Category Archives: Pipeline

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

In part I of this three-part series I introduced the idea of an inverted funnel whereby marketing can derive a required demand generation budget using the sales target and historical conversion rates.  In order to focus on the funnel itself, I made the simplifying assumption that the company’s new ARR target was constant each quarter. 

In part II, I made things more realistic both by quarterizing the model (with increasing quarterly targets) and accounting for the phase lag between opportunity generation and closing that’s more commonly known as “the sales cycle.”  We modeled that phase lag using the average sales cycle length.  For example, if your average sales cycle is 90 days, then opportunities generated in 1Q19 will be modeled  as closing in 2Q19 [1].

There are two things I dislike about this approach:

  • Using the average sales cycle loses information contained in the underlying distribution.  While deals on average may close in 90 days, some deals close in 30 while others may close in 180. 
  • Focusing only on the average often leads marketing to a sense of helplessness. I can’t count the number of times I have heard, “well, it’s week 2 and the pipeline’s light but with a 90-day sales cycle there is nothing we can do to help.”  That’s wrong.  Some deals close more quickly than others (e.g., upsell) so what can we do to find more of them, fast [2].

As a reminder, time-based close rates come from doing a cohort analysis where we take opportunities created in a given quarter and then track not only what percentage of them eventually close, but when they close, by quarter after their creation. 

This allows us to calculate average close rates for opportunities in different periods (e.g., in-quarter, in 2 quarters, or cumulative within 3 quarters) as well an overall (in this case, six-quarter) close rate, i.e., the cumulative sum.  In this example, you can see an overall close rate of 18.7% meaning that, on average, within 6 quarters we close 18.7% of the opportunities that sales accepts.  This is well within what I consider the standard range of 15 to 22%.

Previously, I argued this technique can be quite useful for forecasting; it can also be quite useful in planning.  At the risk of over-engineering, let’s use the concept of time-based close rates  to build an inverted funnel for our 2020 marketing demand generation plan.

To walk through the model, we start with our sales targets and average sales price (ASP) assumptions in order to calculate how many closed opportunities we will need per quarter.  We then drop to the opportunity sourcing section where we use historical opportunity generation and historical time-based close rates to estimate how many closed opportunities we can expect from the existing (and aging) pipeline that we have already generated.  Then we can plug our opportunity generation targets from our demand generation plan into the model (i.e., the orange cells).  The model then calculates a surplus or (gap) between the number of closed opportunities we need and those the model predicts. 

I didn’t do it in the spreadsheet, but to turn that opportunity creation gap into ARR dollars just multiply by the ASP.  For example, in 2Q20 this model says we are 1.1 opportunities short, and thus we’d forecast coming in $137.5K (1.1 * $125K) short of the new ARR plan number.  This helps you figure out if you have the right opportunity generation plan, not just overall, but with respect to timing and historical close rates.

When you discover a gap there are lots of ways to fix it.  For example, in the above model, while we are generating enough opportunities in the early part of the year to largely achieve those targets, we are not generating enough opportunities to support the big uptick in 4Q20.  The model shows us coming in 10.8 opportunities short in 4Q20 – i.e., anticipating a new ARR shortfall of more than $1.3M.  That’s not good enough.  In order to achieve the 4Q20 target we are going to need to generate more opportunities earlier in the year.

I played with the drivers above to do just that, generating an extra 275 opportunities across the year generating surpluses in 1Q20 and 3Q20 that more than offset the small gaps in 2Q20 and 4Q20.  If everything happened exactly according to the model we’d get ahead of plan and 1Q20 and 3Q20 and then fall back to it in 2Q20 and 4Q20 though, in reality, the company would likely backlog deals in some way [3] if it found itself ahead of plan nearing the end of one quarter with a slightly light pipeline the next. 

In concluding this three-part series, I should be clear that while I often refer to “the funnel” as if it’s the only one in the company, most companies don’t have just one inverted funnel.   The VP of Americas marketing will be building and managing one funnel that may look quite different from the VP of EMEA marketing.  Within the Americas, the VP may need to break sales into two funnels:  one for inside/corporate sales (with faster cycles and smaller ASPs) and one for field sales with slower sales cycles, higher ASPS, and often higher close rates.  In large companies, General Managers of product lines (e.g., the Service Cloud GM at Salesforce) will need to manage their own product-specific inverted funnel that cuts across geographies and channels. There’s a funnel for every key sales target in a company and they need to manage them all.

You can download the spreadsheet used in this post, here.

Notes

[1] Most would argue there are two phase lags: the one from new lead to opportunity and the one from opportunity (SQL) creation to close. The latter is the sales cycle.

[2] As another example, inside sales deals tend to close faster than field sales deals.

[3] Doing this could range from taking (e.g., co-signing) the deal one day late to, if policy allows, refusing to accept the order to, if policy enables, taking payment terms that require pushing the deal one quarter back.  The only thing you don’t want to is to have the customer fail to sign the contract because you never know if your sponsor quits (or gets fired) on the first day of the next quarter.  If a deal is on the table, take it.  Work with sales and finance management to figure out how to book it.

The Evolution of Software Marketing: Hey Marketing, Go Get [This]!

As loyal readers know, I’m a reductionist, always trying to find the shortest, simplest way of saying things even if some degree of precision gets lost in the process and even if things end up more subtle than they initially appear.

For example, my marketing mission statement of “makes sales easier” is sometimes misinterpreted as relegating marketing to a purely tactical role, when it actually encompasses far more than that.  Yes, marketing can make sales easier through tactical means like lead generation and sales support, but marketing can also makes sales easier through more leveraged means such as competitive analysis and sales enablement or even more leveraged means such as influencer relations and solutions development or the most leveraged means of picking which markets the company competes in and (with product management) designing products to be easily salable within them.

“Make sales easier” does not just mean lead generation and tactical sales support.

So, in this reductionist spirit, I thought I’d do a historical review of the evolution of enterprise software marketing by looking at its top objective during the thirty-odd years (or should I say thirty odd years) of my career, cast through a fill-in-the-blank lens of, “Hey Marketing, go get [this].”

Hey Marketing, Go Get Leads

In the old days, leads were the focus.  They were tracked on paper and the goal was a big a pile as possible.  These were the days of tradeshow models and free beer:  do anything to get people come by the booth – regardless of whether they have any interest in or ability to buy the software.  Students, consultants, who cares?  Run their card and throw them in the pile.  We’ll celebrate the depth of the pile at the end of the show.

Hey Marketing, Go Get Qualified Leads

Then somebody figured out that all those students and consultants and self-employed people who worked at companies way outside the company’s target customer size range and couldn’t actually buy our software.  So the focus changed to get qualified leads.  Qualified first basically meant not unqualified:

  • It couldn’t be garbage, illegible, or duplicate
  • It couldn’t be self-employed, students, or consultants
  • It couldn’t be other people who clearly can’t buy the software (e.g., in the wrong country, at too small a company, in a non-applicable industry)

Then people realized that not all not-unqualified leads were the same. 

Enter lead scoring.  The first systems were manual and arbitrarily defined:  e.g., let’s give 10 points for target companies, 10 points for a VP title, and 15 points if they checked buying-within-6-months on the lead form.  Later systems got considerably more sophisticated adding both firmographic and behavioral criteria (e.g., downloaded the Evaluation Guide).  They’d even have decay functions where downloading a white paper got you 10 points, but you’d lose a point every week since if there you had no further activity. 

The problem was, of course, that no one ever did any regressions to see if A leads actually were more likely to close than B leads and so on.  At one company I ran, our single largest customer was initially scored a D lead because the contact downloaded a white paper using his Yahoo email address.  Given such stories and a general lack of faith in the scoring system, operationally nobody ever treated an A lead differently from a D lead – they’d all get “6×6’ed” (6 emails and 6 calls) anyway by the sales development reps (SDRs).  If the score didn’t differentiate the likelihood of closing and the SDR process was score-invariant, what good was scoring? The answer: not much.

Hey Marketing, Go Get Pipeline

Since it was seemingly too hard to figure out what a qualified lead was, the emphasis shifted.  Instead of “go get leads” it became, “go get pipeline.”  After all, regardless of score, the only leads we care about are those that turn into pipeline.  So, go get that.

Marketing shifted emphasis from leads to pipeline as salesforce automation (SFA) systems were increasingly in place that made pipeline easier to track.  The problem was that nobody put really good gates on what it took to get into the pipeline.  Worse yet, incentives backfired as SDRs, who were at the time almost always mapped directly to quota-carrying reps (QCRs), were paid incentives when leads were accepted as opportunities.  “Heck,” thinks the QCR, “I’ll scratch my SDR’s back in order to make sure he/she keeps scratching mine:  I’ll accept a bunch of unqualified opportunities, my SDR will get paid a $200 bonus on each, and in a few months I’ll just mark them no decision.  No harm, no foul. “Except the pipeline ends up full of junk and the 3x self-fulfilling pipeline coverage prophecy is developed.  Unless you have 3x coverage, your sales manager will beat you up, so go get 3x coverage regardless of whether it’s real or not.  So QCRs stuff bad opportunities into the pipeline which in turn converts at a lower rate which in turn increases the coverage goal – i.e., “heck, we’re only converting pipeline at 25%, so now we need 4x coverage!”  And so on.

At one point in my career I actually met a company with 100x pipeline coverage and 1% conversion rates. 

Hey Marketing, Go Get Qualified Opportunities (SQLs)

Enter the sales qualified lead (SQL). Companies realize they need to put real emphasis on someone, somewhere in the process defining what’s real and what not.  That someone ends up the QCR and it’s now their job to qualify opportunities as they are passed over and only accept those that both look real and meet documented criteria.  Management is now focused on SQLs.  SQL-based metrics, such as cost-per-SQL or SQL-to-close-rate, are created and benchmarked.  QCRs can no longer just accept everything and no-decision it later and, in fact, there’s less incentive to anyway as SDRs are no longer basically working for the QCRs, but instead for “the process” and they’re increasingly reporting into marketing to boot.  Yes, SDRs will be paid on SQLs accepted by sales, but sales is going to be held highly accountable for what happens to the SQLs they accept. 

Hey Marketing, Go Get Qualified Opportunities Efficiently

At this point we’ve got marketing focused on SQL generation and we’ve built a metrics-driven inbound SDR team to process all leads. We’ve eliminated the cracks between sales and marketing and, if we’re good, we’ve got metrics and reporting in place such that we can easily see if leads or opportunities are getting stuck in the pipeline. Operationally, we’re tight.

But are we efficient? This is also the era of SaaS metrics and companies are increasingly focused not just on growth, but growth efficiency.  Customer acquisition cost (CAC) becomes a key industry metric which puts pressure on both sales and marketing to improve efficiency.  Sales responds by staffing up sales enablement and sales productivity functions. Marketing responds with attribution as a way to try and measure the relative effectiveness of different campaigns.

Until now, campaign efficiency tended to be measured a last-touch attribution basis. So when marketers tried to calculate the effectiveness of various marketing campaigns, they’d get a list of closed deals, and allocate the resultant sales to campaigns by looking at the last thing someone did before buying. The predictable result: down-funnel campaigns and tools got all of the credit and up-funnel campaigns (e.g., advertising) got none.

People pretty quickly realized this was a flawed way to look at things so, happily, marketers didn’t shoot the propellers off their marketing planes by immediately stopping all top-of-funnel activity. Instead, they kept trying to find better means of attribution.

Attribution systems, like Bizible, came along which tried to capture the full richness of enterprise sales. That meant modeling many different contacts over a long period of time interacting with the company via various mechanisms and campaigns. In some ways attribution became like search: it wasn’t whether you got the one right answer, it was whether search engine A helped you find relevant documents better than search engine B. Right was kind of out the question. I feel the same way about attribution. Some folks feel it doesn’t work at all. My instinct is that there is no “right” answer but with a good attribution system you can do better at assessing relative campaign efficiency than you can with the alternatives (e.g., first- or last-touch attribution).

After all, it’s called the marketing mix for a reason.

Hey Marketing, Go Get Qualified Opportunities That Close

After the quixotic dalliance with campaign efficiency, sales got marketing focused back on what mattered most to them. Sales knew that while the bar for becoming a SQL was now standardized, that not all SQLs that cleared it were created equal. Some SQLs closed bigger, faster, and at higher rates than others. So, hey marketing, figure out which ones those are and go get more like them.

Thus was born the ideal customer profile (ICP). In seed-stage startups the ICP is something the founders imagine — based on the product and target market they have in mind, here’s who we should sell to. In growth-stage startups, say $10M in ARR and up, it’s no longer about vision, it’s about math.

Companies in this size range should have enough data to be able to say “who are our most successful customers” and “what do they have in common.” This involves doing a regression between various attributes of customers (e.g., vertical industry, size, number of employees, related systems, contract size, …) and some success criteria. I’d note that choosing the success criteria to regress against is harder than meetings the eye: when we say we find to find prospects most like our successful customers, how are we defining success?

  • Where we closed a big deal? (But what if it came at really high cost?)
  • Where we closed a deal quickly? (But what if they never implemented?)
  • Where they implemented successfully? (But what if they didn’t renew?)
  • Where they renewed once? (But what if they didn’t renew because of uncontrollable factor such as being acquired?)
  • Where they gave us a high NPS score? (But what if, despite that, they didn’t renew?)

The Devil really is in the detail here. I’ll dig deeper into this and other ICP-related issues one day in a subsequent post. Meantime, TOPO has some great posts that you can read.

Once you determine what an ideal customer looks like, you can then build a target list of them and enter into the world of account-based marketing (ABM).

Hey Marketing, Go Get Opportunities that Turn into Customers Who Renew

While sales may be focused simply on opportunities that close bigger and faster than the rest, what the company actually wants is happy customers (to spread positive word of mouth) who renew. Sales is typically compensated on new orders, but the company builds value by building its ARR base. A $100M ARR company with a CAC ratio of 1.5 and churn rate of 20% needs to spend $30M on sales and marketing just to refill the $20M lost to churn. (I love to multiply dollar-churn by the CAC ratio to figure out the real cost of churn.)

What the company wants is customers who don’t churn, i.e., those that have a high lifetime value (LTV). So marketing should orient its ICP (i.e., define success in terms of) not just likelihood to {close, close big, close fast} but around likelihood to renew, and potentially not just once. Defining different success criteria may well produce a different ICP.

Hey Marketing, Go Get Opportunities that Turn into Customers Who Expand

In the end, the company doesn’t just want customers who renew, even if for a long time. To really the build the value of the ARR base, the company wants customers who (1) are relatively easily won (win rate) and relatively quickly (average sales cycle) sold, (2) who not only renew multiple times, but who (3) expand their contracts over time.

Enter net dollar expansion rate (NDER), the metric that is quickly replacing churn and LTV, particularly with public SaaS companies. In my upcoming SaaStr 2020 talk, Churn is Dead, Love Live Net Dollar Expansion Rate, I’ll go into why this happening and why companies should increasingly focus on this metric when it comes to thinking about the long-term value of their ARR base.

In reality, the ultimate ICP is built around customers who meet the three above criteria: we can sell them fairly easily, they renew, and they expand. That’s what marketing needs to go get!

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

In the previous post, I introduced the idea of an inverted demand generation (demandgen) funnel which we can use to calculate a marketing demandgen budget based given a sales target, an average sales price (ASP), and a set of conversion rates along the funnel. This is a handy tool, isn’t hard to make, and will force you into the very good habit of measuring (and presumably improving) a set of conversion rates along your demand funnel.

In the previous post, as a simplifying assumption, we assumed a steady-state situation where a company had a $2M new ARR target every quarter. The steady-state assumption allowed us to ignore two very real factors that we are going to address today:

  • Time. There are two phase-lags along the funnel. MQLs might take a quarter to turn into SALs and SALs might take two quarters to turn into closed deals. So any MQL we generate now won’t likely become a closed deal until 3 quarters from now.
  • Growth. No SaaS company wants to operate at steady state; sales targets go up every year. Thus if we generate only enough MQLs to hit this-quarter’s target we will invariably come up short because those MQLs are working to support a (presumably larger) target 3 quarters in the future.

In order to solve these problems we will start with the inverted funnel model from the previous post and do three things:

  • Quarter-ize it. Instead of just showing one steady-state quarter (or a single year), we are going to stretch the model out across quarters.
  • Phase shift it. If SALs take two quarters to close and MQLs take 1 quarter to become SALS we will reflect this in the model, by saying 4Q20 deals need come from SALs generated in 2Q20 which in turn come from MQLs generated in 1Q20.
  • Extend it. Because of the three-quarter phase shift, the vast majority of the MQLs we’ll be generating 2020 are actually to support 2021 business, so we need to extend the model in 2021 (with a growth assumption) in order to determine how big of a business we need to support.

Here’s what the model looks like when you do this:

You can see that this model generates a varying demandgen budget based on the future sales targets and if you play with the drivers, you can see the impact of growth. At 50% new ARR growth, we need a $1.47M demandgen budget in 2020, at 0% we’d need $1.09M, and at 100% we’d need $1.85M.

Rather than walk through the phase-shifting with words, let me activate Excel’s trace-precedents feature so you can see how things flow:

With these corrections, we have transformed the inverted funnel into a pretty realistic tool for modeling MQL requirements of the company’s future growth plan.

Other Considerations

In reality, your business may consist of multiple funnels with different assumption sets.

  • Partner-sourced deals are likely to have smaller deal sizes (due to margin given to the channel) but faster conversion timeframes and higher conversion rates. (Because we will learn about deals later in the cycle, hear only about the good ones, and the partner may expedite the evaluation process.)
  • Upsell business will almost certainly have smaller deal sizes, faster conversion timeframes, and much higher conversion rates than business to entirely new customers.
  • Corporate (or inside) sales is likely to have a materially different funnel from enterprise sales. Using a single funnel that averages the two might work, provided your mix isn’t changing, but it is likely to leave corporate sales starving for opportunities (since they do much smaller deals, they need many more opportunities).

How many of these funnels you need is up to you. Because the model is particularly sensitive to deal size (given a constant set of conversion rates) I would say that if a certain type of business has a very different ASP from the main business, then it likely needs its own funnel. So instead of building one funnel that averages everything across your company, you might be three — e.g.,

  • A new business funnel
  • An upsell funnel
  • A channel funnel

In part III of this series, we’ll discuss how to combine the idea of the inverted funnel with time-based close rates to create an even more accurate model of your demand funnel.

The spreadsheet I made for this series of posts is available here.

The Most Important Chart for Managing the Pipeline: The Opportunity Histogram

In my last post, I made the case that the simplest, most intuitive metric for understanding whether you have too much, too little, or just the right amount of pipeline is opportunities/salesrep, calculated for both the current-quarter and the all-quarters pipeline.

This post builds upon the prior one by examining potential (and usually inevitable) problems with pipeline distribution.  If the problem uncovered by the first post was that “ARR hides weak opportunity count,” the problem uncovered by this post is that “averages hide uneven distributions.”

In reality, the pipeline is almost never evenly distributed:

  • Despite the salesops team’s best effort to create equal territories at the start of the year, opportunities invariably end up unevenly distributed across them.
  • If you view marketing as dropping leads from airplanes, the odds that those leads fall evenly over your territories is zero.  In some cases, marketing can control where leads land (e.g., a local CFO event in Chicago), but in most cases they cannot.
  • Tenured salesreps (who have had more time to develop their territories) usually have more opportunities than junior ones.
  • Warm territories tend to have more opportunities than cold ones [1].
  • High-activity salesreps [2] tend to have more opportunities than their more average-activity counterparts.

The result is that even my favorite pipeline metric, opportunities/salesrep, can be misleading because it’s a mathematical average and a single average can be produced by very different distributions.  So, much as I generally prefer tables of numbers to charts, here’s a case where we’re going to need a chart to get a look at the distribution.

Here’s an example:

oppty histo

Let’s say this company thinks its salesreps need 7 this-quarter and 16 all-quarters opportunities in order to be successful.  The averages here, shown by the blue and orange dotted lines respectively, say they’re in great shape — the average this-quarter opportunities/salesrep is 7.1 and the average all-quarters is 16.6.

But behind that lies a terrible distribution:  only 4 salesreps (reps 2, 7, 10, and 13) have more than 7 opportunities in the current quarter.  The other 11 are all starving to various degrees with 5 reps having 4 or fewer opportunities.

The all-quarters pipeline is somewhat healthier.  There are 8 reps above the target of 16, but nevertheless, certain reps are starving on both a this-quarter and all-quarters basis (reps 4, 11, 12, and 14) and have little chance at either short- or mid-term success.

Now that we can use this chart to highlight this problem, let’s examine the three ways to solve it.

  • Generate more opportunities, ideally in a super-targeted way to help the starving reps without further burying the loaded reps.  Sales loves to ask for this solution.  In practice, it’s hard to execute and inherently phase-lagged.
  • Reduce the number of reps.  If reps 4, 11, and 12 have been at the company for a long time and continuously struggled to hit their numbers, we can “Lord of the Flies” them, and reassign their opportunities to some of the surviving reps.  The problem here is that you’re reducing sales quota capacity — it’s a potentially good short-term fix that hurts long-term growth [3].
  • Reallocate opportunities from loaded reps to starving reps.  Sales management usually loathes this “Robin Hood” approach because there are few things more difficult than taking an opportunity from a sales rep.  (Think:  you can pry it from my cold dead fingers.)  This is a real problem because it is the best solution to the problem [4] — there is no way that reps 7 and 13 can actively service all their opportunities and the company is likely to be losing deals it could have won because of it [5].

You can download the spreadsheet for this post, here.

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Notes

[1] The distinction here is whether the territory has been continuously and actively covered (warm) vs. either totally uncovered or partially covered by another rep who did not actively manage it (cold).

[2] Yes, David C., if you’re reading this while doing a demo from the back seat of your car that someone else is driving on the NJ Turnpike, you are the archtype!

[3] It’s also a bad solution if they are proven salesreps simply caught in a pipeline crunch, perhaps after having had a blow-out result in the prior quarter.

[4] Other solutions include negotiating with the reps — e.g., “if you hand off these four opportunities I’ll uplift the commissions twenty percent and you’ll split it with salesrep I assign them to — 60% of something is a lot more than 100% of zero, which is what you’ll get if you can’t put enough time into the deal.”

[5] Better yet, in anticipation of the inevitable opportunity distribution problem, sales management can and should leave fallow (i.e., unmapped) territories, so they can do dynamic rebalancing as opportunities are created without enduring the painful “taking” of an opportunity from a salesrep who thinks they own it.

Do We Have Enough Pipeline? The One Simple Metric Many Folks Forget.

Pipeline is a frequently scrutinized SaaS company metric because it’s one of relatively few leading indicators in a SaaS business — i.e., indicators that don’t just tell us about the past but that help inform us about the future, providing important clues to our anticipated performance this quarter, next quarter, and the one after that.

Thus, pipeline gets examined a lot.  Boards and investors love to look at:

  • Aggregate pipeline for the year, and how it’s changing [1]
  • Pipeline coverage for the quarter and whether a company has the magical 3x coverage ratio that most require [2]
  • Pipeline with and without the high funnel (i.e., pipeline excluding stage 1 and stage 2 opportunities) [3]
  • Pipeline scrubbing and the process a company uses to keep its pipeline from getting inflated full of junk including, among other things, rolling hairballs.
  • Expected values of the pipeline that create triangulation forecasts, such as stage-weighted expected value or forecast-category-weighted expected value.

But how much pipeline is enough?

“I’ve got too much pipeline, I wish the company would stop sending so many opportunities my way”  — Things I Have Never Heard a Salesperson Say.

Some try to focus on building an annual pipeline.  I think that’s misguided.  Don’t focus on the long-term and hope the short-term takes care of itself; focus consistently on the short-term and long-term will automatically take care of itself.  I made this somewhat “surprised that it’s seen as contrarian” argument in I’ve Got a Crazy Idea:  How About We Focus on Next-Quarter’s Pipeline?

But somehow, amidst all the frenzy a very simple concept gets lost.  How many opportunities can a salesperson realistically handle at one time? 

Clearly, we want to avoid under-utilizing salespeople — the case when they are carrying too few opportunities.  But we also want to avoid them carrying too many — opportunities will fall through the cracks, prospect voice mails will go unreturned, and presentations and demos will either be hastily assembled or the team will request extensions to deadlines [4].

So what’s the magic metric to inform you if you have too little, too much, or just the right amount of pipeline?  Opportunities/salesrep — measured both this-quarter and for all-quarters.

What numbers define an acceptable range?

My first answer is to ask salesreps and sales managers before they know what you’re up to.  “Hey Sarah, out of curiosity, how many current-quarter opportunities do you think a salesrep can actually handle?”  Poll a bunch of your team and see what you get.

Next, here are some rough ranges that I’ve seen [5]:

  • Enterprise reps:  6 to 8 this-quarter and 12 to 15 all-quarters opportunities
  • Corporate reps:  10 to 12 this-quarter and 15 to 20 all-quarters opportunities

I’ve been in meetings where the CRO says “we have enough pipeline” only to discover that they are carrying only 2.5 current-quarter opportunities per salesrep [6].  I then ask two questions:  (1) what’s your close rate and (2) what’s your average sales price (ASP)?  If the CRO says 40% and $125K, I then conclude the average salesrep will win one (0.4 * 2.5 = 1), $125K deal in the quarter, about half a typical quota.  I then ask:  what do the salesreps carrying 2.5 current-quarter opportunities actually do all day?  You told me they could carry 8 opportunities and they’re carrying about a quarter of that?  Silence usually follows.

Conversely, I’ve been in meetings where the average enterprise salesrep is carrying close to 30 large, complex opportunities.  I think:  there’s no way the salesreps are adequately servicing all those deals.  In such situations, I have had SDRs crying in my office saying a prospect they handed off to sales weeks ago called them back, furious about the poor service they were getting [7].  I’ve had customers call me saying their salesrep canceled a live demo on five minutes’ notice via a chickenshit voicemail to their desk line after they’d assembled a room full of VIPs to see it [8].  Bad things happen when your salesreps are carrying too many opportunities.

If you’re in this situation, hire more reps.  Give deals to partners.  Move deals from enterprise to corporate sales.  But don’t let opportunities that cost the company between $2,000 and $8,000 to create just rot on the table.  As I reminded salesreps when I was a CEO:  they’re not your opportunities, they’re my opportunities — I paid for them.

Hopefully, I’ve made the case that going forward, while you should keep tracking pipeline on an ARR basis and looking at ARR conversion rates, you should add opportunity count and opportunity count / salesrep to your reports on the current-quarter and the all-quarters pipeline.  It’s the easiest and most intuitive way to understand the amount of your pipeline relative to your ability to process it.

# # #

Notes

[1] With an eye to two rules of thumb:  [a] that annual starting pipeline often approximate’s this year’s annual sales and [b] that the YoY growth rate in the size of the pipeline predicts YoY growth rate in sales.

[2] Pipeline coverage = pipeline / plan.  So if you have 300 units of pipeline and a new ARR plan of 100 units, then you have 3.0x pipeline coverage.

[3] Though there’s a better way to solve this problem — rather than excluding early-stage opportunities that have been created with a placeholder value, simply create new opportunities with value of $0.  That way, there’s nothing to exclude and it creates a best-practice (at most companies) that sales can’t change that $0 to a value without socializing the value with the customer first.

[4] The High Crime of a company slowing down its own sales cycles!  Never forget the sales adage:  “time kills all deals.”

[5] You can do a rough check on these numbers using close rates and ASPs.  If your enterprise quota is $300K/quarter, your ASP $100K, and your close rate 33%, a salesrep will need 9 current-quarter opportunities to make their number.

[6] The anemic pipeline hidden, on an ARR basis, by (unrealistically) large deal sizes.

[7] And they actually first went to HR seeking advice about what to do, because they didn’t want “rat out” the offending salesrep.

[8] Invoking my foundational training in customer support, I listened actively, empathized, and offered to assign a new salesrep — the top rep in the company — to the account, if they’d give us one more chance.  That salesrep turned a deal that the soon-to-be-former salesrep was too busy to work on, into the deal of the quarter.

I’ve Got a Crazy Idea:  How About We Focus on Next-Quarter’s Pipeline?

I’m frankly shocked by how many startups treat pipeline as a monolith.

Sample CMO:  “we’re in great shape because we have a total pipeline of $32M covering a forward-four-quarter (F4Q) sales target of $10M, so 3.2x coverage.  Next slide, please.”

Regardless of your view on the appropriate magic pipeline coverage number (e.g., 2x, 3x, 4x), I’ve got a slew of serious problems with this.  What do I think when someone says this?

“Wait, hang on.  How is that pipeline distributed by quarter?  By stage?  By forecast category?  By salesrep?  You can’t just look at it as a giant lump and declare that you’re in great shape because you have 3x the F4Q coverage.  That’s lazy thinking.  And, by the way, you probably don’t even need 3x  the F4Q target, but you sure as hell need 3x this quarter’s coverage [1] and better be building to start next quarter with 3x as well.  You do understand that sales can starve to death and we can go out of business – the whole time with 3x pipeline coverage — if it’s all pipeline that’s 3 and 4 quarters, out?”

I’ve got a crazy idea.  How about as a first step, we stop looking at annual pipeline [2] and start looking at this-quarter pipeline and, most importantly, next-quarter pipeline?

What people tell me when I say this:  “No, no, Dave.  We can’t do that.  That’s myopic.  You need to look further out.  You can’t drive looking at the hood ornament.  Plus, with a 90-day average sales cycle (ASC) there’s nothing we can do anyway about the short term.  You need to think big picture.”

I then imagine the CMO talking to the head of demandgen:  “Yep, it’s week 1 and we only have 2.1x pipeline coverage.  But with a 90-day sales cycle, there’s nothing we can do.  Looks like we’re going to hit the iceberg.  At least we made our 3x coverage OKR on a rolling basis.  Hey, let’s go grab a flat white.”

I loathe this attitude for several reasons:

  • It’s parochial. The purpose of marketing OKRs is to enable sales to hit sales OKRs.  Who cares if marketing hit its pipeline OKR but sales is nevertheless flying off a cliff?  Marketing just had a poorly chosen OKR.
  • It’s defeatist. If “when the going gets tough, the tough get a flat white” is your motto, you shouldn’t work in startup marketing.
  • It’s wrong. The A in ASC stands for average.  Your average sales cycle.  It’s not your minimum sales cycle.  If your average sales cycle is 90 days [3] then you have lots of deals that close faster than 90 days, so instead of getting a flat white marketing should be focused on finding a bunch of those, pronto [4].

Here’s my crazy idea.  Never look at rolling F4Q pipeline again.  It doesn’t matter.  What you really need to do is start every quarter with 3.0x [5] pipeline.  After all, if you started every quarter with 3.0x pipeline coverage wouldn’t that mean you are teed up for success every quarter?  Instead of focusing on the long-term and hoping the short-term works out, let’s continually focus on the short-term and know the long-term will work out.

This brings to mind Kellogg’s fourth law of startups:  you have to survive short-term in order to exist long-term.

This-Quarter Pipeline
This process starts by looking at the this-quarter (aka, current-quarter) pipeline.  While it’s true that in many companies marketing will have a limited ability to impact the current-quarter pipeline — especially once you’re 5-6 weeks in — you should nevertheless always be looking at current-quarter pipeline and current-quarter pipeline coverage calculated on a to-go basis.  You don’t need 3x the plan number every single week; you need 3x coverage of the to-go number to get to plan.  To-go pipeline coverage provides an indicator of confidence in your forecast (think “just how lucky to do we have to get”) and over time the ratio can be used as an alternative forecasting mechanism [6].

this qtr togo

In the above example, we can see a few interesting patterns.

  • We start the quarter with high coverage, but it quickly becomes clear that’s because the pipeline has not yet been cleaned up. Because salespeople are usually “animals that think in 90-day increments” [7], next quarter is effectively eternity from the point of view of most salesreps, so they tend to dump troubled deals in next-quarter [8] regardless of whether they actually have a next-quarter natural close date.
  • Between weeks 1 and 3, we see $2,250K of current-quarter pipeline vaporize as part of sales’ cleanup. Note that $250K was closed – the best way for dollars to exit the pipeline!  I always do my snapshot pipeline analytics in week 3 to provide enough time for sales to clean up before trying to analyze the data.  (And if it’s not clean by week 3, then you have a different conversation with sales [9].)
  • Going forward, we burn off more pipeline to fall into the 2.6 to 2.8 coverage range but from weeks 5 to 9 we are generally closing and burning off pipeline [10] at the same rate – hence the coverage ratio is running in a stable, if somewhat tight, range.

Next-Quarter Pipeline
Let’s now look at next-quarter pipeline.  While I think sales needs to be focused on this-quarter pipeline and closing it, marketing needs to be primarily focused on next-quarter pipeline and generating it.  Let’s look at an example:

next qtr pipe

Now we can see that next-quarter plan is $3,250K and we start this quarter with $3,500K in next-quarter pipeline or 1.1x coverage.  The 1.1x is nominally scary but do recall we have 12 weeks to generate more next-quarter pipeline before we want to start next quarter with 3x coverage, or a total pipeline of $9,750K.  Once you start tracking this way and build some history, you’ll know what your company’s requirements are.  In my experience, 1.5x next-quarter coverage in week 3 is tight but works [11].

The primary point here is that given:

  • Your knowledge of history and your pipeline coverage requirements
  • Your marketing plans for the current quarter
  • The trends you’re seeing in the data
  • Normal spillover patterns

That marketing should be able to forecast next quarter’s starting pipeline coverage.  So, pipeline coverage isn’t just an iceberg that marketing thinks we’ll hit or miss.  It’s something can marketing can forecast.  And if you can forecast it, then you adjust your plans accordingly to do something about it.

Let’s stick with our example and make a forecast for next-quarter starting pipeline [12]

  • Note that we are generating about $250K of net next-quarter pipeline per week from weeks 4 to 9.
  • Assume that we are continuing at steady-state the programs generating that pipeline and ergo we can assume that over the next four weeks we’ll generate another $1M.
  • Assume we are doing a big webinar that we think will generate another $750K in next-quarter pipeline.
  • Assume that 35% of the surplus this-quarter pipeline slips to next-quarter [13]

If you do this in a spreadsheet, you get the following.  Note that in this example we are forecasting a shortfall of $93K in starting next-quarter pipeline coverage.  Were we forecasting a significant gap, we might divert marketing money into demand generation in order to close the gap.

fc next qtr

All-Quarters Pipeline
Finally, let’s close with how I think about all-quarters pipeline.

all qtr

While I don’t think it’s the primary pipeline metric, I do think it’s worth tracking for several reasons:

  • So you can see if pipeline is evaporating or sloshing. When a $1M forecast deal is lost, it comes out of both current-quarter and all-quarters pipeline.  When it slips, however, current-quarter goes down by $1M but all-quarters stays the same.  By looking at current-quarter, next-quarter, and all-quarters at the same time in a compact space you can get sense for what is happening overall to your pipeline.  There’s nowhere to hide when you’re looking at all-quarters pipeline.
  • So you can get a sense for the size of opportunities in your pipeline.  Note that if you create opportunities with a placeholder value then there’s not much  purpose in doing this (which is just one reason why I don’t recommend creating opportunities with a placeholder value) [14].
  • So you can get a sense of your salesreps’ capacity. The very first number I look at when a company is missing its numbers is opportunities/rep.  In my experience, a typical rep can handle 8-12 current-quarter and 15-20 all-quarters opportunities [15].  If your reps are carrying only 5 opportunities each, I don’t know how they can make their numbers.  If they’re carrying 50, I think either your definition of opportunity is wrong or you need to transfer some budget from marketing to sales and hire more reps.

The spreadsheet I used in this post is available for download here.

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Notes

[1] Assuming you’re in the first few weeks of the quarter, for now.

[2] Which is usually done using forward four quarters.

[3] And ASC follows a normal distribution.

[4] Typically, they are smaller deals, or deals at smaller companies, or upsells to existing customers.  But they’re out there.

[5] Or, whatever your favorite coverage ratio is.  Debating that is not the point of this post.

[6] Once you build up some history you can use coverage ratios to predict sales as a way of triangulating on the forecast.

[7] As a former board member always told me — a quote that rivals “think of salespeople as single-celled organisms driven by their comp plan” in terms of pith.

[8] Or sometimes, fourth-quarter which is another popular pipeline dumping ground.  (As is first-quarter next year for the truly crafty.)

[9] That is, one about how they are going to get their shit together and manage the pipeline better, the first piece of which is getting it clean by week 3, often best accomplished by one or more pipeline scrub meetings in weeks 1 and 2.

[10] Burning off takes one of three forms:  closed/won, lost or no-decision, or slipping to a subsequent quarter.  It’s only really “burned off” from the perspective of the current-quarter in the last case.

[11] This depends massively on your specific business (and sales cycle length) so you really need to build up your own history.

[12] Technically speaking, I’m making a forecast for day-1 pipeline, not week-3 pipeline.  Once you get this down you can use any patterns you want to correct it for week 3, if desired.  In reality, I’d rather uplift from week 3 to get day-1 so I can keep marketing focused on generating pipeline for day-1, even though I know a lot will be burned off before I snapshot my analytics in week 3.

[13] Surplus in the sense that it’s leftover after we use what we need to get to plan.  Such surplus pipeline goes three places:  lost/no-decision, next-quarter, or some future quarter.  I often assume 1/3rd  goes to each as a rule of thumb.

[14] As a matter of principle I don’t think an opportunity should have a value associated with it until a salesrep has socialized a price point with the customer.  (Think:  “you do know it cost about $150K per year to subscribe to this software, right?”)  Perversely, some folks create opportunities in stage 1 with a placeholder value only to later exclude stage 1 opportunities in all pipeline analytics. Doing so gets the same result analytically but is an inferior sales process in my opinion.

[15] Once you’re looking at opportunities/rep, you need to not stop with the average but make a histogram.  An 80-opportunity world where 10 reps have 8 opportunities each is a very different world from one where 2 reps have 30 opportunities each and the other 8 have an average of 2.5.

How To Sales Manage Upside and Unlikely Deals

If your sales organization is like most, you classify sales opportunities in about four categories, such as:

  • Commit, which are 90% likely to close
  • Forecast, which are 70% likely to close
  • Upside, which are 33% likely to close
  • Unlikely, which are 5% likely to close

And then, provided you have sufficient pipeline, your sales management team basically puts all of its effort into and attention on the commit and forecast deals.  They’re the ones that get deal reviews.  They’re the ones where the team does multiple dry runs before big demos and presentations.  They’re the deals that get discussed every week on the forecast call.

The others ones?  No such much.  Sure, the salesreps who own them will continue to toil away.  But they won’t get much, if any, management attention.  You’ll probably lose 75% of them and it won’t actually matter much, provided you have enough high-probability deals to make your forecast and plan.

But, what a waste.  Those opportunities probably each cost the company $2500 to $5000 to generate and many multiples of that to pursue.  But they’re basically ignored by most sales management teams.

The classical solution to this problem is to tell the sales managers to focus on everything.  But it doesn’t work.   A smart sales manager knows the only thing that really matters is making his/her number and doing that typically involves closing almost all the committed and most of the forecast deals.  So that is where their energy goes.

jumpballThe better way to handle these deals is to recognize they’re more likely to be lost than won (e.g., calling them jump-balls, 50/50 balls, or face-offs, depending on your favorite sport), find the most creative non-quota-carrying manager in the sales organization (e.g., VP of salesops) and have him/her manage these low-probability, high-risk deals in the last month of the quarter using non-traditional (i.e., Crazy Ivan) tactics.

This only works if you have happen to have a VP of salesops, enablement, alliances, etc., who has the experience, passion, and creativity to pull it off, but if you do it’s a simply fantastic way to allow core sales management to focus on the core deals that will make or break the quarter while still applying attention and creativity to the lower probability deals that can drive you well over your targets.

This is not as crazy as it might sound, because those in sales ops or productivity positions typically do have prior sales management experience.  Thus, this becomes a great way to keep their saw sharp and keep them close/relevant to the reality of the field in performing their regular job.  What could be better than a VP of sales productivity who works on closing deals 4 months/year?

If your VP of sales ops or sales enablement doesn’t have the background or interest to do this, maybe they should.  If not, and/or you are operating at bigger scale, why not promote a salesperson with management potential into jump-ball, overlay deal management as their first move into sales management?