Category Archives: Marketing

Marketing Exists to Make Sales Easier

Many moons ago when I was young product marketing manager, I heard a new VP of Marketing speak at a marketing all-hands meeting.  He spoke with a kiwi accent and his name was Chris Greendale.  What he said were six words that changed my career:

Marketing exists to make sales easier

While this has clearly been a theme in Kellblog posts over the years, I realized that I’ve actually never done a dedicated post on it, despite having written reductionist mission statement posts for both professional services (“maximize ARR without losing money”) and human resources (“help managers manage”).

Being a math type, I love deriving things from first principles and this seemed the perfect first principle from which to derive marketing.  First, you hire a team to build your product.  Then, you hire a team to sell it.  The only reason you need marketing is to help the second team do its job better.

At my next job, I remember bumping into Larry, our fresh from the used-car lot VP of Business Development, who in frustration (as he often was), one day came to work with a bunch of t-shirts that looked something like this

Enterprise software is a two-engine plane and those two engines are quota-carrying salesreps (QCRs) who sell the software and storypoint-burning developers (DEVs) who write it [1].

Everyone else is “the help” — including marketing, finance, sales supporting roles (e.g., SCs, SDRs), engineering-supporting roles (e.g., QA, PM, TPM), customer service, and yes, the CEO.  The faster you understand this, in my humble opinion, the better.

And, while we’re in realization mode, the other thing to internalize is that it costs about twice as much to sell an enterprise software product as it does to build it.  Per KeyBanc, typical S&M spend is 45% of revenue and R&D runs about half that.

But back to the mantra, make sales easier.  Why did I like it so much?

First, it put marketing in its proper place.  At the time, there was something of a power struggle between sales and marketing, and CPG/brand management types were trying to argue that product marketing mangers should be the generals and that sales were just the foot-soldiers.  Looking both around me and at the P&L that just seemed wrong.  Maybe it worked in consumer products [2] but this was enterprise software.  Sales had all the budget and all the power to go with it.  We should help them and, ego aside, there’s nothing wrong with being a helper.

In fact, if you define your mission statement as “help” and remember that “help is defined in the mind of the recipient,” you’ve already gone a long way to aligning your sales and marketing.

Second, there was nothing written in stone that limited the scope of that help. Narrow thinking might limit marketing to a servile role.  That’s not my intent.  Help could take many forms, and while the primary form of requested help has evolved over time, help can include both the tactical and the strategic:

  • Giving sales qualified leads to work on.
  • Building training and tools that helps sales sell more.
  • Providing competitive information that helps win more deals.
  • Creating an ideal customer profile (ICP) that helps sales focus on the most winnable deals.
  • Building industry-specific messaging that helps sell in given verticals
  • Working with PM [3] to build product that is inherently more salable [4].
  • Corporate strategy development to put the company in the right markets with the right offerings.

When I say help, I don’t mean lowercase-h tactical help.  I mean help in all its forms, which can and should include the “tough love” form of help:  “I know you think you want that, but let me demonstrate that I’ve heard your request and now explain why I think it’s not a good idea.”

Being helpful doesn’t mean saying yes to everything.  I hearken back to Miracle on 34th Street whenever I’m drawn into this problem (quote adapted):

Kris Kringle:  No, but don’t you see, dear?  Some <salespeople> wish for things they couldn’t possibly use like real locomotives or B-29s.

If sales is asking you for a real locomotive or a B-29 you need to tell them.

For the rest of my marketing career, I took Greendale’s mantra and made it my own.  If sales were my customer and I were helping them, then:

  • We’d run sales satisfaction surveys to see how happy sales was with marketing and where they wanted us to invest and improve [5].
  • We’d make ourselves accountable.  One of the biggest stresses in the sales/marketing relationship was, to paraphrase an old joke, sales felt like the pig while marketing was the chicken.  We’d publish objectives, measure ourselves, and be honest about hits and misses.
  • We’d bring data to the party.  We’d leverage syndicated and custom research to try and made data-driven as opposed to opinion-driven decisions.
  • We’d stop back-seat drivers.  I’d remind anyone that got too uppity that “quotas are available” and they should go take one [6].
  • We wouldn’t be the marketing police, scolding people for using out-of-date materials.  If sales were using a deck we’d decommissioned quarters ago, our first response wouldn’t be “stop!” but “why?”
  • We’d market marketing.  We’d devote some time to internal marketing to let the sales organization know what we were doing and why.

We’d even do something that tested the limits of HR (particularly when I was in France).  I’d use the sales satisfaction survey to rank every customer-facing marketer on a matrix.

This gave me hard data on who sales knew in the department and what they thought of them.  If we’re going to make messaging for sales to present to customers, we’d better prepared to — and be good at — presenting it ourselves [7].

Overall, the mantra served me well, taking me from product marketing director to VP of product marketing to VP of corporate marketing to overall VP of marketing and a great run at Business Objects.  I’ve had plenty of people challenge me on it over the years — usually it’s because they understand it as purely tactical.  But it’s served me well and I encourage you to use it as your North Star in leading your marketing team.

After all, who doesn’t like help?

# # #

Notes

[1] You’d be wise to add those two figures to your one-page key metrics.  Somehow it’s always easier to hire the supporting staff than the “engine” staff, so keep an eye on the raw numbers of QCRs and DEVs and, for more fun, track their density in their respective organizations (QCRs/sales and DEVs/eng).

[2] Shout out to my daughter Stephanie who works in brand management on a consumer product and who can now inform me directly of how things work in that world — and it is different.

[3] PM = product management.

[4] Either in the sense of better solves the problem or in the tactical sense of wipes out competitive differentiation.

[5] One of my favorite results was the sales and SCs often wanted exactly the same thing, but that sales wanted it more (i.e., roughly the same priority curve but sales would rank everything even more important than the SCs).

[6] Most didn’t, but a few did, and some did remarkably well.

[7] We were probably a $100M company around the time we started this, so I’m not suggesting it for a 2-PMM startup.  And yes, I’d put myself on the matrix as well.

A Missive to Marketing: Impose Simplicity

Markets are complex. Customers are complex. Products are complex, sometimes very. Heck, the world is complex. What’s a marketer to do?

Great marketing is about making things simple. We do that by imposing simplicity on a complex world. We might be attacked for so doing — people might accuse us of over-simplification. And we don’t want that either because we need to stay credible. Paraphrasing Einstein, we want to make things as simple as possible, but no simpler.

Consider product marketing. Enterprise software products are enormously complex, built by scores (or hundreds) of developers across quarters and years. They have deep functionality and subtle differences.

But a product marketer, operating in a TLDR world, can never say:

The difference between our product and their product is actually quite subtle and ultimately is about 100 little things; there’s really no one big thing that separates them.

No, no, no. The successful product marketer finds the most important subtle differences, groups them, and amplifies them. Here are our three silver bullet features. Here’s our white paper on The Five Things You Should Look For in a Schmumble.

In so doing, black-and-white is infinitely superior to gray. While sometimes unavoidable, speaking gray (i.e., “our schmumble is better than their schmumble”) is infinitely inferior to speaking black-and-white (e.g., “we have a schmumble; they don’t.”)

The successful product marketer takes a complex, gray world and transforms it into a simple, black-and-white one. If you don’t have row-level locking, you’re screwed. If you don’t have semi-additive measures, you’re screwed. If you don’t financial consolidation, you’re screwed.  If you don’t have hyperblocks, you’re screwed. 

The great marketer imposes simplicity on the product.

Consider corporate marketing, where the goal is simple. Take a complex competitive landscape and position the company as the leader. Not a leader. The leader. “A leader” is complex because it means there are multiple different companies, each of a different size, and each with its own angle on what constitutes the best product. That means customers need to understand all the competitors and their relative strengths and weaknesses. That’s a lot of work.

“The leader” is simple. Define the space in as simple terms as possible — carving it up to make yourself the leader — and then declare yourself the leader. It’s not always possible to do this — at one point, I called out Brio for effectively claiming they were the leading business intelligence vendor — on Great America Parkway in Santa Clara, California.

But if you can do it credibly, then back it up with awards, customer wins, customer counts, and financing rounds. It’s safe to buy from the leader.

The great marketer imposes simplicity on the market.

Consider customer targeting. The world is complex and gray when it comes to targeting. An ideal customer profile (ICP), typically the result of a regression used to identify the best target companies, isn’t black and white. It might output a score that varies from to 0.0 to 1.0. That’s gray. You need to make that black-and-white so sales can use it — e.g., by using it to identify named accounts for sales and account-based marketing (ABM), by using the score to create tiers that follow different processes in the high funnel, or by looking at the model to derive simple rules to say when some opportunities look better than others (e.g., we double our win rate when the customer is using Spark).

The great marketer imposes simplicity on targeting.

Consider messaging. The database reveals that the key contacts at our top 50 customers have over 80 different titles. If we stopped there, we’d end up wasting money buying overly broad lists and with an overly generic message. The great marketer interviews a broad set of customers and discovers there effectively two canonical personas in that set. Precise titles and hierarchical levels aside, there are two different animals: data analysts and data architects. And a VP of data architecture thinks a lot more like a director of data architecture than a VP of data analysis.

They look at things differently. The have different missions within the organization. They have different career backgrounds. They will respond different to sales and marketing messaging. If you want architects to come to your webinar, talk about data transformation initiatives and enterprise architecture. If you want data analytics people to come to your webinar talk about better decisions made more quickly on higher-quality data.

If you want to sell a data architect convince them your system is built for scalablity. If you want to sell a data analyst, convince them they’ll be more productive and make better analyses.

The great marketer imposes simplicity on messaging.

The hardest part of all this is believing with conviction that you have to do it. You’ll be accused of being inaccurate. Others will say you’re over-simplifying. You’ll be told, “well, it’s really not that simple” over and over again. You yourself will start to wonder.

Don’t forget that simplicity isn’t easy. Just as it takes a tough man to make a tender chicken, it takes a tough marketer to make a simple message. It’s your job.  A key skill in marketing is the ability to impose simplicity on a complex world.

Your career will depend on it.

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.