Category Archives: Startups

Foreword to The Next CMO: A Guide to Marketing Operational Excellence

The folks at Plannuh, specifically Peter Mahoney, Scott Todaro, and Dan Faulkner, asked me to write the foreword for their new book, The Next CMO:  A Guide to Marketing Operational Excellence.  (Free download here.)

Here’s what I wrote for them.

CMO is a hard job. Early in my career I worked for CMOs, in sort of an endless revolving-door progression, at one point having 7 bosses in 5 years. I have been a CMO, for over 12 years at three different companies. I have managed CMOs, working as CEO for over a decade at two different companies. And I have guided CMOs, serving as an independent director on the board of five different companies.  Let’s just say I’ve spent a lot of time in and around the CMO role.

In the past two decades, no executive suite role has changed more and more quickly than the CMO. Marketers of yesteryear could focus on strategic positioning and branding, leaving such banalities as lead generation to sales-aligned field marketing teams, managing scraps of paper in cardboard boxes.

Sales and marketing automation systems changed everything. Concepts like pipeline, conversion rates, and velocity were born. From lead generation sprung lead nurturing. Attribution emerged to solve one of the world’s oldest marketing problems.

Artificial intelligence (AI) arrived at the scene, helping with areas like lead scoring and prioritization. The demand for analytics followed suit. Marketing ops arose as the cousin of sales ops.

Digital marketing changed everything again. Spend became even more accountable. Pay-per-click replaced pay-per-view which replaced just-pay. Targeting became more precise both via search and the rise of social media. Content marketing emerged to supplement declining traditional public relations. If yesterday’s marketing was leaflets dropped from airplanes, today’s is A/B-tested, laser-guided, call-to-action missiles.

Technology came at CMOs faster than they could keep up. Software could power your website, run your resource center, generate your landing pages, test your messaging, drive repeatable SDR processes, identify your ideal customer, drive account-based marketing, and even record and analyze prospect conversations.

What’s more, as CEOs and boards knew that entirely new classes of questions were becoming answerable, they started asking them.

  • What percent of the pipeline are prospects within our ideal customer profile?
  • What’s the stage-weighted expected value of the pipeline?
    Forecast-category weighted?
  • What’s our week 3 pipeline conversion rate for new logo vs upsell opportunities?
  • What’s our cost per opportunity and how does it vary by channel and geography?
  • What’s marketing’s contribution to our customer acquisition cost (CAC) ratio and how are we improving it?

And dozens and dozens more.

The hardest job in the C-suite got harder. Today’s CMOs need to be visionary strategists by day and operational tacticians by night. Operational marketing has become the sine qua non of modern marketing. If the website is optimized, if the demand generation machine is running effectively, if marketing events are executed flawlessly, if quality pipeline is being generated efficiently, if that pipeline is converting in line with industry benchmarks, and if and only if all that is being done within the constraints of the marketing budget — spending neither too little nor too much — then and only then does the CMO get the chance to be “strategic.”

Operational excellence is thus a necessary but not sufficient condition for CMO success. So it’s well worth mastering and this book is the ideal guide to building and managing your own integrated marketing machine.

There’s no one better to write this book than the leadership team at Plannuh, Peter, Scott, and Dan. With their experience running marketing teams from startups through multi-billion dollar public companies, teaching and mentoring generations of marketers, and now building a platform that codifies their thinking into a scalable SaaS platform, this guide is certain to raise the IQ of your marketing function.

– Dave Kellogg

How To Get Sales and Marketing Working Together (Presentation)

I spoke this morning to a private equity (PE) firm’s gathering of portfolio company CEOs, CROs, and CMOs.  Our topic, one of my favorites, was how to get sales and marketing working together to drive business results.  While I talked about the predictable subject of alignment, I covered it with an interesting three-level angle (philosophical, strategic, operational).  I prefaced the alignment discussion with examples of what typically goes wrong in the sales/marketing relationship, later revealing that I believe most of the commonly-observed “problems” between sales and marketing are, in fact, symptoms of four underlying problems:

  • Unrealistic plans
  • Function-led mentality
  • Blame culture
  • Non-alignment

I’ve embedded the presentation below and it’s also available on Slideshare.

Are We Due for a SaaSacre?

I was playing around on the enterprise comps [1] section of Meritech‘s website today and a few of the charts I found caught my attention.  Here’s the first one, which shows the progression of the EV/NTM revenue multiple [2] for a set of 50+ high-growth SaaS companies over the past 15 or so years [3].

meritech saas multiples

While the green line (equity-value-weighted [4]) is the most dramatic, the one I gravitate to is the blue line:  the median EV/NTM revenue multiple.  Looking at the blue line, you can see that while it’s pretty volatile, eyeballing it, I’d say it normally runs in the range between 5x and 10x.  Sometimes (e.g., 2008) it can get well below 5x.  Sometimes (e.g., in 2013) it can get well above 10x.  As of the last data point in this series (7/14/20) it stood at 13.8x, down from an all-time high of 14.9x.  Only in 2013 did it get close to these levels.

If you believe in regression to the mean [5], that means you believe the multiples are due to drop back to the 5-10 range over time.  Since mean reversion can come with over-correction (e.g., 2008, 2015) it’s not outrageous to think that multiples could drop towards the middle or bottom of that range, i.e., closer to 5 than 10 [6].

Ceteris paribus, that means the potential for a 33% to 66% downside in these stocks. It also suggests that — barring structural change [7] that moves baseline multiples to a different level — the primary source of potential upside in these stocks is not continued multiple expansion, but positive NTM revenue surprises [8].

I always love Rule of 40 charts, so the next fun chart that caught my eye was this one.  meritech r40 score While this chart doesn’t speak to valuations over time, it does speak to the relationship between a company’s Rule of 40 Score and its EV/NTM revenue multiple.  Higher valuations primarily just shift the Y axis, as they have done here, uplifting the maximum Y-value by nearly three times since I last blogged about such a chart [9].  The explanatory power of the Rule of 40 in explaining valuation multiple is down since I last looked, by about half from an R-squared of 0.58 to 0.29.  Implied ARR growth alone has a higher explanatory power (0.39) than the Rule of 40.

To me, this all suggests that in these frothy times, the balance of growth and profit (which is what Rule of 40 measures) matters less than other factors, such as growth, leadership, scarcity value and hype, among others.

Finally, to come back to valuation multiples, let’s look at a metric that’s new to me, growth-adjusted EV/R multiples.

meritech r40 growth adjusted

I’ve seen growth-adjusted price/earnings ratios (i.e., PEG ratios) before, but I’ve not seen someone do the same thing with EV/R multiples.  The basic idea is to normalize for growth in looking at a multiple, such as P/E or — why not — EV/R.  For example, Coupa, trading at (a lofty) 40.8x EV/R is growing at 21%, so divide 40.8 by 21 to get 1.98x.  Zoom, by comparison looks to be similarly expensive at 38.3x EV/R but is growing at 139%, so divide 38.3 by 139 to get 0.28x, making Zoom a relative bargain when examined in this light [10].

This is a cool metric.  I like financial metrics that normalize things [11].  I’m surprised I’ve not seen someone do it to EV/R ratios before.  Here’s an interesting observation I just made using it:

  • To the extent a “cheap” PE firm might pay 4x revenues for a company growing 20%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • To the extent a “crazy” VC firm might pay 15x revenues for a company growing at 75%, they are buying in at a 0.2 growth-adjusted EV/R ratio.
  • The observant reader may notice they are both paying the same ratio for growth-adjusted EV/R. Given this, perhaps the real difference isn’t that one is cheap and the other free-spending, but that they pay the same for growth while taking on very different risk profiles.

The other thing the observant reader will notice is that in both those pseudo-random yet nevertheless realistic examples, the professionals were paying 0.2.  The public market median today is 0.7.

See here for the original charts and data on the Meritech site.

Disclaimer:  I am not a financial analyst and do not make buy/sell recommendations.  I own positions in a wide range of public and private technology companies.  See complete disclaimers in my FAQ.

# # #

Notes 
[1] Comps = comparables.

[2] EV/NTM Revenue = enterprise value / next twelve months revenue, a so-called “forward” multiple.

[3] Per the footer, since Salesforce’s June, 2004 IPO.

[4] As are most stock indexes. See here for more.

[5] And not everybody does.  People often believe “this time it’s different” based on irrational folly, but sometimes this time really is different (e.g., structural change).  For example, software multiples have structurally increased over the past 20 years because the underlying business model changed from one-shot to recurring, ergo increasing the value of the revenue.

[6] And that’s not to mention external risk factors such as pandemic or election uncertainty.  Presumably these are already priced into the market in some way, but changes to how they are priced in could result in swings either direction.

[7] You might argue a scarcity premium for such leaders constitutes a form of structural change. I’m sure there are other arguments as well.

[8] To the extent a stock price is determined by some metric * some multiple, the price goes up either due to increasing the multiple (aka, multiple expansion) or increasing the metric (or both).

[9] While not a scientific way to look at this, the last time I blogged on a Rule of 40 chart, the Y axis topped out at 18x, with the highest data point at nearly 16x.  Here the Y axis tops out at 60x, with the highest data point just above 50x.

[10] In English, to the extent you’re paying for EV/R multiple in order to buy growth, Zoom buys you 7x more growth per EV/R point than Coupa.

[11] As an operator, I don’t like compound operational metrics because you need to un-tangle them to figure out what to fix (e.g., is a broken LTV/CAC due to LTV or CAC?), but as investor I like compound metrics as much as the next person.

 

The Pipeline Chicken or Egg Problem

The other day I heard a startup executive say, “we will start to accelerate sales hiring — hiring reps beyond the current staffing levels and the current plan — once we start to see the pipeline to support it.”

To mix metaphors, what comes first: the pipeline or the egg?  To un-mix them, what comes first:  the pipeline or the reps to prosecute it?  Unlike the chicken or the egg problem, I think this one has a clear answer: the reps.

My answer comes part from experience and part from math.

First, the experience part:  long ago I noticed that the number of opportunities in the pipeline of a software company tends to be a linear function of the number of reps, with a slope in the 12-18 range as a function of business model [1].  That is, in my 12 years of being a startup CEO, my all-quarters, scrubbed [2] pipeline usually had somewhere between 12 and 18 opportunities per rep and the primary way it went up was not by doing more marketing, but by hiring more reps.

Put differently, I see pipeline as a lagging indicator driven by your capacity and not a leading indicator driven by opportunity creation in your marketing funnel.

Why?  Because of the human factor:  whether they realize it or not, reps and their managers tend to apply a floating bar on opportunity acceptance that keeps them operating around their opportunity-handling capacity.  Why’s that?  It’s partially due to the self-fulfilling 3x pipeline prophecy:  if you’re not carrying enough pipeline, someone’s going to yell at you until you do, which will tend to drop your bar on opportunity acceptance.  On the flip side, if you’re carrying more opportunities than your capacity — and anyone is paying attention — your manager might take opportunities away from you, or worse yet hire another rep and split your territory.  These factors tends to raise the bar, so reps cherry pick the best opportunities and reject lesser ones that they’d might otherwise accept in a tougher environment.

So unless you’re running a real machine with air-tight definitions and little/no discretion (which I wouldn’t advise), the number of opportunities in your pipeline is going to be some constant times the number of reps.

Second, the math part.  If you’re running a reasonably tight ship, you have a financial model and an inverted funnel model that goes along with it.  You’re using historical costs and conversion rates along with future ARR targets to say, roughly, “if we need $4.0M in New ARR in 3 quarters, and we insert a bunch of math, then we’re going to need to generate 400 SALs this quarter and $X of marketing budget to do it.”  So unless there’s some discontinuity in your business, your pipeline generation doesn’t reflect market demand; it reflects your financial and demandgen funnel models.

To paraphrase Chester Karrass, you don’t get the pipeline you deserve, you get the one you plan for.  Sure, if your execution is bad you might fall significantly short on achieving your pipeline generation goal.  But it’s quite rare to come in way over it.

So what should be your trigger for hiring more reps?  That’s probably the subject of another post, but I’d look first externally at market share (are you gaining or losing, and how fast) and then internally at the CAC ratio.

CAC is the ultimate measure of your sales & marketing efficiency and looking at it should eliminate the need to look more deeply at quota attainment percentages, close rates, opportunity cost generation, etc.  If one or more of those things are badly out of whack, it will show up in your CAC.

So I’d say my quick rule is if your CAC is normal (1.5 or less in enterprise), your churn is normal (<10% gross), and your net dollar expansion rate is good enough (105%+), then you should probably hire more reps.  But we’ll dive more into that in another post.

# # #

Notes

[1]  It’s a broad range, but it gets tighter when you break it down by business model.  In my experience, roughly speaking in:

  • Classic enterprise on-premises ($350K ASP with elephants over $1M), it runs closer to 8-10
  • Medium ARR SaaS ($75K ASP), it runs from 12-15
  • Corporate ARR SaaS ($25K ASP) where it ran 16-20

[2] The scrubbed part is super important.  I’ve seen companies with 100x pipeline coverage and 1% conversation rates. That just means a total lack of pipeline discipline and ergo meaningless metrics.  You should have written definitions of how to manage pipeline and enforce them through periodic scrubs.  Otherwise you’re building analytic castles in the sand.

Book Review of Good Strategy, Bad Strategy by Richard Rumelt

Good Strategy, Bad Strategy by UCLA Anderson professor Richard Rumelt is by far my favorite book on strategy.  In this post I’ll explain why I love this book, provide an overview of Rumelt’s core concepts, and offer a few thoughts on (and dare I say an enhancement to) his strategy framework.

Why I Love This Book
I love this book for two reasons.  First, he skillfully eviscerates all of the garbage that far too often passes for strategy in corporate America.  It’s borderline therapeutic to watch him tear down case after case of junk that is pitched by executives and consultants as strategy.  His four telltales:

  • Fluff.  Corporate doublespeak that,“uses ‘Sunday’ words and apparently esoteric concepts to create the illusion of high-level thinking.”
  • Failure to face the challenge“Bad strategy fails to recognize of define the challenge.  If you can’t define the challenge, you cannot evaluate a strategy.”
  • Mistaking goals for strategy.  Here at the center of the OKR universe, it’s common to find companies with lists of “statements of desire” rather than “plans for overcoming obstacles.” [1]
  • Bad strategic objectives“Strategic objectives are ‘bad’ when they fail to address critical issues or when they are impracticable.”

His dismemberment of bad strategy is so surgical and so deft that it alone is worth the price of the book.

The second thing I love about this book is focus.  As my high school Latin teacher, Mr. Maddaloni, always reminded us:  focus is singular [2].  Most companies — often due to the group consensus process used to create strategy — fail at rising to the challenge of picking and end up with multiple, strategic foci instead of a single, strategic focus [3].

This can reflect avoidance of a dead moose issue threatening the company or simply lead to a laundry list of incoherent and unattainable goals.  Either way, Rumelt’s approach sidesteps this problem by forcing the company to focus on a single issue.

The Core Concepts of Good Strategy, Bad Strategy
Per Rumelt, “good strategy is coherent action backed up by an argument, an effective mixture of thought and action with a basic underlying structure called the kernel.”

Excerpt:

The kernel of a strategy contains three elements:

A diagnosis that defines or explains the nature of the challenge. A good diagnosis simplifies the often overwhelming complexity of reality by identifying certain aspects of the situation as critical.

A guiding policy for dealing with the challenge. This is an overall approach chosen to cope with or overcome the obstacles identified in the diagnosis.

A set of coherent actions that are designed to carry out the guiding policy. These are steps that are coordinated with one another to work together in accomplishing the guiding policy.

This is brilliant in its simplicity and in its recognition that a huge part of strategy is an accurate and insightful simplification of the situation:  determining which elements are essential and boiling it down to a short, simple narrative as to “what’s going on”  and ergo what to do about it.

I use a trick to indirectly make this point when I’m in a strategy meeting.  At some point the discussion inevitably fades into, “argh, this is so complicated, there are so, so many things to consider” and room is lost to a sense of hopelessness.  I’ll then ask one of the participants, “can you tell me the story of the last company you worked at?”

You’ll usually hear something like this in response:

  • “We pushed too far up market without the product to support it.”
  • “We got caught in a squeeze between a high-end enterprise vendor and low-end velocity disrupter.”
  • “We got out-marketed by a company with more capital and a more aggressive team.”

I’ll then say, “why do you suppose it’s so easy for us to tell short, simple stories about our prior employers but nearly impossible to make one about us?  What do you think we’ll say in four years about this company?”  It’s the same idea as Rumelt’s — to force simplification of the story to its core narrative and to focus on one thing in the diagnosis.  We do it naturally when looking at the past.  In the present, we resist it like the plague.

I believe that 80% of strategy is the diagnosis — and sometimes the diagnosis simply can’t get made through a group process, but instead has to be decided by the CEO [4] [5].  The other half, to paraphrase Yogi Berra, is the guiding policy and coherent actions.

Thoughts on the Framework
While I love the fact that Rumelt forces executives to diagnose the single most important challenge facing the company — and avoid creating lists of many such challenges — doing so is quite difficult for both good and bad reasons.

The good reason is that it forces “table stakes” conversations, well, “off the table.”  If it’s a discussion about something that everyone in the industry must do (e.g., build quality product, train and scale sales), then it’s almost definitionally not the single most important challenge facing that company.  That’s good, because while those table stakes operations are undoubtedly hard work, they are not strategic.  Operating executives too often confuse the two.

The bad reason it’s difficult is that you might get it wrong.  And in this framework, where everything is tied to a diagnosis about the company’s single-most important challenge, if you get the diagnosis wrong, the whole strategy collapses along with it.

The hardest part I’ve found is balancing immediate vs. longer-term challenges.  For example, say it’s 2003 and you’re at CRM leader Siebel Systems.

  • Your most immediate challenge is likely your direct competition, PeopleSoft or Oracle who are much larger than you and providing broad suites.
  • Your biggest strategic challenge is your indirect competitor Salesforce.com, who is disrupting the business model with software as a service.

Perhaps one of my friends who worked at Siebel at the time can weigh in with an informed comment, but my guess is that Siebel (who was doing $1.4B in annual revenue) minimized Salesforce (who reported doing a mere $65M in its S-1) and, to the extent they would have used a framework like this, would have picked the wrong challenge and gotten the wrong strategy as a result.

Another potential criticism of this framework is that it tends to orient you to competitive threats in a Silicon Valley that would much rather talk about vision (and making the world a better place) than competition.  In my experience, there are few vendors who have the luxury of being totally vision-driven, those who claim otherwise are often practicing revisionism [6], and there’s nothing in the framework, per se, that says the central challenge has to be competition-related.  It could be about building the product, creating distribution channels, or landing your first ten customers.  The framework doesn’t dictate the nature of the challenge, it simply demands that you pick one.

My last thought on the framework is that it appears to be missing an element [7].  In order to make a guiding policy from a diagnosis it helps to have a set of beliefs (or assumptions) as the bridge in between, because these beliefs are neither an explicit part of the guiding policy nor necessarily documented in the diagnosis.

So my slightly revised format of the template is:

  • Diagnosis:  the single most important challenge faced by the company (whether immediate or strategic)
  • Beliefs:  a short list of key assumptions that bridge from the diagnosis to the guiding policy.
  • Guiding policy:  the overall approach to dealing with the challenge
  • Coherent actions:  a set of actions designed to carry out the guiding policy

Or, in English form, given the diagnosis and this set of beliefs, we have chosen this guiding policy which is to be carried out through this set of coherent actions.

Closing Thoughts
I’d say that while I love this book it might have been better titled Bad Strategy, Good Strategy because it’s stronger at tearing apart the garbage that masquerades as strategy than at helping you build good strategy yourself [8].  That said, if you can learn by example and through emulation of the many good strategy examples Rumelt provides, it should be enough to help you and your company not only avoid falling for garbage instead of strategy, but building a good strategy yourself.

I’ll end with the best news of all:  I wrote Rumelt to ask him a few questions and he told me that he’s working on a new book that should address some of my issues.  I can’t wait to read it.

# # #

Notes
[1] OKRs are great and I love OKRs.  But OKRs are for establishing clarity about goals, their unambiguous measurement, and (typically by omission) their priority.  OKRs should be implied by a strategy, but the existence of OKRs (particularly an overly long or incoherent set) does not imply the existence of strategy.

[2] The plural, of course, being foci.

[3] A common case of this is simply failing to make a strategy at all, instead saying (as I’ve actually heard at strategy meetings), “well we’re going to need two financial goals, two sales goals, two product goals, a marketing goal, a customer goal, an alliances goal, and a people goal, so there you go, that’s 10, so let’s just sit down and start making them.  I know the people goal (“attract, develop, and retain the best talent”) and customer goal (“delight our customers”) already, so there’s only 8 more to go.”

[4] I’m slightly twisting Rumelt’s example of a Condorcet Paradox which was really about strategy formulation, not diagnosis, but to the extent that people often gun jump in offering a diagnosis that leads to their desired strategy it still holds.  Adapting his example, the Services person wants a diagnosis that leads to Solutions, the design head wants a diagnosis that leads to Chips, and the systems person wants a diagnosis that leads to Boxes.  The paradox actually occurs not there, but in how each ranks the relative strategies.

[5] If everyone on the team can agree to it, I’d argue it’s almost definitionally a bad strategy.  In a good strategy choices are made, some areas are resources, others are starved, and some are discontinued.  The Chips person voting for Solutions would be, as the saying goes, like the turkeys voting for Thanksgiving.

[6] In conference talks and podcasts it’s far cooler to talk about being vision-driven than talking about competitive strategies; thus I have found the best companies talk little about the competition externally, but are fiercely competitive internally.

[7] Hat tip to my friend Raj Gossain for figuring this out.

[8] By this I mean that while the book provides examples of good strategy, and a simple framework for expressing it, I find the framework missing an important element (beliefs) and the book doesn’t even attempt to outline a process whereby an executive team can work together to devise a good strategy.

Ten Pearls Of Enterprise Software Startup Wisdom From My Friend Mark Tice

I was talking with my old friend, Mark Tice, the other day and he referred to a startup mistake as, “on his top ten list.”  Ever the blogger, I replied, “what are the other nine?”

Mark’s been a startup CEO twice, selling two companies in strategic acquisitions, and he’s run worldwide sales and channels a few times.  I first met Mark at BusinessObjects, where he ran our alliances, we worked together for a while at MarkLogic, and we’ve stayed in touch ever since.  Mark’s a seasoned startup executive, he’s go-to-market oriented, and he has some large-company chops that he developed earlier in his career.

Here’s an edited version of Mark’s top ten enterprise software startup mistakes list, along with a few comments prefaced by DK.

1. Thinking that your first VP of Sales will take you from $0 to $100M.  Startups should hire the right person for the next 18-24 months; anything beyond that is a bonus.  (DK:  Boards will often push you to hire someone “bigger” and that’s often a mistake.) 

2. Expecting the sales leader to figure out positioning and pricing.  They should  have input, but startups should hire a VP of Marketing with strong product marketing skills at the same time as the first VP of Sales. (DK:  I think the highest-risk job in Silicon Valley is first VP of Sales at a startup and this is one reason why.)

3. Hiring the wrong VP Sales due to incomplete vetting and then giving them too much runway to perform.  Candidates should give a presentation to your team and run through their pipeline with little to no preparation (and you should see if they pay attention to stage, last step, next step, keys to winning).  You should leverage backdoor references.  Finally, you should hire fast and fire faster — i.e., you’ll know after 3 months; don’t wait for more proof or think that time is going to make things better.  (DK:  a lot of CEOs and boards wait too long in denial on a bad VP of Sales hire.  Yes, starting over is difficult to ponder, but the only thing worse is the damage the wrong person does in the meantime.)

4. Marketing and selling a platform as a vertical application.  Having a platform is good to the extent it means there is a potentially large TAM, but marketing and selling it as an application is bad because the product is not complete enough to deliver on the value proposition of an application.  Align the product, its positioning, and its sales team — because the rep who can sell an analytic platform is very different from the rep who can sell a solution to streamline clinical trials.  (DK:  I think this happens when a company is founded around the idea of a platform, but it doesn’t get traction so they then fall back into a vertical strategy without deeply embracing the vertical.  That embrace needs to be deeper than just go-to-market; it has to include product in some way.)

5. Ignoring churn greater than 15%.  If your churn is greater than 15%, you have a problem with product, market, or most likely both. Don’t ignore it — fix it ASAP at all costs.  It’s easy to say it will get better with the next release, but it will probably just get a bit less bad.  It will be harder to fix than you think. (DK:  if your SaaS bucket is too leaky, you can’t build value.  Finding the root cause problem here is key and you’ll need a lot of intellectual honesty to do so.)

6. Waiting too long to create Customer Success and give it renewals.  After you have five customers, you need to implement Customer Success for renewals and upsells so Sales can focus on new logos. Make it work. (DK:  Truer words have never been spoken; so many startups avoid doing this.  While the upsell model can be a little tricky, one thing is crystal clear:  Customer Success needs to focus on renewals so sales can focus on new ARR.)

7. Pricing that doesn’t match the sales channel.  Subscriptions under $50K should only be sold direct if it’s a pilot leading to a much larger deployment.  Customers should become profitable during year two of their subscription. Having a bunch of customers paying $10K/year (or less) might make you feel good, but you’ll get crushed if you have a direct sales team acquiring them. (DK:  Yes, you need to match price point to distribution channel. That means your actual street price, not the price you’re hoping one day to get.)

8. Believing that share ownership automatically aligns interests.  You and your investors both own material stakes in your company.  But that doesn’t automatically align your interests.  All other things being equal, your investors want your company to succeed, but they also have other interests, like their own careers and driving a return for their investors.  Moreover, wanting you to succeed and being able to offer truly helpful advice are two different things.  Most dangerous are the investors who are very smart, very opinionated, and very convincing, but who lack operating experience.  Thinking that all of their advice is good is a bit like believing that a person who reads a lot will be a good author — they’ll be able to tell you if your go-to-market plan is good, but they won’t write it for you. (DK:  See my posts on interest mis-alignments in Silicon Valley startups and taking advice from successful people.)

9. Making decisions to please your investors/board rather than doing what’s best for your company. This is like believing that lying to your spouse is good for your marriage. It leads to a bad outcome in most cases.  (DK: There is a temptation to do this, especially over the long term, for fear of some mental tally that you need to keep in balance.  While you need to manage this, and the people on your board, you must always do what you think is right for company.  Perversely at times, it’s what they (should, at least) want you to do, too.)

10. Not hiring a sales/go-to-market advisor because they’re too expensive.  A go-to-market mistake will cost you $500K+ and a year of time. Hire an advisor for $50K to make sure you don’t make obvious mistakes.  It’s money well spent.  (DK:  And now for a word from our sponsor.)

Thanks Mark.  It’s a great list.

Branded Features: Resist the Temptation

Software startups seem drawn by sirens to brand their features. Hey, Apple does it.  Think:  Siri, Facetime.  Microsoft tries it:  Cortana.  Starbucks even brands a cup size:  Venti.  So if they can do it, we should too, right?

Wrong [1].

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But it’s so cool.  Imagine it’s a long time ago and we’re about to launch the first DBMS with stored procedures [2].  Shouldn’t we call them Intelliprocs™ as opposed to plain, old stored procedures?

Then our competitors won’t be able to copy Intelliprocs!

Wrong.  If that’s what you want, get a patent, not a trademark.

Oh, well, then our competitors won’t be able to call them Intelliprocs.

That’s correct.

But we’ll make Intelliprocs the industry term and we’ll  be widely acknowledged as having created both the term and the feature.  Customers will ask competitors if they have Intelliprocs, too!  It will be like going to Peet’s and asking for a Venti latte!

Wrong.  In fact, calling them Intelliprocs and trademarking it virtually guarantees that won’t happen.  The industry will be forced to call them anything but Intelliprocs.  Furthermore, Intelliprocs sounds stupid, and no self-respecting database architect is going to call them that.

By the way, we’re a small company.  Most prospective customers are yet to hear of us here at Sybase [2], so we’re going to dilute our branding efforts.  Instead trying to make people know that Sybase means fast relational DBMS, we’re going split our efforts between that and getting them to first learn the term Intelliprocs and second that Intelliprocs come from Sybase.  That’s three branding efforts when we should be putting all our wood behind one arrow.

Plus, where does it end?  If we call stored procedures Intelliprocs, should call fast commit QuickCommit™, on-line backup ContinuBack™, optimistic locking OptiLock™, group commit GroupFlush™.  You’ll need a thesaurus to understand us when we speak.  And to what end?

If we want the industry to use our language and to know that we invented [3] these features, we should give them common, descriptive names so that others will use them, and then we can market — to the industry and its influencers — the fact that we were the first to deliver them.  That’s how you get known as an innovator.

We’ll save money by not having to register all those marks in 47 countries around the world.

Speaking of international, descriptive features names translate better into other languages than branded names.  If you think we’ll be hard to understand when we speak English, imagine how hard it will be when we’re speaking French, Greek, or Mandarin — with all these untranslatable, English-rooted, branded feature names popping up every two seconds.

We’ll be in a way better position, legally, when it comes to defense.  If we name stored procedures descriptively, it will help us if someone else claims our name is their mark.  We’ll just argue, correctly, that it’s a descriptive name and not a brand.  The more “brandy” we name them, the harder that is to do.  So our branding strategy should be to have one brand, Sybase, and then name everything (e.g., products, features) else descriptively.  It the best marketing, and the best legal, strategy.

Last of all, remember branding first principles.  It’s Jell-O brand gelatin.  Levi’s brand denim jeans.  Kleenex brand tissues.  Zoom brand videoconferencing.  Tinder brand dating.  While you certainly can brand features, the primary purpose is to name and differentiate your company’s offering from the other ones.  Branding is not first and foremost about features.  It’s about companies.

So, if you’re not a multi-billion-dollar company, then maybe you shouldn’t emulate the marketing strategy of one.  If you take a breath, pause, and think about what it means to create branded features — to your branding, to your comprehensibility, to your industry leadership, to your international operations, to your legal strategy (and associated costs) — you’ll decide to pass on branded features every single time.

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Notes

[1] This post is a fresh take on a post I did in 2006 entitled On Branded Features, which actually uses the same example.

[2] This is a fictitious conversation about a real example.  Sybase was the first relational database to introduce stored procedures.

[3] See note 2.

[4] Closer to reality, first brought to the relational DBMS more than invented.

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