Category Archives: Marketing

Stopping the Sales & Marketing Double Drowning

I earned my spending money in high school and partially paid for college by working as a lifeguard and water safety instructor. Working at a lovely suburban country club you don’t make a lot of saves. One day, working from the deep-end chair, I noticed two little kids hanging on a lane line. That was against the rules. I blew my whistle and shouted, “off!”

Still young enough to be obedient (i.e., under 11), the two kids let go of the line. The trouble was they couldn’t swim. Each grabbed the other and they sank to the bottom. “Oh my God,” I thought as I dove off the chair to make the save, “I just provoked a double drowning.”

While that was happily the last actual (and yes, averted) double drowning I have witnessed, I’ve seen a lot of metaphorical ones since. They involve adults, not kids. And it’s always the VP of Sales in a deadly embrace with the VP of Marketing. Sure, it may not be an exactly simultaneous death — sometimes they might leave a few months apart — but make no mistake, in the end they’re both gone and they drowned each other.

How To Recognize the Deadly Embrace

I believe the hardest job in software is the VP of Sales in an early-stage startup. Why? Because almost everything is unknown.

  • Is the product salable?
  • How much will people pay for it?
  • What’s a good lead?
  • Who should we call on?
  • What’s the ideal customer profile?
  • What should we say / message?
  • Who else is being evaluated?
  • What are their strengths/weaknesses?
  • What profile of rep should I hire?
  • How much can they be expected to sell?
  • What tools do they need?
  • Which use-cases should we sell to?
  • What “plays” should we run?

You might argue every startup less then $50M in ARR is still figuring out some of this. Yes, you get product-market fit in the single-digit millions (or not at all). But to get a truly repeatable, debugged sales model takes a lot longer.

This painful period presents a great opportunity for sales and marketing to blow each other up. It all begins with sales signing up for (or being coerced into) an unrealistic number. Then, there aren’t enough leads. Or, if there are, the leads are weak. Or the leads don’t become pipeline. Or pipeline doesn’t close.

At each step one side can easily blame the other.

Sales SaysMarketing Says
There aren’t enough leadsThere are, but they’re all stuck with your “generation Z” SDRs
The SDRs are great, I hired themThe SQL acceptance rate says they are passing garbage to sales.
The SQLs aren’t bad, there just aren’t enough of themYour reps are greasing the SDRs by accepting bad SQLs
We’re not getting 80% of pipeline from marketingWe’re delivering our target of 70% and then some
But the pipeline is low quality, look at the poor close rateThe close rate is poor because of your knuckleheaded sellers
Those knuckleheads all crushed it at my last companyYour derail rate’s insane
Lots of deals in this space end up no-decisionMaybe they derail because we don’t follow-up fast enough
Our message isn’t crisp or consistentOur messaging is fine, the analysts love it
We’re the greatest thing nobody’s ever heard of We’ve got a superior product that your team can’t sell
We’re being out-marketed!We’re being out-sold!

Once this ping-pong match starts, it’s hard to stop. People feel blamed. People get defensive. Anecdotal bloody shirts are waived in front of the organization — e.g., “marketing counted five grad students who visited the booth as MQLs!” or “we lost an opportunity at BigCo because our seller was late for the big meeting!”

With each claim and counter-claim sales and marketing tighten the deadly embrace. Often the struggling CRO is fired for missing too many quarters, guns still blazing as he/she dies. (Or even beyond the grave if they continue to trash the CMO post departure.) Sometimes the besieged CMO quits in anticipation of termination. Heck, I even had one quit after I explicitly told them “I know you’re under attack, but it’s unfair and I’ve got your back.”

Either way, in whatever order, they go down together. Each one mortally wounds the spirit, the confidence, or the pleasure-in-work of the other.

How to Break Out of It

Like real double drownings, it’s hard for one of the participants to do an escape maneuver. The good news is that it’s not hard to know there’s a problem because the mess is clearly visible to the entire organization. Everyone sees the double downing. Heck, employees’ spouses probably even know about it. However, only the CEO can stop it and — trust me — everyone’s waiting for them to do so.

The CEO has four basic options:

  • Take some pressure off. If the primary reason you’re missing plan is because the plan is too aggressive, go to the board and reduce the targets. (Yes, even if it means reducing some expense budget as well.) As Mike Moritz said to me when I started at MarkLogic: “make a plan that you can beat.” Tell them both that you’re taking off the pressure, them them why (because they’re not collaborating), and tell them that you’ve done your part and now it’s time for them to do theirs: collaborate non-defensively to solve problems.
  • Force them to work together. This the old “this shit needs to stop and I’m going to fire one of the two of you, maybe both, if you can’t work together” meeting. A derivation is to put both in a room and tell them not to leave until either they agree to work together or come out with a piece of paper with one name on it (i.e., the one who’s leaving). The key here for them to understand that you are sufficiently committed to ending the bullshit that you are willing to fire one or both of them to end it. In my experience this option tends not to work, I think because each secretly believes they will be the winner if you are forced to choose.
  • Fire one of the participants. This has the effect of rewarding the survivor as the victor. If done too late (before death but after the mortal wound — i.e., after the victor is far along in finding another job), it can still result in the loss of both. To the extent one person clearly picked the fight, my tendency is to want to reward the victim, not the aggressor — but that discounts the possibility the aggressor is either correct and/or more highly skilled. If they are both equally skilled and equally at fault, a rational alternative is to flip a coin and tell them: “I value you both, you are unable to work together, I think you’re equally to blame, so I’m going to flip a coin and fire one of you: heads or tails.” An alternative is to fire one and demote the other — that way it’s very clear to all involved that there was no winner. If fights have winners, you’re incenting fighting.
  • Fire both. I love this option. While it’s not always practical, boy does it send a strong message about collaboration to the rest of the organization: “if you fight, are asked to stop, and you don’t — you’re gone.” Put differently: “I’m not firing them for fighting, I’m firing them for insubordination because I told them not to fight.” Odds are you might lose both anyway so one could argue this is simply a proactive way of dealing with the inevitable.

One of the hardest things for executives is to maintain the balance between healthy cross-functional tension and accountability and unhealthy in-fighting and politics. It’s the CEO’s job to set the tone for collaboration in the company. While Larry Ellison and his disciplines may love “two execs enter, one exec leaves” cage fights as a form of corporate Darwinism, most CEOs prefer a tone of professional collaboration. When that breaks down, weak CEOs get frustrated and complain about their executive team. Strong ones take definitive action to define what is and what isn’t acceptable behavior in the organization and put clear actions behind their words.

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.

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

Does my company spend too much on marketing? Too little? How I do know? What is the right level of marketing spend at an enterprise software startup? I get asked these questions all the time by startup CEOs, CMOs, marketing VPs, and marketing directors.

You can turn to financial benchmarks, like the KeyBanc Annual SaaS Survey for some great high-level answers. You can subscribe to SiriusDecisions for best practices and survey data. Or you can buy detailed benchmark data [1] from OPEXEngine. These are all great sources and I recommend them heartily to anyone who can afford them.

But, in addition to sometimes being too high-level [2], there is one key problem with all these forms of benchmark data: they’re not about you. They’re not based on your operating history. While I certainly recommend that executives know their relevant financial benchmarks, there’s a difference between knowing what’s typical for the industry and what’s typical for you.

So, if you want to know if your company is spending enough on marketing [3], the first thing you should do is to make an inverted demand generation (aka, demandgen) funnel to figure out if you’re spending enough on demandgen. It’s quite simple and I’m frankly surprised how few folks take the time to do it.

Here’s an inverted demandgen funnel in its simplest form:

Inverted demandgen funnel

Let’s walk through the model. Note that all orange cells are drivers (inputs) and the white cells are calculations (outputs). This model assumes a steady-state situation [4] where the company’s new ARR target is $2,000,000 each quarter. From there, we simply walk up the funnel using historical deal sizes and conversion rates [5].

  • With an average sales price (ASP) of $75,000, the company needs to close 27 opportunities each quarter.
  • With a 20% sales qualified lead (SQL) to close rate we will need 133 SQLs per quarter.
  • If marketing is responsible for generating 80% of the sales pipeline, then marketing will need to generate 107 of those SQLs.
  • If our sales development representatives (SDRs) can output 2.5 opportunities per week then we will need 5 SDRs (rounding up).
  • With an 80% SAL to SQL conversion rate we will need 133 SALs per quarter.
  • With a 10% MQL to SAL conversion rate we will need 1,333 MQLs per quarter.
  • With a cost of $250 per MQL, we will need a demandgen budget [6] of $333,333 per quarter.

The world’s simplest way to calculate the overall marketing budget at this point would be to annualize demandgen to $1.3M and then double it, assuming the traditional 50/50 people/programs ratio [7].

Not accounting for phase lag or growth (which will be the subjects of part II and part III of this post), let’s improve our inverted funnel by adding benchmark and historical data.

Let’s look at what’s changed. I’ve added two columns, one with 2019 actuals and one with benchmark data from our favorite source. I’ve left the $2M target in both columns because I want to compare funnels to see what it would take to generate $2M using either last year’s or our benchmark’s conversion rates. Because I didn’t want to change the orange indicators (of driver cells) in the left column, when we have deviations from the benchmark I color-coded the benchmark column instead. While our projected 20% SQL-to-close rate is an improvement from the 18% rate in 2019, we are still well below the benchmark figure of 25% — hence I coded the benchmark red to indicate a problem in this row. Our 10% MQL-to-SQL conversion rate in the 2020 budget is a little below the benchmark figure of 12%, so I coded it yellow. Our $250 cost/MQL is well below the benchmark figure of $325 so I coded it green.

Finally, I added a row to show the relative efficiency improvement of the proposed 2020 budget compared to last year’s actuals and the benchmark. This is critical — this is the proof that marketing is raising the bar on itself and committed to efficiency improvement in the coming year. While our proposed funnel is overall 13% more efficient than the 2019 funnel, we still have work to do over the next few years because we are 23% less efficient than we would be if we were at the benchmark on all rates.

However, because we can’t count on fixing everything at once, we are taking a conservative approach where we show material improvement over last year’s actuals, but not overnight convergence to the benchmark — which could take us from kaizen-land to fantasy-land and result in a critical pipeline shortage downstream.

Moreover because this approach shows not only a 13% overall efficiency improvement but precisely where you expect it to come from, the CEO can challenge sales and marketing leadership:

  • Why are we expecting to increase our ASP by $5K to $75K?
  • Why do you think we can improve the SQL-to-close rate from 18% to 20% — and what you are doing to drive that improvement? [8]
  • What are we doing to improve the MQL-to-SAL conversion rate?
  • How are we going to improve our already excellent cost per MQL by $25?

In part II and part III of this post, we’ll discuss two ways of modeling phase-lag, modeling growth, and the separation of the new business and upsell funnels.

You can download my spreadsheet for this post, here.

Notes

[1] For marketing or virtually anything else.

[2] i.e., looking at either S&M aggregated or even marketing overall.

[3] The other two pillars of marketing are product marketing and communications. The high-level benchmarks can help you analyze spend on these two areas by subtracting your calculated demandgen budget from the total marketing budget suggested by a benchmark to see “what’s left” for the other two pillars. Caution: sometimes that result is negative!

[4] The astute reader will instantly see two problems: (a) phase-lag introduced by both the lead maturation (name to MQL) and sales (SQL to close) cycles and (b) growth. That is, in a normal high-growth startup, you need enough leads not to generate this quarter’s new ARR target but the target 3-4 quarters out, which is likely to be significantly larger. Assuming a steady-state situation gets rid of both these problems and simplifies the model. See part II and part III of this post for how I like to manage that added real-world complexity.

[5] Hint: if you’re not tracking these rates, the first good thing about this model is that it will force you to do so.

[6] When I say demandgen budget, I mean money spent on generating leads through marketing campaigns. Sometimes that very directly (e.g., adwords). Other times it’s a bit indirectly (e.g., an SEO program). I do not include demandgen staff because I am trying to calculate the marginal cost of generating an extra MQL. That is, I’m not trying to calculate what the company spends, in total, on demandgen activities (which would include salary, benefits, stock-based comp, etc. for demandgen staff) but instead the marketing programs cost to generate a lead (e.g., in case we need to figure out how much to budget to generate 200 more of them).

[7] In an increasingly tech-heavy world where marketing needs to invest a lot in infrastructure as well, I have adapted the traditional 50/50 people/programs rule to a more modern 45/45/10 people/programs/infrastructure rule, or even an infrastructure-heavy split of 40/40/20.

[8] Better closing tools, an ROI calculator, or a new sales training program could all be valid explanations for assuming an improved close rate.

Should SDRs Report to Sales or Marketing?

Slowly and steadily, over the past decade, the industry has evolved from a mentality of “all salesreps must do everything” – including some percent of their time prospecting — to one of specialization.  We, with the help of books like Predictable Revenue, have collectively decided that in-bound lead processing is different from outbound lead prospecting is different from low-end, velocity sales is different from high-end, enterprise sales.

Despite the old-school, almost-character-building emphasis on prospecting, we have collectively realized that having our top hunters dialing for dollars and digging through inbound leads isn’t, well, the best use of their time.

Industrialization typically involves specialization and the industrialization of once purely artisanal software sales has been no exception.  As part of this specialization the sales development representative (SDR) role has risen to prominence.  In this post, we’ll do a quick review of what SDRs typically do and discuss the relative merits of having them report into sales vs. marketing.

“Everyone under 25 in San Francisco is an SDR.” – Anonymous startup CEO

SDRs Bridge the Two Departments

SDRs typically form the bridge between sales and marketing.  A typical SDR job is take inbound leads from marketing, perform some basic BANT-style [1] qualification on them, and then pass them to sales if indicated. While SDRs typically have activity quotas (e.g., 50 calls/day) they should be primarily measured on the number of opportunities they create per week. In enterprise software, typically that quota is 2-3 oppties/week. 

As companies get bigger they tend to separate SDRs into two groups:

  • Inbound SDRs, those who only process in-bound leads, and
  • Outbound SDRs, those who primarily do targeted outreach over the phone or email

Being an SDR is a hard job.  Typical SDR challenges include:

  • Adhering to service-level agreements for all leads (i.e., touches with timeframes)
  • Contacting prospects in an increasingly spam-hostile, call-hostile environment
  • Figuring out which leads to work on the hardest (e.g., which merit homework to customize the message and which don’t)
  • Remembering that their job is to sell meetings and not product [2]
  • Supporting multiple salespeople with often conflicting priorities [3]
  • Managing the conflict between supporting salespeople and executing the process
  • Getting salespeople to show-up at the hand-off meeting [4]
  • Avoiding burnout in a high-pressure environment

To Which Department Should SDRs Report:  Sales or Marketing?

Historically, SDRs reported to sales.  That’s probably because sales first decided to fund SDR teams as a way getting inbound lead management out of the hands of salespeople [5].  Doing so would:

  • Enable the company to consistently respond in a timely manner to all inquiries
  • Free up sales to spend more time on selling
  • Avoid the problem of individual reps not processing new leads once they are “full up” on opportunities [6]

The problem is that most enterprise software sales VPs are not particularly process-oriented [7], because they grew up in a pre-industrialized era of sales [8].  In fact, nothing drives me crazier than an old-school, artisanal, deal-person CRO insisting on owning the SDR organization despite the total inability to manage it.  They rationalize:  “Oh, I can hire someone process-oriented to manage it.”  And I think:  “but what can that person learn from you [9] about how to manage it?”  And the answer is nothing.  Your desire to own it is either pure ego or simply a ploy to enrich your resume.

I’ll say again because it drives me crazy:  do not be the VP of Sales who insists on owning the SDR organization in the annual planning meeting but then shows zero interest in it for the rest of the year.  You’re not helping anyone!

As mentioned in a footnote in a prior post, I greatly prefer SDRs reporting to marketing versus sales.  Why?

  • Marketing leadgen and nurture people are metrics- and process-oriented animals, naturally suited to manage a process-oriented department.
  • It provides a simple, clear conceptual model:  marketing is the opportunity creation factory and sales is the opportunity closing machine.

In short, marketing’s job is to make opportunities.  Sales’ job is to close them.

# # #

Notes

[1] BANT = budget, authority, need, time-frame.

[2] Most early- and mid-stage startups put SDRs in their regular sales training sessions which I think does them a disservice.  Normal sales training is about selling products/solutions.  SDRs “sell” meetings.  They should not attempt to build business value or differentiation. Training them to do so tempts them to do – even when it is not their job.

[3] A typical QCR:SDR ratio is 3-4:1, though I’ve seen as low as 1:1 and as high as 6:1

[4] Believe it or not, this sometimes happens (typically when your reps are already carrying a lot of oppties).  Few things reflect worse on the company than a last-minute rescheduling of the meet-your-salesperson call. You don’t get a second chance to make a firm impression.

[5] Although most early models had wide bypass rules  – e.g.,  “leads with VP title at this list of key accounts will get passed directly to reps for qualification” – reflecting a lack of trust in marketing beyond dropping leaflets from airplanes.

[6] That problem could still exist at hand-off (i.e., opportunity creation) time but at least we have combed through the leads to find the good ones, and reports can easily identify overloaded reps.

[7] While they may be process-oriented when it comes to the sales process for a deal moving across stages during a quarter, that is not quite the same thing as a velocity mentality driven by daily or weekly goals with tracking metrics.  If you will, there’s process-oriented and Process-Oriented.

[8] One simple test:  if your sales org doesn’t have monthly cadence (e.g., goals, forecasts) then your sales VP is probably not capital P process-oriented.

[9] On the theory you should always build organizations where people can learn from their managers.

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.