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In prior posts I have discussed how to make a proper sales bookings productivity model and how to use the concept of ramped rep equivalents (RREs) in sales analytics and modeling. When it comes to setting drivers for both, corporate leaders tend to lean towards benchmarks and industry norms for the values. For example, two such common norms are:
Setting steady-state (or terminal) productivity at $1,200K of new ARR per rep in enterprise SaaS businesses
Using a {0%, 25%, 50%, 100%} productivity ramp for new salesreps in their {1st, 2nd, 3rd, 4th} quarters with the company (and 100% thereafter)
In this post, I’ll discuss how you can determine if either of those assumptions are reasonable at your company, given its history.
To do so, I’m introducing one of my favorite charts, the Rep Ramp Chart. Unlike most sales analytics, which align sales along fiscal quarters, this chart aligns sales relative to a rep’s tenure with the company.
You start by listing every rep your company has ever hired [1] in order by hire date. You then record their sales productivity (typically measured in new ARR bookings [2]) for their series of quarters with the company [3], up to and including their current-quarter forecast (which you shade in green). Reps who leave the company are shaded black. Reps who get promoted out of quota-carrying roles (e.g., sales management) are shaded blue. Future periods are shaded grey. Add a 4+ quarter average productivity column for each row, and average each of the figures in the columns [4].
Here’s what you get:
Despite having only a relatively small amount of data [5], we can still interpret this a little.
The relative absence of black lines means we’re pretty good at sales hiring. I’ve seen real charts with 5 black lines in a row, usually down to a single bad management hire.
The absence of black lines that “start late” — for example {0, 25, 75, 25, 55, black} — is also good. Our reps are either “failing fast” or succeeding, but things are not dragging on forever when they’re not working.
Our average 4Q+ productivity is $308K per quarter, almost exactly $1,200K per year so it does seem valid to use that figure in our modeling.
Entering $300K as target productivity then shows the empirical rep ramp as a percent of steady-state productivity, exactly how sales leaders think of it. In this case, we see a {10%, 38%, 76%, 85%, 98%} empirical ramp across the first five quarters. If our bookings model assumed {0%, 25%, 50%, 100%, 100%}, you’d say our model is somewhat pessimistic in the first three quarters, a little optimistic in the 4th, and pretty much on-target (a tiny bit optimistic) in the 5th. If we had more data, we might adjust it a bit based on that.
I love this chart because it presents unadulterated history and lets you examine the validity of two hugely important drivers in your sales bookings capacity model — drivers, by the way, that are often completely unquestioned [6]. For that reason, I encourage everyone to make this a standard slide in your Sales ops review (aka, QBR) template. Note that since different types of rep ramp differently and hit different steady-state productivity levels, you should create one rep ramp per major type of rep in your company. For example, corporate (or inside) sales reps will typically ramp more quickly to lower productivity levels than field reps who will ramp more slowly to higher productivity. Channels reps will ramp differently from direct reps. International reps may need their own chart as well.
[1] Sales management may want to omit those no longer with the company, but that also omits their data, and might omit important patterns of hiring failure, so don’t omit anyone. You can always exclude certain rows from the analysis without removing them from the chart (i.e., hiding them).
[2] New ARR bookings typically includes new ARR to both new and existing customers.
[3] You’ll need as many columns to do this as your longest tenured rep has been with the company, so it can get wide. Let it. There’s data in there.
[4] Ensuring empty cells are not confused with cells whose value is zero. Excel ignores empty cells in calculating averages but will average your 0’s in when you probably don’t want them.
[5] In order to keep it easily and quickly grasped
I was tempted to stop there because I’ve been writing a lot of long posts lately and because I do believe the answer is that simple. First let me explain the controversy and then I’ll explain my view on it.
In days of yore, chief revenue officer (CRO) was just a gussied-up title for VP of Sales. If someone was particularly good, particularly senior, or particularly hard to recruit you might call them CRO. But the job was always the same: go sell software.
Back in the pre-subscription era, basically all the revenue — save for a little bit of services and some maintenance that practically renewed itself — came from sales anyway. Chief revenue officer meant chief sales officer meant VP of Sales. All basically the same thing. By the way, as the person responsible for effectively all of the company’s revenue, one heck of a powerful person in the organization.
Then the subscription era came along. I remember the day at Salesforce when it really hit me. Frank, the head of Sales, had a $1B number. But Maria, the head of Customer Success [1], had a $2B number. There’s a new sheriff in SaaS town, I realized, the person who owns renewals always has a bigger number than the person who runs sales [2], and the bigger you get the larger that difference.
Details of how things worked at Salesforce aside, I realized that the creation of Customer Success — particularly if it owned renewals — represented an opportunity to change the power structure within a software company. It meant Sales could be focused on customer acquisition and that Customer Success could be, definitionally, focused on customer success because it owned renewals. It presented the opportunity to have an important check and balance in an industry where companies were typically sales-dominated to a fault. Best of all, the check would be coming not just from a well-meaning person whose mission was to care about customer success, but from someone running a significantly larger amount of revenue than the head of Sales.
Then two complications came along.
The first complication was expansion ARR (annual recurring revenue). Subscriptions are great, but they’re even better when they get bigger every year — and heck you need a certain amount of that just to offset the natural shrinkage (i.e., churn) that occurs when customers unsubscribe. Expansion take two forms
Incidental: price increases, extra seats, edition upsells, the kind of “fries with your burger” sales that are a step up from order-taking, but don’t require a lot of salespersonship.
Non-incidental: cross-selling a complementary product, potentially to a different buyer within the account (e.g., selling Service Cloud to a VP of Service where the VP of Sales is using Sales Cloud) or an effectively new sale into different division of an existing account (e.g., selling GE Lighting when GE Aviation is already a customer).
While it was usually quite clear that Sales owned new customer acquisition and Customer Success owned renewals, expansion threw a monkey wrench in the machinery. New sales models, and new metaphors to go with them, emerged. For example:
Hunter-only. Sales does everything, new customer acquisition, both types of expansion, and even works on renewals. Customer success is more focused on adoption and technical support.
Hunter/farmer. Sales does new customer acquisition and non-incidental expansion and Customer Success does renewals and incidental expansion.
Hunter/hunter. Where Sales itself is effectively split in two, with one team owning new customer acquisition after which accounts are quickly passed to a very sales-y customer success team whose primary job is to expand the account.
Farmers with shotguns. A variation of hunter/hunter where an initial penetration Sales team focuses on “land” (e.g, with a $25K deal) and then passes the account to a high-end enterprise “expand” team chartered with major expansions (e.g., to $1M).
While different circumstances call for different models, expansion significantly complicated the picture.
The second complication was the rise of the chief revenue officer (CRO). Generally speaking, sales leaders:
Didn’t like their diminished status, owning only a portion of company revenue
Were attracted to the buffer value in managing the ARR pool [3]
Witnessed too many incidents where Customer Success (who they often viewed as overgrown support people) bungled expansion opportunities and/or failed to maximize deals
Could exploit the fact that the check-and-balance between Sales and Customer Success resulted in the CEO getting sucked into a lot of messy operational issues
On this basis, Sales leaders increasingly (if not selflessly) argued that it was better for the CEO and the company if all revenue rolled up under a single person (i.e., me). A lot of CEOs bought it. While I’ve run it both ways, I was never one of them.
I think Customer Success should report into the CEO in early- and mid-stage startups. Why?
I want the sales team focused on sales. Not account management. Not adoption. Not renewals. Not incidental expansion. I want them focused on winning new deals either at new customers or different divisions of existing customers (non-incidental expansion). Sales is hard. They need to be focused on selling. New ARR is their metric.
I want the check and balance. Sales can be tempted in SaaS companies to book business that they know probably won’t renew. A smart SaaS company does not want that business. Since the VP of Customer Success is going to be measured, inter alia, on gross churn, they have a strong incentive call sales out and, if needed, put processes in place to prevent inception churn. The only thing worse than dealing with the problems caused by this check and balance is not hearing about those problems. When one exec owns pouring water into the bucket and a different one owns stopping it from leaking out, you create a healthy tension within the organization.
They can work together without reporting to a single person. Or, better put, they are always going to report to a single person (you or the CRO) so the question is who? If you build compensation plans and operational models correctly, Customer Success will flip major expansions to Sales and Sales will flip incidental expansions back to Customer Success. Remember the two rules in building a Customer Success model — never pair our farmer against the competitor’s hunter, and never use a hunter when a farmer will do.
I want the training ground for sales. A lot of companies take fresh sales development reps (SDRs) and promote them directly to salesreps. While it sometimes works, it’s risky. Why not have two paths? One where they can move directly into sales and one where they can move into Customer Success, close 12 deals per quarter instead of 3, hone their skills on incidental expansion, and, if you have the right model, close any non-incidental expansion the salesrep thinks they can handle?
I want the Customer Success team to be more sales-y than support-y. Ironically, when Customer Success is in Sales you often end up with a more support-oriented Customer Success team. Why? The salesreps have all the power; they want to keep everything sales-y to themselves, and Customer Success gets relegated to a more support-like role. It doesn’t have to be this way; it just often is. In my generally preferred model, Customer Success is renewals- and expansion-focused, not support-focused, and that enables them to add more value to the business. For example, when a customer is facing a non-support technical challenge (e.g., making a new set of reports), their first instinct will be to sell them professional services, not simply build it for the customer themselves. To latter is to turn Customer Success into free consulting and support, starting a cycle that only spirals. The former is keep Customer Success focused on leveraging the resources of the company and its partners to drive adoption, successful achievement of business objectives, renewals, and expansion.
Does this mean a SaaS company can’t have a CRO role if Customer Success does not report into them? No. You can call the person chartered with hitting new ARR goals whatever you want to — EVP of Sales, CRO, Santa Claus, Chief Sales Officer, or even President/CRO if you must. You just shouldn’t have Customer Success report into them.
Personally, I’ve always preferred Sales leaders who like the word “sales” in their title. That way, as one of my favorites always said, “they’re not surprised when I ask for money.”
# # #
[1] At Salesforce then called Customers for Life.
[2] Corner cases aside and assuming either annual contracts or that ownership is ownership, even if every customer technically isn’t renewing every year.
[3] Ending ARR is usually a far less volatile metric than new ARR.
I’ve seen numerous startups try numerous ways to calculate their sales capacity. Most are too back-of-the-envelope and too 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 [1].
Thus, to me, everything needs to begin with a sales productivity model that is Einsteinian in the sense that it is as simple as possible but no simpler.
What does such a model need to take into account?
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 [2]. 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 [3].
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 [4], 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 [5].
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 [6].
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.
# # #
Notes
[1] 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.
[2] 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.
[3] 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.
[4] 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.”
[5] 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).
[6] 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.
Slowly and steadily, over the past decade, the industry has evolved from a mentality of “all salesreps must do everything” – including some percent of their time prospecting — to one of specialization. We, with the help of books like Predictable Revenue, have collectively decided that in-bound lead processing is different from outbound lead prospecting is different from low-end, velocity sales is different from high-end, enterprise sales.
Despite the old-school, almost-character-building emphasis on prospecting, we have collectively realized that having our top hunters dialing for dollars and digging through inbound leads isn’t, well, the best use of their time.
Industrialization typically involves specialization and the industrialization
of once purely artisanal software sales has been no exception. As part of this specialization the sales
development representative (SDR) role has risen to prominence. In this post, we’ll do a quick review of what
SDRs typically do and discuss the relative merits of having them report into
sales vs. marketing.
“Everyone under 25 in San Francisco is an SDR.” – Anonymous startup CEO
SDRs Bridge the Two
Departments
SDRs typically form the bridge between sales and marketing. A typical SDR job is take inbound leads from
marketing, perform some basic BANT-style [1] qualification on them, and then
pass them to sales if indicated. While SDRs typically have activity quotas
(e.g., 50 calls/day) they should be primarily measured on the number of
opportunities they create per week. In enterprise software, typically that quota
is 2-3 oppties/week.
As companies get bigger they tend to separate SDRs into two
groups:
Inbound SDRs, those who only process in-bound
leads, and
Outbound SDRs, those who primarily do targeted outreach
over the phone or email
Being an SDR is a hard job.
Typical SDR challenges include:
Adhering to service-level agreements for all leads (i.e., touches with timeframes)
Contacting prospects in an increasingly spam-hostile, call-hostile environment
Figuring out which leads to work on the hardest (e.g., which merit homework to customize the message and which don’t)
Remembering that their job is to sell meetings and not product [2]
Supporting multiple salespeople with often conflicting priorities [3]
Managing the conflict between supporting salespeople and executing the process
Getting salespeople to show-up at the hand-off meeting [4]
Avoiding burnout in a high-pressure environment
To Which Department
Should SDRs Report: Sales or Marketing?
Historically, SDRs reported to sales. That’s probably because sales first decided to fund SDR teams as a way getting inbound lead management out of the hands of salespeople [5]. Doing so would:
Enable the company to consistently respond in a
timely manner to all inquiries
Free up sales to spend more time on selling
Avoid the problem of individual reps not
processing new leads once they are “full up” on opportunities [6]
The problem is that most enterprise software sales VPs are not particularly process-oriented [7], because they grew up in a pre-industrialized era of sales [8]. In fact, nothing drives me crazier than an old-school, artisanal, deal-person CRO insisting on owning the SDR organization despite the total inability to manage it. They rationalize: “Oh, I can hire someone process-oriented to manage it.” And I think: “but what can that person learn from you [9] about how to manage it?” And the answer is nothing. Your desire to own it is either pure ego or simply a ploy to enrich your resume.
I’ll say again because it drives me crazy: do not be the VP of Sales who insists on owning the SDR organization in the annual planning meeting but then shows zero interest in it for the rest of the year. You’re not helping anyone!
As mentioned in a footnote in a prior post, I greatly prefer SDRs reporting to marketing versus sales. Why?
Marketing leadgen and nurture people are metrics- and process-oriented animals, naturally suited to manage a process-oriented department.
It provides a simple, clear conceptual model: marketing is the opportunity creation factory and sales is the opportunity closing machine.
In short, marketing’s job is to make opportunities. Sales’ job is to close them.
# # #
Notes
[1] BANT = budget, authority, need, time-frame.
[2] Most early- and mid-stage startups put SDRs in their regular sales training sessions which I think does them a disservice. Normal sales training is about selling products/solutions. SDRs “sell” meetings. They should not attempt to build business value or differentiation. Training them to do so tempts them to do – even when it is not their job.
[3] A typical QCR:SDR ratio is 3-4:1, though I’ve seen as
low as 1:1 and as high as 6:1
[4] Believe it or not, this sometimes happens (typically when your reps are already carrying a lot of oppties). Few things reflect worse on the company than a last-minute rescheduling of the meet-your-salesperson call. You don’t get a second chance to make a firm impression.
[5] Although most early models had wide bypass rules – e.g., “leads with VP title at this list of key accounts will get passed directly to reps for qualification” – reflecting a lack of trust in marketing beyond dropping leaflets from airplanes.
[6] That problem could still exist at hand-off (i.e., opportunity creation) time but at least we have combed through the leads to find the good ones, and reports can easily identify overloaded reps.
[7] While they may be process-oriented when it comes to the sales process for a deal moving across stages during a quarter, that is not quite the same thing as a velocity mentality driven by daily or weekly goals with tracking metrics. If you will, there’s process-oriented and Process-Oriented.
[8] One simple test:
if your sales org doesn’t have monthly cadence (e.g., goals, forecasts)
then your sales VP is probably not capital P process-oriented.
[9] On the theory you should always build organizations where people can learn from their managers.
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 [1] 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 [2].
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 [3]”. 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 [4]. I think the ASC 606 approach makes a lot of sense for more mature companies, which have a history of making these deals work [5].
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 [6] [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 [8]. 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 [9]) to the company and when you tier your customers for customer support/success purposes you might do so by TCV as opposed to ARR [10]. 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 [11]. 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 [12] 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 [13]. Think: “hey, if you wanted to get paid on a three-year $3M ARR deal, then you should have brought me one of those [14].”
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 [14]. 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.
[1] I am not an accountant. I’m a former CEO and strategic marketer who’s pretty good at finance.
[2] 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.)
[3] 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.
[4] Which I define as all the future revenue over time if every contract played out until its end.
[5] Ergo, you have high empirical confidence that you are going to get all the revenue in the contract over time.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.)
[11] 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.
[12] In the event your compensation plan offers a kicker for multi-year contracts.
[13] 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.
[14] 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, FloQast, GainSight, Hex, Logikcull, MongoDB, Pigment, Recorded Future, and Tableau.
I currently serve on the boards of Cyber Guru (cybersecurity training) and Scoro (work management).
I previously served on boards including Alation (data intelligence), Aster Data (big data), Granular (agtech), Nuxeo (content services), Profisee (MDM), and SMA Technologies (workload automation).
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