Tag Archives: Funnel

What Do “Pipeline Coverage” and “Forecast” Mean When Your Sales Cycle is 30 Days?

I grew up in enterprise.  I have already written a post on the tricky problem of mapping one’s mindset from enterprise to velocity SaaS, meaning smaller deals, shorter contract durations (e.g., month-to-month), and/or monthly-varying pricing [1].  That post was focused on what, if anything, “annual recurring revenue” (ARR) means such an environment, and how that impacts metrics that rely on ARR as part of their definition (e.g., CAC ratio).

In this post, I’ll continue in the velocity SaaS direction by exploring short average sales cycles (ASC), as opposed to short contracts.  Specifically, what does it mean in short ASC companies when you discuss common concepts like pipeline coverage and the sales forecast?

Let’s demonstrate the problem.

In enterprise, quarterly pipeline (defined as the sum of the values of opportunities with a close date in the quarter) is somewhat intertwined the notion of long sales cycles.  Meaning that in a company with 9–12-month sales cycles, virtually every deal that has a chance of closing within the quarter is already in the pipeline at the start of the quarter.  Thus, you can meaningfully calculate “coverage” for the quarter by dividing the quarterly starting pipeline by the quarterly sales target.  Most sales VPs like a 3x ratio [2].

Thus, the concept of pipeline coverage implicitly assumes a sales cycle (significantly) longer than the coverage period.  That’s why most companies don’t look at out-quarter pipeline coverage much (though they should) and if they do, they expect a much lower coverage ratio.

Now, let’s imagine an average sales cycle of 30 days and — rather than futzing with cohorts, statistics, and distributions [3] — let’s assume that all oppties are won or lost in exactly 30 days [4].

In this scenario, at the start of the quarter, what is the pipeline coverage ratio? It’s 1.0x.  Why?  We have zero pipeline for months 2 and 3 of the quarter.  If we assume that we have 3.0x coverage for month one and that the quarterly goal is evenly distributed across months, then we’d have 3.0x, 0.0x, and 0.0x for the three months of the quarter, or 1.0x overall [5].

In this example, quarterly pipeline coverage is basically meaningless because two-thirds of the pipeline you need to close during the quarter hasn’t been created yet.  Assuming a 30-day MQL-to-opportunity lag, one-third is working its way through the high funnel and the other third is still a wink in marketing’s eye.

If quarterly pipeline coverage is basically meaningless in short ASC companies, then what is meaningful?

  • Examining monthly pipeline coverage. Instead of week-3 quarterly pipeline coverage [6], we should look at day-3 monthly pipeline coverage — dividing the starting monthly pipeline by the monthly sales target. (After that, you can use to-go pipeline coverage to get continuous insight.)
  • Treating months 2 and 3 the way you’d treat next-quarter and the quarter thereafter in enterprise. Using a pipeline progression chart to see how the out-month pipeline is shaping up.
  • Getting marketing to forecast starting pipeline for month 2 and month 3, based on what they have already generated in the high funnel and their current pipeline generation plans for month 2.

Inherent in my point of view is that the definition of “coverage” is based on opportunities that already exist in the pipeline. Call me untrusting, but somehow I can’t feel covered by something that hasn’t been created yet.  Some might define quarterly coverage in this environment using month 1 pipeline plus month 2 pipeline forecast and month 3 pipeline plan.  But to me, that’s not coverage.  And it’s objectively not the same thing as pipeline coverage when we use the term in enterprise.

Now, let’s zip back to reality for a minute.  In the velocity companies that I work with, ASC is closer to 60 days and with a pretty broad distribution where maybe 90% of the deals close within 30 and 120 days.  Happily, this means you will have month 2 and month 3 opportunities in the starting quarter pipeline, but it nevertheless also means you will be increasingly reliant on to-be-generated opportunities across the months of the quarter.

In this case, I would make a three-layer forecast:

  • Sales (from existing opportunities). Forecast month 1, 2, and 3 sales using the normal sales forecasting process.
  • Marketing, from the high funnel. Use existing MQLs and your standard conversion rates, ideally time-based time-based (not just the total rate, but the rate split by time period)
  • Marketing, from planned demandgen. Forecast responses, then use standard conversion rates and ideally time-based. (Ideally you can start with your inverted funnel model.)

This approach is preferable to looking only at pipeline generation (pipegen) because a pipegen approach:

  • Tends to ignore the oppties that are already there
  • Almost always ignores that time-based nature of close rates
  • Uses an average sales price (ASP) as the proxy value for an opportunity [7].

In the example above you can clearly see how much of the forecast comes from existing opportunities (51%), how much from the existing high funnel (36%), and how much from planned demandgen activities (13%).

Finally, I have the same problem with the word “forecast” as I do with “coverage” in the short ASC world. They’re not quite the same thing as they are in enteprise. First, let me define “forecast,” along with its cousins, “plan” and “model.”

  • The plan is about accountability. It’s what we signed up for and accountable to. Budget is a synonym [8].
  • The model is a driver-based model of the business. It’s a calculated output (e.g., opportunities generated) given assumptions for a number of inputs and the way they interact (e.g., demandgen spend, MQLs generated, conversion rates).
  • The forecast is about prediction. It’s someone’s latest prediction for an output (e.g., bookings) given all available information at the time it’s made.

The plan is what we were willing to sign up for last December (when we received board approval). The forecast is what we think is going to happen now.  We used models to help build the original plan and we can certainly re-run those models today using actuals as inputs to see what they produce.

In enterprise, the sales forecast is all about the deals in play.  What if Mike closes deals A, B, and either C or D.  The buyer at deal E promised me they’d give us the order.  Given everything we know about Sally’s deal F, what value do we think it will close at?  Sales VPs spend hours in Excel (or a modern forecasting tool like Clari) running scenarios to arrive a number.  It’s usually more about different combinations of deals than it is about probabilities and expected values.

In the velocity world, as discussed above, the forecast cannot be only about existing deals. If you want to forecast a quarter, you’ll need to include results from the high-funnel and planned demangen. I’d still call it a forecast, but I’d know that it’s not quite the same thing as a forecast in enterprise. And by presenting in the three layers above, you can remind everyone of that.

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[1] Monthly-varying SaaS is a different concept, which I used in that post, featuring short contracts (e.g., month-to-month) where the spend can vary every month, usually as the result of a flexible user-based pricing model, a consumption-based pricing model, or a hybrid pricing model (e.g., base + overage).  In such environments, simple SaaS concepts like ARR can quickly lose meaning, as do the metrics that rely on them (e.g., CAC ratio).

[2] Which I think had its ancient origins in the idea that you win 33%, lose 33%, and 33% slip. (Thus assuming a 50% competitive win rate.) Regardless of its roots, 3x (starting) coverage is a widely accepted norm, so much so that I fear it’s often a self-fufilling prophecy.

[3] We’re ignoring the distribution of average sales cycle length for closed/won deals, its standard deviation, and the fact the three different outcomes (i.e., win, loss, slip) will likely have three different average opportunity cycle lengths (e.g., you usually lose faster than you win), each with its own distribution.

[4] And, most unrealistically, that deals never slip to a subsequent period. We’re also assuming that all opportunities are generated on the first day of month, an exactly 30-day lag from MQL to opportunity, and that all MQLs are generated on the first day of month, and convert in exactly 30 days. (And, for the detail-oriented, that every month is 30 days.) Overall, with these simplifying assumptions, you start every month with only the opportunities generated from MQLs generated the prior month and only those opportunities. There is no leftover pipeline sloshing around to confuse things.

[5] The reality is likely somewhat less than 1.0x because we’d normally expected to some backloading (“linearity”) of the quarterly target across the months of the quarter.  In enterprise, that backloading is severe (e.g., most enterprise cash models assume a 10/20/70 distribution). In velocity SaaS, I’ve seen from 30/30/40 (i.e., pretty flat) to 10/20/70 (i.e., as backloaded as enterprise), typically reflecting a quarterly (as opposed to a monthly) sales cadence which is usually a mistake in a velocity model.

[6] To intelligently compare pipeline across quarters we need to fix a point in time to snapshot it. In enterprise, I prefer day one of week three because it’s early enough to take actions (e.g., reducing expenses), but late enough so sales can no longer credibly claim they need more time for pipeline cleanup (aka, scrubbing).

[7] In enterprise, this is a major sin because deal sizes vary significantly and values should be inserted only after discovery and price-point socialization (e.g., “you do know that this costs $150K?”)  In velocity, it’s a lesser sin because the deal sizes tend to be more similar.  Either way, if all we’re doing is counting opportunities and multiplying by a constant, then why not just admit it and count opportunities directly? The more sophisticated the proxy, the more I like it (e.g., using $10K for SMB, $25K for MM, and $75K for ENT).

[8] Technically, I’d say budget is a synonym for the financial part of the plan. That is, a budget is only one part of a plan. A plan would also include strategic goals, objectives for attaining them, and organization structure.

How to Simplify Your Marketing Funnel:  Seeing the Unit Cost Forest for the Conversion Rates Trees

Let’s say you’re a CEO.  You don’t come from a marketing background.  At every quarterly business review (QBR) and board meeting, your marketing head presents a chart like this:

What happens next?  More than likely, after 10 or 15 minutes of effectively random probes into this minefield of numbers, you do what any good CEO would under the circumstances.  You say:

“Next slide, please.”

To paraphrase Thoreau, the mass of CEOs lead lives of quiet marketing desperation. Slides like this are why. What’s wrong with this slide? [1]

Well, to get this out of my system, there are a number of what I’d call mechanical problems:

  • It mixes different time periods as the reader scans across columns making it difficult to spot trends.  Better to group quarters and years on the right.
  • It has excess precision.  Too many digits are unnecessary and impede comprehension.  Better to show pageviews by the thousand, demandgen by the kilodollar ($K), cost/MQL without the pennies, and conversion rates to the percentage point, not the basis point.
  • It contains too many rows.  Even if they’re all of interest (and they aren’t), it’s simply too much.
  • It fails to use formatting, such as commas, to make figures more easily grasped.

These details aren’t nits [2].  Particularly if you’re a finance or ops person (e.g., saleops, marketingops), your job is to present data in a way that is clear, consistent, and comprehensible.  In short, your job is to “light shit up” when there are problems.  This slide does anything but.

More importantly, there are what I’d call conceptual problems with the slide:

  • It’s a sea of numbers that drowns the reader in data, making it impossible to find insights.  To paraphrase the old saw, “all these trees are making it hard for me to see the forest.”
  • It’s supposed to be a summary of the funnel for a board meeting or QBR.  This summary doesn’t summarize.
  • It contains numerous rows that are not appropriate for such a summary and serve only to cognitively overload the reader.
  • Worst yet, it omits rows of high potential interest.  Specifically, unit cost (e.g., cost/oppty) rows that can help readers understand the viability of the business model [3].

In the above table, I tried to hide a big problem floating in that sea of numbers. Did you find it? Did the slide help you do so?

Before transforming the table into something more useful, let’s talk briefly about what we’re going to do. Three simple things:

  • Take hops down the funnel instead of steps.  Instead of looking at each conversion rate as we descend, we will look only at MQLs, stage 1 and stage 2 oppties, closed/won deals, and associated conversion rates between them. Any problems involving intermediate conversion rates between those hops will usually show up in those numbers, anyway [4].
  • Add cost information.  Ultimately, the business cares about how much things cost, not just what the rates are compared to benchmarks and to history.
  • Be sensitive to cognitive overload, both in terms of the size of the table and the total number of digits we’re going to put before the reader.

In addition, I’m going to keep website unique visitors not because it strictly helps the funnel analysis, but simply because I think it’s a good leading indicator [5], and I’m going to add information about new ARR booked and the average sales price (ASP). In the end, the point of all this marketing is to bring in new ARR. Finally I’m going to add highlighting [6].

Here’s our chart, simplified and transformed [7]:

Here you can see a few important things that are not even present in the original chart:

  • Demandgen cost per deal has increased from $6.8K to $10.1K
  • Demandgen cost per stage-2 oppty has stayed remarkably constant at $2.2K
  • The stage2-to-close rate has dropped by a third, from 33% to 22%
  • The new ARR ASP (average sales price) has dropped from $33K to $26K, about 22%

Thus, while we are generating stage 2 oppties at the same cost, they are closing both at a much lower rate and for less value.  We can finally see what’s going on. We have a mid-to- low funnel problem in converting oppties to deals and in closing those deals at our historical value. Note that this analysis doesn’t tell us precisely what the problem is, but it does tell us where to go look. For that reason, I refer to this kind of chart as a smoke detector [8].

As part of the next-level investigation we might actually go back to the original chart. When I built the exercise, I tried to confine the problem to a single row, demo to shortlist conversion, which drops nearly monotonically across the year.

To understand why demo-to-shortlist is falling, I’d start asking sales questions, listening to demo calls, and speaking with prospects (who both kept and excluded us after the demo) to try and understand why we decreasingly reach the short list. Generically, I’d look to possible explanations such as:

  • A new demo script, that is perhaps less compelling than the old one
  • A new demo methodology, perhaps we’ve moved to a less customized boiler room approach to save money
  • A change in demo staffing, perhaps putting more junior SCs on demos or having sales take over basic demos
  • A new competitor in the market, who perhaps neutralizes some our once-differentiating features
  • A loss of market leadership, such that we are decreasingly seen as a must-evaluate product

The great irony of this example is that while I was trying to type numbers that didn’t vary that much (using mental math) across most rows, I failed pretty badly at so doing. My intent was to have every rate stay roughly constant while demo-to-shortlist fell by around 25 percentage points across the year. However, when I look at the data after the fact:

  • Meeting-to-SQL fell by more than 20 percentage points across the year
  • This was somewhat offset by MQL-to-appointment rising 17.5 percentage points across the year

So if this were real data, I’d have to go investigate those changes, too.

The point of this post is not that the next-level analysis and detailed step-by-step conversion rates are useless. The point is that unless you summarize (e.g., by analyzing hops) and map to business metrics that executives care about (e.g., cost/deal) that you will lose your audience (and maybe yourself) in the process.

And remember, we’d addressed just one form of funnel complexity in this example. Marketing-inbound funnel analysis. We haven’t looked across pipeline sources (e.g., partner, outbound, sales). We haven’t touched on attribution or marketing channel analysis. But when we approach those problems, we should do it the same way. Keep it simple. Come at it top down. Peel back the onion for the audience.

The spreadsheet I used for this post can be found on Scribd or Google Drive.

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[1] Let put aside of the question of whether it should be a chart.  Yes, there certainly is a time and place for charts, but in my experience, they are far too often a waste of space, using an entire screen to show 12 data points. (This always reminds me of the Hyderabadi taxi driver who once told me that lines on the roadway were a waste of paint.) Conversely, I’ve never met a board who can’t handle a well-prepared table full of numbers.  Let’s just stipulate here that a table is the right answer, and then make the best of that table, which is really the purpose of this post.

[2] “They’re important,” the author screams into the void.  My reputation notwithstanding, it’s not for obsessive-compulsive reasons, it’s for comprehensibility.  (Or perhaps, I’m obsessive about comprehensibility!) 

[3] For example, if your demandgen cost/opportunity is $4K and your close rate is 25%, then your demandgen cost/deal is $16K.  If, continuing the example, demandgen is 50% of your total marketing cost and sales & marketing contribute equally to your CAC, then you are spending $64K in total S&M cost per deal.  If your ARR ASP (average sales price) is $32K, then your CAC ratio will be around 2.0. If your ARR ASP is $128K, then your CAC ratio will be around 0.5. I say “around” because I presume you’re not operating at steady state and certain accounting conventions (e.g., amortizing commissions in sales expense) can cause variations with this back-of-the-envelope CAC ratio approach.

[4] Unless they magically happen to offset each other, as coincidentally largely happened when I created my synthetic data set (which you see if you read to the end of the post). Thus, this is not to say that no one should ever look at step-by-step conversion rates. It is to say that they have no business in a C-level summary.

[5] I think every marketer should track and share unique visitors. It’s a good leading indicator, if only loosely coupled to the demandgen funnel. It can be benchmarked against the competition (if somewhat imprecisely) and should be. The first time you do so is often sobering.

[6] You could argue this is cheating and that I could easily improve the wall of numbers chart by adding highlighting. While highlighting could quickly take you to the problem row, it’s not always the case that one row is so clearly responsible. (I contained the problem to one row here to make my life easier in making the slide, not because I think it’s common in reality, where stage defnitions are rarely so clear and used so consistently.)

[7] In addition to many other changes, I’m switching to my preferred nomenclature of stage-1 and stage-2 opportunity as opposed to SAL, SQL/SAO and such. Also, please note that at the risk of complexifying the chart, I’m separating stage1 and stage 2 oppties (instead of, say, just looking at stage 2s) because that is often the handoff point between SDRs and sales which makes it worth closely monitoring.

[8] Much as an employee engagement survey tells you, “there’s a management problem in product management,” but doesn’t tell you precisely what it is. But you know where to go to start asking questions.

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.


[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.

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.

A Historical Perspective on Why SAL and SQL Appear to be Defined Backwards

Most startups today use some variation on the now fairly standard terms SAL (sales accepted lead) and SQL (sales qualified lead).  Below see the classic [1] lead funnel model from marketing bellwether Sirius Decisions that defines this.

One great thing about working as an independent board member and consultant is that you get to work with lots of companies. In doing this, I’ve noticed that while virtually everyone uses the terminology SQL and SAL, that some people define SQL before SAL and others define SAL before SQL.

Why’s that?  I think the terminology was poorly chosen and is confusing.  After all, what sounds like it comes first:  sales accepting a lead or sales qualifying a lead?  A lot of folks would say, “well you need to accept it before you can qualify it.”  But others would say “you need to qualify it before you can accept it.”  And therein lies the problem.

The correct answer, as seen above, is that SAL comes before SQL.  I have a simple way of remembering this:  A comes before Q in the alphabet, and SAL comes before SQL in the funnel. Until I came up with that I was perpetually confused.

More importantly, I think I also have a way of explaining it.  Start by remembering two things:

  • This model was defined at a time when sales development reps (SDRs) generally reported to sales, not marketing [2].
  • This model was defined from the point of view of marketing.

Thus, sales accepting the lead didn’t mean a quota-carrying rep (QCR) accepted the lead – it meant an SDR, who works in the sales department, accepted the lead.  So it’s sales accepting the lead in the sense that the sales department accepted it.  Think: we, marketing, passed it to sales.

After the SDR worked on the lead, if they decided to pass it to a QCR, the QCR would do an initial qualification call, and then the QCR would decide whether to accept it.  So it’s a sales qualified lead, in the sense that a salesperson has qualified it and decided to accept it as an opportunity.

Think: accepted by an SDR, qualified by a salesrep.

Personally, I prefer avoid the semantic swamp and just say “stage 1 opportunity” and “stage 2 opportunity” in order to keep things simple and clear.

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[1] This model has since been replaced with a newer demand unit waterfall model that nevertheless still uses the term SQL but seems to abandon SAL.

[2] I greatly prefer SDRs reporting to marketing for two reasons:  [a] unless you are running a pure velocity sales model, your sales leadership is more likely to deal-people than process-people – and running the SDRs is a process-oriented job and [b] it eliminates a potential crack in the funnel by passing leads to sales “too early”.  When SDRs report to marketing, you have a clean conceptual model:  marketing is the opportunity creation factory and sales is the opportunity closing factory.