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.
# # #
 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:
This means that the financial planning and analysis (FP&A) team at many companies is so busy doing other things that it doesn’t have time to focus on what it does best and where it can add the most value: analysis.
This begs the question: where did the A go? What are the other things that are taking up so much time? The answer: data prep and spreadsheet jockeying. These functions suck time away and the soul from the FP&A function.
Data-related tasks — such as finding, integrating, and preparing data — take up more than 2/3rds of FP&A’s time. Put differently, FP&A spends twice as much time getting ready to analyze data than it does analyzing it. It might even be worse, depending on whether periodic and ad hoc reporting is included in data-related task or further carved out of the 28% of time remaining for analytics, as I suspect it is.
It’s not just finance who loves spreadsheets. The business does do: salesops, marketingops, supply chain planners, professional services ops, and customer support all love spreadsheets, too. When I worked at Salesforce, we had one of the most sophisticated sales strategy and planning teams I’ve ever seen. Their tool of choice? Excel.
This comes back to haunt finance in three ways:
Warring models, for example, when the salesops new bookings model doesn’t foot to the finance one because they make different ramping and turnover assumptions. These waste time with potential endless fights.
Non-integrated models. Say sales and finance finally agree on a bookings target and to hire 5 more salespeople to support it. Now we need to call marketing to update their leadgen model to ensure there’s enough budget to support them, customer service to ensure we’re staffed to handle the incremental customers they sign, professional services to ensure we’re have adequate consulting resources, and on and on. Forget any of these steps and you’ll start the year out of balance, with unattainable targets somewhere.
Excel inundation. FP&A develops battle fatigue dealing with and integrating some many different versions of so many spreadsheets, often late and night and under deadline pressure. Mistakes gets made.
So how can prevent FP&A from being run over by these forces? The answer is to automate, automate, and integrate.
Automate data integration and preparation. Let’s free up time by use software that lets you “set and forget” data refreshes. You should be able to setup a connector to a data source one, and then have it automatically run at periodic intervals going forward. No more mailing spreadsheets around.
Automate periodic FP&A tasks. Use software where you can invest in building the perfect monthly board pack, monthly management reports, quarterly ops review decks, and quarterly board reports once, and then automatically refresh it every period through these templates. This not only free up time and reduces drudgery; it eliminates plenty of mistakes as well.
Integrate planning across the organization. Move to a cloud-based enterprise performance platform (like Host Analytics) that not only accomplishes the prior two goals, but also offers a modeling platform that can be used across the organization to put finance, salesops, marketingops, professional services, supply chain, HR, and everyone else across the organization on a common footing.
Since the obligatory groundwork in FP&A is always heavy, you’re not going to succeed in putting the A back in FP&A simply by working harder and later. The only way to put the A back in FP&A is to create time. And you can do that with two doses of automation and one of integration.
The single most misunderstood software-as-a-service (SaaS) metric I’ve encountered is the CAC Payback Period (CPP), a compound metric that is generally defined as the months of contribution margin to pay back the cost of acquiring a customer. Bessemer defines the CPP as:
I quibble with some of the Bessemerisms in the definition. For example, (1) most enterprise SaaS companies should use annual recurring revenue (ARR), not monthly recurring revenue (MRR), because most enterprise companies are doing annual, not monthly, contracts, (2) the “committed” MRR concept is an overreach because it includes “anticipated” churn which is basically impossible to measure and often unknown, and (3) I don’t know why they use the prior period for both S&M costs and new ARR – almost everybody else uses prior-period S&M divided by current-period ARR in customer acquisition cost (CAC) calculations on the theory that last quarter’s S&M generated this quarter’s new ARR.
Switching to ARR nomenclature, and with a quick sleight of mathematical hand for simplification, I define the CAC Payback Period (CPP) as follows:
Let’s run some numbers.
If your company has a CAC ratio of 1.5 and subscription gross margins of 75%, then your CPP = 24 months.
If your company has a CAC ratio of 1.2 and subscription gross margins of 80%, then your CPP = 18 months.
If you company has a CAC ratio of 0.8 and subscription gross margins of 80%, then your CPP = 12 months.
All seems pretty simple, right? Not so fast. There are two things that constantly confound people when looking at CAC Payback Period (CPP).
They forget payback metrics are risk metrics, not return metrics
They fail to correctly interpret the impact of annual or multi-year contracts
Payback Metrics are for Risk, Not Return
Quick, basic MBA question: you have two projects, both require an investment of 100 units, and you have only 100 units to invest. Which do you pick?
Project A: which has a payback period of 12 months
Project B: which has a payback period of 6 months
Quick, which do you pick? Well, project B. Duh. But wait — now I tell you this:
Payback is all about how long your money is committed (so it can’t be used for other projects) and at risk (meaning you might not get it back). Payback doesn’t tell you anything about return. In capital budgeting, NPV tells you about return. In a SaaS business, customer lifetime value (LTV) tells you about return.
There are situations where it makes a lot of sense to look at CPP. For example, if you’re running a monthly SaaS service with a high churn rate then you need to look closely how long you’re putting your money at risk because there is a very real chance you won’t recoup your CAC investment, let alone get any return on it. Consider a monthly SaaS company with a $3500 customer acquisition cost, subscription gross margin of 70%, a monthly fee of $150, and 3% monthly churn. I’ll calculate the ratios and examine the CAC recovery of a 100 customer cohort.
While the CPP formula outputs a long 33.3 month CAC Payback Period, reality is far, far worse. One problem with the CPP formula is that it does not factor in churn and how exposed a cohort is to it — the more chances customers have to not renew during the payback period, the more you need to consider the possibility of non-renewal in your math . In this example, when you properly account for churn, you still have $6 worth of CAC to recover after 30 years! You literally never get back your CAC.
Soapbox: this is another case where using a model is infinitely preferable to back-of-the-envelope (BOTE) analysis using SaaS metrics. If you want to understand the financials of a SaaS company, then build a driver-based model and vary the drivers. In this case and many others, BOTE analysis fails due to subtle complexity, whereas a well-built model will always produce correct answers, even if they are counter-intuitive.
Such cases aside, the real problem with being too focused on CAC Payback Period is that CPP is a risk metric that tells you nothing about returns. Companies are in business to get returns, not simply to minimize risk, so to properly analyze a SaaS business we need to look at both.
The Impact of Annual and Multi-Year Prepaid Contracts on CAC Payback Period
The CPP formula outputs a payback period in months, but most enterprise SaaS businesses today run on an annual rhythm. Despite pricing that is sometimes still stated per-user, per-month, SaaS companies realized years ago that enterprise customers preferred annual contracts and actually disliked monthly invoicing. Just as MRR is a bit of a relic from the old SaaS days, so is a CAC Payback Period stated in months.
In a one-hundred-percent annual prepaid contract world, the CPP formula should output in multiples of 12, rounding up for all values greater than 12. For example, if a company’s CAC Payback Period is notionally 13 months, in reality it is 24 months because the leftover 1/13 of the cost isn’t collected until the a customer’s second payment at month 24. (And that’s only if the customer chooses to renew — see above discussion of churn.)
In an annual prepaid world, if your CAC Payback Period is less than or equal to 12 months, then it should be rounded down to one day because you are invoicing the entire year up-front and at-once. Even if the formula says the CPP is notionally 12.0 months, in an annual prepaid world your CAC investment money is at risk for just one day.
So, wait a minute. What is the actual CAC Payback Period in this case? 12.0 months or 1 day? It’s 1 day.
Anyone who argues 12.0 months is forgetting the point of the metric. Payback periods are risk metrics and measured by the amount of time it takes to get your investment back . If you want to look at S&M efficiency, look at the CAC ratio. If you want to know about the efficiency of running the SaaS service, look at subscription gross margins. If you want to talk about lifetime value, then look at LTV/CAC. CAC Payback Period is a risk metric that measures how long your CAC investment is “on the table” before getting paid back. In this instance the 12 months generated by the standard formula is incorrect because the formula misses the prepayment and the correct answer is 1 day.
A lot of very smart people get stuck here. They say, “yes, sure, it’s 1 day – but really, it’s not. It’s 12 months.” No. It’s 1 day.
If you want to look at something other than payback, then pick another metric. But the CPP is 1 day. You asked how long it takes for the company to recoup the money it spends to acquire a customer. For CPPs less than or equal to 12 in a one-hundred percent annual prepaid world, the answer is one day.
It gets harder. Imagine a company that sells in a sticky category (e.g., where typical lifetimes may be 10 years) and thus is a high-consideration purchase where prospective customers do deep evaluations before making a decision (e.g., ERP). As a result of all that homework, customers are happy to sign long contracts and thus the company does only 3-year prepaid contracts. Now, let’s look at CAC Payback Period. Adapting our rules above, any output from the formula greater than 36 months should be rounded up in multiples of 36 months and, similarly, any output less than or equal to 36 months should be rounded down to 1 day.
Here we go again. Say the CAC Payback Period formula outputs 33 months. Is the real CPP 33 months or 1 day? Same argument. It’s 1 day. But the formula outputs 33 months. Yes, but the CAC recovery time is 1 day. If you want to look at something else, then pick another metric.
It gets even harder. Now imagine a company that does half 1-year deals and half 3-year deals (on an ARR-weighted basis). Let’s assume it has a CAC ratio of 1.5, 75% subscription gross margins, and thus a notional CAC Payback Period of 24 months. Let’s see what really happens using a model:
Using this model, you can see that the actual CAC Payback Period is 1 day. Why? We need to recoup $1.5M in CAC. On day 1 we invoice $2.0M, resulting in $1.5M in contribution margin, and thus leaving $0 in CAC that needs to be recovered.
While I have not yet devised general rounding rules for this situation, the model again demonstrates the key point – that the mix of 1-year and 3-year payment structure confounds the CPP formula resulting in a notional CPP of 24 months, when in reality it is again 1 day. If you want to make rounding rules beware the temptation to treat the average contract duration (ACD) as a rounding multiple because it’s incorrect — while the ACD is 2 years in the above example, not a single customer is paying you at two-year intervals: half are paying you every year while half are paying you every three. That complexity, combined with the reality that the mix is pretty unlikely to be 50/50, suggests it’s just easier to use a model than devise a generalized rounding formula.
But pulling back up, let’s make sure we drive the key point home. The CAC Payback Period is the single most often misunderstood SaaS metric because people forget that payback metrics are about risk, not return, and because the basic formulas – like those for many SaaS metrics – assume a monthly model that simply does not apply in today’s enterprise SaaS world, and fail to handle common cases like annual or multi-year prepaid contracts.
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 This is a huge omission for a metric that was defined in terms of MRR and which thus assumes a monthly business model. As the example shows, the formula (which fails to account for churn) outputs a CAC payback of 33 months, but in reality it’s never. Quite a difference!
 If I wanted to be even more rigorous, I would argue that you should not include subscription gross margin in the calculation of CAC Payback Period. If your CAC ratio is 1.0 and you do annual prepaid contracts, then you immediately recoup 100% of your CAC investment on day 1. Yes, a new customer comes with a future liability attached (you need to bear the costs of running the service for them for one year), but if you’re looking at a payback metric that shouldn’t matter. You got your money back. Yes, going forward, you need to spend about 30% (a typical subscription COGS figure) of that money over the next year to pay for operating the service, but you got your money back in one day. Payback is 1 day, not 1/0.7 = 17 months as the formula calculates.
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 Cyral, FloQast, Fortella, GainSight, Kelda, 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).