Category Archives: Sales

Traditional B2B Sales is Dead, Long Live the UCE?

In the land of disruption, there’s always something dying and something lining up to replace it, so we’re pretty used to hearing things like “on-premise is dead, long live SaaS.”  Sometimes, they’re right.  Despite the 2008-era views of our resident luddite below, SaaS really did kill on-premises.

Sometimes they’re wrong.  Despite years of hearing, “the data warehouse is dead, long live the data lake,” the data warehouse is doing just fine, thanks.  Snowflake can tell you 60 billion reasons why.

Sometimes, they’re both right and wrong.  Data lakes are doing pretty well, too.  Not everything is zero sum.

You don’t hear this just about technologies, but business models, too.

When the Internet eliminated sellers’ monopoly power over information, I heard, “traditional B2B sales is dead, long live facilitating buying processes.”  This was right and wrong.  B2B sales wasn’t dead, it just changed.  When buyers can get more information themselves and advance further without needing sellers, reframing sales as facilitating buying is a good idea.

When product-led growth (PLG) became the rage, you started to hear it again:  “traditional B2B sales is dead, long live PLG.”  While companies like Atlassian really did dispense with traditional B2B sales, other companies — like Zendesk, Slack, and Twilio — showed the power of blending the two models.  Heck, even Atlassian eventually blended them.

I think of PLG as embracing the continuation of a trend already started by the Internet.  In phase one, buyers no longer needed sellers to get basic product information.  (It’s almost hard to believe, but back in the day, if you wanted even a white paper let alone a demo, you had to talk to a seller.)  In phase two, buyers no longer needed sellers to get hands-on product trial.  It’s the same transformation, just applied to the next two phases down the funnel.

While some companies consider trials customers (and ergo need to count them in churn), I think most enterprise startups should consider trials leads, and the ones who do the right things with the product become leads worthy of passing to sales.  Because they’re qualified by product usage and not marketing actions, they’re called PQLs instead of MQLs.  (Ask my friends at Correlated, or any of the new PLG CRMs, to learn more.)

The other day an old friend of mine, now a highfalutin GM at a big-name software company, forwarded me this article, Traditional B2B Sales and Marketing are Becoming Obsolete.  So, anticipating the content, I donned my “it’s PLG and enterprise, not PLG or enterprise,” gloves and got ready to fight.

But I was surprised.  Instead of saying, “B2B sales is dead, long live PLG,” the article threw me a curveball:

“B2B sales is dead, long live the unified commercial engine (UCE).”

Huh.  The what?

Who wrote this, I think?  Ah, it’s some guy from Gartner.  Before I can add, “and they should stick to IT prognostication,” I see that “some guy” is Brent Adamson, coauthor of The Challenger Sale, one of my top five favorite sales books.

Darn.  Now I have to read this eight-page article and figure out what I think.  The rest of this post is the result.

The Article:  Summary and Analysis
The article argues that it’s no longer enough to try and integrate (in the sense of align) sales and marketing, we should instead unify them.  That’s because buyers have more access to information (including hands-on trials), buyers have access to that information via multiple channels (e.g., vendor websites, review sites), and buyers don’t want to interact with salespeople (which is not exactly new, though he argues that younger people want to interact with sellers even less than older ones).

Sales is thus fighting for relevancy in the buying process and seeking to regain customer access.  The linear model is dead, long live the unified model.  In short:

Helping today’s B2B buyers buy isn’t a sales challenge, nearly so much as an information challenge (or, alternatively, an information opportunity).

He begins with motherhood and apple pie:

The companies that best provide customers the information they most urgently seek, specifically through the channels they most clearly prefer, are in a far better position to drive commercial success in today’s rapidly evolving digital commercial landscape.

He moves into rhetoric to amp things up:

While once a relatively accurate proxy for the underlying buying behavior it was meant to approximate, the serial commercial engine is hopelessly out of date — and dangerously out of sync — with how today’s B2B buyers buy.

With a requisite Gartner dash of profundity:

Today’s buyers are not only channel agnostic in terms of behavior, they’re digitally dominant in terms of preference.

I think that means people like to research shit online before buying it.  Got it.  Stipulated.

I always say that any good sales pitch is 80% tee-up and 20% knockdown.  Now, on the receiving end of such a pitch, I need to advise some caution in that approach.  At some point people want to hear your solution; I’m on page 6 of what’s barely 8 pages and still waiting.  It’s always easier to agree on the problem than the solution (e.g., child poverty, wealth inequality, climate change).  It’s why the 80/20 formula works — you get people agreeing with you, sounding smart, heads nodding, and then you shift to a credible solution that drives your agenda.

But you can’t wait too long to shift to the solution (so I should probably revise my rule to 60/40).  And you should introduce the solution from first principles, not via a case study (which you can always present later, as proof). And if you’re going to introduce the solution via a case study anyway, it shouldn’t be a 1300-person company based in Calgary that I’ve never heard of.

Yet, here I am, about to learn how SMART Technologies found the answer to this pervasive problem by “rebuilding it from the ground up.”  But first, I need to learn about SMART Technologies.  I am now at page 6.75 of an 8.25 page article and still not heard the solution.

The answer:  completely dismantle traditional sales, marketing, success, and service altogether and reconfigure them into a unified commercial engine (UCE).

I’m now thinking:

  • Can you partially dismantle something?
  • Can you completely dismantle something without it being altogether?
  • Where did success and service sneak into things?  While I’d certainly, almost definitionally, want to put all customer-facing teams into a unified engine, how is it that success and service are totally omitted from the argument’s tee-up?

You create a UCE by:

Careful mapping of customers’ buying journeys across a range of predictable “jobs to be done” as part of a typical educational technology purchase.

Never one to miss a gratuitous Clayton Christensen reference, I have to observe that while I am big believer in his work and the jobs-to-be-done framework, I think this is something of a misapplication.  Christensen’s point was about innovation — if you think of products as hired instead of bought, and hired to do specific jobs, then you will anchor yourself in the customer’s point of view when contemplating new products and features.  Think:  not how can we make this milkshake tastier, but how can we make this milkshake more effective when it’s hired as a one-handed commuter breakfast.  What we’re talking about at SMART is simply mapping customer journeys.

When you do that careful mapping, this happens (or, at least, this is what happened at SMART):

Through that initiative the team identified five common buying jobs (Learn, Buy, Order/Install, Adopt, Support) and established an internal team specifically deployed to support each one, reassigning nearly every member of legacy marketing, sales, service, and success staff as a result. In all, over 250 team members received new job designations as part of the process.

You can’t do a re-org these days without creating a center of excellence, so SMART created three:

SMART created three centers of excellence, where they consolidated otherwise duplicative efforts across traditional functional boundaries, one for data and analytics, and one for customer insights and positioning, and one for creative and digital experience.

Those, by the way, sound like a good idea.  I like centralized, specialized support teams, particularly in areas where we’re trying to present one face to the customer.

And then, the re-organization:

Finally, the team then deployed their staff in geographically aligned “pods,” where each pod contains members supporting each of the respective five buying jobs. So, the pod for the southeast United States, for example, is made up of combination of individuals tasked with supporting the entire range of customer jobs from Learn to Support across all relevant digital and in-person channels (including third-party distribution).

In short, run your regions in the USA more like you run countries in Europe.

It’s neither a bad idea nor some insanely different approach.  It does create the need, however, for sophisticated regional leaders who are capable of aligning on both dimensions of the matrix.  Concretely:  is the French country marketing manager part of the French team or the marketing team?  Answer:  it’s a trick question.  The answer is both and they need to learn which way to look, when, as they face managerial decisions — e.g., look to the CMO for questions on messaging and positioning, look to the French country manager when prioritizing campaigns and investments.

I’m going to ignore the end of the article where the VPs of sales and marketing proudly introduce themselves “the former heads” of their respective departments, because they both seem to still work at the company and do something, though the article doesn’t say what their new titles and jobs are.  I’ll assume, hype and semantics aside, that they’ve implemented some sort of functional vs. pod matrix.  As one does with countries in Europe.

Before wrapping up, let me challenge some of the more detailed points in the tee-up.

  • Yes, the machine is by default linear.  But that’s just the first pass.
  • Contacts that don’t make it MQL or SQL get put into nurture and nurture is not linear.  Nurture is a popcorn machine where we dump kernels in, expose them to heat, and over time and in a pretty random order, the kernels pop into recycled MQLs.  I’ve run companies where half of all MQLs are recycled.
  • There’s the question of whether we should nurture people or accounts, as we would in account-based marketing.  Nurturing accounts is definitely not linear, it’s like having one popcorn machine per account.
  • There is no 11th Commandment where God said that nurture shalt be digital only.  While a lot of nurture is automated digital, marketers should remember that a nurture track, broadly defined, can also involve live events (e.g., C-level dinners), dimensional marketing (e.g., mailing a coffee-table book or a Moleskine), and live interactions  (e.g., SDR or AE outreach).  Nurture doesn’t definitionally mean a sequence of emails, nor should it.
  • The part of the linear handoff I detest is when sales “waives off” marketing once an opportunity is in play.  This happens less frequently than it used to, but it reveals a deep lack of trust that should be fixed by destroying walls, not erecting them.

I’ll conclude by saying I think the article misses the most important point in organizational design.  When it comes down the game on the field, who calls the plays and the audibles?  Sure, we have a playbook, and we all know the play we’re supposed to be running.  But things have changed.  There’s a new participant in the meeting.  They mentioned a new competitor who we didn’t know was in the deal.

With a group of talented people, they’ll usually be several different and vocal opinions expressed on how to proceed.  The AE may want to reschedule the meeting.  The SC wants to proceed with the demo.  The consultant thinks we shouldn’t be in the deal in the first place.  The sales manager thinks we can win it because the champion has our back.  What do we do?  Who calls the plays and the audibles that modify them?

In my mind, the person with the quota wins.  As the old joke goes about breakfast:  the chicken is participating, but the pig is committed.  In a world where accountability for results legitimizes decision-making authority, it’s not enough to have a pod/commune and say we can all work it out.  Sometimes we can’t.  And often we can’t fast enough.

Whether you call that person the regional pod leader or the country manager, the role needs to exist and they need to take lead on deal strategy. Everything else is a supporting resource.  Which is why I think marketing alignment with sales is enough.  Yes, we need to collaborate and yes we need smart managers to work in the functional/regional matrix.  Yes, in the case of marketing, we need field marketing to ensure ground-level local alignment.

But do we need to reorganize everything into regional pods?  No.  We just need to work together and be aware that buyers have more information, options, and control than ever before.

# # #

End note
The irony is I ran a company in a pod structure, MarkLogic, where we had vertical pods (aka, business units) where we didn’t have sellers, SCs, and consultants.  We had Federal sellers, Federal SCs, and Federal consultants — all working for a VP/GM of Federal.  Ditto for information & media.  But we did it not in the name of “traditional B2B sales is dead because buyers have more information,” but in the name of a vertical go-to-market strategy where we wanted specialization and alignment.  Pods can work.  It’s all about strategy first and organizational design to support strategy.

The Sales/Marketing Expense Ratio

Question:  how much does a $15M SaaS company spend on sales and marketing as a percent of ARR?  Answer:  35% (with 45% and 15% as the top and bottom quartiles).

Charts like this, from OpenView’s 2021 Financial & Operating Benchmarks survey, help to answer questions like that all the time.

Good SaaS executives keep these metrics in mind, and you can get them from KeyBanc, RevOps Squared, OpenView, or for bigger/public companies, sites like Meritech Public Comps, Public Comps, or Clouded Judgement.

A great revops or FP&A person will give the answer from multiple sources and explain the differences among them.  Moreover, they’d observe that sales and marketing (S&M) expense really should vary with growth rate, and they’d know that KeyBanc tracks that:

So if that $15M SaaS company is growing at 25%, then median S&M spend is 20% of revenue, whereas if it’s growing at 70%, then median S&M spend bulks up to 46%.

But that’s all SaaS Metrics 101.  Today, I’d like to hop to the 201 level by introducing a simple that metric that can reveal a lot and on which few people focus:  the sales/marketing expense ratio, which just equals sales expense divided by marketing expense.

To introduce the idea — quick, tell me what’s happening at this company:

My take:

  • The company is high relative to the benchmark
  • The company is not making much progress towards the benchmark
  • Sales is getting less efficient while marketing is getting more efficient

This situation is very common.  Sometimes, it’s justified bottom-up — e.g., we’re building a partners function in sales that is only slowly becoming productive and we’ve upgraded both marketing leadership and the martech stack to improve marketing efficiency.

Normally, it’s not.  In fact, normally, there’s no justification whatsoever.  When you ask, you get, “well, that’s just how the budget process worked out, the real focus was on improving S&M and we did.  Next question, please.”

Yes, you did improve S&M, but you put the “S&M” improvement 100% on the back of marketing (in fact, 200%) and with no bottom-up justification for why sales needs to get more expensive while marketing is going to magically become more efficient.  This is a mistake.  The likely result is underfed sellers screaming for pipeline, forming an angry mob with dogs and torches headed to the CMO’s office.

Let me tell you what’s going on when this happens:

  • Your CRO is a better negotiator than your CMO.  They better be.  If they’re not, you have an additional problem.
  • Your CRO has more negotiating leverage than the CMO.  They are negotiating the company number directly with the CEO and indirectly with the board.  This is high-stakes, board-level poker.
  • There’s usually no broken-out benchmark, typically only a combined benchmark, and given the prior two points, the CRO is just fine with that.
  • It’s easy to think that hiring sellers “leads directly” to new ARR than investing in marketing.  Why?  Because in enterprise software the bookings capacity model is typically driven off the number of sellers.  Yes, this is intellectually lazy and only works on the margin, but deep down, it’s what a lot of CEOs and CFOs feel.

So the CMO gets asked to suck it up, the board doesn’t notice the problem, the CFO notices but doesn’t want to rock the boat, and the CEO is just happy to get the plan approved.

Hopefully the CRO has the decency to attend the CMO’s going-away party in the fall.  Because if this process repeats itself for even a few years, that’s how it’s going to end.

So how do we fix this?

1. Shine a light on the problem, by adding the sales/marketing ratio to the in-line metrics presented in the plan.

I prefer to show it this way, which makes it clear we used to spend $2 in sales for every $1 in marketing, but that has crept up to over $3.  Showing the metric gives people the chance to ask the all-important question:  why?

The other way to show this is via “sales composition,” i.e., sales as a percent of sales and marketing:

In this case, you can say that sales has risen from two-thirds to three-quarters of S&M expense, and again ask why.  I think the former presentation is more intuitive, but the advantage of this presentation is that KeyBanc benchmarks it in this form:

2. Shine a light on your inverted funnel model.  Sometimes you can squeeze marketing expense just on the people side, but the real way you usually cut to these targets is by making a series of seemingly innocuous assumptions in your funnel.  Consider:

Saying, we need to take MQL to SQL from 10% to 12%, SQL to SAL up from 65% to 70%, and SAL to close up from 15% to 20% all sounds pretty reasonable.  When you combine these effects, however, you’re saying that you’re going to cut the cost of generating an opportunity by more than a third, from $2700 to $1800.  That should get some attention — without any explanation other than the compound effect of small tweaks, it sounds like an Excel-induced hallucination to me.

3. Get the CRO on your side.  Make them understand that squeezing marketing too hard for purely top-down reasons increases their risk on the plan.  Get them to go to bat for you saying, “we need to ensure we feed the sellers enough pipeline.”  Most boards solve for growth with one eye on the CAC and not the opposite.

4. Get the CFO on your side.  In my experience, the hardest person to convince in these debates is the CEO, not the CFO.  Why?  Because the CEO is the one and only person who must negotiate the plan target with the CRO and that’s always something of a painful process.  So, if you get the CRO and CFO on your side, you will greatly increase your odds of getting the CEO to along with you.  You win the CFO over by emphasizing risk.  Think:  “we’ve (finally) got the CRO signed up for the number, but we’ve squeezed marketing too hard and that’s adding risk to the plan” and then say the magic words, “we don’t want to miss plan — do we, CFO?”  They never do.

Conclusion
In a world where sales has more political power, better negotiating skills, and more negotiating leverage than their marketing colleagues, the somewhat natural state of affairs is for this ratio to slowly increase over time.  The question is:  should it?  Everyone on the e-team needs to take accountability for thinking about that and ensuring the company gets the right, not just the easy, answer.  And the CMO has the unique responsibility of ensuring they do.

Appearance on the Precursive Podcast: The Role of Services in Today’s SaaS Market

A few weeks back, I sat down with Jonathan Corrie, cofounder and CEO of Precursive — a Salesforce-native professional services (PS) delivery cloud that provides PS automation, task, and resource management — to discuss one of my favorite topics, the role of professional services in today’s SaaS businesses.

Jonathan released the 48-minute podcast today, available on both Apple and Spotify.

Topics we discussed included:

  • The Hippocratic oath and executive compensation plans (do no harm).
  • How to frame the sales / services working relationship (i.e., no chucking deals over the fence).
  • Why to put an andon cord in place to stop zero-odds-of-success deals early in the sales process.
  • How to package services, including the risks of tshirt-sized QuickStart packages.
  • How to market methodology instead of packages to convince customers of what matters:  success.
  • The myth of services cannibalization of ARR.  (This drives me crazy.)
  • The alternatives test:  would a customer pay someone else to be successful with your software?
  • Selling mistake-avoidance to IT vs. selling success to line-of-business executives.
  • How and why to bridge “air gaps” between functions (e.g., sales, customer success, services).
  • How to position the sales to CSM “handoff” as à la prochaine and not adieu.
  • The perils of checklist-driven onboarding approaches.
  • The beauty of defining organizational roles with self-introductions (e.g., “my name is Dave and my job is to get your renewal”).
  • The three types of CSMs — the best friend, the seller, and the consultant — and how to blend them and build career paths within the organization.
  • Top professional services metrics.  Caring about (versus maximizing) services margin via compensation plan gates.
  • The loose coupling between NPS and renewal.

Thanks again to Jonathan for having me, and the episode is available here.

Why You Should Always Create Sales Opportunities at Zero Dollar Value

Quiz:  Your marketing team generates an MQL.  It’s passed to an SDR, who does basic BANT-style qualification and decides it’s real.  They create a sales opportunity in your pipeline and pass it to a seller.  What number is in the opportunity’s value field at this time?

Four answers I hear frequently:

  • I don’t know.  C’mon Dave, that’s a detail, why would I care about that?  Keep reading.
  • Some semi-random proxy value, say $25K.  Because, well, we’ve always done it that way, and I’m not sure why.
  • Our average sales price (ASP), say $100K.  For extra credit, our segment-specific ASP:  SMB opportunities get valued at $25K and enterprise ones get valued at $100K.
  • Zero dollars.   And that’s the only way I’d ever do it.

What’s my answer?  Zero dollars (and that’s the only way I’d ever do it).  Before I tell you why, let’s remind ourselves why we should care about the answer to this question.

Do you ever look at:

  • Pipeline coverage, as a way to determine your confidence about the future or to give investors confidence in the future?
  • Pipeline conversion rates (on a regular or to-go basis) as a way of measuring pipeline quality or triangulating the forecast?
  • Pipeline generation efficiency (e.g., pipe-to-spend ratio) in order to determine which programs or channels are better than others?

If the answer to any of those question is yes, you need to care about your definition of pipeline.  And while many people think about stage (e.g., should that SDR-created, stage-one opportunity even be considered pipeline?), few people seem to think as much about value.

In a typical funnel [1], by the time you get to stage 3 or 4 of your sales process you may have weeded out half your pipeline.  Now imagine it’s early in a quarter and your pipeline is loaded with stage 2 and stage 3 opportunities, all valued at $100K.  You may have a big air bubble in your pipe.

You think, alas, no worries, Dave, I can handle that in other ways:

  • When we say pipeline around here, we actually mean stage 4+ pipeline, so we just exclude all those opportunities.
  • When we look at stage-weighted pipeline, we weight at 0% all the stage 2 and 3 opportunities, so they’re effectively ignored.

Doing this will bleed a lot of air out of the pipeline, but let’s step back for a minute.  You’re telling me that you’re putting in a $100K placeholder value at opportunity creation time and then systematically ignoring it?  Yes.  Well, tell me again, why are you putting it in the first place?!

The answer to that question is usually:

  • We want to show a big pipeline to get everyone excited.
  • That’s how everybody does it.
  • We want to be able to compare against companies that use placeholder values.

Before challenging those answers, let me object to the air bleeding processes mentioned above:

  • Pipeline should mean pipeline.  If there’s no adjective before the word pipeline, it means the sum of the value of all opportunities with a close date in the period.  It’s sloppy to say, “pipeline” and then revise to, “oh, I mean current-quarter s3+ pipeline.”  They’re not the same.  Which one are you using when?
  • Pipeline that’s ignored in analytics is usually ignored in operations.  If your company defines “demo” as stage 4 (which you shouldn’t) and measures conversion rates from stage 4, I can guarantee you one thing:   the stage 1-3 pipeline is a garbage dump.   I have literally never met a company that does analytics from stage 3 or stage 4 where this is not true.  As Drucker said, what gets measured, gets managed.  And conversely.  This is bad practice.  All pipeline is valuable.  It should all be inspected, scrubbed, and managed.  That doesn’t happen when you systematically ignore part of it.
  • How do I know if a given $100K opportunity has a real or placeholder value?  You can’t.  Maybe you have a rule that says by stage 3 all values need to be validated, but do you know if that happened?  If you create opportunities with $0 value and say, “don’t enter a value unless it’s socialized with the customer,” then you’ll know.  Otherwise you’ll never be able to tell the difference between a real $100K and a fake one [2].
  • Stage weights should come from regressions, not thin air.  For those regressions to work, stage definitions should come from clear rules.  Then, and only then, can you say things like, “given our (consistent) definition of stage 2 opportunity, we typically see 8% of stage 2 ARR value converted in the current quarter and 9% more converted in the quarter after that.” [3]  Arbitrarily zeroing-out certain stages due to poor pipeline discipline and despite their actual conversion rates is bad practice.

Let’s close with challenging the three answers above:

  • Everybody does it.  Ask your parents about Johnny and bridges.  That’s not a good reason to do the wrong thing when derived from first principles.
  • We want to get people excited.  Good.  How about we get them excited by creating a real pipeline that converts at a healthy rate [4], instead of giving everyone a false sense of security with an inflated big number?
  • We want to be able to compare to (i.e., benchmark against) others who use placeholder values?  Super.  Then create a new metric called “implied pipeline” where you take all the zero-dollar opportunities and substitute an appropriate placeholder value.  You can compare to Johnny without following him off the bridge.

# # #

Notes
[1] While stage definitions and conversions vary widely, to make this concrete, here’s one sample funnel that I think is realistic:  stage 1 = BANT, stage 2 = sales accepted with 80% conversion from prior stage, stage 3 = deep dive completed with 80% conversion, stage 4 = solution fit confirmed with 50% conversion, stage 5 = vendor of choice with 60% conversion, stage 6 = win with 80% conversion.  Overall, that implies a s2-to-close rate of 16%, which is in the 10 to 25% range that I typically see.

[2] The hack solution to this is to use $99.999K as the placeholder — i.e., a value that people are unlikely to enter and then ignore that.  Which leads again to the question of why to put fake data into the system only to carefully ignore it in reporting and analytics?  (And hope that you always remember to ignore it.)

[3] This in turn relies on both a consistent definition of close date and a reference to which week of the quarter you’re talking about — such conversion rates vary across the week of the quarter.

[4] One of my CMO friends pointed out that sometimes this “excitement” takes dysfunctional forms — e.g., when sales wants to “cry poor” either to defend a weak forecast or argue for more investment, they can artificially hold oppties at zero value for an extended period (“uninflated balloons”).  This, however, is easily caught when the e-staff is looking at both pipeline (dollar) coverage as well as count (i.e.,  opportunities/rep).

How Much Should You Bet on Educating the Market?

Using the Marketing Fundamental Tension Quadrant to Map Your Demandgen and Communications Strategy

Years ago I wrote a post on what I call the fundamental tension in marketing:  the gap between what we want to say and what our audience wants to hear.

For example, let’s say we’re a supply chain software company.  Our founders are super excited about our AI/ML-based algorithms for demand prediction.  Our audience, on the other hand, barely understands AI/ML [1] and wants to hear about reducing the cost of carrying inventory and matching marketing programs to inventory levels [2].

How then should we market our supply chain software?  Let’s use the following quadrant to help.

Let’s map AI/ML as a marketing message onto this framework.  Do we care about it?  Yes, a lot.  Does our audience?  No.  We’re in Box 4:  we care and they don’t, so we conclude that must therefore educate (as we might dangerously consider them) the unwashed in order to make them care about AI/ML.  We can write a white paper entitled, The Importance of AI/ML in Supply Chain Systems.  We can run a webinar with the same title.  By the way, should we expect a lot of people to attend that webinar?  No.  Why?  Because no one cares.

Market education is hard.  That’s not to say you shouldn’t do it, but realize that you are trying, in a world of competing priorities, to add one to the list and move it up to the top.  It can be done:  digital transformation is widely viewed as business priorities today.  But that took an enormous amount of work from almost the entire software industry.  Your one startup isn’t going to change the VP of Supply Chain’s priorities overnight.

Every good demandgen leader knows it’s far easier to start with things the audience already cares about and then bridge to things your company wants to talk about.  Using the movie theatre metaphor of the prior post, you put “Reduce Inventory Costs” on the marquee and you feature “AI/ML” in a lead role in the movie.

How do you determine those priorities?  I’ll scream it:  MARKET RESEARCH.  You find existing and/or run proprietary market studies targeting your business buyers, asking about their priorities.  Then you create marketing campaigns that bridge from buyer priorities to your messages.  If you’re lucky, you’re in Box 2 and everything aligns without the bridge.  But most software marketers should spend the majority of their time in Box 1, bridging between what’s important to the audience and what’s important to the company.

If you fail to build the bridge in Box 1 you’ll have a webinar full of people of who won’t buy anything.  If you put all your investment into Box 4 you’ll run a lot of empty webinars.

The number one mistake startup marketers make is that they try educate the market on too many things.  You need to care about AI/ML.  And reporting.  And, oh by the way, analytics.  And CuteName.  And features 5, 6, and 7.  And, no, no we’re not feature-driven marketing because we remember to mention benefits somewhere.  We are evangelists.  We are storytellers!

But you’re telling stories that people don’t want to hear.

My rule is simple:  every startup should have one — and only one — Box 4 message and supporting campaigns.  Sticking with our example:

  • We should have a superb white paper on the importance of AI/ML in supply chain systems.
  • We should make claims in our PR boilerplate and About Us page related to our pioneering AI/ML in supply chain systems.
  • We should run a strong analyst relations (AR) program to get thought leaders on board with the importance of AI/ML in supply chain.
  • We should commit to this message for, by marketing standards, an extraordinarily long time; it’s literally a decade-long commitment.  So choose it wisely.

To blast through 30 years of personal industry history:  for Oracle it was row-level locking; for BusinessObjects, the semantic layer; for Endeca, the MDEX engine; for MongoDB, NoSQL [3]; for Salesforce, SaaS (branded as No Software); for Anaplan, the hypercube; for GainSight, customer success; and for Alation, the data catalog [4].

To net out the art of enterprise software marketing, it’s:

  • Stay out of Box 3
  • If you’re lucky, you’re in your Box 2 [5].  Talk about what you want to say because it’s what they want to hear.
  • Spend most of your time in Box 1, bridging from what they want to hear to what you want to say.  This keeps butts in seats at programs and primes them towards your selling agenda.
  • Make one and only one bet in Box 4, use AR to help evangelize it, and produce a small number of very high quality deliverables to tell the story.

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Notes

[1] Much as I barely understand a MacPherson strut, despite having been subjected to hearing about it by years of feature-driven automotive marketing.

[2] In other words, “sell what’s on the truck.”  An old example, but likely still true:  the shirt color worn by the model in a catalog typically gets 5x the orders of any other color; so why not do color selection driven by inventory levels instead of graphic design preferences?

[3] Or, as I always preferred, MyNoSQL, simultaneously implying both cheap and easy (MySQL) and document-oriented (NoSQL).  By the way, this claim is somewhat less clear to me than the proceeding two.

[4]  The more the company is the sole pioneer of a category, the more the evangelization is about the category itself.  The more the company emerges as the leader in a competitive market, the more the evangelization is about the special sauce.  For example, I can’t even name a GainSight competitor so their message was almost purely category evangelical.  Alation, by comparison, was close to but not quite a sole pioneer so I wrestled with saying “machine-learning data catalog” (which embeds the special sauce), but settled on data catalog because they were, in my estimation, the lead category pioneer.  See my FAQ for disclaimers as I have relationships past or present with many of the companies mentioned.

[5]  Any space-pioneering application is probably in Box 2.  Any technology platform is almost always in Box 3 or 4.  Any competitive emerging space probably places you in Box 1 — i.e., needing to do a lot of bridging from more generic buyer needs to your special sauce for meeting them.