise comps  section of Meritech‘s website today and a few of the charts I found caught my attention. Here’s the first one, which shows the progression of the EV/NTM revenue multiple  for a set of 50+ high-growth SaaS companies over the past 15 or so years .
While the green line (equity-value-weighted ) is the most dramatic, the one I gravitate to is the blue line: the median EV/NTM revenue multiple. Looking at the blue line, you can see that while it’s pretty volatile, eyeballing it, I’d say it normally runs in the range between 5x and 10x. Sometimes (e.g., 2008) it can get well below 5x. Sometimes (e.g., in 2013) it can get well above 10x. As of the last data point in this series (7/14/20) it stood at 13.8x, down from an all-time high of 14.9x. Only in 2013 did it get close to these levels.
If you believe in regression to the mean , that means you believe the multiples are due to drop back to the 5-10 range over time. Since mean reversion can come with over-correction (e.g., 2008, 2015) it’s not outrageous to think that multiples could drop towards the middle or bottom of that range, i.e., closer to 5 than 10 .
Ceteris paribus, that means the potential for a 33% to 66% downside in these stocks. It also suggests that — barring structural change  that moves baseline multiples to a different level — the primary source of potential upside in these stocks is not continued multiple expansion, but positive NTM revenue surprises .
I always love Rule of 40 charts, so the next fun chart that caught my eye was this one. While this chart doesn’t speak to valuations over time, it does speak to the relationship between a company’s Rule of 40 Score and its EV/NTM revenue multiple. Higher valuations primarily just shift the Y axis, as they have done here, uplifting the maximum Y-value by nearly three times since I last blogged about such a chart . The explanatory power of the Rule of 40 in explaining valuation multiple is down since I last looked, by about half from an R-squared of 0.58 to 0.29. Implied ARR growth alone has a higher explanatory power (0.39) than the Rule of 40.
To me, this all suggests that in these frothy times, the balance of growth and profit (which is what Rule of 40 measures) matters less than other factors, such as growth, leadership, scarcity value and hype, among others.
Finally, to come back to valuation multiples, let’s look at a metric that’s new to me, growth-adjusted EV/R multiples.
I’ve seen growth-adjusted price/earnings ratios (i.e., PEG ratios) before, but I’ve not seen someone do the same thing with EV/R multiples. The basic idea is to normalize for growth in looking at a multiple, such as P/E or — why not — EV/R. For example, Coupa, trading at (a lofty) 40.8x EV/R is growing at 21%, so divide 40.8 by 21 to get 1.98x. Zoom, by comparison looks to be similarly expensive at 38.3x EV/R but is growing at 139%, so divide 38.3 by 139 to get 0.28x, making Zoom a relative bargain when examined in this light .
This is a cool metric. I like financial metrics that normalize things . I’m surprised I’ve not seen someone do it to EV/R ratios before. Here’s an interesting observation I just made using it:
To the extent a “cheap” PE firm might pay 4x revenues for a company growing 20%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
To the extent a “crazy” VC firm might pay 15x revenues for a company growing at 75%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
The observant reader may notice they are both paying the same ratio for growth-adjusted EV/R. Given this, perhaps the real difference isn’t that one is cheap and the other free-spending, but that they pay the same for growth while taking on very different risk profiles.
The other thing the observant reader will notice is that in both those pseudo-random yet nevertheless realistic examples, the professionals were paying 0.2. The public market median today is 0.7.
See here for the original charts and data on the Meritech site.
Disclaimer: I am not a financial analyst and do not make buy/sell recommendations. I own positions in a wide range of public and private technology companies. See complete disclaimers in my FAQ.
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 Comps = comparables.
 EV/NTM Revenue = enterprise value / next twelve months revenue, a so-called “forward” multiple.
 Per the footer, since Salesforce’s June, 2004 IPO.
 And not everybody does. People often believe “this time it’s different” based on irrational folly, but sometimes this time really is different (e.g., structural change). For example, software multiples have structurally increased over the past 20 years because the underlying business model changed from one-shot to recurring, ergo increasing the value of the revenue.
 And that’s not to mention external risk factors such as pandemic or election uncertainty. Presumably these are already priced into the market in some way, but changes to how they are priced in could result in swings either direction.
 You might argue a scarcity premium for such leaders constitutes a form of structural change. I’m sure there are other arguments as well.
 To the extent a stock price is determined by some metric * some multiple, the price goes up either due to increasing the multiple (aka, multiple expansion) or increasing the metric (or both).
 While not a scientific way to look at this, the last time I blogged on a Rule of 40 chart, the Y axis topped out at 18x, with the highest data point at nearly 16x. Here the Y axis tops out at 60x, with the highest data point just above 50x.
 In English, to the extent you’re paying for EV/R multiple in order to buy growth, Zoom buys you 7x more growth per EV/R point than Coupa.
 As an operator, I don’t like compound operational metrics because you need to un-tangle them to figure out what to fix (e.g., is a broken LTV/CAC due to LTV or CAC?), but as investor I like compound metrics as much as the next person.
The other day I heard a startup executive say, “we will start to accelerate sales hiring — hiring reps beyond the current staffing levels and the current plan — once we start to see the pipeline to support it.”
What comes first: the pipeline or the egg? Or, to unmix metaphors, what comes first: the pipeline or the reps to prosecute it? Unlike the chicken or the egg problem, I think this one has a clear answer: the reps.
My answer comes part from experience and part from math.
First, the experience part: long ago I noticed that the number of opportunities in the pipeline of a software company tends to be a linear function of the number of reps, with a slope in the 12-18 range as a function of business model . That is, in my 12 years of being a startup CEO, my all-quarters, scrubbed  pipeline usually had somewhere between 12 and 18 opportunities per rep and the primary way it went up was not by doing more marketing, but by hiring more reps.
Put differently, I see pipeline as a lagging indicator driven by your capacity and not a leading indicator driven by opportunity creation in your marketing funnel.
Why? Because of the human factor: whether they realize it or not, reps and their managers tend to apply a floating bar on opportunity acceptance that keeps them operating around their opportunity-handling capacity. Why’s that? It’s partially due to the self-fulfilling 3x pipeline prophecy: if you’re not carrying enough pipeline, someone’s going to yell at you until you do, which will tend to drop your bar on opportunity acceptance. On the flip side, if you’re carrying more opportunities than your capacity — and anyone is paying attention — your manager might take opportunities away from you, or worse yet hire another rep and split your territory. These factors tends to raise the bar, so reps cherry pick the best opportunities and reject lesser ones that they’d might otherwise accept in a tougher environment.
So unless you’re running a real machine with air-tight definitions and little/no discretion (which I wouldn’t advise), the number of opportunities in your pipeline is going to be some constant times the number of reps.
Second, the math part. If you’re running a reasonably tight ship, you have a financial model and an inverted funnel model that goes along with it. You’re using historical costs and conversion rates along with future ARR targets to say, roughly, “if we need $4.0M in New ARR in 3 quarters, and we insert a bunch of math, then we’re going to need to generate 400 SALs this quarter and $X of marketing budget to do it.” So unless there’s some discontinuity in your business, your pipeline generation doesn’t reflect market demand; it reflects your financial and demandgen funnel models.
To paraphrase Chester Karrass, you don’t get the pipeline you deserve, you get the one you plan for. Sure, if your execution is bad you might fall significantly short on achieving your pipeline generation goal. But it’s quite rare to come in way over it.
So what should be your trigger for hiring more reps? That’s probably the subject of another post, but I’d look first externally at market share (are you gaining or losing, and how fast) and then internally at the CAC ratio.
CAC is the ultimate measure of your sales & marketing efficiency and looking at it should eliminate the need to look more deeply at quota attainment percentages, close rates, opportunity cost generation, etc. If one or more of those things are badly out of whack, it will show up in your CAC.
So I’d say my quick rule is if your CAC is normal (1.5 or less in enterprise), your churn is normal (<10% gross), and your net dollar expansion rate is good enough (105%+), then you should probably hire more reps. But we’ll dive more into that in another post.
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 It’s a broad range, but it gets tighter when you break it down by business model. In my experience, roughly speaking in:
Classic enterprise on-premises ($350K ASP with elephants over $1M), it runs closer to 8-10
Medium ARR SaaS ($75K ASP), it runs from 12-15
Corporate ARR SaaS ($25K ASP) where it ran 16-20
 The scrubbed part is super important. I’ve seen companies with 100x pipeline coverage and 1% conversation rates. That just means a total lack of pipeline discipline and ergo meaningless metrics. You should have written definitions of how to manage pipeline and enforce them through periodic scrubs. Otherwise you’re building analytic castles in the sand.
This is part II in this series. Part I is here and covers the basics of management education, employee communications, and simple steps to help slow virus transmission while keeping the business moving forward.
In this part, we’ll provide:
A short list of links to what other companies are doing, largely when it comes to travel and in-office work policies.
A discussion of financial planning and scenario analysis to help you financially navigate these tricky waters.
I have broken out the list of useful information links and resources (that was formerly in this post) to a separate, part III of this series.
What Other Companies are Saying and Doing
Relatively few companies have made public statements about their response policies. Here are a few of the ones who have:
Financial Planning and Scenario Analysis: Extending the Runway
It’s also time to break out your driver-based financial model, and if you don’t have one, then it’s time to have your head of finance (or financial planning & analysis) build one.
Cash is oxygen for startups and if there are going to be some rough times before this threat clears, your job is to make absolutely sure you have the cash to get through it. Remember one of my favorite all-time startup quotes from Sequoia founder Don Valentine: “all companies go out of business for the same reason. They run out of money.”
In my opinion you should model three scenarios for three years, that look roughly like:
No impact. You execute your current 2020 operating plan. Then think about the odds of that happening. They’re probably pretty low unless you’re in a counter-cyclical business like videoconferencing (in which case you probably increase targets) or a semi-counter-cyclical one like analytics/BI (in which case maybe you hold them flat).
20% bookings impact in 2020. You miss plan bookings targets by 20%. Decide if you should apply this 20% miss to new bookings (from new customers), expansion bookings (new sales to existing customers), renewal bookings — or all three. Or model a different percent miss on each of those targets as it makes sense for your business. The point here is to take a moderately severe scenario and then determine how much shorter this makes your cash runway. Then think about steps you can take to get that lost runway back, such as holding costs flat, reducing costs, raising debt, or — if you’re lucky and/or have strong insiders — raising equity.
40% bookings impact in 2020. Do the same analysis as in the prior paragraph but with a truly major bookings miss. Again, decide whether and to what extent that miss hits new bookings, expansion bookings, and renewal bookings. Then go look at your cash runway. If you have debt make sure you have all covenant compliance tests built into your model that display green/red — you shouldn’t have to notice a broken covenant, it should light up in big letters (YES/NO) in a good model. Then, as in the prior step, think about how to get that lost runway back.
Once you have looked at and internalized these models, it’s time for you and your CFO to call your lead investors to discuss your findings. And then schedule a discussion of the scenario analysis at your next board meeting.
Please note that it’s not lost on me that accelerating out of the turn when things improve can be an excellent way to grab share in your market. But in order to so, you need to have lots of cash ready to spend in, say, 6-12 months when that happens. Coming out of the corner on fumes isn’t going to let you do that. And, as many once-prodigal, now-thrifty founders have told me: “the shitty thing is that once you’ve spent the money you can’t get it back.” Without dilution. With debt. Maybe without undesirable structure and terms.
Now is the time to think realistically about how much fuel you have in the tank, if you can get more, how long should it last, and how much you want in the tank 6-12 months out.
I’ve seen numerous startups try numerous ways to calculate their sales capacity. Most are too back-of-the-envelope and to top-down for my taste. Such models are, in my humble opinion, dangerous because the combination of relatively small errors in ramping, sales productivity, and sales turnover (with associated ramp resets) can result in a relatively big mistake in setting an operating plan. Building off quota, instead of productivity, is another mistake for many reasons .
Sales productivity, measured in ARR/rep, and at steady state (i.e., after a rep is fully ramped). This is not quota (what you ask them to sell), this is productivity (what you actually expect them to sell) and it should be based on historical reality, with perhaps incremental, well justified, annual improvement.
Rep hiring plans, measured by new hires per quarter, which should be realistic in terms of your ability to recruit and close new reps.
Rep ramping, typically a vector that has percentage of steady-state productivity in the rep’s first, second, third, and fourth quarters . This should be based in historical data as well.
Rep turnover, the annual rate at which sales reps leave the company for either voluntary or involuntary reasons.
Judgment, the model should have the built-in ability to let the CEO and/or sales VP manually adjust the output and provide analytical support for so doing .
Quota over-assignment, the extent to which you assign more quota at the “street” level (i.e., sum of the reps) beyond the operating plan targets
For extra credit and to help maintain organizational alignment — while you’re making a bookings model, with a little bit of extra math you can set pipeline goals for the company’s core pipeline generation sources , so I recommend doing so.
If your company is large or complex you will probably need to create an overall bookings model that aggregates models for the various pieces of your business. For example, inside sales reps tend to have lower quotas and faster ramps than their external counterparts, so you’d want to make one model for inside sales, another for field sales, and then sum them together for the company model.
In this post, I’ll do two things: I’ll walk you through what I view as a simple-yet-comprehensive productivity model and then I’ll show you two important and arguably clever ways in which to use it.
Walking Through the Model
Let’s take a quick walk through the model. Cells in Excel “input” format (orange and blue) are either data or drivers that need to be entered; uncolored cells are either working calculations or outputs of the model.
You need to enter data into the model for 1Q20 (let’s pretend we’re making the model in December 2019) by entering what we expect to start the year with in terms of sales reps by tenure (column D). The “first/hired quarter” row represents our hiring plans for the year. The rest of this block is a waterfall that ages the rep downward as we move across quarters. Next to the block ramp assumption, which expresses, as a percentage of steady-state productivity, how much we expect a rep to sell as their tenure increases with the company. I’ve modeled a pretty slow ramp that takes five quarters to get to 100% productivity.
To the right of that we have more assumptions:
Annual turnover, the annual rate at which sales reps leave the company for any reason. This drives attriting reps in row 12 which silently assumes that every departing rep was at steady state, a tacit fairly conservative assumption in the model.
Steady-state productivity, how much we expect a rep to actually sell per year once they are fully ramped.
Quota over-assignment. I believe it’s best to start with a productivity model and uplift it to generate quotas .
The next block down calculates ramped rep equivalents (RREs), a very handy concept that far too few organizations use to convert the ramp-state to a single number equivalent to the number of fully ramped reps. The steady-state row shows the number of fully ramped reps, a row that board members and investors will frequently ask about, particularly if you’re not proactively showing them RREs.
After that we calculate “productivity capacity,” which is a mouthful, but I want to disambiguate it from quota capacity, so it’s worth the extra syllables. After that, I add a critical row called judgment, which allows the Sales VP or CEO to play with the model so that they’re not potentially signing up for targets that are straight model output, but instead also informed by their knowledge of the state of the deals and the pipeline. Judgment can be negative (reducing targets), positive (increasing targets) or zero-sum where you have the same annual target but allocate it differently across quarters.
The section in italics, linearity and growth analysis, is there to help the Sales VP analyze the results of using the judgment row. After changing targets, he/she can quickly see how the target is spread out across quarters and halves, and how any modifications affect both sequential and quarterly growth rates. I have spent many hours tweaking an operating plan using this part of the sheet, before presenting it to the board.
The next row shows quota capacity, which uplifts productivity capacity by the over-assignment percentage assumption higher up in the model. This represents the minimum quota the Sales VP should assign at street level to have the assumed level of over-assignment. Ideally this figure dovetails into a quota-assignment model.
Finally, while we’re at it, we’re only a few clicks away from generating the day-one pipeline coverage / contribution goals from our major pipeline sources: marketing, alliances, and outbound SDRs. In this model, I start by assuming that sales or customer success managers (CSMs) generate the pipeline for upsell (i.e., sales to existing customers). Therefore, when we’re looking at coverage, we really mean to say coverage of the newbiz ARR target (i.e., new ARR from new customers). So, we first reduce the ARR goal by a percentage and then multiple it by the desired pipeline coverage ratio and then allocate the result across the pipeline-sources by presumably agreed-to percentages .
Building the next-level models to support pipeline generation goals is beyond the scope of this post, but I have a few relevant posts on the subject including this three-part series, here, here, and here.
Two Clever Ways to Use the Model
The sad reality is that this kind of model gets a lot attention at the end of a fiscal year (while you’re making the plan for next year) and then typically gets thrown in the closet and ignored until it’s planning season again.
That’s too bad because this model can be used both as an evaluation tool and a predictive tool throughout the year.
Let’s show that via an all-too-common example. Let’s say we start 2020 with a new VP of Sales we just hired in November 2019 with hiring and performance targets in our original model (above) but with judgment set to zero so plan is equal to the capacity model.
Our “world-class” VP immediately proceeds to drive out a large number of salespeople. While he hires 3 “all-star” reps during 1Q20, all 5 reps hired by his predecessor in the past 6 months leave the company along with, worse yet, two fully ramped reps. Thus, instead of ending the quarter with 20 reps, we end with 12. Worse yet, the VP delivers new ARR of $2,000K vs. a target of $3,125K, 64% of plan. Realizing she has a disaster on her hands, the CEO “fails fast” and fires the newly hired VP of sales after 5 months. She then appoints the RVP of Central, Joe, to acting VP of Sales on 4/2. Joe proceeds to deliver 59%, 67%, and 75% of plan in 2Q20, 3Q20, and 4Q20.
Our question: is Joe doing a good job?
At first blush, he appears more zero than hero: 59%, 67%, and 75% of plan is no way to go through life.
But to really answer this question we cannot reasonably evaluate Joe relative to the original operating plan. He was handed a demoralized organization that was about 60% of its target size on 4/2. In order to evaluate Joe’s performance, we need to compare it not to the original operating plan, but to the capacity model re-run with the actual rep hiring and aging at the start of each quarter.
When you do this you see, for example, that while Joe is constantly underperforming plan, he is also constantly outperforming the capacity model, delivering 101%, 103%, and 109% of model capacity in 2Q through 4Q.
If you looked at Joe the way most companies look at key metrics, he’d be fired. But if you read this chart to the bottom you finally get the complete picture. Joe is running a significantly smaller sales organization at above-model efficiency. While Joe got handed an organization that was 8 heads under plan, he did more than double the organization to 26 heads and consistently outperformed the capacity model. Joe is a hero, not a zero. But you’d never know if you didn’t look at his performance relative to the actual sales capacity he was managing.
Second, I’ll say the other clever way to use a capacity model is as a forecasting tool. I have found a good capacity model, re-run at the start of the quarter with then-current sales hiring/aging is a very valuable predictive tool, often predicting the quarterly sales result better than my VP of Sales. Along with rep-level, manager-level, and VP-level forecasts and stage-weighted and forecast-category-weighted expected pipeline values, you can use the re-run sales capacity model as a great tool to triangulate on the sales forecast.
You can download the four-tab spreadsheet model I built for this post, here.
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 Starting with quota starts you in the wrong mental place — what you want people to do, as opposed to productivity (what they have historically done). Additionally, there are clear instances where quotas get assigned against which we have little to no actual productivity assumption (e.g., a second-quarter rep typically has zero productivity but will nevertheless be assigned some partial quota). Sales most certainly has a quota-allocation problem, but that should be a separate, second exercise after building a corporate sales productivity model on which to base the operating plan.
 A typically such vector might be (0%, 25%, 50%, 100%) or (0%, 33%, 66%, 100%) reflecting the percentage of steady-state productivity they are expected to achieve in their first, second, third, and fourth quarters of employment.
 Without such a row, the plan is either de-linked from the model or the plan is the pure output of the model without any human judgement attached. This row is typically used to re-balance the annual number across quarters and/or to either add or subtract cushion relative to the model.
 Back in the day at Salesforce, we called pipeline generation sources “horsemen” I think (in a rather bad joke) because there were four of them (marketing, alliances, sales, and SDRs/outbound). That term was later dropped probably both because of the apocalypse reference and its non gender-neutrality. However, I’ve never known what to call them since, other than the rather sterile, “pipeline sources.”
 Many salesops people do it the reverse way — I think because they see the problem as allocating quota whereas I see the the problem as building an achievable operating plan. Starting with quota poses several problems, from the semantic (lopping 20% off quota is not 20% over-assignment, it’s actually 25% because over-assignment is relative to the smaller number) to the mathematical (first-quarter reps get assigned quota but we can realistically expect a 0% yield) to the procedural (quotas should be custom-tailored based on known state of the territory and this cannot really be built into a productivity model).
 One advantages of having those percentages here is they are placed front-and-center in the company’s bookings model which will force discussion and agreement. Otherwise, if not documented centrally, they will end up in different models across the organization with no real idea of whether they either foot to the bookings model or even sum to 100% across sources.
Knowing that CFO transformation is one of my favorite subjects (having run a planning company for six years) the folks at Sage invited me to moderate a set of panels at their San Francisco and New York media events this week. Sage is launching the results of a research study where they surveyed over 500 CFOs of primarily medium-size businesses about the ongoing transformation of the CFO role, and their outlook on topics ranging from the evolution of finance to advanced automation (robotic process automation, or RPA), artificial intelligence (AI), and machine-learning.
98% of CFOs say their job has changed in the past 5 years. Fortunately, none of our panelists were in the other 2%.
94% believe that financial management tools success increase productivity in the department.
46% of finance professionals reported an increasing demand for business counsel beyond just basic reporting and analytics. Finding more time to offer such counsel is an ongoing theme in CFO transformation.
92% are hopeful that AI/ML can further increase automation in finance and help create more such time for strategic matters.
Yet 83% say that their organizations may be yet be culturally ready to adopt more automation technology.
While only 25% saw themselves as change agents, nearly 75% reported that they were leading digital transformation efforts at their organizations. Humble people, those CFOs.
In addition to a great joke (question: “how do you know when you’re talking to an extroverted accountant?” answer: “they’re the one looking at your shoes when they’re talking.”) we heard a few colorful stories as well, my favorite from Jack who at age 19 was hauled into the CFO’s office for questioning as to why Jack referred to him as the “CFNo.” Expecting to be fired from his first job, Jack was instead thanked, “that’s quite a compliment,” said the CFO 1.0, “I’m pleased to hear you’ve been calling me the CFKnow.” Jack dodged a bullet on that one, for sure.
Thanks to Sage for inviting me and best of luck on the continuing journey to transform finance. You can get a copy of the full Sage CFO 3.0 study here.
In this post we’re going to look at the management accounting side of multi-year SaaS deals that grow in value over time. I’ve been asked about this a few times lately, less because people value my accounting knowledge  but rather because people are curious about the CAC impact of such deals and how to compensate sales on them.
Say you sign a three-year deal with a customer that ramps in payment structure: year 1 costs $1M, year 2 costs $2M, and year 3 costs $3M. Let’s say in this example the customer is getting the exact same value in all 3 years (e.g., the right for 1,000 people to use a SaaS service) – so the payment structure is purely financial in nature and not related to customer value.
Equal Value: The Price-Ramped Deal
The question on my mind is how do I look at this from a new ARR bookings, ending ARR, CAC, and sales compensation perspective?
GAAP is being conservative and saying “no cash, no revenue.” For an early stage startup with no history of actually making these deals come true, that is not a bad position. I like the concept of GAAP unbilled deferred revenue, but I don’t actually know anyone who tracks it, let alone discloses it. Folks might release backlog in some sort of unbilled total contract value (TCV) metric which I suspect is similar .
ASC 606 is being aggressive and mathematical – “hey, if it’s a 3-year, $6M deal, then that’s $2M/year, let’s just smooth it all out ”. While “unbilled A/R” strikes me as (another) oxymoron I see why they need it and I do like the idea of ASC 606 revenue backlog . I think the ASC 606 approach makes a lot of sense for more mature companies, which have a history of making these deals work .
Now, from an internal, management accounting perspective, what do you want to do with this deal in terms of new ARR bookings, ending ARR balance, CAC ratio, and sales comp? We could say:
It’s $2M in new ARR today
Ergo calculate this quarter’s CAC with it counted as $2M
Add $2M in ending ARR
Pay the salesrep on a $2M ARR deal – and let our intelligently designed compensation plan protect us in terms of the delayed cash collections  [6A]
And I’d be OK with that treatment. Moreover, it jibes with my definition of ARR which is:
That’s because ASC 606 also flattens out the uneven cash flows into a flat revenue stream.
Now, personally, I don’t want to be financing my customers when I’m at a high-burn startup, so I’m going to try and avoid deals like this. But if I have to do one, and we’re a mature enough business to be quite sure that years 2 and 3 are really coming, then I’m OK to treat it this way. If I’m not sure we’ll get paid in years 2 and 3 – say it’s for a brand-new product that has never been used at this scale – then I might revert to the more GAAP-oriented, 1-2-3 approach, effectively treating the deal not as a price ramp, but as an auto-expander.
Increasing Value: The Auto-Expanding Deal
Let’s say we have a different use-case. We sell a SaaS platform and year 1 will be exclusively focused on developing a custom SaaS app, we will roll it to 500 users day 1 of year 2, and we will roll it to 500 more users on day 1 of year 3. Further assume that the customer gets the same value from each of these phases and each phase continues until the end of the contract . Also assume the customer expects that going forward, they will be paying $3M/year plus annual inflation adjustments.
Oy veh. Now it’s much harder. The ramped shape of the curve is not about financing at all. It’s about the value received by the customer and the ramped shape of the payments perfectly reflects the ramped shape of the value received. Moreover, not all application development projects succeed and if they fall behind on building the customized application they will likely delay the planned roll-outs and try to delay the payments along with them. Moreover, since we’re an early-stage startup we don’t have enough history to know if they’ll succeed at all.
This needs to be seen as an auto-expanding deal: $1M of new-business ARR in year 1, $1M of pre-sold upsell ARR in year 2, and another $1M of pre-sold upsell ARR in year 3.
When you celebrate it at the company kickoff you can say the customer has made a $6M commitment (total contract value, or TCV ) to the company and when you tier your customers for customer support/success purposes you might do so by TCV as opposed to ARR . When you talk to investors you can say that $1M of next year’s and $1M of the subsequent year’s upsell is already under contract, ergo increasing your confidence in your three-year plan. Or you could roll it all together into a statement about backlog or RPO . That part’s relatively easy.
The hard part is figuring out sales compensation and CAC. While your rep will surely argue this is a $2M ARR deal (if not a $3M ARR deal) and that he/she should be paid accordingly, hopefully you have an ARR-driven (and not a total bookings-driven) compensation plan and we’ve already established that we can’t see this as $2M or $3M ARR deal. Not yet, at least.
This deal is a layer cake: it’s a three-year $1M ARR deal  that has a one-year-delayed, two-year $1M ARR deal layered atop it, and a two-year-delayed, one-year $1M ARR deal atop that. And that, in my opinion, is how you should pay it out . Think: “hey, if you wanted to get paid on a three-year $3M ARR deal, then you should have brought me one of those .”
Finally, what to do about the CAC? One might argue that the full cost of sale for the eventual $3M in ARR was born up-front. Another might argue that, no, plenty of account management will be required to ensure we actually get the pre-sold upsell. The easiest and most consistent thing to do is to treat the ARR as we mentioned (1+1+1) and calculate the CAC, as you normally would, using the ARR that we put in the pool.
If you do a lot of these deals, then you would see a high new-business CAC ratio that is easily explained by stellar net-dollar expansion rates (173% if these were all you did). Think: “yes, we spend a lot up-front to get a customer, but after we hook them, they triple by year three.”
Personally, I think any investor would quickly understand (and fall in love with) those numbers. If you disagree, then you could always calculate some supplemental CAC ratio designed to better amortize the cost of sale across the total ARR . Since you can’t have your cake and eat it too, this will make the initial CAC look better but your upsell CAC and net-dollar expansion rates worse.
As always, I think the right answer is to stick with the fundamental metrics and let them tell the story, rather than invent new metrics or worse yet, new definitions for standard metrics, which can sow the seeds of complexity and potential distrust.
 I am not an accountant. I’m a former CEO and strategic marketer who’s pretty good at finance.
 And which I like better as “unbilled deferred revenue” is somewhat oxymoronical to me. (Deferred revenue is revenue that you’ve billed, but you have not yet earned.)
 I know in some cases, e.g., prepaid, flat multi-year deals, ASC 606 can actually decide there is a material financing event and kind of separate that from the core deal. While pure in spirit, it strikes me as complex and the last time I looked closely at it, it actually inflated revenue as opposed to deflating it.
 Which I define as all the future revenue over time if every contract played out until its end.
 Ergo, you have high empirical confidence that you are going to get all the revenue in the contract over time.
 Good comp plans pay only a portion of large commissions on receipt of the order and defer the balance until the collection of cash. If you call this a $2M ARR deal, you do the comp math as if it’s $2M, but pay out the cash as dictated by the terms in your comp plan. (That is, make it equivalent to a $2M ARR deal with crazy-delayed payment terms.) You also retire $2M of quota, in terms of triggering accelerators and qualifying for club.
[6A] This then begs the question of how to comp the $1M in pre-sold upsell in Year 3. As with any of the cases of pre-sold upsell in this post, my inclination is to pay the rep on it when we get the cash but not on the terms/rates of the Year 1 comp plan, but to “build it in” into their comp plan in year 3, either directly into the structure (which I don’t like because I want reps primarily focused on new ARR) or as a bonus on top of a normal OTE. You get a reward for pre-sold upsell, but you need to stay here to get it and you don’t year 1 comp plan rates.
 That is, if all your contracts are signed on the last day of the quarter, and you don’t sign any new ones, or churn any existing ones until the last day of the quarter, and no one does a mid-quarter expansion, and you don’t have to worry about any effects due to delayed start dates, then the ARR balance on the last day of the quarter / 4 = next quarter’s subscription revenue.
 Development is not “over” and that value released – assume they continue to fully exploit all the development environments as they continue to build out their app.
 Note that TCV can be seen as an “evil” metric in SaaS and rightfully so when you try to pretend that TCV is ARR (e.g., calling a three-year $100K deal “a $300K deal,” kind of implying the $300K is ARR when it’s not). In this usage, where you’re trying to express total commitment made to the company to emphasize the importance of the customer, I think it’s fine to talk about TCV – particularly because it also indirectly highlights the built-in upsell yet to come.
 Or perhaps some intelligent mix thereof. In this case, I’d want to weight towards TCV because if they are not successful in year 1, then I fail to collect 5/6th of the deal. While I’d never tell an investor this was a $6M ARR deal (because it’s not true), I’d happily tell my Customer Success team that this a $6M TCV customer who we better take care of. (And yes, you should probably give equal care to a $2M ARR customer who buys on one-year contracts – in reality, either way, they’d both end up “Tier 1” and that should be all that matters.)
 Or you could of the ASC 606 revenue backlog and/or Remaining Performance Obligation (RPO) – and frankly, I’d have trouble distinguishing between the two at this point. I think RPO includes deferred revenue whereas ASC 606 revenue backlog doesn’t.
 In the event your compensation plan offers a kicker for multi-year contracts.
 And while you should factor in the pre-committed upsell in setting the reps targets in years 2 and 3, you shouldn’t go so far as to give them a normal upsell target with the committed upsell atop it. There is surely middle ground to be had. My inclination is to give the rep a “normal” comp plan and build in collecting the $1M as a bonus on top — but, not of course at regular new ARR rates. The alternative is to build (all or some of) it into the quota which will possibly demotivate the rep by raising targets and reducing rates, especially if you just pile $1M on top of a $1M quota.
 This ain’t one – e.g., it has $6M of TCV as opposed to $9M.
Overall, I can say that at Host Analytics, we are honored to a leader in both MQs again this year. We are also honored to be the only cloud pure-play vendor to be a leader in both MQs and we believe that speaks volumes about the depth and breadth of EPM functionality that we bring to the cloud.
So, if all you wanted was the links, thanks for visiting. If, however, you’re looking for some Kellblog editorial on these MQs, then please continue on.
The first thing the astute reader will notice is that the category name, which Gartner formerly referred to as corporate performance management (CPM), and which others often referred to as enterprise performance management (EPM), is entirely missing from these MQs. That’s no accident. Gartner decided last fall to move away from CPM as a uber category descriptor in favor of referring more directly to the two related, but pretty different, categories beneath it. Thus, in the future you won’t be hearing “CPM” from Gartner anymore, though I know that some vendors — including Host Analytics — will continue to use EPM/CPM until we can find a more suitable capstone name for the category.
Personally, I’m in favor of this move for two simple reasons.
CPM was a forced, analyst-driven category in the first place, dating back to Howard Dresner’s predictions that financial planning/budgeting would converge with business intelligence. While Howard published the research that launched a thousand ships in terms of BI and financial planning industry consolidation (e.g., Cognos/Adaytum, BusinessObjects/SRC/Cartesis, Hyperion/Brio), the actual software itself never converged. CPM never became like CRM — a true convergence of sales force automation (SFA) and contact center. In each case, the two companies could be put under one roof, but they sold fundamentally different value propositions to very different buyers and thus never came together as one.
In accordance with the prior point, few customers actually refer to the category by CPM/EPM. They say things much more akin to “financial planning” and “consolidation and close management.” Since I like referring to things in the words that customers use, I am again in favor of this change.
It does, however, create one problem — Gartner has basically punted on trying to name a capstone category to include vendors who sell both financial planning and financial consolidation software. Since we at Host Analytics think that’s important, and since we believe there are key advantages to buying both from the same vendor, we’d prefer if there were a single, standard capstone term. If it were easy, I suppose a name would have already emerged .
How Not To Use Magic Quadrants
While they are Gartner’s flagship deliverable, magic quadrants (MQs) can generate a lot of confusion. MQs don’t tell you which vendor is “best” because there is no universal best in any category. MQs don’t tell you which vendor to pick to solve your problem because different solutions are designed around meeting different requirements. MQs don’t predict the future of vendors — last-year’s movement vectors rarely predict this year’s positions. And the folks I know at Gartner generally strongly dislike vector analysis of MQs because they view vendor placement as relative to each other at any moment in time .
Many things that customers seem to want from Gartner MQs are actually delivered by Gartner’s Critical Capabilities reports which get less attention because they don’t produce a simple, dramatic 2×2 output, but which are far better suited for determine the suitability of different products to different use-cases.
How To Use A Gartner Magic Quadrant?
In my experience after 25+ in enterprise software, I would use MQs for their overall purpose: to group vendors into 4 different buckets: leaders, challengers, visionaries, and niche players. That’s it. If you want to know who the leaders are in a category, look top right. If you want to know who the visionaries are, look bottom right. You want to know which big companies are putting resources into the category but who thus far are lacking strategy/vision, then look top-left at the challengers quadrant.
But should you, in my humble opinion, get particularly excited about millimeter differences on either axes? No. Why? Because what drives those deltas may have little, none, or in fact a counter-correlation to your situation. In my experience, the analysts pay a lot of attention to the quadrants in which vendors end up in  so quadrant-placement, I’d say, is quite closely watched by the analysts. Dot-placement, while closely watched by vendors, save for dramatic differences, doesn’t change much in the real world. After all, they are called the magic quadrants, not the magic dots.
All that said, let me wind up with some observations on the MQs themselves.
Quick Thoughts on the 2018 Cloud FP&A Solutions MQ
While the MQs were published at the end of July 2018, they were based on information about the vendors gathered in and largely about 2017. While there is always some phase-lag between the end of data collection and the publication data, this year it was rather unusually long — meaning that a lot may have changed in the market in the first half of 2018 that customers should be aware of. For that reason, if you’re a Gartner customer and using either the MQs or critical capabilities reports that accompany them, you should probably setup an appointment to call the analysts to ensure you’re working off the latest data.
Here are some of my quick thoughts the Cloud FP&A Solutions magic quadrant:
Gartner says the FP&A market is accelerating its shift from on-premises cloud. I agree.
Gartner allows three types of “cloud” vendors into this (and the other) MQ: cloud-only vendors, on-premise vendors with new built-for-the-cloud solutions, and on-premises vendors who allow their software to be run hosted on a third-party cloud platform. While I understand their need to be inclusive, I think this is pretty broad — the total cost of ownership, cash flows, and incentives are quite different between pure cloud vendors and hosted on-premises solutions. Caveat emptor.
To qualify for the MQ vendors must support at least two of the four following components of FP&A: planning/budgeting, integrated financial planning, forecasting/modeling, management/performance reporting. Thus the MQ is not terribly homogeneous in terms of vendor profile and use-cases.
For the second year in a row, (1) Host is a leader in this MQ and (2) is the only cloud pure-play vendor who is a leader in both. We think this says a lot about the breadth and depth of our product line.
Customer references for Host cited ease of use, price, and solution flexibility as top three purchasing criteria. We think this very much represents our philosophy of complex EPM made easy.
Quick Thoughts on the 2018 Cloud Financial Close Solutions MQ
Here are some of my quick thoughts on the Cloud Financial Close Solutions magic quadrant:
Gartner says that in the past two years the financial close market has shifted from mature on-premises to cloud solutions. I agree.
While Gartner again allowed all three types of cloud vendors in this MQ, I believe some of the vendors in this MQ do just-enough, just-cloud-enough business to clear the bar, but are fundamentally still offering on-premise wolves in cloud sheep’s clothing. Customers should look to things like total cost of ownership, upgrade frequency, and upgrade phase lags in order to flesh out real vs. fake cloud offerings.
This MQ is more of a mixed bag than the FP&A MQ or, for that matter, most Gartner MQs. In general, MQs plot substitutes against each other — each dot on an MQ usually represents a vendor who does basically the same thing. This is not true for the Cloud Financial Close (CFC) MQ — e.g., Workiva is a disclosure management vendor (and a partner of Host Analytics). However, they do not offer financial consolidation software, as does say Host Analytics or Oracle.
Because the scope of this MQ is broad and both general and specialist vendors are included, customers should either call the Gartner for help (if they are Gartner customers) or just be mindful of the mixing and segmentation — e.g., Floqast (in SMB and MM) and Blackline (in enterprise) both do account reconciliation, but they are naturally segmented by customer size (and both are partners of Host, which does financial consolidation but not account reconciliation).
Net: while I love that the analysts are willing to put different types of close-related, office-of-the-CFO-oriented vendors on the same MQ, it does require more than the usual amount of mindfulness in interpreting it.
 For Gartner, this is likely more than a semantic issue. They are pretty strong believers in a “post-modern” ERP vision which eschews the idea of a monolithic application that includes all services, in favor of using and integrating a series of cloud-based services. Since we are also huge believers in integrating best-of-breed cloud services, it’s hard for us to take too much issue with that. So we’ll simply have to clearly articulate the advantages of using Host Planning and Host Consolidations together — from our viewpoint, two best-of-breed cloud services that happen to come from a single vendor.
 And not something done against absolute scales where you can track movement over time. See, for example, the two explicit disclaimers in the FP&A MQ:
I’m Dave Kellogg, consultant, independent director, advisor, and blogger focused on enterprise software startups.
I bring a unique perspective to startup challenges having 10 years’ experience at each of the CEO, CMO, and independent director levels across 10+ companies ranging in size from zero to over $1B in revenues.
From 2012 to 2018, I was CEO of cloud enterprise performance management vendor Host Analytics, where we quintupled ARR while halving customer acquisition costs in a competitive market, ultimately selling the company in a private equity transaction.
Previously, I was SVP/GM of Service Cloud at Salesforce and CEO at NoSQL database provider MarkLogic, which we grew from zero to $80M in run-rate revenues during my tenure. Before that, I was CMO at Business Objects for nearly a decade as we grew from $30M to over $1B. I started my career in technical and product marketing positions at Ingres and Versant.
I love disruption, startups, and Silicon Valley and have had the pleasure of working in varied capacities with companies including Bluecore, Cyral, FloQast, Fortella, GainSight, MongoDB, Plannuh, Recorded Future, and Tableau. I currently sit on the boards of Alation (data catalogs), Nuxeo (content management) and Profisee (master data management). I previously sat on the boards of agtech leader Granular (acquired by DuPont for $300M) and big data leader Aster Data (acquired by Teradata for $325M).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).