Category Archives: Metrics

The Pipeline Progression Chart:  Why I Like It Better Than Just Tracking Rolling Four-Quarter Pipeline

When asked, “how is it going?” many companies will respond with something akin to, “things are looking strong, the pipeline is up to $50M.”

Not a bad statement, but certainly an imprecise one.  “Over what timeframe?” you might ask.  To which you’ll typically hear one of two answers

  • “Uh, that’s the whole thing.” I don’t love this answer as many companies –particularly the ones who answer with all-quarter pipeline — let junk opportunities get parked in the 5Q+ pipeline.  (You can fix this by including a timeframe as part of the definition of opportunity and ensuring you review the entire pipeline whenever you do a pipeline scrub.)
  • “That’s the rolling four-quarter (R4Q) pipeline.” I don’t love this answer either because, in my experience, companies who focus on R4Q pipeline as their top pipeline metric tend not to put enough emphasis on pipeline timing.  It’s too easy to say in January, “this year’s number is $20M and we’ve got $50M in the pipeline already (2.5x pipeline coverage) so we are golden.”  The problem, of course, is if 80% of that pipeline is backloaded into Q4, then while “the year may look great,” you’re going to need to survive three wasteland quarters to get there.  Even if that $40M Q4 pipeline were real, which it usually isn’t, most sales VPs won’t be around in October to close it.

I never look at rolling-four-quarter pipeline for the simple reason that I’ve never had a rolling-four-quarter sales target.  We have quarterly targets.  Instead of looking at R4Q  pipeline and hoping it’s well distributed (over time and across sellers), my philosophy is the opposite:

Let’s focus on ensuring we start every quarter with 3.0x pipeline coverage.  If we do that, the year takes care of itself, as does the year after that.

Once you accept this viewpoint, a few things happen:

  • Someone needs to start forecasting day-1 next-quarter pipeline coverage. What’s the point of focusing on next-quarter coverage if no one is tracking it and taking corrective actions as needed?  As mentioned, I think that person should be the CMO.
  • We need to start tracking the progression of the pipeline over time. This quarter’s starting pipeline is largely composed of last-quarter’s next-quarter pipeline and so on.  Since there are so many ebbs and flows in the pipeline the best way to track this is via periodic snapshots.

Towards that end, here’s a chart I find useful:

Let’s examine it.

  • Each row is a snapshot of the pipeline, broken down by quarter, taken on the first day of the quarter. (Some allow a week or two, for pipeline cleanup before snapshotting, which is fine.)
  • We’re tracking pipeline dollars, not opportunity count, which generally works better if you have a range of deal sizes and/or a multi-modal distribution of average sales prices. Doing so, however, can leave you overconfident if you create new opportunities with a high placeholder value.  (See this post for what to do about that.)
  • We show pipeline coverage in the block on the right. Most people want this-quarter coverage of around 3.0.  Targets for next-quarter and N+2 quarter are usually less well understood because many people don’t track them.  Coverage needed in the out quarters is a function of your sales cycle length, but the easiest thing is to just start tracking it so you get a sense for what out-quarter coverage normally is.  If you’re worried about that 1.6x next-quarter coverage shown on the 7/1 snapshot, read this post for ideas on how to generate pipeline in a hurry.
  • It’s good to carry at least one year’s prior snapshots so you can see historical progression.  Even more is better.
  • I’m assuming bigger deals and longer sales cycles (e.g., 6 to 12 months) so you will actually have material pipeline in the out-quarters.  For a velocity model with 25-day sales cycles, I’d take this template but just switch the whole things to months.

The most fun part of this chart is this you read it diagonally.  The $7M in starting this-quarter pipeline at the 7/1/21 snapshot is largely composed of the $6.5M in next-quarter pipeline at the 4/1/21 snapshot and the $3M in pipeline at the 1/1/21 snapshot.  You can kind of see the elephant go through the snake.

When you add this chart to your mix, you’re giving yourself an early warning system for pipeline shortages beyond simply forecasting starting next-quarter pipeline.  You should do this, particularly with big deals and long sales cycles, because one quarter’s notice is usually not enough time to fix the problem.  Yes, you can and should always try to mitigate problems (and never give-up saying, “looks like we’re going to hit the iceberg”), but if you give yourself more advance notice, you’ll give yourself more options and a better chance at reaching the goal:  starting every quarter with 3.0x coverage.

Add this slide to your QBR template now!

Crash Course in Customer Success SaaS Metrics: Appearance on the ChurnZero podcast.

Earlier this week I appeared on a webinar with You Mon Tsang, founder and CEO of ChurnZero, a SaaS application aimed at helping subscription businesses reduce churn.

In this post, I will share the video of event, provide a link to the slides, provide a link to the Q&A wrap-up they posted, embed the video below, embed the slides below that, and finally provide a quick summary below that.

Here’s the video:

Here’s a copy of the slides:

Here’s a quick list of the topics we discussed:

  • ARR and MRR, and when to use which
  • Logo retention rate, why a count-based rate works best when your customers are more or less “all the same” on deal size, and that you should use a dollar-based rate when they’re not.
  • Available-to-renew (ATR) logo retention rate, which factors in only those customers who had a chance to renew or not.  If you’re an ARR-based company but do multi-year contracts not every customer has the chance to get out every year.
  • Gross revenue retention rate, and why it’s gathering steam as an important metric.  (Sometimes great expansion is hiding major churn and just looking at churn before expansion will reveal that.)
  • Net revenue retention (NRR), aka net dollar retention (NDR) for those who work only in dollars, which is probably the hottest SaaS metrics after ARR and ARR growth.
  • Lifetime value (LTV), and its fairly severe limitations.  I gave a talk on this at SaaStr two years back.
  • Customer acquisition cost (CAC) and the CAC ratio.  How it differs for new customer and expansion ARR.
  • LTV/CAC ratio.  An attempt to measure what something costs against what it’s worth, but one that has generally failed and is now being replaced by NRR.
  •  Benchmarks for many of these metrics from the KeyBanc 2021 SaaS Survey.

Thanks to all those who attended and thanks to You Mon for inviting me and Cori for executing it so well.

The Sales/Marketing Expense Ratio

Question:  how much does a $15M SaaS company spend on sales and marketing as a percent of ARR?  Answer:  35% (with 45% and 15% as the top and bottom quartiles).

Charts like this, from OpenView’s 2021 Financial & Operating Benchmarks survey, help to answer questions like that all the time.

Good SaaS executives keep these metrics in mind, and you can get them from KeyBanc, RevOps Squared, OpenView, or for bigger/public companies, sites like Meritech Public Comps, Public Comps, or Clouded Judgement.

A great revops or FP&A person will give the answer from multiple sources and explain the differences among them.  Moreover, they’d observe that sales and marketing (S&M) expense really should vary with growth rate, and they’d know that KeyBanc tracks that:

So if that $15M SaaS company is growing at 25%, then median S&M spend is 20% of revenue, whereas if it’s growing at 70%, then median S&M spend bulks up to 46%.

But that’s all SaaS Metrics 101.  Today, I’d like to hop to the 201 level by introducing a simple that metric that can reveal a lot and on which few people focus:  the sales/marketing expense ratio, which just equals sales expense divided by marketing expense.

To introduce the idea — quick, tell me what’s happening at this company:

My take:

  • The company is high relative to the benchmark
  • The company is not making much progress towards the benchmark
  • Sales is getting less efficient while marketing is getting more efficient

This situation is very common.  Sometimes, it’s justified bottom-up — e.g., we’re building a partners function in sales that is only slowly becoming productive and we’ve upgraded both marketing leadership and the martech stack to improve marketing efficiency.

Normally, it’s not.  In fact, normally, there’s no justification whatsoever.  When you ask, you get, “well, that’s just how the budget process worked out, the real focus was on improving S&M and we did.  Next question, please.”

Yes, you did improve S&M, but you put the “S&M” improvement 100% on the back of marketing (in fact, 200%) and with no bottom-up justification for why sales needs to get more expensive while marketing is going to magically become more efficient.  This is a mistake.  The likely result is underfed sellers screaming for pipeline, forming an angry mob with dogs and torches headed to the CMO’s office.

Let me tell you what’s going on when this happens:

  • Your CRO is a better negotiator than your CMO.  They better be.  If they’re not, you have an additional problem.
  • Your CRO has more negotiating leverage than the CMO.  They are negotiating the company number directly with the CEO and indirectly with the board.  This is high-stakes, board-level poker.
  • There’s usually no broken-out benchmark, typically only a combined benchmark, and given the prior two points, the CRO is just fine with that.
  • It’s easy to think that hiring sellers “leads directly” to new ARR than investing in marketing.  Why?  Because in enterprise software the bookings capacity model is typically driven off the number of sellers.  Yes, this is intellectually lazy and only works on the margin, but deep down, it’s what a lot of CEOs and CFOs feel.

So the CMO gets asked to suck it up, the board doesn’t notice the problem, the CFO notices but doesn’t want to rock the boat, and the CEO is just happy to get the plan approved.

Hopefully the CRO has the decency to attend the CMO’s going-away party in the fall.  Because if this process repeats itself for even a few years, that’s how it’s going to end.

So how do we fix this?

1. Shine a light on the problem, by adding the sales/marketing ratio to the in-line metrics presented in the plan.

I prefer to show it this way, which makes it clear we used to spend $2 in sales for every $1 in marketing, but that has crept up to over $3.  Showing the metric gives people the chance to ask the all-important question:  why?

The other way to show this is via “sales composition,” i.e., sales as a percent of sales and marketing:

In this case, you can say that sales has risen from two-thirds to three-quarters of S&M expense, and again ask why.  I think the former presentation is more intuitive, but the advantage of this presentation is that KeyBanc benchmarks it in this form:

2. Shine a light on your inverted funnel model.  Sometimes you can squeeze marketing expense just on the people side, but the real way you usually cut to these targets is by making a series of seemingly innocuous assumptions in your funnel.  Consider:

Saying, we need to take MQL to SQL from 10% to 12%, SQL to SAL up from 65% to 70%, and SAL to close up from 15% to 20% all sounds pretty reasonable.  When you combine these effects, however, you’re saying that you’re going to cut the cost of generating an opportunity by more than a third, from $2700 to $1800.  That should get some attention — without any explanation other than the compound effect of small tweaks, it sounds like an Excel-induced hallucination to me.

3. Get the CRO on your side.  Make them understand that squeezing marketing too hard for purely top-down reasons increases their risk on the plan.  Get them to go to bat for you saying, “we need to ensure we feed the sellers enough pipeline.”  Most boards solve for growth with one eye on the CAC and not the opposite.

4. Get the CFO on your side.  In my experience, the hardest person to convince in these debates is the CEO, not the CFO.  Why?  Because the CEO is the one and only person who must negotiate the plan target with the CRO and that’s always something of a painful process.  So, if you get the CRO and CFO on your side, you will greatly increase your odds of getting the CEO to along with you.  You win the CFO over by emphasizing risk.  Think:  “we’ve (finally) got the CRO signed up for the number, but we’ve squeezed marketing too hard and that’s adding risk to the plan” and then say the magic words, “we don’t want to miss plan — do we, CFO?”  They never do.

Conclusion
In a world where sales has more political power, better negotiating skills, and more negotiating leverage than their marketing colleagues, the somewhat natural state of affairs is for this ratio to slowly increase over time.  The question is:  should it?  Everyone on the e-team needs to take accountability for thinking about that and ensuring the company gets the right, not just the easy, answer.  And the CMO has the unique responsibility of ensuring they do.

Why You Should Always Create Sales Opportunities at Zero Dollar Value

Quiz:  Your marketing team generates an MQL.  It’s passed to an SDR, who does basic BANT-style qualification and decides it’s real.  They create a sales opportunity in your pipeline and pass it to a seller.  What number is in the opportunity’s value field at this time?

Four answers I hear frequently:

  • I don’t know.  C’mon Dave, that’s a detail, why would I care about that?  Keep reading.
  • Some semi-random proxy value, say $25K.  Because, well, we’ve always done it that way, and I’m not sure why.
  • Our average sales price (ASP), say $100K.  For extra credit, our segment-specific ASP:  SMB opportunities get valued at $25K and enterprise ones get valued at $100K.
  • Zero dollars.   And that’s the only way I’d ever do it.

What’s my answer?  Zero dollars (and that’s the only way I’d ever do it).  Before I tell you why, let’s remind ourselves why we should care about the answer to this question.

Do you ever look at:

  • Pipeline coverage, as a way to determine your confidence about the future or to give investors confidence in the future?
  • Pipeline conversion rates (on a regular or to-go basis) as a way of measuring pipeline quality or triangulating the forecast?
  • Pipeline generation efficiency (e.g., pipe-to-spend ratio) in order to determine which programs or channels are better than others?

If the answer to any of those question is yes, you need to care about your definition of pipeline.  And while many people think about stage (e.g., should that SDR-created, stage-one opportunity even be considered pipeline?), few people seem to think as much about value.

In a typical funnel [1], by the time you get to stage 3 or 4 of your sales process you may have weeded out half your pipeline.  Now imagine it’s early in a quarter and your pipeline is loaded with stage 2 and stage 3 opportunities, all valued at $100K.  You may have a big air bubble in your pipe.

You think, alas, no worries, Dave, I can handle that in other ways:

  • When we say pipeline around here, we actually mean stage 4+ pipeline, so we just exclude all those opportunities.
  • When we look at stage-weighted pipeline, we weight at 0% all the stage 2 and 3 opportunities, so they’re effectively ignored.

Doing this will bleed a lot of air out of the pipeline, but let’s step back for a minute.  You’re telling me that you’re putting in a $100K placeholder value at opportunity creation time and then systematically ignoring it?  Yes.  Well, tell me again, why are you putting it in the first place?!

The answer to that question is usually:

  • We want to show a big pipeline to get everyone excited.
  • That’s how everybody does it.
  • We want to be able to compare against companies that use placeholder values.

Before challenging those answers, let me object to the air bleeding processes mentioned above:

  • Pipeline should mean pipeline.  If there’s no adjective before the word pipeline, it means the sum of the value of all opportunities with a close date in the period.  It’s sloppy to say, “pipeline” and then revise to, “oh, I mean current-quarter s3+ pipeline.”  They’re not the same.  Which one are you using when?
  • Pipeline that’s ignored in analytics is usually ignored in operations.  If your company defines “demo” as stage 4 (which you shouldn’t) and measures conversion rates from stage 4, I can guarantee you one thing:   the stage 1-3 pipeline is a garbage dump.   I have literally never met a company that does analytics from stage 3 or stage 4 where this is not true.  As Drucker said, what gets measured, gets managed.  And conversely.  This is bad practice.  All pipeline is valuable.  It should all be inspected, scrubbed, and managed.  That doesn’t happen when you systematically ignore part of it.
  • How do I know if a given $100K opportunity has a real or placeholder value?  You can’t.  Maybe you have a rule that says by stage 3 all values need to be validated, but do you know if that happened?  If you create opportunities with $0 value and say, “don’t enter a value unless it’s socialized with the customer,” then you’ll know.  Otherwise you’ll never be able to tell the difference between a real $100K and a fake one [2].
  • Stage weights should come from regressions, not thin air.  For those regressions to work, stage definitions should come from clear rules.  Then, and only then, can you say things like, “given our (consistent) definition of stage 2 opportunity, we typically see 8% of stage 2 ARR value converted in the current quarter and 9% more converted in the quarter after that.” [3]  Arbitrarily zeroing-out certain stages due to poor pipeline discipline and despite their actual conversion rates is bad practice.

Let’s close with challenging the three answers above:

  • Everybody does it.  Ask your parents about Johnny and bridges.  That’s not a good reason to do the wrong thing when derived from first principles.
  • We want to get people excited.  Good.  How about we get them excited by creating a real pipeline that converts at a healthy rate [4], instead of giving everyone a false sense of security with an inflated big number?
  • We want to be able to compare to (i.e., benchmark against) others who use placeholder values?  Super.  Then create a new metric called “implied pipeline” where you take all the zero-dollar opportunities and substitute an appropriate placeholder value.  You can compare to Johnny without following him off the bridge.

# # #

Notes
[1] While stage definitions and conversions vary widely, to make this concrete, here’s one sample funnel that I think is realistic:  stage 1 = BANT, stage 2 = sales accepted with 80% conversion from prior stage, stage 3 = deep dive completed with 80% conversion, stage 4 = solution fit confirmed with 50% conversion, stage 5 = vendor of choice with 60% conversion, stage 6 = win with 80% conversion.  Overall, that implies a s2-to-close rate of 16%, which is in the 10 to 25% range that I typically see.

[2] The hack solution to this is to use $99.999K as the placeholder — i.e., a value that people are unlikely to enter and then ignore that.  Which leads again to the question of why to put fake data into the system only to carefully ignore it in reporting and analytics?  (And hope that you always remember to ignore it.)

[3] This in turn relies on both a consistent definition of close date and a reference to which week of the quarter you’re talking about — such conversion rates vary across the week of the quarter.

[4] One of my CMO friends pointed out that sometimes this “excitement” takes dysfunctional forms — e.g., when sales wants to “cry poor” either to defend a weak forecast or argue for more investment, they can artificially hold oppties at zero value for an extended period (“uninflated balloons”).  This, however, is easily caught when the e-staff is looking at both pipeline (dollar) coverage as well as count (i.e.,  opportunities/rep).

Named To Top 25 All-Time SaaStr Podcast, Twice

I’m revising this post because I learned today that I’ve been named not once (as I thought yesterday) but twice to the SaaStr All-Time Top 25 List of podcast episodes (see Top 1-12 and Top 13-25).  Apologies for the confusion, but wow, what an honor.  This puts me in the company of legends like David Skok, Mark Suster, Nick Mehta, and Tomasz Tunguz — and dare I say that by my quick tally only two people made the list twice:  David Skok and me.

Thank you to everyone who listened and helped drive my episodes to the top of the charts!

Here are the two episodes that made the list:

In both episodes the interviewer was the ever-dynamic Harry Stebbings of 20VC fame.