The Two Engines of SaaS: QCRs and DEVs

I remember one day, years ago, when I was a VP at $10M startup and Larry, the head of sales, came in one day handing out t-shirts that said:

“Code, sell, or get out of the way.”

Neither I, nor the rest of marketing team, took this particularly well because the shirt obviously devalued the contributions of F&A, HR, and marketing.  But, ever seeking objectivity, I did concede that the shirt had a certain commonsense appeal.  If you could only hire one person at a startup, it would be someone to write the product.  And if you could only hire one more, it would be someone to sell it.

This became yet another event that reconfirmed my belief in my “marketing exists to make sales easier” mantra.  After all, if you’re not coding or selling, at least you can help someone who is.

Over time, Larry’s t-shirt morphed in my mind into a new mantra:

“A SaaS company is a two-engine plane.  The left engine is DEVs.  The right is QCRs.”

QCR meaning quota-carrying (sales) representative and DEV meaning developer (or, for symmetry and emphasis, storypoint-burning developer).  People who sell with truly incremental quota, and people who write code and burndown storypoints in the process.

It’s a much nicer way of saying “code, sell, or get out of the way,” but it’s basically the same idea.  And it’s true.  While Larry was coming from a largely incorrect “protest overhead and process” viewpoint, I’m coming from a different one:  hiring.

The two hardest lines in a company headcount plan to keep at-plan are guess which two?  QCRs and DEVs.  Forget other departments for a minute — I’m saying is the the hardest line for the VP of Engineering to stay fully staffed on is DEVs, and the hardest line for the VP of Sales to stay fully staffed on is QCRs.

Why is this?

  • They are two, critical highly in-demand positions, so the market is inherently tight.
  • Given their importance, the hiring VPs can be gun-shy about making mistakes and lose candidates due to hesitation or indecision.
  • Both come with a short-term tax and mid-term payoff because on-boarding new hires slows down the rest of the team, a possible source of passive resistance.
  • Sales managers dislike splitting territories because it makes them unpopular, which could drive more foot-dragging.
  • It’s just plain easier to find the associated support functions — (e.g,. program managers, QA engineers, techops, salesops, sales productivity, overlays, CSMs, managers in general) than it is find the QCRs and DEVs.

Let me be clear:  this is not to say that all the supporting functions within sales and engineering do not add value, nor is this to say that supporting corporate functions beyond sales and engineering do not add value — it is to say, however, that far too often companies take their eye off the ball and staff the support functions before, not after, those they are supporting.  That’s a mistake.

What happens if you manage this poorly?  On the sales side, for example, you end up with an organization that has 1 SVP of Sales, 1 VP of sales consulting, 4 sales consultants, 1 director of sales ops, 1 director of sales productivity, 1 manager of sales development reps (SDRs), 4 SDRs, an executive assistant, and 4 quota-carrying salespeople.  So only 22% of the people in your sales organization actually carry a quota.

“Uh, other than QCRs, we’re doing great on sales hiring,”  says the sales VP.  “Other than that, Mrs. Lincoln, how did you find the play?” thinks the board.

Because I’ve seen this happen so often, and because I’ve seen companies accused of it both rightfully and unjustly, I’d decided to create two new metrics:

  • QCR density = number of QCRs / total sales headcount
  • DEV density = numbers of DEVs / total engineering headcount

The bad news is I don’t have a lot of benchmark data to share here.  In my experience, both numbers want to run in the 40% range.

The good news is that if you run a ratio-driven staffing model (which you should do for both sales and engineering), you should be able to calculate what these densities should be when you are fully staffed.

Let’s conclude with a simple model that does just that on the sales side, producing a result in the 38% to 46% range.

qcr dens

Finally, let me add that having such a model helps you understand whether, for example, your QCR density is low due to slow QCR hiring (and/or bad retention) against a good model, or on-pace hiring against a “fat” model.  The former is an execution problem, the latter is a problem with your model.

Talking Competition: Methinks Thou Doth Protest Too Much.

From time to time marketers and executives need to talk about the competition with those outside the company, including analysts, partners, and prospective investors.  In this post, we’ll cover my 4 rules for this type of communication.

Be Consistent. 
The biggest mistake people make is inconsistency, often because they’re trying to downplay a certain competitor.   Example:

“Oh, TechMo.  No, we never see them.  They’re like nowhere.  And you know their technology is really non-scaleable because it runs out of address space in the Java virtual machine.  And their list-based engine doesn’t scale because it didn’t scale when the same three founders, Mo, Larry, and Curly, did their last startup which used primarily the same idea.  And while I know they’re up to 150 employees, they must be in trouble because in the past 6 months they’ve lost their VP of Sales, Jon Smith, and their VP of Product Management, Paula Sands, and that new appexchange-like thing they launched last week, with 37 solutions, well it’s a not real either because 15 of the 37 solutions aren’t even built by partners, and they’re more prototypes than applications, and — another thing — I heard that TechMo World last week in Vegas had only 400 attendees and customers didn’t react well to the announcement they made about vertical strategy.  Yes, TechMo’s nobody to us.  We hardly ever see them.”

— Would-Be Dismissive Product Marketer.

What’s the one thing the listener is thinking on hearing all this?

“Holy Cow, these guys are tracking TechMo’s every move.  They sure know a lot about somebody they supposedly never see.”

Or, in other words, “the lady doth protest too much, methinks.”  (Hamlet.)

Don’t be this person.  Stay credible.  Be consistent.  If you’re going to be dismissive of someone, dismiss them.  But don’t try to dismiss them, then bleed fear and guilt all over the audience.  Line up your words, your attitude, and your behavior.

Cede, But Cede Carefully.
Some people say never cede anything at all, but I think that’s dangerous, particularly when dealing with sophisticated audiences like industry analysts, prospective investors, or channel partners (who work in the field every day).

I think ceding builds your credibility, but you need to be careful and precise in so doing so.  To take an old example, from BusinessObjects days:

  • Bad/sloppy:  Brio is doing pretty well.
  • Good/careful:  Brio is doing pretty well — in the USA, with companies where the end-users have a strong voice in the process, and they prioritize UI over security and administration.

It’s called positioning for a reason.  You’re supposed to be able to say what you do well, what your competitors do well, and what the difference is.  If you just go on singing “anything you can do I can do better, I can do anything better than you,” then you’re not going to build much credibility with your audience.

  • Bad/sloppy:  Competitor X seems to have some traction in the market.
  • Good/careful:  Competitor X is appearing in high-end deals, has a “fake cloud” offering, and competes well against entrenched Oracle product Y.

Don’t give competitor X an ounce more than they deserve and don’t forget to point out their limitations along the way.  When it comes to credit, give it where due, but be stingy — don’t give a drop more.

This will build your credibility in being reasonably objective.  More important, it also forces you to build some positioning.  As long you are claiming universal superiority — that no one will believe — you’re letting yourself off the hook for doing your job, in building credible positioning.

Keep Your Facts Straight
Be sure of what you say.  It’s far better to say less and be correct than to add just one more point you’re not sure of and get quickly contradicted.  Why?  Because your credibility is now in question as are all your other assertions — even the correct ones.

If you’re sure about something, then say it.  If you’re not sure but think it’s probable then weasel-word it — “we’re hearing,” “I heard from customers that,” “you can see several reviews on Glassdoor where former employees say,” or simply “we think.”  But don’t assert something as fact unless you are sure it is and you’re ready to defend it.

Read the Audience to Avoid the Blindside Hit
I warn every marketer and product manager I know about the blindside hit.  When you’re doing a briefing with hardened industry analyst on a market they’ve covered for 20 years, you’re as vulnerable to a blindside hit as an NFL quarterback.

You make some assertions, and you’re feeling good.  But you stop paying attention to the audience.  You don’t notice the body language showing that they’re not buying it anymore.  You don’t read the warning signs.  You miss the building tension in their voice.   You don’t know that the vendor you’re attacking is the analyst’s favorite and they just had a big steak dinner at the roadshow they did last week in Cleveland.

And then you make one too many false claims and then like a safety on a blitz, the analyst sees a hole in the offensive line, accelerates through it, and hits you in the back at full speed.  BOOM.  You awake a few minutes later and discover you’re strapped to a stretcher with a neck collar on and the CMO and the analyst relations director are carrying you out of the meeting.

“Sorry, Brian got a little ahead of himself, there.  Bob will take it from here.”

quarterback blindside hit

Product marketer carried out of industry analyst briefing. Don’t let this be you.


“Always Scrubbing the Pipeline” Means “Never Scrubbing the Pipeline.”

Perhaps you’ve seen this movie:

CEO:  “Wow the quarterly pipeline dropped 20% this week.  What’s going on sales VP?”

Sales VP:  “Well, that’s because we cleaned it up this week.”

CEO:  “That sounds great, but you said that last week.”

VP of Sales: “Well, that’s because we scrubbed it then, too.”

CEO:  “So shouldn’t it have been clean after last week’s cleaning?  Why did it require so much more cleaning that it dropped another 20% this week.”

VP of Sales:  “Well, you know it’s a big job and you can’t clean up the whole pipeline in a week.”

CEO:  “Should I expect it to drop another 20% next week?”

VP of Sales:  “Uh.”

CEO:  “Soon you’re going to say that we don’t have enough to make our numbers.”

VP of Sales:  “Well, I did mean to mention that I’ve been thinking of cutting the forecast because we just don’t have enough opportunities to work on.”

CEO:  “But we started the quarter with 3.2x pipeline coverage, shouldn’t that be enough?”

VP of Sales:  “Normally, yes.  But the pipeline wasn’t really clean.  Some of those opportunities weren’t real opportunities.” [1]

CEO:  “What does ‘clean’ mean?  When does it get clean?  Once clean, how long does it stay clean.”

VP of Sales:  “Well, look our view here is that we should always be scrubbing, so we’re constantly scrubbing the pipeline, always finding new things.”

What’s wrong with this conversation?  A lot. This Sales VP:

  • Has no clear definition of a scrubbed pipeline.
  • Has no process for scrubbing the pipeline.
  • Takes no accountability for the pipeline and its quality.

In my experience, the statement “we always scrub the pipeline” means precisely one thing:  “we never scrub the pipeline.”

Should that matter?  Well, using some quick assumptions [2], the average first-line enterprise sales manager is managing pipeline that cost $50,000 to generate per rep, so if they’re managing 6-8 reps they are managing pipeline that cost the company $300,000 – $400,000.  Sales managers need to manage that pipeline.  The way to manage it is through periodic, disciplined scrubs [3].

Now some managers don’t play the “always scrubbing” card.  Instead, they say “we scrub the pipeline every week on my sales forecast call.”  But once understand what a pipeline scrub looks like and remember the purpose of a forecast call [4], you realize that it’s impossible to do both at once.

How to Properly Scrub the Pipeline

While everyone will want to take their own unique angle on how to approach this, the core of a pipeline scrub is to review all the opportunities (this quarter and out quarters) in every sales rep’s pipeline to ensure that they are classified correctly with respect to:

  • Close date (which determines what quarter pipeline it’s in)
  • Stage (along a series of well defined and verifiable stages)
  • Forecast category (e.g., forecast, commit, upside)
  • Value (following specific rules about how and when to value opportunities)

These rules should be documented in a living document called something like Pipeline Management Rules (PMR) to which managers should refer during the pipeline scrub (e.g., “Jimmy, tell me what’s the rule for picking a close date in the PMR document”).

The other important thing about pipeline scrubs is timing, because pipeline scrubs will affect your sales analytics (e.g., pipeline coverage ratios, pipeline conversion rates, stage- and forecast-category weighted expected values).  Ergo, I picked a few fixed weeks per quarter (weeks 3, 6, and 9) to present scrubbed pipeline and then we typically use the week 3 snapshot for most of our early-quarter pipeline analytics [5].

The goal of the pipeline scrub is to ensure that the entire pipeline is fairly represented with respect to those rules.  By following this disciplined procedure you can ensure that your sales forecasting and analytics are not a castle built on a sand foundation, but an edifice built on bedrock.


[1] If you haven’t gone insane yet, this one should push you over.  Wait, whose job it is to accept opportunities into the pipeline?  Sales!  Once an opportunity gets into what’s known as either “stage 2” or “sales accepted lead” status, sales doesn’t get to play that card.  This represents a total failure to accept accountability.

[2] 10 this-quarter and 10 out-quarter opportunities per rep * $2,500 mean cost per opportunity = $50,000.

[3]  I am not arguing that you can’t also clean up opportunities along the way, but that needs to be a supplement to, not a substitute for, a proper pipeline scrubbing process.

[4] A forecast call is usually focused on the current quarter and on the opportunities that are expected to close in order to make the forecast.  Thus, low-probability and out-quarter opportunities are easily overlooked.

[5] Implying of course that sales perform the scrubs during weeks 2, 5, and 8 so the resulted can be presented on Monday morning of weeks 3, 6, and 9.

Bookings vs. Billings in a SaaS Company

Financial analysts speak a lot about “billings” in a public SaaS companies, but in private VC-backed SaaS companies, you rarely hear discussion of this metric.  In this post, we’ll use a model of a private SaaS company (where we know all the internal metrics), to show how financial analysts use rules of thumb to estimate and/or impute internal SaaS metrics using external ones – and to see what can go wrong in that process.

For reference, here’s an example of sell-side financial analyst research on a public SaaS company that talks about billings [1].


Let’s start with a quick model that builds up a SaaS company from scratch [1].  To simplify the model we assume all deals (both new and renewal) are for one year only and are booked on the last day of the quarter (so zero revenue is ever recognized in-quarter from a deal).  This also means next-quarter’s revenue is this-quarter’s ending annual recurring revenue (ARR) divided by 4.


Available to renew (ATR) is total subscription bookings (new and renewal) from four quarters prior.  Renew bookings are ATR * (1 – churn rate).  The trickiest part of this model is the deferred revenue (DR) waterfall where we need to remember that the total deferred revenue balance is the sum of DR leftover from the current and the prior three quarters.

If you’re not convinced the model is working and/or want to play with it, you can download it, then see how things work by setting some drivers to boundary conditions (e.g., churn to 0%, QoQ sales growth to 0, or setting starting ARR to some fixed number [2]).

 The Fun Part:  Imputing Internal Metrics from External Ones

Now that we know what’s going on the inside, let’s look in from the outside [3]:

  • All public SaaS companies release subscription revenues [4]
  • All public SaaS companies release deferred revenues (i.e., on the balance sheet)
  • Few SaaS companies directly release ARR
  • Few SaaS companies release ATR churn rates, favoring cohort retention rates where upsell both masks and typically exceeds churn [5]

It wasn’t that long ago when a key reason for moving towards the SaaS business model was that SaaS smoothed revenues relative to the all-up-front, lumpy on-premises model.  If we could smooth out some of that volatility then we could present better software companies to Wall Street.  So the industry did [6], and the result?  Wall Street immediately sought a way to look through the smoothing and see what’s really going on in the inherently lumpy, backloaded world of enterprise software sales.

Enter billings, the best answer they could find to do this.  Billings is defined as revenue plus change in deferred revenue for a period.  Conceptually, when a SaaS order with a one-year prepayment term is signed, 100% of it goes to deferred revenue and is burned down 1/12th every month after that.  To make it simple, imagine a SaaS company sells nothing in a quarter:  revenue will burn down by 1/4th of starting deferred revenue [7] and the change in deferred revenue will equal revenue – thus revenue plus change in deferred revenue equals zero.  Now imagine the company took an order for $50K on the last day of the quarter.  Revenue from that order will be $0, change in deferred will be +$50K, implying new sales of $50K [8].

Eureka!  We can see inside the SaaS machine.  But we can’t.

Limitations of Billings as a SaaS Metric

If you want to know what investors really care about when it comes to SaaS metrics, ask the VC and PE folks who get to see everything and don’t have to impute outside-in.  They care about

Of those, public company investors only get a clear look at subscription gross margins and the customer acquisition cost (CAC) ratio.  So, looking outside-in, you can figure out how efficiency a company runs its SaaS service and how efficiently it acquires customers [9].

But you typically can’t get a handle on churn, so you can’t calculate LTV/CAC or CAC Payback Period.  Published cohort growth, however, can give you comfort around potential churn issues.

But you can’t get a precise handle on sales growth – and that’s a huge issue as sales growth is the number one driver of SaaS company valuation [10].  That’s where billings comes into play.  Billings isn’t perfect because it shows what I call “total subscription bookings” (new ARR bookings plus renewal bookings) [11], so we can’t tell the difference between a good sales and weak renewals quarter and a bad sales and a good renewals quarter.

Moreover, billings has two other key weaknesses as a metric:

  • Billings is dependent on prepaid contract duration
  • Companies can defer processing orders (e.g., during crunch time at quarter’s end, particularly if they are already at plan) thus making them invisible even from a billings perspective [12]

Let’s examine how billings depends on contract duration.  Imagine it’s the last day of new SaaS company’s first quarter.  The customer offers to pay the company:

  • 100 units for a prepaid one-year subscription
  • 200 units for a prepaid two-year subscription
  • 300 units for a prepaid three-year subscription

From an ARR perspective, each of the three possible structures represents 100 units of ARR [13].  However, from a deferred revenue perspective, they look like 100, 200, 300 units, respectively.  Worse yet, looking solely at deferred revenue at the end of the quarter, you can’t know if 300 units represents three 100-unit one-year prepay customers or a single 100-unit ARR customer who’s done a three-year prepay.

In fact, when I was at Salesforce we had the opposite thing happen.  Small and medium businesses were having a tough time in 2012 and many customers who’d historically renewed on one-year payment cycles started asking for bi-annual payments.  Lacking enough controls around a rarely-used payment option, a small wave of customers asked for and got these terms.  They were happy customers.  They were renewing their contracts, but from a deferred revenue perspective, suddenly someone who looked like 100 units of deferred revenue for an end-of-quarter renewal suddenly looked 50.  When Wall St. saw the resultant less-than-expected deferred revenue (and ergo less-than-expected billings), they assumed it meant slower new sales.  In fact, it meant easier payment terms on renewals – a misread on the business situation made possible by the limitations of the metric.

This issue only gets more complex when a company is enabling some varying mix of one through five year deals combined with partial up-front payments (e.g., a five-year contract with years 1-3 paid up front, but years 4 and 5 paid annually).  This starts to make it really hard to know what’s in deferred revenue and to try and use billings as a metric.

Let’s close with an excerpt from the Zuora S-1 on billings that echoes many of the points I’ve made above.



[1] Source:  William Blair, Inc., Zendesk Strong Start to 2018 by Bhavan Suri.

[2] Even though it’s not labelled as a driver and will break the renewals calculations, implicitly assuming all of it renews one year later (and is not spread over quarters in anyway).

[3] I’m not a financial analyst so I’m not the best person to declare which metrics are most typically released by public companies, so my data is somewhat anecdotal.  Since I do try to read interesting S-1s as they go by, I’m probably biased towards companies that have recently filed to go public.

[4] As distinct from services revenues.

[5] Sometimes, however, those rates are survivor biased.

[6] And it worked to the extent that from a valuation perspective, a dollar of SaaS revenue is equivalent to $2 to $4 of on-premises revenue.  Because it’s less volatile, SaaS revenue is more valuable than on-premises revenue.

[7] Provided no customers expire before the last day of the quarter

[8] Now imagine that order happens on some day other than the last day of the quarter.  Some piece, X, will be taken as revenue during the quarter and 50 – X will show up in deferred revenue.  So revenue plus change in deferred revenue = it’s baseline + X + 50 – X = baseline + 50.

[9] Though not with the same clarity VCs can see it — VCs can see composition of new ARR (upsell vs. new business) and sales customers (new customer acquisition vs. customer success) and thus can create more precise metrics.  For example, a company that has a solid overall CAC ratio may be revealed to have expensive new business acquisition costs offset by high, low-cost upsell.

[10] You can see subscription revenue growth, but that is smoothed/damped, and we want a faster way to get the equivalent of New ARR growth – what has sales done for us lately?

[11] It is useful from a cash forecasting perspective because all those subscription billings should be collectible within 30-60 days.

[12] Moving the deferred revenue impact of one or more orders from Q(n) to Q(n+1) in what we might have called “backlogging” back in the day.  While revenue is unaffected in the SaaS case, the DR picture looks different as a backlogged order won’t appear in DR until the end of Q(n+1) and then at 75, not 100, units.

[13] Normally, in real life, they would ask a small discount in return for the prepay, e.g., offer 190 for two years or 270 for three years.  I’ll ignore that for now to keep it simple.

Write Actionable Emails! (aka, If You’re Going to Make a Proposal, Make One)

As CEO of a company, I can’t tell you the number of times, I get emails like this:


I know our policy is that we don’t pay both the salesreps their high-rate commissions on low-profit, one-of items, but we ended up doing a $50K/year pass-along storage fee for Acme, because they are managing a huge amount of data.  Because it recurs we’re considering it ARR at the corporate level.  The rep is OK because he is being paid well on the rest of the $500K deal, but I worry that the sales managers and sales consultants who also get paid on new ARR bookings won’t get 100% of their payout if we don’t pay them on this – can we please do that?


I find this email a non-actionable, incomplete proposal better suited for a philosophy class than a business discussion.  The mail does ask for approval, so you might think it’s actionable – but is it really?  What’s missing?  Three things.

  • A complete, concrete proposal: taking everything into account – all groups, any existing relevant policies, and any relevant precedent — what do you want to do?  Suppose the SDRs are also paid on total bookings, have you simply overlooked them and will be back asking for more once you’ve figured that out or are you saying you don’t want to pay them like the sales managers and SCs?
  • Numbers: what’s it going to cost the company?  First principles are fine, but you must translate them into recommended actions and identified costs.  I don’t mind back-of-the-envelope calculations, but I do need to be sure you’ve included everything in your analysis.  If the issue is complex or expensive, then I’d want a well thought-out and clearly documented spreadsheet cost analysis.  I get the qualitative arguments, but if you are just giving me passion and philosophy with no idea of what it’s going to cost, then I have no way of answering.
  • One or more alternatives:  if I don’t want to approve your primary proposal, do you have a preferred backup?  What is your plan B and what would it cost the company and why do you prefer plan A to it?
  • Bonus: a proposal to change existing polices so this situation won’t be ambiguous in the future and require another escalation.

So, let’s re-craft this email into something I’d rather receive:


Per our policy we didn’t payout the salesrep on the $50K of ARR we took as a pass-along storage fee on the Acme account.  That’s OK with the rep because such one-of items are clearly excluded in our compensation plan terms and conditions [link to document], but I’ve discovered that the SC and manager compensation plans lack the same exclusionary language.  Ergo, this time, I recommend that we payout the SCs and the managers on this $50K of ARR (total cost $2.5K as it pushes some folks into accelerators).  Additionally, I intend to immediately update and re-issue the T&C document for sales management and SC comp plans.  Can I get your approval on this proposal?

By the way, if you’re opposed to this, can we please just go and payout the SCs (total cost $1.0K) as I believe it’s more important to them than the managers.  Either way, these are small numbers so let’s get this behind us quickly and move onto more important items.


Ah.  I feel better already.

The proposer is referring to our existing policies – even providing me with links to them – applying them, noticing problems with them, and making a concrete proposal for what to do about it, along with a backup.  Kelly’s telling me correct costs – e.g., not forgetting the impact of accelerators – for approving the proposal.  And even correcting our policies so this situation won’t ever again require an escalation.

The Leaky Bucket, Net New ARR, and the SaaS Growth Efficiency Index

My ears always perk up when I hear someone say “net new ARR” — because I’m trying to figure out which, of typically two, ways they are using the term:

  • To mean ARR from net new customers, in which case, I don’t know why they need the word “net” in there.  I call this new business ARR (sometimes abbreviated to newbiz ARR), and we’ll discuss this more down below.
  • To mean net change in ARR during a period, meaning for example, if you sold $2,000K of new ARR and churned $400K during a given quarter, that net new ARR would be $1,600K.  This is the correct way to use this term.

Let’s do a quick review of what I call leaky bucket analysis.  Think of a SaaS company as a leaky bucket full of ARR.

  • Every quarter, sales dumps new ARR into the bucket.
  • Every quarter, customer success does its best to keep water from leaking out.

Net new ARR is the change in the water level of the bucket.  Is it a useful metric?  Yes and no.  On the yes side:

  • Sometimes it’s all you get.  For public companies that either release (or where analysts impute) ARR, it’s all you get.  You can’t see the full leaky bucket analysis.
  • It’s useful for measuring overall growth efficiency with metrics like cash burn per dollar of net new ARR or S&M expense per dollar of net new ARR.  Recall that customer acquisition cost (CAC) focuses only on sales efficiency and won’t detect the situation where it’s cheap to add new ARR only to have it immediately leak out.

If I were to define an overall SaaS growth efficiency index (GEI), I wouldn’t do it the way Zuora does (which is effectively an extra-loaded CAC), I would define it as:

Growth efficiency index = -1 * (cashflow from operations) / (net new ARR)

In English, how much cash are you burning to generate a dollar of net new ARR.  I like this because it’s very macro.  I don’t care if you’re burning cash as a result of inefficient sales, high churn, big professional services losses, or high R&D investment.  I just want to know how much cash you’re burning to make the water level move up by one dollar.

So we can see already that net new ARR is already a useful metric, if a sometimes confused term.  However, on the no side, here’s what I don’t like about it.

  • Like any compound metric, as they say at French railroad crossings, un train peut en cacher un autre (one train can hide another).  This means that while net new ARR can highlight a problem you won’t immediately know where to go fix it — is weak net new ARR driven by a sales problem (poor new ARR), a product-driven churn problem, a customer-success-driven churn problem, or all three?

Finally, let’s end this post by taking a look and then a deeper look at the SaaS leaky bucket and how I think it’s best presented.


For example, above, you can quickly see that a massive 167% year-over-year increase in churn ARR was the cause for weak 1Q17 net new ARR.  While this format is clear and simple, one disadvantage of this simpler format is that it hides the difference between new ARR from new customers (newbiz ARR) and new ARR from existing customers (upsell ARR).  Since that can be an important distinction (as struggling sales teams often over-rely on sales to existing customers), this slightly more complex form breaks that out as well.


In addition to breaking out new ARR into its two sub-types, this format adds three rows of percentages, the most important of which is upsell % of new ARR, which shows to what extent your new ARR is coming from existing versus new customers.  While the “correct” value will vary as a function of your market, your business model, and your evolutionary phase, I generally believe that figures below 20% indicate that you may be failing to adequately monetize your installed base and figures above 40% indicate that you are not getting enough new business and the sales force may be too huddled around existing customers.

My Appearance on DisrupTV Episode 100

Last week I sat down with interviewers Doug Henschen, Vala Afshar, and a bit of Ray Wang (live from a 777 taxiing en route to Tokyo) to participate in Episode 100 of DisrupTV along with fellow guests DataStax CEO Billy Bosworth and big data / science recruiter Virginia Backaitis.

We covered a full gamut of topics, including:

  • The impact of artificial intelligence (AI) and machine learning (ML) on the enterprise performance management (EPM) market.
  • Why I joined Host Analytics some 5 years ago.
  • What it’s like competing with Oracle … for basically your entire career.
  • What it’s like selling enterprise software both upwind and downwind.
  • How I ended up on the board of Alation and what I like about data catalogs.
  • What I learned working at Salesforce (hint:  shoshin)
  • Other lessons from BusinessObjects, MarkLogic, and even Ingres.

DisrupTV Episode 100, Featuring Dave Kellogg, Billy Bosworth, Virginia Backaitis from Constellation Research on Vimeo.