Tag Archives: MQL

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 Four Levers of SaaS

There are a lot of SaaS posts out there with some pretty fancy math in them.  I’m a math guy, so I like to geek on SaaS metrics myself.  But, in the heat of battle running a SaaS company, sometimes you just need to keep it simple.

Here’s the picture I keep on my wall to help me do that.

It reminds me that new ARR in any given period is the product of four levers.

  • The MQL to stage 2 opportunity conversion rate (MTS2CR), the rate at which MQLs convert to stage 2, or sales-accepted, opportunities.  Typically they pass through a stage 1 phase first when a sales development rep (SDR) believes there is a real opportunity, but a salesperson has not yet agreed.
  • The stage 2 to close rate (S2TCR), the rate at which stage 2 opportunities close into deals, and avoid being lost to a competitor or derailed (e.g., having the evaluation project cancelled).
  • The annual recurring revenue average sales price (ARR ASP), the average deal size, expressed in ARR.

That’s it.  Those four levers will predict your quarterly new ARR every time.

Aside:  before diving into each of the four levers, let me note that sales velocity is omitted from this model.  That keeps it simple, but it does overlook a potentially important lever.  So if you think you have a sales velocity (i.e., sales cycle length) problem, go look at a different model that includes this lever and suggests ways to decrease it.

So now that we have identified the four levers, let’s focus on what we can do about them in order to increase our quarterly new ARR.

Marketing Qualified Leads (MQLs)

Getting MQLs is the domain of marketing, which should be constantly measuring the cost effectiveness of various marketing programs in terms of generating MQLs (cost/MQL).  This isn’t easy because most leads will require numerous touches over time in order to graduate to MQL status, but marketing needs to stay atop that complexity (e.g., by assigning credits to various programs as MQL-threshold points accumulate).

The best marketers understand the demand is variable and have designed their programs mix so they can scale spending quickly in response to increased needs.  Nothing is worse than an MQL shortage and a marketing department that’s not ready to spend incremental money to address it.

The general rule is to constantly A/B test your programs and nurture streams and do more of what’s working and less of what isn’t.

MQL to Stage 2 Opportunity Conversion Rate

Increasing the MQL to stage 2 opportunity conversion rate (MTS2CR) requires either generating better MQLs or doing a better job handling them so that they convert into stage 2 opportunities.

Generating better MQLs can be accomplished by analyzing past programs to determine which generated the best-converting MQLs and increasing them, putting a higher gate on what you pass over to sales (using predictive or behavioral scoring), or using buyer personas to optimize what you say to buyers, when, and through which channels.

Do a better job handling your existing MQLs comes down ensuring your operational processes work and you don’t let leads fall between the cracks.  Basic activity and aging reports are a start.  Establishing a formal service-level agreement between sales and marketing is a common next step.

Moving up a level and checking that your whole process fits well with the customer’s buying journey is also key.  While each step of your process might individually make sense, when assembled the process may not — e.g., are you irritating customers by triple-qualifying them with an SDR, a salesrep, and a solution consultant each doing basic discovery?

The Stage 2 to Close Rate

Once created, one of three things can happen to a stage 2 opportunity:  you can win it, you can lose it, or it can derail (i.e., anything else, such as project cancellation or “slips” to the distant future).

Increasing your win rate can be accomplished through better product positioning, sales tools, and sales training, improved competitive intelligence, improved buzz/aura, improved case studies and customer references, and better pricing and discounting strategy.  That’s not to mention more strategic approaches via improved sales methodology and process or product improvements, in terms of functionality, non-functional requirements, and product design.

Decreasing your loss rate can be accomplished through better up-front sales qualification, better sales tools and training, improved competitive strategy and tactics, and better pricing and discounting.  Improved sales management can also play a key role in catching in-trouble deals early and escalating to get the necessary resources deployed to win.

Reducing your derail rate is hard because project slips or cancellations seem mostly out of your control.  What’s the best way to reduce your derail rate?  Focus on velocity — take deals off the table before the company has a chance to prioritize another project, do a reorganization, or hire a new executive that kills it.  The longer a deal hangs around, the more likely something bad happens to it.  As the adage goes, time kills all deals.

ARR ASP

The easiest way to increase ARR ASP is to not shrink it through last-minute discounting.  Adopt a formal discount policy with approvals so that, in the words of one famous sales leader, “your rep is more afraid of his/her sales manager than the customer” when it comes to speaking about discounts.

Selling value and product differentiation are two other discount reduction strategies.  The more customers see real value and a concrete return for their business the less they will focus on price.  Additionally, the more they see your offering as unique, the less price pressure you will face from the competition.  Conversely, the more they see your product as a cost and your company as one of several suppliers from whom they can buy the same capabilities, the more discount pressure you will face.

Up-selling to a higher edition or cross selling (“fries with your burger?”) are both ways to increase your ASP as well.  Just be careful to avoid customers feeling nickled and dimed in the process.

For SaaS businesses, remember that multi-year deals typically do not help your ARR ASP (though, if prepaid, they do help with year-one cash).  In fact, it’s usually the opposite — a small ARR discount is typically traded for the multi-year commitment.  My general rule of thumb is to offer a multi-year discount that’s less than your churn rate and everybody wins.

Conclusion

Hopefully this framework will make it easier for you to diagnose and act upon the problems that can impede achieving your company’s new ARR goals.  Always remember that any new ARR problem can be broken down into some combination of an MQL problem, an MQL to stage 2 conversion rate problem, a stage 2 to close rate problem, or an average sales price problem.  By focusing on these four levers, you should be able to optimize the productivity of your SaaS sales model.

 

 

Lead Nurturing, Fast and Slow

I’ll borrow the title of one of my favorite books (Thinking, Fast and Slow) to make a few important points about lead nurturing in this post.

While there is a strong argument that buyers should be nurtured before, during, and after the initial sale, I’m going to speak in this post about pre-sales lead nurturing, the purpose of which is to turn prospective buyers into marketing qualified leads, or MQLs.

For a widely used term, you’d be surprised how hard it is to find a good definition of MQL on the web. HubSpot’s definition, while a tad self serving, isn’t bad:

A marketing qualified lead (MQL) is a lead judged more likely to become a customer compared to other leads based on lead intelligence, often informed by closed-loop analytics.

An MQL is someone judged to be more likely to buy than the rest.  That works for me.  Typically, MQLs are defined by a set of rules like:

  1. New
  2. A predictive lead score of A, B, or C
  3. Correct geography
  4. At a company bigger than some threshold
  5. “Raised their hand.”  Took activity that indicates interest (i.e., they are not just  a name on purchased list) or increasingly, took multiple actions that accumulated points in a behavioral tracking system that exceed some threshold.

The first point (the newness criterion) was a trap that I slipped in to see if you were paying attention.  While some marketers will argue that MQLs need to be “new” (and there are some good reasons for this) others will increasingly question — in a lead nurturing world — what “new” actually means and why “new” matters.

After all, what should matter is that we have found a person more likely to buy than the other people.  Whether they’ve been in our database 2 hours, 2 weeks, or 2 years shouldn’t matter.  Or should it?

I think it does matter because:

  • Marketing needs to watch its image in front of sales.  Declaring someone who’s come to our last 3 annual roadshows an MQL strikes me as a “Kick Me” sign, regardless of whether she’s just accumulated 50 points.  There is a difference between someone who is new and someone we’ve been recycling for several years.
  • Marketing needs to track how many are new vs. recycled (1) to avoid a seemingly in-built tendency to be new-obsessed, (2) because few companies actually want 100% of either, and (3) because new and recycled MQLs will likely show very different downstream conversion rates, which should not be averaged away.

That’s why, in my view, a “new MQL” is a contact who has become an MQL for the first time (i.e., they are not necessarily new to our database, but they are new in hitting the MQL criteria).  After that, if they don’t buy on the first round and if they later come back to life again (by accumulating enough points in the nurture system), they are a “recycled MQL.”

MQLs = new MQLs + recycled MQLs

When I first heard the term “nurture” about a decade ago, to me it was all about recycling.  Nurture was what you did to people who were interested in your stuff, but who weren’t ready to buy now.  The purpose, then, of nurture would be some combination of (1) maintaining awareness and positive opinion so that the customer would call when they were ready to buy, and (2) attempting to accelerate the customer’s buying timeframe by marketing the benefits of acting sooner rather than later.

Nurture, then, was a process that should take quarters or years — not days or weeks.  Nurture could include emails, but it wouldn’t be limited to them.  We might invite nurtured leads to local events, mail them schwag (aka, “dimensional pieces“), and even call them from time to time.

I now call this path “slow nurture” because marketers seem to increasingly define “nurture” as the process by which you take a new inquiry (or name) and turn them into an MQL.  It becomes largely about email and is a speedy process that executes in hours, days, or maybe weeks.  I now call this “fast nurture.”

Both types of nurture should involve point accumulation, use tracks, and be A/B tested.  But there is a fundamental difference between fast nurture and slow nurture, related primarily to frequency.

This is what fast nurturing all too often feels like:

That’s why I also call fast nurture speed-bagging.

If you speed-bag someone who plans to buy in 12 months, what happens?  You irritate the heck out of them.  “Hey, I just wanted to read that white paper and you’ve emailed and called 4 times in a week.  Go away.”  Then they  hit unsubscribe or junk-sender.

And that’s it.  You’re done.  You spent real money finding someone, they were the right person, they even have plans to buy — just not now — and you speed-bagged them into blocking your communications.  Epic fail.

That’s why marketers need to think about Nurture, Fast and Slow.  They need to never fast-nurture slow-nurture prospects.  And they need worry about just how much they are speed-bagging even the fast-nurture prospects.  Particularly in markets where the challenge is more finding the right buyer at right time than simply finding the right buyer, matching the pace of the nurture to the pace of the buyer is everything.