Tag Archives: Funnel

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.

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.

# # #

Notes

[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.