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.
I’m Dave Kellogg, advisor, director, consultant, angel investor, and blogger focused on enterprise software startups. I am an executive-in-residence (EIR) at Balderton Capital and principal of my own eponymous consulting business.
I bring an uncommon 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 EPM 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 the $500M Service Cloud business at Salesforce; CEO of NoSQL database provider MarkLogic, which we grew from zero to $80M over 6 years; and CMO at Business Objects for nearly a decade as we grew from $30M to over $1B in revenues. 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, GainSight, MongoDB, Recorded Future, and Tableau.
I previously sat on the boards of Granular (agtech, acquired by DuPont), Aster Data (big data, acquired by Teradata), and Nuxeo (content services, acquired by Hyland), and Profisee (MDM, exited to Pamlico).
I periodically speak to strategy and entrepreneurship classes at the Haas School of Business (UC Berkeley) and Hautes Études Commerciales de Paris (HEC).