Category Archives: Metrics

Video of My SaaStr 2020 Presentation: Churn is Dead, Long Live Net Dollar Retention

Thanks to everyone who attended my SaaStr 2020 presentation and thanks to those who provided me with great feedback and questions on the content of the session.  The slides from the presentation are available here.  The purpose of this post is to share the video of the session, courtesy of the folks at SaaStr.  Enjoy!

 

Appearance on the CFO Bookshelf Podcast with Mark Gandy

Just a quick post to highlight a recent interview I did on the CFO Bookshelf podcast with Mark Gandy.  The podcast episode, entitled Dave Kellogg Address The Rule of 40, EPM, SaaS Metrics and More, reflects the fun and somewhat wandering romp we had through a bunch of interesting topics.

Among other things, we talked about:

  • Why marketing is a great perch from which to become a CEO
  • Some reasons CEOs might not want to blog (and the dangers of so doing)
  • A discussion of the EPM market today
  • A discussion of BI and visualization, particularly as it relates to EPM
  • The Rule of 40 and small businesses
  • Some of my favorite SaaS operating metrics
  • My thoughts on NPS (net promoter score)
  • Why I like driver-based modeling (and what it has in common with prime factorization)
  • Why I still believe in the “CFO as business partner” trope

You can find the episode here on the web, here on Apple Podcasts, and here on Google Podcasts.

Mark was a great host, and thanks for having me.

Are We Due for a SaaSacre?

I was playing around on the enterprise comps [1] section of Meritech‘s website today and a few of the charts I found caught my attention.  Here’s the first one, which shows the progression of the EV/NTM revenue multiple [2] for a set of 50+ high-growth SaaS companies over the past 15 or so years [3].

meritech saas multiples

While the green line (equity-value-weighted [4]) is the most dramatic, the one I gravitate to is the blue line:  the median EV/NTM revenue multiple.  Looking at the blue line, you can see that while it’s pretty volatile, eyeballing it, I’d say it normally runs in the range between 5x and 10x.  Sometimes (e.g., 2008) it can get well below 5x.  Sometimes (e.g., in 2013) it can get well above 10x.  As of the last data point in this series (7/14/20) it stood at 13.8x, down from an all-time high of 14.9x.  Only in 2013 did it get close to these levels.

If you believe in regression to the mean [5], that means you believe the multiples are due to drop back to the 5-10 range over time.  Since mean reversion can come with over-correction (e.g., 2008, 2015) it’s not outrageous to think that multiples could drop towards the middle or bottom of that range, i.e., closer to 5 than 10 [6].

Ceteris paribus, that means the potential for a 33% to 66% downside in these stocks. It also suggests that — barring structural change [7] that moves baseline multiples to a different level — the primary source of potential upside in these stocks is not continued multiple expansion, but positive NTM revenue surprises [8].

I always love Rule of 40 charts, so the next fun chart that caught my eye was this one.  meritech r40 score While this chart doesn’t speak to valuations over time, it does speak to the relationship between a company’s Rule of 40 Score and its EV/NTM revenue multiple.  Higher valuations primarily just shift the Y axis, as they have done here, uplifting the maximum Y-value by nearly three times since I last blogged about such a chart [9].  The explanatory power of the Rule of 40 in explaining valuation multiple is down since I last looked, by about half from an R-squared of 0.58 to 0.29.  Implied ARR growth alone has a higher explanatory power (0.39) than the Rule of 40.

To me, this all suggests that in these frothy times, the balance of growth and profit (which is what Rule of 40 measures) matters less than other factors, such as growth, leadership, scarcity value and hype, among others.

Finally, to come back to valuation multiples, let’s look at a metric that’s new to me, growth-adjusted EV/R multiples.

meritech r40 growth adjusted

I’ve seen growth-adjusted price/earnings ratios (i.e., PEG ratios) before, but I’ve not seen someone do the same thing with EV/R multiples.  The basic idea is to normalize for growth in looking at a multiple, such as P/E or — why not — EV/R.  For example, Coupa, trading at (a lofty) 40.8x EV/R is growing at 21%, so divide 40.8 by 21 to get 1.98x.  Zoom, by comparison looks to be similarly expensive at 38.3x EV/R but is growing at 139%, so divide 38.3 by 139 to get 0.28x, making Zoom a relative bargain when examined in this light [10].

This is a cool metric.  I like financial metrics that normalize things [11].  I’m surprised I’ve not seen someone do it to EV/R ratios before.  Here’s an interesting observation I just made using it:

  • To the extent a “cheap” PE firm might pay 4x revenues for a company growing 20%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • To the extent a “crazy” VC firm might pay 15x revenues for a company growing at 75%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • The observant reader may notice they are both paying the same ratio for growth-adjusted EV/R. Given this, perhaps the real difference isn’t that one is cheap and the other free-spending, but that they pay the same for growth while taking on very different risk profiles.

The other thing the observant reader will notice is that in both those pseudo-random yet nevertheless realistic examples, the professionals were paying 0.2.  The public market median today is 0.7.

See here for the original charts and data on the Meritech site.

Disclaimer:  I am not a financial analyst and do not make buy/sell recommendations.  I own positions in a wide range of public and private technology companies.  See complete disclaimers in my FAQ.

# # #

Notes 
[1] Comps = comparables.

[2] EV/NTM Revenue = enterprise value / next twelve months revenue, a so-called “forward” multiple.

[3] Per the footer, since Salesforce’s June, 2004 IPO.

[4] As are most stock indexes. See here for more.

[5] And not everybody does.  People often believe “this time it’s different” based on irrational folly, but sometimes this time really is different (e.g., structural change).  For example, software multiples have structurally increased over the past 20 years because the underlying business model changed from one-shot to recurring, ergo increasing the value of the revenue.

[6] And that’s not to mention external risk factors such as pandemic or election uncertainty.  Presumably these are already priced into the market in some way, but changes to how they are priced in could result in swings either direction.

[7] You might argue a scarcity premium for such leaders constitutes a form of structural change. I’m sure there are other arguments as well.

[8] To the extent a stock price is determined by some metric * some multiple, the price goes up either due to increasing the multiple (aka, multiple expansion) or increasing the metric (or both).

[9] While not a scientific way to look at this, the last time I blogged on a Rule of 40 chart, the Y axis topped out at 18x, with the highest data point at nearly 16x.  Here the Y axis tops out at 60x, with the highest data point just above 50x.

[10] In English, to the extent you’re paying for EV/R multiple in order to buy growth, Zoom buys you 7x more growth per EV/R point than Coupa.

[11] As an operator, I don’t like compound operational metrics because you need to un-tangle them to figure out what to fix (e.g., is a broken LTV/CAC due to LTV or CAC?), but as investor I like compound metrics as much as the next person.

 

Kellblog on SaaS Metrics, A Comprehensive Introduction Podcast

I’m pleased to announce that I was recently featured in a six-part SaaS podcast mini-series on SaaShimi hosted by Aznaur Midov, VP at PNC Technology Finance Group, a debt provider who works primarily with private equity (PE) firms for SaaS buyouts, growth capital, and recapitalizations.

Let’s talk first about the mini-series.  It’s quite a line-up:

  • A Brief History of SaaS with Phil Wainewright, co-founder of Diginomica and recognized authority on cloud computing.
  • Key SaaS Metrics with me.
  • Building a Sales Org with Jacco van der Kooij, founder and CEO of Winning by Design
  • Building a Marketing Org with my old friend Tracy Eiler, CMO at InsideView and author of Aligned to Achieve, a book on aligning sales and marketing.
  • Building a Customer Success Org with Ed Daly, SVP of Customer Success and Growth at Okta.
  • Raising Capital with my friend Bruce Cleveland, partner at Wildcat Ventures and former operational executive at Oracle and Siebel.

The series is available on RedCircle, Apple podcasts, and Spotify.

Now, let’s talk about my episode.  The first thing you’ll notice is Aznaur did the interviews live, with a high-quality rig, and you can hear it in the audio which is much higher quality than the typical podcast.

In terms of the content, Aznaur did his homework, came prepared with a great set of questions in a logical order, and you can hear that in the podcast.  His goal was to do an interview that effectively functioned as a “SaaS Metrics 101” class and I think he succeeded.

Here is a rough outline of the metrics we touched on in the 38-minute episode:

  • ARR vs. ACV (annual recurring revenue vs. annual contract value)
  • ARR vs. MRR (ARR vs. monthly recurring revenue)
  • TCV (total contract value)
  • RPO (remaining performance obligation)
  • Bookings
  • Average contract duration (ACD)
  • Customer acquisition cost
  • Customer acquisition cost (CAC) ratio
  • CAC Payback Period
  • Renewal and churn rates
  • ARR- vs. ATR-based churn rates (ATR = available to renew)
  • Compound vs. standalone metrics
  • Net dollar expansion rate (NDER)
  • Survivor bias in churn rates
  • The problem with long customer lifetimes (due to low churn rates)
  • LTV/CAC (LTV = lifetime value)
  • Net promoter score (NPS)
  • The loose correlation between NPS and renewals
  • Intent to renew
  • Billings
  • Services gross margin
  • Cash burn rate
  • The investor vs. the operator view on metrics

The First Three Slides of a SaaS Board Deck, with Company Key Metrics

I’m a SaaS metrics nut and I go to a lot of SaaS board meetings, so I’m constantly thinking about (among other things) how to produce a minimal set of metrics that holistically describe a SaaS company.  In a prior post, I made a nice one-slide metrics summary for an investor deck.  Here, I’m changing to board mode and suggesting what I view as a great set of three slides for starting a (post-quarter) board meeting, two of which are loaded with carefully-chosen metrics.

Slide 1:  The Good, The Bad, and the Ugly
The first slide (after you’ve reviewed the agenda) should be a high-level summary of the good and the bad  — with an equal number of each [1] — and should be used both to address issues in real-time and tee-up subsequent discussions of items slated to be covered later in the meeting.  I’d often have the e-staff owner of the relevant bullet provide a thirty- to sixty-second update rather than present everything myself.

slide 0The next slide should be a table of metrics.  While you may think this is an “eye chart,” I’ve never met a venture capitalist (or a CFO) who’s afraid of a table of numbers.  Most visualizations (e.g., Excel charts) have far less information density than a good table of numbers and while sometimes a picture is worth a thousand words, I recommend saving the pictures for the specific cases where they are needed [2].  By default, give me numbers.

Present in Trailing 9 Quarter Format
I always recommend presenting numbers with context, which is the thing that’s almost always missing or in short supply.  What do I mean by context? If you say we did $3,350K (see below) in new ARR in 1Q20, I don’t necessarily know if that’s good or bad.  Independent board members might sit on three to six boards, venture capitalists (VCs) might sit on a dozen.  Good with numbers or not, it’s hard to memorize 12 companies’ quarterly operating plans and historical results across one or two dozen metrics.

With a trailing nine quarter (T9Q) format, I get plenty of context.  I know we came up short of the new ARR plan because the plan % column shows we’re at 96%.  I can look back to 1Q19 and see $2,250K, so we’ve grown new ARR, nearly 50% YoY.  I can look across the row and see  a nice general progression, with only a slight down-dip from 4Q19 to 1Q20, pretty good in enterprise software. Or, I can look at the bottom of the block and see ending ARR and its growth — the two best numbers for valuing a SaaS company — are $32.6M and 42% respectively.  This format gives me two full years to compare so I can look at both sequential and year-over-year (YoY) trends, which is critical because enterprise software is a seasonal business.

What’s more, if you distribute (or keep handy during the meeting) the underlying spreadsheet, you’ll see that I did everyone the courtesy of hiding a fair bit of next-level detail with grouped rows — so we get a clean summary here, but are one-click away from answering obvious next-level questions, like how did new ARR split between new logos and upsell?

Slide 2:  Key Operating Metrics

Since annual recurring revenue (ARR) is everything in a SaaS company, this slide starts with the SaaS leaky bucket, starting ARR + new ARR – churn ARR = ending ARR.

After that, I show net new ARR, an interesting metric for a financial investor (e.g., your VCs), but somewhat less interesting as an operator.  Financially, I want to know how much the company spent on S&M to increase the “water level” in the leaky bucket by what amount [3].  As an operator, I don’t like net new ARR because it’s a compound metric that’s great for telling me there is a problem somewhere (e.g., it didn’t go up enough) but provides no value in telling me why [4].

After that, I show upsell ARR as a percent of new ARR, so we can see how much we’re selling to new vs. existing customers in a single row.  Then, I do the math for the reader on new ARR YoY growth [5].  Ultimately, we want to judge sales by how fast they are increasing the water they dump into the bucket — new ARR growth (and not net new ARR growth which mixes in how effective customer success is at preventing leakage).

The next block shows the CAC ratio, the amount the company pays in sales & marketing cost for $1 of new ARR.  Then we show the churn rate, in its toughest form — gross churn ARR divided not by the entire starting ARR pool, but only by that part which is available-to-renew (ATR) in the current period. No smoothing or anything that could hide fluctuations — after all, it’s the fluctuations we’re primarily interested in [6] [7].  We finish this customer-centric block with the number of customers and the net promoter score (NPS) of your primary buyer persona [8].

Moving to the next block we start by showing the ending period quota-carrying sales reps (QCRs) and code-writing developers (DEVs).  These are critical numbers because they are, in a sense, the two engines of the SaaS airplane and they’re often the two areas where you fall furthest behind in your hiring.  Finally, we keep track of total employees, an area where high-growth companies often fall way behind, and employee satisfaction either via NPS or an engagement score. [9]

Slide 3:  P&L and Cash Metrics

slide 3 newYour next (and final [10]) key metrics slide should include metrics from the P&L and about cash.

We start with revenue split by license vs. professional services and do the math for the reader on the mix — I think a typical enterprise SaaS company should run between 10% and 20% services revenue.  We then show gross margins on both lines of business, so we can see if our subscription margins are normal (70% to 80%) and to see if we’re losing money in services and to what extent [11].

We then show the three major opex lines as a percent of revenue, so we can see the trend and how it’s converging.  These are commonly benchmarked numbers so I’m showing them in % of revenue form in the summary, but in the underlying sheet you can ungroup to find actual dollars.

Moving to the final block, we show cashflow from operations (i.e., burn rate) as well as ending cash which, depending on your favorite metaphor is either the altimeter of the SaaS plane or the amount of oxygen left in the scuba tank.  We then show Rule of 40 Score a popular measure of balancing growth vs. profitability [12].  We conclude with CAC Payback Period, a popular compound measure among VCs, that I could have put on the operating metrics but put here because you need several P&L metrics to build it.

I encourage you to take these three slides as a starting point and make them your own, aligning with your strategy — but keeping the key ideas of what and how to present them to your board.

You can download the spreadsheet here.

# # #

Notes

[1] I do believe showing a balance is important to avoid getting labeled as having a half-empty or hall-full perspective.

[2] I am certainly not anti-visualization or anti-chart.  However, most people don’t make good ones so I’d take a table numbers over almost any chart I’ve ever seen in a board meeting.  Yes, there is a time and a place for powerful visualizations but, e.g., presenting single numbers as dials wastes space without adding value.

[3] Kind of a more demanding CAC ratio, calculated on net new ARR as opposed simply to new ARR.  For public companies you have to calculate that way because you don’t know new and churn ARR.  For private ones, I like staying pure and keeping CAC the measure of what it costs to add a $1 of ARR to the bucket, regardless of whether it stays in for a long time or quickly leaks out.

[4] Did sales have a bad quarter getting new logos, did account management fail at expansion ARR, or did customer success let too much churn leak out in the form of failed or shrinking renewals?  You can’t tell from this one number.

[5] There are a lot of judgement calls here in what math you for the reader vs. bloating the spreadsheet.  For things that split in two and add to 100% I often present only one (e.g., % upsell) because the other is trivial to calculate.  I chose to do the math on new ARR YoY growth because I think that’s the best single measure of sales effectiveness.  (Plan performance would be second, but is subject to negotiation and gaming.  Raw growth is a purer measure of performance in some sense.)

[6] Plus, if I want to smooth something, I can select sections in the underlying spreadsheet using the status bar to get averages and/or do my own calculations.  Smoothing something is way easier than un-smoothing it.

[7] Problems are hard to hide in this format anyway because churn ARR is clearly listed in the first block.

[8] Time your quarterly NPS survey so that fresh data arrives in time for your post-quarter ops reviews (aka, QBRs) and the typically-ensuing post-quarter board meeting.

[9] Taking a sort of balanced scorecard of financial, customer, and employee measures.

[10] Before handing off to the team for select departmental review, where your execs will present their own metrics.

[11] Some SaaS companies have heavily negative services gross margins, to the point where investors may want to move those expenses to another department, such as sales (ergo increasing the CAC) or subscription COGS (ergo depressing subscription margins), depending on what the services team is doing.

[12] With the underlying measures (revenue growth, free cashflow margin) available in the sheet as grouped data that’s collapsed in this view.

Does Enterprise SaaS Need a Same-Store Sales Metric?

Enterprise SaaS and retailers have more in common than you might think.

Let’s think about retailers for a minute. Retailers drive growth in two ways:

  • They open new stores
  • They increase sales at existing stores

Opening new stores is great, but it’s an expensive way to drive new sales and requires a lot of up-front investment. It’s also risky because, despite having a small army of MBAs working to determine the right locations, sometimes new locations just don’t work out. Blending the results of these two different activities can blur what’s really happening. For example, consider this company:

Things look reasonable overall, the company is growing at 17%. But when you dig deeper you see that virtually all of the growth is coming from new stores. Revenue from existing stores is virtually flat at 2%.

It’s for this reason that retailers routinely publish same-store sales in their financial results. So you can see not only overall, blended growth but also understand how much of that growth is coming from new store openings vs. increasing sales at existing stores.

Now, let’s think about enterprise software.

Enterprise software vendors drive growth in two ways:

  • They hire new salesreps
  • They increase productivity of existing salesreps

Hiring new salesreps is great, but it’s an expensive way to drive new sales and requires a lot of up-front investment. It’s also risky because, despite having a small army of MBAs working to determine the right territories, hiring profiles and interviewing process, sometimes new salesreps just don’t work out. Blending the results of these two different activities can blur what’s really happening. For example, consider this company:

If you’re feeling a certain déjà vu, you’re right. I simply copy-and-pasted the text, substituting “enterprise software vendor” for “retailer” and “salesrep” for “store.” It’s exactly the same concept.

The problem is that we, as an industry, have basically no metric that addresses it.

  • Revenue, bookings, and billings growth are all blended metrics that mix results from existing and new salespeople [1]
  • Retention and expansion rates are about cohorts, but cohorts of customers, not cohorts of salespeople [2]
  • Sales productivity is typically measured as ARR/salesrep which blends new and existing salesreps [3]
  • Sales per ramped rep, measured as ARR/ramped-rep, starts to get close, but it’s not cohort-based, few companies measure it, and those that do often calculate it wrong [4]

So what we need is a cohort-based metric that compares the productivity of reps here today with those here a year ago [5]. Unlike retail, where stores don’t really ramp [6], we need to consider ramping in defining the cohort, and thus define the year-ago cohort to include only fully-ramped reps [6].

So here’s how I define same-rep sales: sales from reps who were fully ramped a year ago and still here.

Here’s an example of presenting it:

The above table shows same-rep sales via an example where overall sales growth is good at 48%, driven by a 17% increase in same-rep sales and an 89% increase in new-rep sales. Note that enterprise software is a business largely built on the back of sales force expansion so — absent an acquisition or new product launch to put something new in sale’s proverbial bag — I view a 17% increase in same-rep sales as pretty good.

Let’s conclude by sharing a table of sales productivity metrics discussed in this post that I think provides a nice view of sales productivity as related to hiring and ramping.

The spreadsheet I used for this post is available for download, here.

# # #

Notes

[1] Billings is a public company SaaS metric and typically a proxy for bookings.

[2] See here for my thoughts on churn

[3] Public companies never release this but most public and private companies track it.

[4] By taking overall new ARR (i.e., from all reps) and dividing it by the number of ramped reps, thus blending contribution from both new and existing reps in the numerator. Plus, these are usually calculated on a snapshot (not a cohort) basis.

[5] This is not survivor-biased in my mind because I am trying to get a productivity metric. By analogy, I believe stores that closed in the interim are not included in same-store sales calculations.

[6] Or to the extent they do, it takes weeks or months, not quarters. Thus you can simply include all stores open in the year-ago cohort, even if they just opened.

[6] I am trying to avoid seeing an increase in same-rep sales due to ramping — e.g., someone who just started in the year-ago cohort will have year sales, but should increase to full productivity simply by virtue of ramping.

New ARR and CAC in Price-Ramped vs. Auto-Expanding Deals

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 rules define precisely how to take this from a GAAP revenue perspective – and with the adoption of ASC 606 even those rules are changing.  Let’s take an example from this KPMG data sheet on ASC 606 and SaaS.

(Price-Ramped) Year 1 Year 2 Year 3
Payment structure $1M $2M $3M
GAAP revenue $1M $2M $3M
GAAP unbilled deferred revenue $5M $3M $0M
ASC 606 revenue $2M $2M $2M
ASC 606 unbilled accounts receivable $1M $1M $0M
ASC 606 revenue backlog $4M $2M $0M

When I look at this is I see:

  • 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:

End-of-quarter ARR / 4 = next-quarter subscription revenue, if nothing changes [7]

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.

# # #

Notes

For more information on ASC 606 adoption, I suggest this podcast and this web page which outlines the five core principles.

[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.