Category Archives: SaaS

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.

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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.

Should Customer Success Report into the CRO or the CEO?

The CEO.  Thanks for reading.

# # #

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 churnThe 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.

The Zero-Sum Fallacy: ARR vs. Services

Some SaaS startups develop a form of zero-sum delusion early in their evolution, characterized by following set of beliefs.  Believing that:

  • A customer has a fixed budget that is 100% fungible between ARR (annual revenue revenue) and services
  • It is in the company’s best interest to turn as much of the customer’s budget as possible into ARR
  • Customers never think to budget implementation services separately from annual software licensing
  • A $25K StartFast offering that walks through a standard checklist is everything a customer needs for a successful implementation
  • If the StartFast doesn’t work, it’s not a big deal because the Customer Success team’s mission is to offer free clean-up after failed implementations
  • Since the only thing consultants do is implementations, their job title should be “Implementation Consultant”
  • Any solutions practices or offerings should be built by our partners
  • The services team should be introduced as late as possible in the sales cycle; ideally after contract signing, in order to eliminate the chance a post-sales consultant will show up, tell the customer “the truth,” and ruin a deal
  • It is impossible and/or not meaningful to create and run a separate services P&L
  • The need for services is a reflection of failure on the part of the product (even in an enterprise setting)

Zero-sum delusion typically presents with the following metrics:

  • Services being less than 10% of total company revenues
  • Services margins running in the negative 20% to negative 60% range
  • High churn on one-year deals (often 25% or higher) due to failed implementations
  • Competitors winning bigger deals both on the ARR and services side (and associated internal confusion about that)
  • Loss reports indicating that prospects believed the competition “understood our problem better” and acted “more like a partner than a vendor”

Zero-sum delusion is a serious issue for an early-stage SaaS business.  It is often acquired through excess contact with purely financial venture capitalists.  Happily, with critical thinking and by challenging assumptions, it can be overcome.

zerosum

OK, let’s switch to my normal narrative mode and discuss what’s going on here.  First, some SaaS companies deliberately run with a low set-up product, little to no services, and a customer success team that takes care of implementation issues.  Usually these companies sell inexpensive software (e.g., ARR < $25K), use a low-touch sales model, and focus on the small and medium business market [1].  If delivering such an offering is your company’s strategy then you should disregard this post.

However, if your strategy is not to be a low-touch business model disruptor, if you do deals closer to $250K than $25K, if your services attach rate [2] is closer to 10% than 40%, if you consider yourself a somewhat classic enterprise SaaS vendor — basically, if you solve big, hard problems for enterprises and expect to get paid for it — then you should read this post.

Let’s start with a story.  Back in the day at Business Objects, we did a great business grinding out a large number of relatively small (but nevertheless enterprise) deals in the $100K to $200K range.  I remember we were working a deal at a major retailer — call them SeasEdge — against MicroStrategy, a self-funded competitor bootstrapped from a consulting business.

SeasEdge was doing a business intelligence (BI) evaluation and were looking to use BI to improve operational efficiency across a wide range of retail use cases, from supply chain to catalog design.  We had a pretty formulaic sales cycle, from discovery to demo to proposal.  We had financials that Wall Street loved (e.g., high gross margins, a small services business, good sales efficiency) so that meant we ran with a high salesrep-to-SE (sales engineer) ratio and a relatively small, largely tactical professional services team. I remember hearing our sales team’s worries that we were under-servicing the account — the salesrep had a lot of other active opportunities and the SE, who was supporting more than two salesreps, was badly overloaded.  Worse yet, MicroStrategy was swarming on the account, bringing not only a salesrep and an SE but about 5 senior consultants to every meeting.  Although they were a fraction of our size, they looked bigger than we did in this account.

SeasEdge taught me the important lesson that the deal you lose is not necessarily the deal your competitor wins.  We lost a $200K query-and-reporting (Q&R) deal.  MicroStrategy won a $4M retail transformation deal.  We were in the business of banging out $200K Q&R deals so that’s what we saw when we looked at SeasEdge.  MicroStrategy, born from a consultancy, looked at SeasEdge and saw a massive software and services, retail transformation opportunity instead.

I understand this is an extreme example and I’m not suggesting your company get in the business of multi-million dollar services deals [3].  But don’t miss the key lessons either:

  • Make sure you’re selling what the customer is buying.  We were selling Q&R tools.  They were buying retail transformation.
  • People may have more money than you think.  Particularly, when there’s a major business challenge.  We saw only 5% of the eventual budget.
  • A strong professional services organization can help you win deals by allowing you to better understand, more heavily staff, appear more as a partner in, and better solve customer problems in sales opportunities.  Internalize:  a rainmaker professional services leader is pure gold in sales cycles.
  • While partners are awesome, they are not you.  Once in a while, the customer wants “one throat to choke” and if you can’t be that throat then they will likely buy from someone who can.

I call this problem zero-sum delusion because I think the root cause is a fallacy that a zero-sum trade-off exists between ARR and professional services.  The fallacy is that if a customer has only $250K to spend, we should get as much of that $250K as possible in ARR, because ARR recurs and professional services doesn’t [4].  The reality is that most customers, particularly when you’re selling to the information technology (IT) organization, are professional buyers — this isn’t their first rodeo, they know that enterprise software requires professional services, and they budget separately for it.  Moreover, they know that a three-year $250K ARR deal represents a lot of money for their company and they darn well want the project associated with that investment to be successful — and they are willing to pay to ensure that success.

If you combine the zero-sum fallacy with purely financial investors applying pressure to maximize blended gross margins [5] and the fantasy that you can somehow run a low-touch services model when that isn’t actually your company and product strategy, you end up with a full-blown case of zero-sum delusion.

Curing the Zero-Sum Delusion

If your organization has this problem, here are some steps you can take to fix it.

  • Convince yourself it’s not zero sum.  Interview customers.  Look at competitors.  Look at you budget in your own company.  Talk to consultants who help customers buy and implement software.  When you do, you will realize that customers know that enterprise software requires services and they budget accordingly.  You’ll also understand that customers will happily pay to increase the odds of project success; buying quality services is, in effect, an insurance policy on the customer’s job [6].
  • Change your negotiation approach.  If you think it’s zero sum, you’ll create a self-fulfilling prophecy in negotiation.  Don’t frame the problem as zero sum.  Negotiate ARR first, then treat that as fixed.  Add the required services on top, negotiating services not as a zero-sum budget trade-off against ARR, but as a function of the amount of work they want done.  I’ve won deals precisely because we proposed twice the services as our competition because the customer saw we actually wanted to solve their problem, and not just low-ball them on services to sell subscription.
  • Change sales’ mental math.  If you pay salesreps 12% on ARR and 2% on services, if your reps have zero-sum delusion they will see a $250K ARR, $100K services deal as $5K to $10K in lost commission [7].  Per the prior point we want them to see this as a $30K ARR commission opportunity with some services commissions on top — and the higher the services commissions the higher the chance for downstream upsell.  Moreover, once they really get it, they see a 50% chance of winning a 250/25 deal, but a 80% chance of winning a 250/100 deal.  An increase in expected value by over $10K.
  • Put a partner-level, rainmaker leader in charge of your services organization and each region of it.  The lawyer who makes partner isn’t the one with the best legal knowledge; it’s the one with the biggest book of business.  Adopt that mentality and run your services business like, well, a services business.
  • Create a services P&L and let your VP of Services fully manage it.  They will know to get more bookings when the forecast is light. They will increase hiring into a heavy forecast and cut weak performers into a light forecast.  They know how to do this.  Let them.
  • Set your professional services gross margin target at 5-10%.  As an independent business it can easily run in the 30-40% range. As a SaaS adjunct you want services to have time to help sales, time to help broken customers (helping renewals), time to enable partners, and the ability to be agile.  All that costs you some margin.  The mission should be to maximize ARR while not losing money.
  • Constrain services to no more than 20% of revenue.  This limits the blended gross margin impact, is usually fine with the board, keeps you well away from the line where people say “it’s really a services firm,” usually leaves plenty of room for a services partner ecosystem, and most importantly, creates artificial scarcity that will force you to be mindful about where to put your services team versus where to put a partner’s.
  • Force sales to engage with services earlier in the sales cycle.  This is hard and requires trust.  It also requires that the services folks are ready for it.  So wait until the rainmakers in charge have trained, retrained, or cleared people and then begin.  It doesn’t take but a few screw-ups to break the whole process so make sure services understand that they are not on the sales prevention team, but on the solving customer problems team.  When this is working, the customer buys because both the VP of Sales, and more importantly, the VP of Services looked them in the eye and said, “we will make you successful” [8].
  • Outplace any consultant who thinks their mission is “tell the truth” and not help sales.  Nobody’s saying that people should lie, but there is a breed of curmudgeon who loves to “half empty” everything and does so in the name of “telling the truth.”  In reality, they’re telling the truth in the most negative way possible and, if they want to do that, and if they think that helps their credibility, they should go work at independent services firm [9].  You can help them do that.
  • Under no circumstances create a separate services sales team — i.e., hire separate salespeople just to sell services [10]. The margins don’t support it and it’s unnecessary.  If you have strong overall and regional leadership, if those leaders are rainmakers as they should be, then there is absolutely zero reason to hire separate staff to sell services.

# # #

Notes

[1] Yes, they can eventually be enterprise disruptors by bringing this low-touch, cheap-and-cheerful approach to the enterprise (e.g., Zendesk), but that’s not the purpose of this post.

[2] Services attach rate is the ratio of professional services to ARR in a new booking.  For example, if you sell $50K of services as part of a $500K ARR deal, then your attach rate is 10%.

[3] We had neither that staffing levels nor the right kind of consultants to even propose, let alone take on, such an engagement.  The better strategy for us would have been to run behind a Big 4 systems integrator bidding who included our software in their proposal.

[4] Sales compensation plans typically reinforce this as well.  Remediating that is hard and beyond the scope of this post, but at least be aware of the problem.

[5] At the potential expense of maximizing ARR — which should be the point.

[6] If you think from the customer’s perspective.  Their job is to make sure projects succeed.  Bad things sometimes happen when they don’t.

[7] On the theory that the perfect deal, compensation wide, is 100% ARR.  Math wise, 0.12*250+0.02*100 = $32K whereas 0.12*350+0.02*0 = $42K.  More realistically, if they could have held services to $50K, you’d get 0.12*300+0.02*50 = $37K.  Note that this way of thinking is zero-sum and ignores the chance you can expand services while holding ARR constant.

[8] And, no offense, they believed the latter more than the former.  And they know the latter is the person on the hook to make it happen.

[9] Oh, but they want the stock-options upside of working at a vendor!  If that’s true, then they need to get on board and help maximize ARR while, yes, still telling the truth but in a positive way.

[10] Wanting to do so is actually a symptom of advanced zero-sum delusion.

How Startup CEOs Should Think About the Coronavirus, Part II

[Updated 3/10 12:09]

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.

How to Make and Use a Proper Sales Bookings Productivity and Quota Capacity Model

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 [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.

Why Every Startup Needs an Inverted Demand Generation Funnel, Part I

Does my company spend too much on marketing? Too little? How I do know? What is the right level of marketing spend at an enterprise software startup? I get asked these questions all the time by startup CEOs, CMOs, marketing VPs, and marketing directors.

You can turn to financial benchmarks, like the KeyBanc Annual SaaS Survey for some great high-level answers. You can subscribe to SiriusDecisions for best practices and survey data. Or you can buy detailed benchmark data [1] from OPEXEngine. These are all great sources and I recommend them heartily to anyone who can afford them.

But, in addition to sometimes being too high-level [2], there is one key problem with all these forms of benchmark data: they’re not about you. They’re not based on your operating history. While I certainly recommend that executives know their relevant financial benchmarks, there’s a difference between knowing what’s typical for the industry and what’s typical for you.

So, if you want to know if your company is spending enough on marketing [3], the first thing you should do is to make an inverted demand generation (aka, demandgen) funnel to figure out if you’re spending enough on demandgen. It’s quite simple and I’m frankly surprised how few folks take the time to do it.

Here’s an inverted demandgen funnel in its simplest form:

Inverted demandgen funnel

Let’s walk through the model. Note that all orange cells are drivers (inputs) and the white cells are calculations (outputs). This model assumes a steady-state situation [4] where the company’s new ARR target is $2,000,000 each quarter. From there, we simply walk up the funnel using historical deal sizes and conversion rates [5].

  • With an average sales price (ASP) of $75,000, the company needs to close 27 opportunities each quarter.
  • With a 20% sales qualified lead (SQL) to close rate we will need 133 SQLs per quarter.
  • If marketing is responsible for generating 80% of the sales pipeline, then marketing will need to generate 107 of those SQLs.
  • If our sales development representatives (SDRs) can output 2.5 opportunities per week then we will need 5 SDRs (rounding up).
  • With an 80% SAL to SQL conversion rate we will need 133 SALs per quarter.
  • With a 10% MQL to SAL conversion rate we will need 1,333 MQLs per quarter.
  • With a cost of $250 per MQL, we will need a demandgen budget [6] of $333,333 per quarter.

The world’s simplest way to calculate the overall marketing budget at this point would be to annualize demandgen to $1.3M and then double it, assuming the traditional 50/50 people/programs ratio [7].

Not accounting for phase lag or growth (which will be the subjects of part II and part III of this post), let’s improve our inverted funnel by adding benchmark and historical data.

Let’s look at what’s changed. I’ve added two columns, one with 2019 actuals and one with benchmark data from our favorite source. I’ve left the $2M target in both columns because I want to compare funnels to see what it would take to generate $2M using either last year’s or our benchmark’s conversion rates. Because I didn’t want to change the orange indicators (of driver cells) in the left column, when we have deviations from the benchmark I color-coded the benchmark column instead. While our projected 20% SQL-to-close rate is an improvement from the 18% rate in 2019, we are still well below the benchmark figure of 25% — hence I coded the benchmark red to indicate a problem in this row. Our 10% MQL-to-SQL conversion rate in the 2020 budget is a little below the benchmark figure of 12%, so I coded it yellow. Our $250 cost/MQL is well below the benchmark figure of $325 so I coded it green.

Finally, I added a row to show the relative efficiency improvement of the proposed 2020 budget compared to last year’s actuals and the benchmark. This is critical — this is the proof that marketing is raising the bar on itself and committed to efficiency improvement in the coming year. While our proposed funnel is overall 13% more efficient than the 2019 funnel, we still have work to do over the next few years because we are 23% less efficient than we would be if we were at the benchmark on all rates.

However, because we can’t count on fixing everything at once, we are taking a conservative approach where we show material improvement over last year’s actuals, but not overnight convergence to the benchmark — which could take us from kaizen-land to fantasy-land and result in a critical pipeline shortage downstream.

Moreover because this approach shows not only a 13% overall efficiency improvement but precisely where you expect it to come from, the CEO can challenge sales and marketing leadership:

  • Why are we expecting to increase our ASP by $5K to $75K?
  • Why do you think we can improve the SQL-to-close rate from 18% to 20% — and what you are doing to drive that improvement? [8]
  • What are we doing to improve the MQL-to-SAL conversion rate?
  • How are we going to improve our already excellent cost per MQL by $25?

In part II and part III of this post, we’ll discuss two ways of modeling phase-lag, modeling growth, and the separation of the new business and upsell funnels.

You can download my spreadsheet for this post, here.

Notes

[1] For marketing or virtually anything else.

[2] i.e., looking at either S&M aggregated or even marketing overall.

[3] The other two pillars of marketing are product marketing and communications. The high-level benchmarks can help you analyze spend on these two areas by subtracting your calculated demandgen budget from the total marketing budget suggested by a benchmark to see “what’s left” for the other two pillars. Caution: sometimes that result is negative!

[4] The astute reader will instantly see two problems: (a) phase-lag introduced by both the lead maturation (name to MQL) and sales (SQL to close) cycles and (b) growth. That is, in a normal high-growth startup, you need enough leads not to generate this quarter’s new ARR target but the target 3-4 quarters out, which is likely to be significantly larger. Assuming a steady-state situation gets rid of both these problems and simplifies the model. See part II and part III of this post for how I like to manage that added real-world complexity.

[5] Hint: if you’re not tracking these rates, the first good thing about this model is that it will force you to do so.

[6] When I say demandgen budget, I mean money spent on generating leads through marketing campaigns. Sometimes that very directly (e.g., adwords). Other times it’s a bit indirectly (e.g., an SEO program). I do not include demandgen staff because I am trying to calculate the marginal cost of generating an extra MQL. That is, I’m not trying to calculate what the company spends, in total, on demandgen activities (which would include salary, benefits, stock-based comp, etc. for demandgen staff) but instead the marketing programs cost to generate a lead (e.g., in case we need to figure out how much to budget to generate 200 more of them).

[7] In an increasingly tech-heavy world where marketing needs to invest a lot in infrastructure as well, I have adapted the traditional 50/50 people/programs rule to a more modern 45/45/10 people/programs/infrastructure rule, or even an infrastructure-heavy split of 40/40/20.

[8] Better closing tools, an ROI calculator, or a new sales training program could all be valid explanations for assuming an improved close rate.