Category Archives: Pipeline

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

Posted on May 14, 2022 | 3 comments

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 $7.5M 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!

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Posted in Marketing, Metrics, Pipeline, SaaS, Sales, Startups

Understanding SaaS Marketing as an Investor — Appearance on The Novice Investor podcast

Posted on March 10, 2022 | Leave a comment

Just a quick post to highlight a video / podcast episode I recently recorded with Cillian Hilliard, fellow board member at work management leader Scoro, and investor at Kennett Partners.  Cillian runs a great blog (complete with detailed financial models) at The Novice Investor.

(Understanding SaaS Marketing as an Investor – Dave Kellogg from Cillian Hilliard on Vimeo.)

In the episode we discuss:

  • Pipeline, which is used by investors to evaluate trajectory, and how to evaluate a pipeline and tell if it’s real, which ultimately comes down to what I call pipeline discipline.
  • Common problems in the pipeline including air, rolling hairballs, sudden changes / gaming, squatting, tantalizing pipeline, and excess coverage.
  • Detecting repeatability in the sales model and the validity of “just add water” kind of claims.  How to detect gaming.
  • The floating bar problem.  The thinner the pipeline, the lower sellers sets the bar on acceptance and conversely.    The chick/egg problem with pipeline that results.
  • The risks of math and MBA types becoming over-reliant on numbers / models, and how to manage them.  Remember the George Box quote:  “all models are wrong, some are useful,” which I discussed in my SaaStr 2021 presentation.
  • Mitigating this problem by “just talking” and doing periodic win-touch analysis to keep you connected to reality.
  • The attribution problem and my new favorite mug.  How to present attribution data to avoid problems and over-reactions (hint:  put disclaimers up front).

Thanks Cillian for having me on the show.

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Posted in Marketing, Pipeline, SaaS, Sales, Startups

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Why You Should Always Create Sales Opportunities at Zero Dollar Value

Posted on February 13, 2022 | 2 comments

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).

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Posted in Marketing, Metrics, Pipeline, SaaS, Sales, Startups

The Four Sources of Pipeline and The Balance Across Them

Posted on October 21, 2021 | 6 comments

I’ve mentioned this idea a few times of late (e.g., my previous post, my SaaStock EMEA presentation) [1] and I’ve had some follow-up questions from readers, so I thought I’d do a quick post on the subject.

Back in the day at Salesforce, we called pipeline sources “horsemen,” a flawed term both for its embedded gender pronoun and its apocalyptic connotation.  Nevertheless, for me it did serve one purpose — I always remembered there were four of them.

Today, I call them “pipeline sources” but I’ve also heard them referred to as “pipegen sources” (as in pipeline generation) and even “revenue engines” which I think is an over-reach, if not a well intentioned one [2].

While you can define them in different ways, I think a pretty standard way of defining the pipeline sources is as follows:

  • Marketing, also known as “marketing/inbound.”  Opportunities generated as a result of people responding to marketing campaigns [3].
  • SDRs, also known as “SDR/outbound,” to differentiate these truly SDR-generated oppties from marketing/inbound oppties that are also processed by SDRs, but not generated by them [4].
  • Alliances [5].  Opportunities referred to the company by partners, for example, when a regional system integrator brings the company into a deal as a solution for one of its customers.
  • Sales, also known as “sales/outbound,” when a quota-carrying salesrep does their own prospecting, typically found in named-account territory models, and develops an opportunity themselves.

Product-led growth (PLG) companies should probably have a fifth source, product, but I won’t drill into PLG in this post [5A].

Attribution issues (i.e., who gets credit when an opportunity is developed through multiple touches with multiple contacts over multiple quarters [6] [7]) are undoubtedly complex.  See note [8] not for the answer to the attribution riddle, but for my advice on best dealing with the fact that it’s unanswerable.

Now, for the money question:  what’s the right allocation across sources?  I think the following are reasonable targets for a circa $50M enterprise SaaS company for mix of oppties generated by each source (all targets are plus-or-minus 10%):

  • Marketing:  60%
  • SDR/outbound:  10%
  • Alliances:  20%
  • Sales/outbound:  10%

Now, let’s be clear.  This can vary widely.  I’ve seen companies where marketing generates 95% of the pipeline and those where it generates almost none.  SDR/outbound makes the most sense in a named-account sales model, so I personally wouldn’t recommend doing outbound for outbound’s sake [9] [10].  Alliances is often under 20%, because the CEO doesn’t give them a concrete oppty-generation goal (or because they’re focused more on managing technology alliances).  Sales/outbound only makes sense for sellers with named-account territories, despite old-school sales managers’ tendency to want everyone prospecting as a character-building exercise.

And let’s not get so focused on the mix that we forget about the point:  cost-effective opportunity generation (ultimately revealed in the CAC ratio) with broad reach into the target market.

Now, for a few pro tips:

  • Assign the goal as a number of oppties, not a percentage.  For example, if you want 60% from marketing and have an overall goal of 100 oppties, do not set marketing’s goal at 60%, tell them you want 60 oppties.  Why?  Because if the company only generates 50 oppties during the quarter and marketing generates 35 of those, then marketing is popping champagne for generating 70% of the oppties (beating the 60% goal), while they are 15 oppties short of what the company actually needed.
  • Use overallocation when spinning up new pipeline sources.  Say you’ve just created an RSI alliances team and want them generating 10% of oppties.  By default, you’ll drop marketing’s target from 70% to 60% and marketing will build a budget to generate 60% (of say 100) oppties, so 60 oppties.  If they need $3K worth of marketing to generate an oppty, then they’ll ask for $180K of demandgen budget.  But what if alliances flames out?  Far better to tell marketing to generate 70 oppties, give them $210K in budget to do so and effectively over-assign oppty generation to an overall goal of 110 when you need 100.  This way, you’re covered when the new and presumably unpredictable pipeline generation source is coming online [11].

# # #

Notes

[1] Video forthcoming if I can get access to it.

[2]  The good intentions are to keep everyone focused on revenue.  The over-reach is they’re not really engines, more fuel sources.  I am a big believer in the concept of “revenue engines,” but I use the term to refer to independent business units that have an incremental revenue target and succeed or fail in either an uncoupled or loosely coupled manner.  For example, I’d say that geographic units (e.g., Americas, EMEA), channels (e.g., OEM, VAR, enterprise sales, corporate sales), or even product lines (depending on the org) are revenue engines.  The point of having revenue engines is diversification, as with airplanes, they can sputter (or flame-out) independently.  (As one aviation pioneer was reputed to have said:  “why do I only fly four-engine planes across the Atlantic?  Because they don’t make five-engine planes.”)

[3]  I will resist the temptation to deep dive into the rabbit hole of attribution and say two things:  (a) you likely have an attribution mechanism in place today and (b) that system is invariably imperfect so you should make sure you understand how it works and understand its limitations to avoid making myopic decisions.  For example, if an oppty is created after several people downloaded a white paper, a few attended a webinar, an SDR had been doing outreach in the account, the salesperson met a contact on the train, and a  partner was trying to win business in the account, who gets the credit?  It’s not obvious how to do this correctly and if your system is “one oppty, one source” (as I’d usually recommend over some point allocation system), there will invariably be internal jockeying for the credit.

[4]  SDRs are often split inbound vs. outbound not only to ease the tracking but because the nature of the work is fundamentally different.  Hybrid SDR roles are difficult for this reason, particularly in inbound-heavy environments where there is always more inbound work to do.

[5]  My taxonomy is that there are two types of “partners” — “channels” who sell our software and “alliances” who do not.  In this case (where we’re talking about pipeline generation for our direct salesforce), I am speaking of alliance partners, who typically work in a co-sell relationship and bring the company into oppties as a result.  In the case of channels, the question is one of visibility:  are the channels giving us visibility into their oppties (e.g., in our CRM) as you might find with RSIs or are they simply forecasting a number and mailing us a royalty check as you might find with OEMs.

[5A]  Product meaning trials (or downloads in open source land), which effectively become the majority top-of-funnel lead source for PLG companies.  This begs the question:  who drives people to do those trials (typically marketing and/or word of mouth)

[6]  One simple, common example:  a person downloads a white paper they found via through a search advertisement five quarters ago, ends up in our database, receives our periodic newsletter, and then is developed by an SDR through an outreach sequence.  Who gets the credit for the opportunity?  Marketing (for finding them in the first place and providing a baseline nurture program via the newsletter) or SDR/outbound (for developing them into an oppty)?   Most folks would say SDR in this case, but if your company practices “management by reductio ad absurdum” then someone might want to shut down search advertising because it’s “not producing” whereas the SDRs are.  Add some corporate politics where perhaps sales is trying to win points for showing how great they are at managing SDRs after having taken them from marketing and things can get … pretty icky.

[7] Another favorite example:  marketing sponsors a booth at the Snowflake user conference and we find a lead that develops into an opportunity.  Does marketing get the credit (because it’s a marketing program) or alliances (because Snowflake’s a partner).  Add some politics where the alliances team has been seen as underperforming and really needs the credit, and things can get again yucky and confusing, leading you away from the semi-obvious right answer:  marketing, because they ran a tradeshow booth and got a lead.  If you don’t credit marketing here, you are disincenting them from spending money at partner conferences (all I, no RO.)  The full answer here is, IMHO, to credit marketing with being the source of oppty, to track influence ARR by partner so we know how much of our business happens with which partners, and to not incent the technology alliances group with opportunity creation targets.  (Oppty creation, however, should be an important goal for the regional and/or global system integrator alliances teams.)

[8]  My recommended solution here is two-fold:  (a) use whatever attribution mechanism you want, ensuring you understand its limitations, and (b) perform a win-touch analysis at every QBR where a reasonably neutral party like salesops presents the full touch history for a set of representative deals (and/or large) deals won in the prior quarter.  This pulls everyone’s heads of our their spreadsheets and back into reality — and should ease political tensions as well.

[9]  Having an SDR convince someone to take a meeting usually results in a higher no-show rate and a lower overall conversion rate than setting up meetings with people who have engaged with our marketing or our partners already.

[10]  Put differently, you should stalk customers only when you’re quite sure they should buy from you, but they haven’t figured that out yet.

[11] And yes there’s no free lunch here.  Your CAC will increase because you’re paying to generate 110 oppties when you only need 100.  But far better to have the CAC kick up a bit when you’re starting a new program than to miss the number because the pipeline was insufficient.

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Posted in Demandgen, Marketing, Pipeline, Sales, Salesops

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Using This/Next/All-Quarter Analysis To Understand Your Pipeline

Posted on April 30, 2021 | 2 comments

This is the third in a three-post series focused on forecasting and pipeline.  Part I examined triangulation forecasts to improve forecast accuracy and enable better conversations about the forecast.  After a review of pipeline management fundamentals, part II discussed the use of to-go pipeline coverage to provide clarity on how your pipeline is evolving across the weeks of the quarter.  In this, part III, we’ll introduce what I call this/next/all-quarter pipeline analysis as a way of looking at the entire pipeline that is superior to annual or rolling four-quarter pipeline analysis.

Let’s start by unveiling the last block on the sheet we’ve been using the previous two posts.  Here’s the whole thing:

You’ll see two new sections added:  next-quarter pipeline and all-quarters [1] pipeline.  Here’s what we can do when we see all three of them, taken together:

  • We can see slips.  For example, in week 3 while this-quarter pipeline dropped by $3,275K, next-quarter pipeline increased by $2,000K and all-quarters only dropped by $500K.  While there are many moving parts [2], this says to me that pipeline is likely sloshing around between quarters and not being lost.
  • We can see losses.  Similarly, when this-quarter drops, next-quarter is flat, and all-quarters drop, we are probably looking at deals lost from the pipeline [3].
  • We can see wins.  When you add a row at the bottom with quarter-to-date booked new ARR, if that increases, this-quarter pipeline decreases, next-quarter pipeline stays flat, and all-quarters pipeline decreases, we are likely looking at the best way of reducing pipeline:  by winning deals!
  • We can see how we’re building next-quarter’s pipeline.  This keeps us focused on what matters [4].  If you start every quarter with 3.0x coverage you will be fine in the long run without the risk of a tantalizing four-quarter rolling pipeline where overall coverage looks sufficient, but all the closeable deals are always two to four quarters out [5].

Tantalus and his pipeline where all the closeable deals are always two quarters out

  • We can develop a sense how next-quarter pipeline coverage develops over time and get better at forecasting day-1 next-quarter pipeline coverage, which I believe marketing should habitually do [6].
  • We can look at whether we have enough total pipeline to keep our salesreps busy by not just looking at the total dollar volume, but the total count of oppties.  I think this is the simplest and most intuitive way to answer that question.  Typically 15 to 20 all-quarters oppties is the maximum any salesrep can possibly juggle.
  • Finally, there’s nowhere to hide.  Companies that only examine annual or rolling four-quarter pipeline inadvertently turn their 5+ quarter pipeline into a dumping ground full of fake deals, losses positioned as slips, long-term rolling hairballs [7], and oppties used for account squatting.

I hope you’ve enjoyed this three-part series on forecasting and pipeline.  The spreadsheet used in the examples is available here.

# # #

Notes

[1] Apologies for inconsistences in calling this all-quarter vs. all-quarters pipeline.  I may fix it at some point, but first things first.  Ditto for the inconsistency on this-quarter vs. current-quarter.

[2] You can and should have your salesops leader do the deeper analysis of inflows (including new pipegen) and outflows, but I love the first-order simplicity of saying, “this-quarter dropped by $800K, next-quarter increased by $800K and all-quarters was flat, ergo we are probably sloshing” or “this-quarter dropped by $1M, next-quarter was flat, and all-quarters dropped by $1M, so we probably lost $1M worth of deals.”

[3] Lost here in the broad sense meaning deal lost or no decision (aka, derail).  In the former case, someone else wins the deal; in the latter case, no one does.

[4] How do you make 32 quarters in row?  One at a time.

[5] Tantalus was a figure in Greek mythology, famous for his punishment:  standing for eternity in a pool of water below a fruit tree where each time he ducked to drink the water it would recede and each time he reached for a fruit it was just beyond his grasp.

[6] Even though most companies have four different pipeline sources (marketing/inbound, SDR/outbound, sales/outbound, and partners), marketing should, by default, consider themselves the quarterback of the pipeline as they are usually the majority pipeline source and the most able to take corrective actions.

[7] By my definition a normal rolling hairball always sits in this quarter’s pipeline and slips one quarter every quarter.  A long-term rolling hairball is thus one that sits just beyond your pipeline opportunity scrutiny window (e.g., 5 quarters out) and slips one quarter every quarter.

 

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Posted in Forecasting, Pipeline, SaaS, Sales, Salesops