The Four Sources of Pipeline and The Balance Across Them

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.

The Top Two, High-Level Questions About Sales (and Associated Metrics)

“The nice thing about metrics is that there are so many to choose from.” — Adapted from Grace Hopper [1]

“Data, data everywhere.  Nor any drop to drink.” — adapted from Samuel Taylor Coleridge [2]

In a world where many executives are overwhelmed with sales and marketing metrics — from MQL generation to pipeline analysis to close-rates and everything in between — I am writing this post in the spirit of kicking it back up to the CXO-level and answering the question:  when it comes to sales, what do you really need to worry about?

I think can burn it all down to two questions:

  • Are we giving ourselves the chance to hit the number?
  • Are we hitting the number?

That’s it.  In slightly longer form:

  • Are we generating enough pipeline so that we start every quarter with a realistic chance to make the number?
  • Are we converting enough of that pipeline so that we do, in fact, hit the number?

Translating it to metrics:

  • Do we start every quarter with sufficient pipeline coverage?
  • Do we have sufficient pipeline conversion to hit the number?

Who Owns Pipeline Coverage and How to Measure It?
Pipeline coverage is a pretty simple concept:  it’s the dollar value of the pipeline with a close date in a given period divided by the new ARR target for that period.  I have written a lot of pretty in-depth material on managing the pipeline in this blog and I won’t rehash all that here.

The key points are:

  • There are typically four major pipeline generation (pipegen) sources [3] and I like setting quarterly pipegen goals for each, and doing so in terms of opportunity (oppty) count, not pipeline dollars.  Why?  Because it’s more tangible [4] and for early-stage oppties one is simply a proxy for the other — and a gameable one at that [5].
  • I loathe looking at rolling-four-quarter pipeline both because we don’t have rolling-four-quarter sales targets and because doing so often results in a pipeline that resembles a Tantalean punishment where all the deals are two quarters out.
  • Unless delegated, ownership for overall pipeline coverage boomerangs back on the CEO [6].  I think the CMO should be designated the quarterback of the pipeline and be responsible for both (a) hitting the quarterly goal for marketing-generated oppties and (b) forecasting day-one, next-quarter pipeline and taking appropriate remedial action — working across all four sources — to ensure it is adequate.
  • A reasonable pipeline coverage ratio is 3.0x, though you should likely use your historical conversion rates once you have them. [7]
  • Having sufficient aggregate pipeline can mask a feast-or-famine situation with individual sellers, so always keep an eye on the opportunity histogram as well.  Having enough total oppties won’t help you hit the sales target if all the oppties are sitting with three sellers who can’t call everyone all back.
  • Finally, don’t forget the not-so-subtle difference between day-one and week-three pipeline [8].  I like coverage goals focused on day-one pipeline coverage [9], but I prefer doing analytics (e.g., pipeline conversion rates) off week-three snapshots [10].

Who Owns Pipeline Conversion and How to Measure and Improve It?
Unlike pipeline coverage, which usually a joint production of four different teams, pipeline conversion is typically the exclusive the domain of sales [11].  In other words, who owns pipeline conversion?  Sales.

My favorite way to measure pipeline conversion is take a snapshot of the current-quarter pipeline in week 3 of each quarter and then divide the actual quarterly sales by the week 3 pipeline.  For example, if we had $10M in current-quarter new ARR pipeline at the start of week 3, and closed the quarter out with $2.7M in new ARR, then we’d have a 27% week 3 pipeline conversion rate [12].

What’s a good rate?  Generally, it’s the inverse of your desired pipeline coverage ratio.  That is, if you like a 3.0x week 3 pipeline coverage ratio, you’re saying you expect a 33% week 3 pipeline conversation rate.  If you like 4.0x, you’re saying you expect 25% [13].

Should this number be the same as your stage-2-to-close (S2TC) rate?  That is, the close rate of sales-accepted (i.e., “stage 2” in my parlance) oppties.  The answer, somewhat counter-intuitively, is no.  Why?

  • The S2TC rate is count-based, not ARR-dollar-based, and can therefore differ.
  • The S2TC rate is typically cohort-based, not milestone-based — i.e., it takes a cohort of S2 oppties generated in some past quarter and tracks them until they eventually close [14].

While I think the S2TC rate is a better, more accurate measure of what percent of your S2 oppties (eventually) close, it is simply not the same thing as a week-3 pipeline conversion rate [15].  The two are not unrelated, but nor are they the same.

There are a zillion different ways to improve pipeline conversion rates, but they generally fall into these buckets:

  • Generate higher-quality pipeline.  This is almost tautological because my definition of higher-quality pipeline is pipeline that converts at a higher rate.  That said, higher-quality generally means “more, realer” oppties as it’s well known that sellers drop the quality bar on oppties when pipeline is thin, and thus the oppties become less real.  Increasing the percent of pipeline within the ideal customer profile (ICP) is also a good way of improving pipeline quality [16] as is using intent data to find people who are actively out shopping.  High slip and derail percentages are often indicators of low-quality pipeline.
  • Make the product easier to sell.  Make a series of product changes, messaging/positioning changes, and/or create new sales tools that make it easier to sell the product, as measured by close rates or win rates.
  • Make seller hiring profile improvements so that you are hiring sellers who are more likely to be successful in selling your product.  It’s stunning to me how often this simple act is overlooked.  Who you’re hiring has a huge impact on how much they sell.
  • Makes sales process improvements, such as adopting a sales methodology, improving your onboarding and periodic sales training, and/or separating out pipeline scrubs from forecast calls from deal reviews [17].

Interestingly, I didn’t add “change your sales model” to the list as I mentally separate model selection from model execution, but that’s admittedly an arbitrary delineation.  My gut is:  if your pipeline conversion is weak, do the above things to improve execution efficiency of your model.  If your CAC is high, re-evaluate your sales model.  I’ll think some more about that and maybe do a subsequent post [18].

In conclusion, let’s zoom it back up and say:  if you’ve got a problem with your sales performance, there are really only two questions you need to focus on.  While we (perhaps inadvertently) demonstrated that you can drill deeply into them — those two simple questions remain:

  • Are we giving ourselves the chance to hit the number?
  • Are we hitting it?

The first is about pipeline generation and coverage.  The second is about pipeline conversion.

# # #

Notes

[1]  The original quip was about standards:  “the nice thing about standards is that you have so many to chose from.”

[2]  The original line from The Rime of the Ancient Mariner was about water, of course.

[3]  I remember there are four because back in the day at Salesforce they were known, oddly, as the “four horsemen” of the pipeline:  marketing, SDR/outbound, alliances, and sales.

[4]  Think:  “get 10 oppties” instead of “get $500K in pipeline.”

[5]  Think:  ” I know our ASP is $50K and our goal was $500K in pipeline, so we needed 10 deals, but we only got 9, so can you make one of them worth $100K in the pipeline so I can hit my coverage goal?”  Moreover, if you believe that oppties should be created with $0 value until a price is socialized with the customer, the only thing you can reasonably measure is oppty count, not oppty dollars.  (Unless you create an implied pipeline by valuing zero-dollar oppties at your ASP.)

[6]  Typically the four pipeline sources converge in the org chart only at the CEO.

[7]  And yes it will vary across new vs. expansion business, so 3.0x is really more of a blended rate.  Example:  a 75%/25% split between new logo and expansion ARR with coverage ratios of 3.5x and 1.5x respectively yields a perfect, blended 3.0 coverage ratio.

[8]  Because of two, typically offsetting, factors:  sales clean-up during the first few weeks of the quarter which tends to reduce pipeline and (typically marketing-led) pipeline generation during those same few weeks.

[9]  For the simple reason that we know if we hit it immediately at the end of the quarter — and for the more subtle reason that we don’t provide perverse disincentives for cleaning up the pipeline at the start of the quarter.  (Think:  “why did your people push all that stuff out the pipeline right before they snapshotted it to see if I made my coverage goal?”)

[10]  To the extent you have a massive drop-off between day 1 and week 3, it’s a problem and one likely caused by only scrubbing this-quarter pipeline during pipeline scrubs and thus turning next-quarter into an opportunity garbage dump.  Solve this problem by doing pipeline scrubs that scrub the all-quarter pipeline (i.e., oppties in the pipeline with a close date in any future quarter).  However, even when you’re doing that it seems that sales management still needs a week or two at the start of every quarter to really clean things up.  Hence my desire to do analytics based on week 3 snapshots.

[11] Even if you rely on channel partners to make some sales and have two different sales organizations as a result, channel sales is still sales — just sales using a different sales model one where, in effect, channel sales reps function more like direct sales managers.

[12]  Technically, it may not be “conversion” as some closed oppties may not be present in the week 3 pipeline (e.g., if created in week 4 or if pulled forward in week 6 from next quarter).  The shorter your sales cycle, the less well this technique works, but if you are dealing with an average sales cycle of 6-12 months, then this technique works fine.  In that case, in general, if it’s not in the pipeline in week 3 it can’t close.  Moreover, if you have a long sales cycle and nevertheless lose lots of individual oppties from your week 3 pipeline that get replaced by “newly discovered” (yet somehow reasonably mature oppties) and/or oppties that inflate greatly in size, then I think your sales management has a pipeline discipline problem, either allowing or complicit in hiding information that should be clearly shown in the pipeline.

[13]  This assumes you haven’t sold anything by week 3 which, while not atypical, does not happen in more “linear” businesses and/or where sales backlogs orders.  In these cases, you should look at to-go coverage and conversion rates.

[14]  See my writings on time-based close rates and cohort- vs. milestone-based analysis.

[15] The other big problem with the S2TC rate is that it can only be calculated on a lagging basis.  With an average sales cycle of 3 quarters, you won’t be able to accurately measure the S2TC rate of oppties generated in 1Q21 until 4Q21 or 1Q22 (or even later, if your distribution has a long tail — in which case, I’d recommend capping it at some point and talking about a “six-quarter S2TC rate” or such).

[16]  Provided of course you have a data-supported ICP where oppties at companies within the ICP actually do close at a higher rate than those outside.  In my experience, this is usually not the case, as most ICPs are more aspirational than data-driven.

[17]  Many sales managers try to run a single “weekly call” that does all three of these things and thus does each poorly.  I prefer running a forecast call that’s 100% focused on producing a forecast, a pipeline scrub that reviews every oppty in a seller’s pipeline on the key fields (e.g., close date, value, stage, forecast category), and deal reviews that are 100% focused on pulling a team together to get “many eyes” and many ideas on how to help a seller win a deal.

[18] The obvious counter-argument is that improving pipeline conversion, ceteris paribus, increases new ARR which reduces CAC.  But I’m sticking by my guns for now, somewhat arbitrarily saying there’s (a) improving efficiency on an existing sales model (which does improve the CAC), and then there’s (b) fixing a CAC that is fundamentally off because the company has the wrong sales model (e.g., a high-cost field sales team doing small deals).  One is about improving the execution of a sales model; the other is about picking the appropriate sales model.

Video of my Presentation at SaaStr 2021: A CEO’s Guide to Marketing

About two weeks ago I spoke at SaaStr Annual 2021, giving a presentation entitled A CEO’s Guide to Marketing, which discusses why marketing is sometimes seen as a dark art and then discusses 5 things that every CEO (and startup exec) should know about marketing in order to work best with the marketing team.

The slides from that session are here.  Below please find a video captured as part of the Stage A stream.  I start presenting at 8:01 and go for 30 mins.

Thanks for watching!

Join me at SaaStock EMEA for “How to Make a Marketing Machine”

Please join me on October 13th at SaaStock EMEA, a free European SaaS event, for my presentation entitled “How to Make a Marketing Machine” on October 13th at 9:05 am PT (6:05 pm CET).

While west coasters will have to wake-up early to attend some of this event, the overall agenda looks great with a strong speaker line-up including:

  • Founders of companies including Aircall, Amplifyi, Capchase, Chargebee, Clumio, Gainsight, Pitch, Panintelligence, Personio, Productboard, Profitwell, Slack, and Talkdesk.
  • Executives from companies including Algolia, AWS, Celigo, Contentful, Freshworks, Intercom, Yellowfin, Zephr, and Zoominfo.
  • VCs from partnerships including Accel, Index, Point Nine, Seedcamp, Sequoia, and of course, Balderton.

The event runs for 3 days (Oct 12 – 14), about 4 hours every day starting at 5:30 am PT (2:30 pm CET).  Check out the full schedule here.

With a 20-minute slot, I had one of two angles to take on my topic, How To Build a Marketing Machine.

  • I could emphasize “marketing,” and attempt a warp-speed, how-to session that in reality should take 60-90 minutes at any normal pace.
  • I could emphasize “machine,” and focus on what people mean why they say “marketing machine” and how to build one.  This is the angle I decided to take.

As such, we’ll discuss the following topics in the sure-to-be still, fast-paced session.

  • What is a marketing machine?
  • How do we model it?
  • How do we measure it?
  • What are its key attributes?
  • How should it function?
  • Why it should be built in layers

It should be a fun and informative session.  Look forward to seeing you there!

Slides from my SaaStr 2021 Presentation: A CEO’s Guide to Marketing

Thanks again to Jason Lemkin and the team at SaaStr for having me as a speaker at this year’s outdoor, live and hybrid online SaaStr Annual 2021 event at the San Mateo Convention Center.  I know the team went to great lengths (and great expense) to enable people both to connect live at an event and make it safe, with both a vaccination card and an onsite Covid test required to gain admission.

In this post, I’ll share the slides from my presentation, A CEO’s Guide to Marketing, which was designed to help startup founders, CEOs, and C-level executives have a little less angst when it comes to marketing in general and the marketing update at quarterly business reviews (QBRs).  I present five things every C-level executive should know about marketing driven by two underlying themes:  (1) use market research to complement the abundant data you have in your internal systems, and (2) keep it simple to avoid being sucked into a vortex of detail and numbers that can leave everyone dazed and confused.

I’ve embedded the slides below.  Don’t miss the appendix with five slides worth of links to resources providing additional information!  Once SaaStr publishes a video of the talk, I’ll embed that in a future post as well.

Thanks for coming!