Kellblog

Strategy as a Series of Beliefs

Posted on May 12, 2024 | Leave a comment

I’m always looking for better ways to distill strategy. My favorite strategy author is Richard Rumelt, who wrote Good Strategy, Bad Strategy and the more recent but less acclaimed follow-on, The Crux.

I love Rumelt’s work for two reasons:

He takes a wrecking ball to the garbage that is often passed off as strategy. Aspirations are not strategy. Goals and OKRs are not strategy. Financial projections and forecasts are not strategy. SWOT analyses and five forces analyses are not strategy. Driving results is not strategy. Deciding to be a butcher, baker, or candlestick maker is not strategy. You may, like me, find reading these takedowns not only educational, but therapeutic.
He whittles strategy down to the head of a pin. First, by defining strategy as identifying and planning to overcome a company’s most important challenge (aka, challenge-driven strategy). Then, by capturing what he calls the kernel of strategy: a diagnosis, a guiding policy, and a set of coherent actions.

Much as I love the kernel idea, in one assignment a few years back we tried to apply this framework and stumbled into a problem. We arrived at a diagnosis fairly easily, but got stuck trying to create a guiding policy. We found that the diagnosis alone wasn’t enough to arrive at a guiding policy. We kept needing to insert a few assumptions (or beliefs) about the future before we could agree on a guiding policy. We drifted to a modified framework that looked like this:

Given diagnosis X,
And beliefs Y,
We choose guiding policy Z,
And coherent actions 1-5 to implement it.

I was so excited with this discovery that I emailed Rumelt. While he kindly did reply, I don’t think my point landed. He directed me to his then-upcoming book and suggested it would be addressed there. The Crux was subsequently published and I don’t think it was. Never meet your heroes, as Flaubert wrote, a little gold always rubs off when you do.

Undeterred, I continued to use Rumelt’s framework, but added beliefs as an explicit part. I’ve always felt that diagnosis was by far the hardest part of strategy, as I believe does Rumelt, given this excerpt from his first book:

“After my colleague John Mamer stepped down as dean of the UCLA Anderson School of Management, he wanted to take a stab at teaching strategy. To acquaint himself with the subject, he sat in on ten of my class sessions. Somewhere around class number seven we were chatting about pedagogy and I noted that many of the lessons learned in a strategy course come in the form of the questions asked as study assignments and asked in class. These questions distill decades of experience about useful things to think about in exploring complex situations. John gave me a sidelong look and said, “It looks to me as if there is really only one question you are asking in each case. That question is ‘What’s going on here?’ ” John’s comment was something I had never heard said explicitly, but it was instantly and obviously correct. A great deal of strategy work is trying to figure out what is going on. Not just deciding what to do, but the more fundamental problem of comprehending the situation.”

I believe Rumelt would say that what I call beliefs are simply part of the diagnosis. For example, he said, “Netflix’s overall challenge (in 2018) was that it could no longer count on contracting for existing good TV and studio films at reasonable prices.” I’d argue that Rumelt’s Netflix diagnosis is actually two statements in one. Writing from the viewpoint of Netflix:

That today we find ourselves increasingly hit with large price increases and/or a non-desire to renew distribution agreements for content.
We believe the vast majority of the content producers will enter the content distribution business via streaming services in the next few years and ergo will not want or need to work with us.

First, that’s one hell of a “gnarly challenge” as Rumelt likes to call the crux issue. Second, I like splitting it because, particularly when working with a good-sized group to build strategy, it helps to distinguish between what we are seeing right now versus what we anticipate in the future. The former are facts, the latter are beliefs — and most of the interesting debate is not about the facts, but the beliefs.

I was happy with this modified framework until a strange thing happened the other day. I was talking with a founder and — lightbulb moment — I realized I could further distill strategy simply by looking only at beliefs. Not a laundry list of them (which can easily get generated in such a process), but what I call the primary belief, the big one, the one that resolves the crux issue and drives all the rest.

I immediately tried to apply this idea to my experience at Business Objects where, for nearly a decade, I worked as one of the top executives as we grew the company from $30M to $1B in revenues. I easily distilled our history into four eras categorized by our primary belief:

Era 1 (1990-1994). We believe enterprises will pay 5-10x the price of commodity query and reporting (Q&R) tools for an enterprise Q&R solution.
Era 2 (1995-1998). We believe that desktop Q&R and online analytical processing (OLAP) tools should be integrated, and not existing as two separate products.
Era 3 (1999-2002). We believe the Internet will require a wholesale rewrite of business intelligence (BI) tools and enable both existing internal and new external use-cases for BI.
Era 4 (2003-2005). We believe that customers will increasingly want to buy an integrated suite of BI tools, including Q&R, OLAP, and enterprise reporting.

These beliefs were largely heretical at the time. $500/seat for a Q&R tool? Insane. Integrating Q&R and OLAP? Can’t be done (and “they” were nearly right). Extranet BI? Never, corporate data is highly proprietary. BI suites? No, customers still want best of breed!

But those four beliefs drove us from $0 to $1B in revenues. The beliefs alone are not enough, of course. You need to build sub-strategies (e.g., product, go-to-market) and execute against them. In era 1, we needed a highly targeted strategy to break into the market with this radical idea. In era 2, we had to first arrive at the belief when the market had established Q&R and OLAP as separate categories, and then we needed to build and market the integrated product. In era 3, there were several different web product strategies and we eventually selected, and executed successfully against, a good one.

But I can and will argue that it all flowed from the underlying primary belief.

I worked with Alation in various capacities for many years, so I feel I know their evolution pretty well. Let me try the same exercise (as an outsider looking in), separating Alation’s history into three eras categorized by primary belief:

Era 1 (search and discovery). We believe that companies will need a centralized data catalog to help people find the data they need, and that machine-learning can help with that finding.
Era 2 (data governance). We believe that data catalogs (almost surprisingly) turn out to be an ideal tool for data governance, particularly the non-invasive variety.
Era 3 (data intelligence platform). We believe that customers will increasingly want to buy a data intelligence platform that includes data search & discovery, governance, and lineage.

I’m probably missing the company’s strong commitment to cloud platforms as part of era 3 and there may be a new era 4, but you get the idea. Again these beliefs were often heretical at the time. A lot of people didn’t believe data catalogs were even needed. Most people believed data governance was a distinct category and that the “prevent access” ethos of data governance ran strongly counter to the “enable access” ethos of data catalogs. Until recently, many people didn’t believe in data intelligence platforms (but with help from IDC and Databricks that debate has been put to bed). Again, beliefs alone are not enough. There are numerous also-ran data catalog companies who presumably shared some of these beliefs, but built the wrong strategies in response or lacked Alation’s relentless drive in execution.

I often say that strategy is best analyzed in reflection. Meaning that somehow everything is clearer and simpler when you look back 10 or 20 years to reflect upon what happened. In fact, I often encourage people to do a future look-back when formulating strategy: “imagine it’s ten years from now and your company won in the market — now tell me why.”

My conclusions from all this are:

Read Rumelt. Both Good Strategy, Bad Strategy and The Crux.
Add beliefs to the framework. More precisely, separate the diagnosis into present truths and future beliefs.
Work to find the one primary belief for your current situation. If you’re a new startup, that belief is probably embedded in the answer to, “why did you found the company?” If you’ve been around for a while, start by analyzing your history and trying to break it into belief-driven eras.
Once you’ve found a potentially era-defining primary belief, resume the Rumelt exercise: define guiding policy and coherent actions around it.

You’re an Operator and Maybe Don’t Even Know It

Posted on May 7, 2024 | 2 comments

Many concepts in Silicon Valley are defined from the point of view of the venture capitalist (VC), not the employee or founder.

For example, the term “exit” is a misnomer when seen from the employee point of view. An IPO is usually anything but an exit for executives who first will be subject to a 6-month lock-up period and restricted to certain trading windows thereafter. A PE acquistion is usually not an exit for the executive team who may be expected to stick around, sign employement letters as a condition of the sale, and rollover a certain percentage of their proceeds into the deal. These transactions are, strictly speaking, best described as an “exit” only for the investors.

So it was with great one surprise one day, a decade or so ago, that I learned I was not a CEO, but an “operator,” when seen from the viewpoint of a VC. Frankly, I found it odd to be referred to as an operator, but I hoped that at least I was a smooth one.

From the VC point of view, there are four types of people:

Founders. The entrepreneur(s) who found a company.
Operators. The executives who build and run companies
Investors. The venture capitalists who invest in startups.
Employees. The 95%+ of the staff not on the executive leadership team (ELT) but who, paraphrasing George Bailey, do most of the working and paying and living and dying in the company.

So, what might you do with this morcel of knowlege? While I believe that the mere act of trying to see things from others’ viewpoint is always developmental, there are some practical takeaways here.

Let’s assume you’re an operator. What should you know and do?

You are not a founder. Many operators, particularly ones who join early, start to think of themselves as one of the founders. While the founders may even treat you as such, from the VC/board point of view you are not. The founder invincability cloak does not apply to you, no matter how much you’ve contributed nor how much you are valued. You are an operator. You may be beloved, but your replacement is always one phone call to 1-800-DAVERSA or 1-800-HEIDRICK away. Don’t overplay your hand. Some do.
You are valued for different things than a founder. Boards value operators for their experience, common sense, reliability, and professionalism. If given only one adjective, I’d say board members want “brilliant” founders, but “solid” executives. Operators are not creating the vision; they’re executing it. I don’t need an artiste to replace my fence, just an experienced fence builder. Be solid and low drama. This doesn’t diminish your value in any way: such operators are often quite hard to find.
You are positioned by expertise and sub-expertise. You are a velocity sales leader. Or a big-deal CRO. Or a demandgen marketer. Or a CRM product leader. You become what you repeatedly do. Actively manage your work so that you position yourself where you want to be. If you want to be positioned as an ABM marketer or a post-agile engineering leader, then go lead an ABM marketing or post-agile engineering team.
Fighting positioning is pointless. Positioning works because the human mind likes to simplify. If you think you can position yourself as the SMB and enterprise, transactional and artisanal, process-driven and relationship-driven sales leader, then you are mistaken. If you fail to position yourself, don’t worry, the market will do it for you — you just may not like the result. Actively position yourself and don’t fight the tide in so doing.
You are positioned by size-range. Think: we need a $10-30M CRO. Or a $30 to $150M CFO. Or a $100M to $300M CEO. The size ranges aren’t fixed, but be aware of what size range you’re positioned in. Avoid the number one mistake I see: leaving a high-growth company too soon because you’re tired of something, thus leaving yourself positioned as a $10-30M operator when — if you’d held on for a bit longer — you might have been a $10-100M one.
Expect to get hired below the top of your size range. No patient wants to hear, “this is the first time I’ve tried a brain surgery this complex, but I’m really confident I can do it.” Expect VCs and boards to think similarly. If you’re positioned as a $10-30M CPO, don’t expect to get hired by a $30M company growing to $100M. Expect to get hired by a $10M company growing to $30M. Per the prior point, ride existing success to increase the top of your range. And beware anyone willing to hire you above it.

I’ll use myself as an example of some of these principles:

Despite having been CEO of two startups from $8-50M and $0-80M for over a decade, I am still widely positioned as a marketing guy. I embrace that both because there’s no point in fighting the tide and I think it’s true. I am a marketing guy. It’s my DNA. I ooze marketing. But I try to remind people of my overall experience with my 10/10/10 message: 10+ years a CMO, 10+ years a CEO, and 10+ boards.
Despite having been CMO of a $1B company and SVP/GM of a $500M business at a $3B company, I get positioned as an early-stage guy, and get size-bucketed in the $0-100M range. While I am certainly strong in that range, I have both signficant experience and clients well beyond $100M. While I can find work beyond that range and enjoy doing it, as a marketer, I must accept that I can’t position at both ends of the scale. Positioning is all about choice and, as the saying goes, “if you try to be everything to everybody, you end up nothing to nobody.”

Or, as I like to say to marketers in career planning: marketer, position thyself!

2 Comments

Posted in Career, Startups, VC

Tagged Exit, Operator

Does Your Marketing Pass the Duck Test?

Posted on May 3, 2024 | Leave a comment

“If a bird walks like a duck, swims like a duck, and quacks like a duck, I call that bird a duck.” — James Whitcomb Riley

Many marketers are in such a hurry to talk about topical issues that they forget the duck test: if it walks like a duck, swims like a duck, and quacks like a duck, then most people will conclude it’s a duck. In logic, they teach that such abductive reasoning can lead to incorrect conclusions — and it can.

But here in marketing, we draw a different conclusion from the duck test. It’s how most peoples’ minds work so we shouldn’t fight against it. There are two common ways that marketers fail the duck test and we’ll cover both of them — and what to do instead — in this post.

Deny Thy Father and Refuse Thy Name
Many marketers are eager to pretend that their product is the latest in-vogue thing (e.g., AI), and can get so busy dressing it up in the latest tech fashion, that they generate more confusion than sales opportunities.

It’s like a replay of the clichéd movie scene:
Man: Who are you?
Woman: Who do you want me to be, baby?

When someone asks your company the equivalent of “who are you?” [1], you need to answer the question and that answer needs to be clear.

Remember, the enemy for most startups isn’t the competition. It’s confusion. The easiest thing for a prospect to do is nothing. If we talk and I leave confused, then I’ll just write off the wasted half-hour and go on with my day.

Consider an answer like this [2] [3], to the question “what is MarkLogic?”

I mean great question. We ask ourselves that all the time. It’s actually hard to answer because there’s nothing else like it. Answering that is like trying to explain the difference between a Cessna and a 747 to someone who’s never seen an airplane. Our marketing people call it an XML Server, but that’s not a great description.

What is it really? Literally, it’s what you get when you lock two search engine PhDs in a garage for two years and tell them to build a database. You know, it looks like a database from the outside, but when you pop open the hood — surprise — you find that it’s built from search engine parts. Search engine style indexing. And it’s schema-free like a search engine so it can handle unstructured, semi-structured, and, of course, structured data as well. Let’s get into those exciting distinctions in a minute.

This thing — whatever you want to call it — it’s the Vegomatic of a data: it slices and dices and chops in every conceivable way. In the end, I think what makes it hard to understand is that it’s basically a hybrid: half search engine, half content application platform, and all database.

Is that clear?

As mud. What’s wrong with that answer?

It’s confusing
It’s long
It’s navel-gazing (let’s talk about me)
It’s bleeding on the customer (sharing internal troubles)

It’s a horrible, horrible answer.

Now before you stop reading, perhaps thinking that this is one specific, dated case study, let me say that I could easily write such a parody for about a quarter of the twenty-something startups I work with today. This is not some ancient example from another world. This is a current problem for many startups, but I’m not going to parody any of them here [4]. Might you suffer from this problem? Go listen to some Gong or Chorus recordings, particularly high funnel (e.g., SDR) and/or discovery calls, and see if anything resonates.

Now, let’s contrast the previous answer with this one:

It’s an XML database system, meaning it’s a database that uses XML documents as its native data modeling element. Now, what did you want to do with it again?

What’s nice about this answer?

It’s short
It’s clear
It’s correct
It leaves an opportunity for follow-up questions [5]

But the really nice part of this answer is that it puts focus back on the customer. The direct cost of all the previous blather is confusion. The opportunity cost of all that blather is you waste precious time you could have spent listening to the customer, learning more about their problem, and trying to decide if you can solve it.

So why didn’t some of our sellers want to give the second answer? They didn’t want to say the X word. XML was cool for a while, but that quickly passed and XML databases were always distinctly uncool. So, some sellers would rather spend five minutes tap dancing around the question rather than directly answering it.

What followed was almost always a difficult conversation [6]. But the flaw in tap-dancing was simple: the customer is going to figure it out anyway [7]. Customers are smart. If it:

Stores data like a database
Builds indexes like a database
And has a query language like a database

Then — quack, quack — it’s a database.

That’s the first way marketers fail the duck test. They’re afraid to say what the product is for fear of scaring people off. But there’s another way to fail the duck test.

Confusing Products and Solutions
The second way to fail the duck test is to rotate so hard to solutions that you basically refuse to say what the product is. You end up dodging the question entirely.

Customer: So, what is it?
Vendor: You can use it to build things, like a deck.
Customer: That’s great, but what is it?
Vendor: You can use it to assemble things, too, like a bed.
Customer: Sure, but what is it?
Vendor: And you can use it for disassembling things too.
Customer: Wait, it’s a drill isn’t it?

Here we have the prospect playing twenty questions to figure out what the product is. Yes, we all know that customers buy solutions to problems [8] and Theodore Levitt’s classic example of customers buying 1/4″ holes, not 1/4″ bits.

But don’t take that in a fundamentalist way. If the customer asks, “what is it?” the answer is not, “a thing that makes holes” but, “a power drill with a 1/4-inch bit.” If they ask why ours is better, we say that our bits are titanium and don’t break. “Feature” need not be a four-letter word to remember that the purpose of the drill is to make a hole and, transitively, that the purpose of the hole is to build a new deck with the ultimate benefit of quality family time.

The point is: knowing what solutions (or use-cases) we want to target does not eliminate the requirement to have strong product messaging. Particularly in unexciting categories, we will need to lead with use-cases, not product superiority, category formation, or market leadership. But, inevitably, even when you lead with use-cases, you will get the question: what is it?

And a short, clear answer – as we discussed above – not only gets the customer what they want, but it lets us have more time for listening and discovery. I see many companies where they rotate so hard to use-case marketing that their product messaging is so weak it actually interferes with discussions of the use-case.

For example, say the product is a data streaming platform (DSP) and the use-case is industrial monitoring for manufacturing facilities. Let’s assume that data streaming platforms are not a hot category, so there aren’t a lot of people out shopping for them. That means we’re not going to target DSP shoppers with a product-oriented superiority message, instead, we are going to target people who have a problem with industrial monitoring.

But when one of those people asks what it is, we’re not going to say, “a thingy that helps you do industrial monitoring.” Instead, we’re going to say, “it’s a data streaming platform, many of our customers use it for industrial monitoring, and here’s why it’s such a great fit for that use-case.”

That is, we map to the use-case. We don’t redefine the product around the use-case. We don’t try to use the use-case to avoid talking about the product. Doing so only confuses people because eventually they figure out it’s not an industrial monitoring application, but a data streaming platform that can be used for industrial monitoring. Unless we are clear that it’s a platform being used for a use-case, then we fail the duck test.

In the end, you will get the right answer if you always remember three things:

Customers are smart
Time spent in hazy product explanations confuses customers and robs time from discovery
If it walks like a duck, swims like a duck, and quacks like a duck — then, for marketing purposes at least — it’s a duck.

# # #

Notes

[1] That is, “what is it?”

[2] I swear this is only partially dramatized, and only because I’ve assembled all the fragments into one single response.

[3] This is circa 2008. Presumably much has changed in the intervening 15 years.

[4] I obviously don’t use more recent examples as a matter of both confidentiality and discretion.

[5] An obvious one might be, “so if it’s a database, does it speak SQL?” (To which the answer was “no, it speaks XQuery,” which could lead to another loop of hopefully tight question/answer follow-ups.)

[6] Because, simply put, nobody wanted to buy an XML database. Gartner had declared the category stillborn around 2002 with a note entitled XML DBMS, The Market That Never Was. The way we sold nearly $200M worth of them (cumulatively) during my tenure was not to sell the product (that nobody wanted) but to sell the problems it could solve.

[7] And when they do, they’re not going to be happy that you seemingly tried to deceive them.

[8] Or hire them to do jobs for them, if you prefer the Jobs To Be Done framework.

The Impact of AI on Go-To-Market: Slides from my Balderton Event

Posted on April 29, 2024 | 4 comments

Last week I hosted an event at the Balderton Capital London headquarters discussing the impact of AI on go-to-market (GTM) functions. The event was inspired by two things:

My aborted attempt to write an AI GTM guide, after I realized just how huge the space was and how fast it was changing. I quickly understood it’d take too long to write and it would be out of date the second it was published. But the exercise nevertheless got me started researching AI and GTM.
The following slide from Battery Ventures that I discussed in my 2024 predictions post. This slide argues that, thanks to AI-driven productivity improvement, you should be able to drive the same quota with a 75-person organization that previously required a 110-person organization. This got me thinking: boards are going to start asking about that 30% productivity improvement in 2H24 and what are we going to say?

*What are going to say when the board asks for that 30%?*

When the market is in a state of confusion and things are moving fast, it’s better to have a conversation than to write a guide. So I found two of the smartest people I know and asked them to join me on a panel:

Alice de Courcy, CMO of Cognism, an amazing company that’s doing some of the best solutions-oriented and thought-leadership (aka “demand generation“) marketing in Europe. Alice is also the author of Diary of First-Time CMO.
Firaas Rasheed, founder and CEO of Hook, a company that’s re-inventing customer success software. Firaas argues that CS software lost the plot and ended up more focused on process (e.g., QBRs and NPS surveys) than on results (e.g., churn prediction and prevention). The company’s origin story is quite compelling and told here.

After a I did brief introduction to set the stage, we focused on four high-level questions that GTM leaders are pondering:

What should I make of all the AI tools flooding the market?
What should my strategy be?
What are my higher-ups expecting?
Where should I start?

Thanks again to Alice and Firaas for joining me, and thanks to everyone who attended. The slides are available in PDF here and are embedded below. Balderton is writing up a summary of the event that, once available, I’ll link to here.

Note: both Cognism and Hook at Balderton portfolio companies.

4 Comments

Posted in AI, Customer Success, GTM, Marketing, Sales

Tagged Balderton

Six Principles to Optimize Your Results and Your Career (Presentation Slides)

Posted on April 20, 2024 | 5 comments

Just a quick post to share the slides of a presentation I recently gave on six principles that can help you optimize both your results and your career.

The material, which should be familar to long-time Kellblog readers, is largely based on posts that I’ve written over the years and the last slide of the deck has links to specific posts. The six principles are:

Answer the effing question (ATFQ). Not answering questions wastes time, frustrates coworkers and executives, and can stall your career.
Know your in-memory analytics. Know what numbers you should know in your sleep, why, and then know them. Executives will often use this as a basic form of competency testing.
Understand the three fundamental layers of management (manager, director, VP). Learn how to think like the next level. It’s not that easy.
Write actionable emails. Write messages that are written to be responded to, quickly and tersely. Have empathy for the recipient.
Be a simplifier. The fastest way to get stuck as a project manager (or equivalent) is to be seen as someone who complexifies simple things instead of simplifying complex things.
Follow the three golden rules of feedack. It has to honest. It has to be timely. And, the tough one, it has to be kind.

I’ve embedded the slides below and you can download a PDF version here.