Strategy 1 — Waves & proving your value

Summary: How to position around a wave that’s sweeping an industry, with B2B SaaS examples in CRM & AI.

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Prove it — in a nutshell:

• Strategy: Prove your new value on a new wave, be that a new technology (AI), an emerging role (a new corporate function), a cultural movement (‘creators’) or an external development (e.g., new regulation).
• Attention: Active right-brain (looking out).
• Action: Prove it — lots of folks try catching a wave; the ones that succeed prove they offer new value.
• Position: You become the famous solution associated with the wave, often as much for your playbook as your product.

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As I write, it’s hard to imagine there’s any other wave worth talking about than AI, so we’re going to talk about AI. But we’re going to talk about how B2B tech works as well, along with past B2B SaaS wave-riding success stories we can learn from.

As a founder or leader, you need to know that wave riding is fundamental to how many great startups succeed. It’s inherent to technology itself — each new wave of innovation drives waves of application, and in B2B, those applications can be worth anywhere up to, in Salesforce’s case, hundreds of billions of dollars.

To understand how that works, though, let’s first understand how technology waves and automation work and what the point of B2B tech fundamentally is.

Waves, automation, & productivity

Technology is a bit like sex in that every generation thinks they’re the first to invent it. With the AI wave, for example, our generation thinks we’ve invented automation that might solve work itself, but that’s been the case for every generation of technology ever.

In 1900, for example, 40% of the U.S. population lived on farms. Now it’s closer to 1%. Why? Science and technology have allowed us to produce far more with far less labor. That trend of abundance from less work led influential British economist John Maynard Keynes to speculate in 1930 that we’d soon be a leisure society working 15 hours per week.

Fast forward to 2019, and OpenAI CEO Sam Altman claimed that OpenAI could “maybe capture the light cone of all future value in the universe." (I suppose that means zero-hour work weeks.) Utopian dreams spring eternal — maybe this time will be different. Maybe this round of technology and innovation will finally Solve Work™ like electrification, or machinery, or computers, or the internet before it. Maybe we can then finally embrace a capitalist leisure society, or fully automated luxury communism, or whatever else lies in wait in this technology-powered utopia.

Maybe!

I suspect we’ll be waiting a while, and, as we wait, speculation about AI-powered utopia will remain oddly correlated with high-stakes AI fundraising rounds. That said, being, shall we say, optimistic on that front doesn’t preclude folks from realizing enormous value for businesses in the here and now. Altman, for example, was early and right enough about the value of AI, which is, in part, why OpenAI is currently valued at $150 billion.

In any case, as a B2B founder, it helps to know why we really do benefit from automation, because that’s probably what you’re selling. I’m no economist, dear reader, but the gist in very simple terms is this: technology and automation improve productivity, improved productivity means less need for labor, excess labor goes and finds other jobs and creates new products and services, and we all benefit. This is the virtuous cycle that improves our overall living standards – improved productivity makes us richer.

(That’s not to say having your livelihood automated away doesn’t hurt — it does, and I’ve personally felt that pain. Modern societies have safety nets for a reason, and those safety nets could be much stronger. But stopping innovation and automation is nonsense.)

This is the nature of B2B tech. That’s what it’s all about. That’s what I love about it, to be honest — it’s being in a feedback loop with reality to improve productivity so that not only do you succeed financially (and perhaps wildly so), but, ideally, you also help make society as a whole that little bit richer, too.

This only happens if you can prove it, however — if you can prove new value from a new wave in a real way. But why do we talk about “waves” of technology, anyway?

Energy waves

Positioning on a wave is great because, in theory, the wave does much of the heavy lifting for you. It’s the groundswell of energy that you can tap that can take you far.

And a wave is a great metaphor: go too early and there’s little momentum there; wait too long and it will pass you by; pretend it’s not there and you risk getting smashed by it. Waves are powerful things, after all.

Waves in B2B include:

• New general-purpose technologies: Generative AI, what blockchain hoped to be (but wasn’t), mobile before that, the internet (which enabled SaaS itself), and so on.
• New corporate disciplines and roles: Think about the rise of customer success (Gainsight), digital product design (Figma, Linear), digital marketing (HubSpot originally), or even ‘creators’ on the micro-business end (Patreon). New roles require new tools.
• New playbooks and trends: There might be a move towards agile (Jira, originally), no-code tools (Webflow, Airtable, Notion), messaging (Intercom, Drift), or the shift to remote work (Zoom, Loom).

Concept-wave

While we talk about waves as things you ride, I like to think about what these startups actually do as selling new concepts. My formula for this approach is:

New concept = new change + new playbook + new tool.

You might think of this as creating a category, but I find a lot of the talk about category creation too broad and aspirational. As an aspiration, it’s fine, but in terms of what you actually do, I find thinking about change + playbook + tool much more concrete and prescriptive.

To sell your new concept, you need to:

• Have the vision: You pick the wave of change you want to ride based on what you see. The more unique your vision and the faster you can act on it, the less likely you’ll be fighting it out with 100 other competitors who all saw the same thing.
• Build the narrative: You make the change that’s happening in the world — one that’s separate from you and your company — the foundation of your right-brain narrative. (Thanks, Andy Raskin, for popularizing that idea.)
• Create your clarity strategy: You write about the new playbook as part of your clarity strategy, talking about the change and playbook perhaps more than your actual tool, while building a tool that fits this new reality like a glove. This is a brand play where you own your message.

If you can ride a wave and build your position in the market in this way, awesome!

If you can’t, though, don’t fake it.

Don’t fake the wave

One of the worst strategic choices I’ve seen founders and marketing leaders make is essentially trying to fake the wave, especially in the category creation sense.

The point of the wave is that the bottom-up energy carries you to fame and fortune. If there’s no energy there, though, what are you doing? What’s going to carry you?

To me, this is a bit like believing that if you splash around enough in a placid lake, a wave will suddenly materialize underneath you and everyone will be very impressed. I think most folks would just look at you like you’re slightly deranged.

This might sound a little obscure in the mid 2020s, but in the late 2010s there was a real craze around coming up with made-up new categories of software. Perhaps it was just a symptom of the tail-end of the classic B2B SaaS ZIRP era, but many VCs really believed it — they bought into this approach entirely. Capital was abundant after all, so why not try? The problem was that fundamental shifts in technology were less so (crypto being a huge red herring), and founders were left trying to wish new categories into existence without a wave to drive them.

Don’t get me wrong, though. New categories do happen occasionally, and there’s nothing wrong with having a fresh concept (as we’ll see below) or picking an attribute (as we’ll see in the next chapter) and trying to be more about something than the competition.

But fake category creation — and fake narratives for that matter (crypto, again) — are a killer because the ones who believe it the most are the ones who are talking about it the most: you and your company. This cuts you off from seeing what’s real, from meaningful learning, and from objective N=1 experiments because you’re too busy trying to splash around to make that wave in a lake happen. But if there’s no wave, it just ain’t gonna happen. You’re in a lake.

Don’t forget to prove it

The other failure mode is to believe a wave alone is enough. You still need to solve a problem. Lots of folks will try to catch the same wave — big waves aren’t secrets, hence the explosion of generative AI startup funding — but very few will prove their value in doing so.

That may seem painfully obvious, but it didn’t appear to be very obvious in the crypto cycle, for example, where almost no one proved any meaningful B2B value. (Unless your business was crime, that is.)

Now take the generative AI wave.

The generative AI wave

While the narrative around AI has been, at times, filled with so much hot air you could probably travel around the world with it, there have been actual, productivity-enhancing B2B tools created (developer tools especially), along with an avenue for future productivity growth (better models and their application), which is what we care about in the B2B world.

Personally, I use gen AI for all kinds of things — proofing this book (spot a typo? Blame Claude), ideation, image generation, Q&A, health consulting, code generation, voice narration, and plenty more. (No writing though, ick.) It’s just fun to play around with.

I’m super excited about all the possibilities for fresh innovation that the generative AI revolution has launched, and I’m a big believer in how Ethan Mollick describes modern AI as a co-intelligence we need to learn to work with.

The key question, in B2B terms, however, is whether we can take this co-intelligence from “fun to play around with” to “delivers meaningful, sustained productivity improvements for the enterprise.”

How do we get there? How do we prove that out? This is where wave-riding, as a positioning strategy, only works if you’re equally dedicated to looking down on the left-brain laser-beam axis to the actual specific value of your tool or solution, not just out on the right-brain radar.

But how do you actually do that? Let’s take a quick detour into the world of Jobs to be Done.

Proving it with Jobs to be Done

In the same sense that we already have examples of founders who had the vision, rode the wave, and got the pot of gold at the end (and we’ll get to them, I promise), we also have examples we can draw from where people spent a lot of time wondering how to actually do the whole innovation thing in a more predictable, less random-walk kind of way.

The framework they landed on — and that the startup world adopted — is called Jobs to be Done, or JTBD.

JTBD has gone through several hype cycles and comes in and out of fashion, but at its core are some very simple ideas that help us go from big vision on a wave to discrete, identifiable productivity improvements that we’re aiming for.

The simplest of those ideas is contained in the name of the theory — focus on the job people are trying to get done.

That is, in B2B especially, very few people wake up in the morning wanting to play around with software. They don’t want to have to think about software at all. They just want to get a particular job done.

This often gets described as a use case or a customer story and so on, and that’s fine. But the magic of JTBD is thinking about the job behind the job so to speak, as that’s fertile ground for innovation. Use cases tend to be very literal — what customers say they want. JTBD tends to be more about what they actually need.

For example, a customer might say their use case is that they “need to see certain data on a dashboard.” You can take that literally and give them what they want, and maybe that’s fine, but it’s not much of an innovation. A JTBD approach would be to investigate what the actual job the data and dashboard exists to serve. Maybe they need to take some action when the dashboard says so? Maybe an LLM-driven agent could now do that for them instead? Maybe there’s some broader workflow that could be automated, enhanced, or otherwise improved?

Or, to put it in simpler terms, imagine a person wants to hang a painting on a wall. Their use case, in terms of the tools they need, might be to hammer a nail in the wall. But from a JTBD point of view, we might ask: how else could they hang a painting? How could we innovate there? What if we had detachable sticky hooks instead? That’s what 3M brought to market, and renters everywhere rejoiced because they solved the core JTBD in a whole new way.

You can see the different kinds of vision and left-brain/right-brain attention at work here. It’s easy to see customer needs in a very literal sense, and sometimes that’s all you need. Sometimes it makes sense to build exactly what they ask for because they know best. But once folks already have a proverbial hammer and nail, what comes next? What can you see, in terms of innovation, that could be ‘out there’ on the horizon instead? What’s your vision for the actual job they want to get done — one that’s stable over time — and not just the slight improvement to how they already do things?

(If you want to nerd out more about this, I explore JTBD more in Positioning Playbook, but plenty of other folks have written about it, too. As a successful theory, JTBD has several parents, including Tony Ulwick, with the original, more left-brain, engineering-driven, map-and-quantify approach; Bob Moesta, with a more right-brain, narrative-driven, change-over-time approach; and, most famously, the late, great Clayton Christensen of ‘disruptive innovation’ fame, who put JTBD on the map in 2003’s The Innovator’s Solution.)

Use it to prove it

Detour over, back to proving value on a wave.

Currently, there’s an incredible amount of pull from the market to put AI to use in helping folks with the jobs they want done and an incredible push from investors who see markets in land-grab mode and are happy to dole out $100M investments to AI PhDs who so much as sneeze.

This can put founders in a tricky position.

On the one hand, there’s incredible pressure on founders to fake the wave — raise too much money, with too big a narrative, and go along with the theater of automating all work forever (for real this time!) while failing to do the left-brain laser-beam work on what productivity improvements can actually be proven, because that threatens the big right-brain narrative.

On the other hand, when there’s so much demand here and now, if you wait around forever forensically proving every single productivity improvement, the wave will pass you by. What can you do? What should you do?

In my view, the best founders will see the hype and use it to prove it. You use the wave to open the doors, be ushered into companies and departments all over the world in almost any conceivable sector, and then apply your vision to methodically proving out your value for concrete jobs-to-be-done, capturing the way your customers innovate with your tools along the way (as we’ll see in the next section).

This is the classic, collaborative, work-with-design-partners approach that balances early adopter enthusiasm with the pragmatic reality of what the technology is currently capable of. By all means, put your flag in the ground about where you want to go — paint the big picture — but then get back to building with real customers on real jobs-to-be-done.

Let’s now look at some of those examples we can learn from, starting with well-funded AI-first companies.

Wave riders — AI examples

Perhaps the biggest question for venture-backed AI startups is: where will the value accrue on the B2B side? Will the winners be:

• The foundational model companies?
• The infrastructure companies one level above?
• The classic B2B SaaS-style platforms for enterprises?
• The end-user PLG plays?

That remains to be seen — perhaps the foundational model companies are the category that will capture all the value, with OpenAI already doing billions in annual revenue and Anthropic rapidly catching up.

But personally, I’m more interested in the application layer, and we’ve already seen breakout PLG hits like Cursor, the “AI-enabled code editor,” which is currently so popular people are making memes on X about how other developers won’t shut up about using Cursor.

Likewise, in the more classic corporate B2B SaaS sense, we’re seeing new categories of LLM-based tools emerge that aim to solve the jobs-to-be-done of a given industry in whole new ways, both speeding them up and, sometimes, automating them entirely.

Let’s look at how this is playing out in one particular area: legal tech. This is a world of masses of words — documents, cases, contracts, etc. — which makes it a good fit for LLMs.

Generative AI & legal tech

Let’s touch on three different legal tech startups with three different strategies to see how they’re trying to ride the AI wave.

Hebbia, who targets investment banks and legal firms (amongst others), has raised $130M to be the “AI platform for knowledge work.” (This is a pivot after an initial search engine play.) Their landing page —no website, just a landing page and a blog — boasts of “1000+ live use cases, driving real business outcomes.”

If you think back to our niche vs. reach discussion, this is big-time brand-building reach across two very potentially lucrative sectors (investment banking and law) with an evidently strong desire to expand beyond those verticals and become the horizontal platform for knowledge work.

Taking a strictly vertical approach, on the other hand, is Harvey, which has raised $100M and calls itself the ’trusted legal AI platform.’ Its product page boasts of ‘a suite of products for all practice areas.’ In the vertical SaaS playbook, the vertical provides the niche, but the play is to then try and satisfy as many jobs-to-be-done as possible within that niche. A bit of niche — one specific vertical, and a bit of reach — products for ‘all practice areas.’

At the more niche end of the spectrum (and more under the umbrella of our ‘find it’ positioning choice) is Spellbook, which has raised $20M. Their homepage leads with the pitch “Draft and review contracts 10x faster with AI” and claims they are the “most popular AI tool for transactional lawyers.” Here, they’ve identified a specific job-to-be-done they can do 10x faster (“Draft and review contracts”) and have picked out a target ICP this is most relevant for (“transactional lawyers”). This is the classic point solution or wedge play to get into the market and build momentum through tight focus.

You can see how different Spellbook’s positioning choices are (contract review for transactional lawyers) from Hebbia (the ‘AI platform for knowledge work’), for example. This is just one small slice of the world of legal tech, but we’ve got:

• Horizontal positioning (with target verticals) as a broad platform play.
• Vertical positioning, as a classic Vertical SaaS-style play.
• Wedge (or point solution) for a target ICP play.

All based on riding the generative AI wave and trying to prove out your value in those specific ways. Which is the right approach? Which should you follow?

No easy answers, but a few observations.

Co-pilot for X & AI for all

Note that Spellbook has, at the time of writing, only raised $20M Series A vs. the $100M+ raised by the others (Series B for Hebbia and Series C for Harvey). Different rounds; different stages; different ambitions. But make no mistake — the race is on to be “the co-pilot for X,” i.e., the co-pilot for lawyers, and perhaps for knowledge workers more broadly, as Hebbia sees it.

If you think back to our discussion of diffusion and brand, it’s popular to use niche positioning to ‘diffuse’ your innovation with a very clearly defined set of customers so you build momentum as they refer you to others and you grow slowly but surely into bigger and bigger segments. We’ll explore that more in the next section, but note that this is more about trying to push an innovation into a market. If you’re a small startup, you can only do so much pushing, so you have to be very deliberate about where you apply your effort (as Spellbook is doing with their tight positioning).

But when there’s insane levels of pull from the market and a wave to ride, the wave itself provides much of the momentum. You’re not trying to convince skeptical buyers one by one; they’re clamoring for it.

This creates the opportunity to cast a much bigger net — why not try and be the platform for knowledge work, for example? Why not try and be the ‘co-pilot for X’ for a specific role or industry, where you know the jobs-to-be-done and what LLMs (and perhaps your custom or fine-tuned models) can do for them?

These aren’t new ideas, of course, and at the extreme end of application-layer startups sits enterprise-focused companies like Glean, which has raised more than $600M (including a $260M Series E) for its “Work AI platform” that provides a search and automation layer above a company’s existing docs and SaaS tools so workers can use AI to pull info from — and update — their CRM, their CS tools, or their company knowledge base, for example. “Work AI for all” is their tagline, and that’s the opposite of niche — it’s maximum right-brain reach.

Either way, there’s so much opportunity for what these strange new co-intelligences can do — and so much demand from the market — that it’s worth thinking hard about your vision and how far you could go with foundational models and, say, $100M (bless you) to make your vision a reality.

Just don’t forget you have to prove it.

Concept creators — CRM

AI wave-riding in B2B is generally about applying this new general purpose technology to business workflows in new, innovative ways. The power of the AI wave is so strong that it, as we’ve discussed, does most of the narrative heavy lifting for you. People know what AI is, know they need it, and want to see what you’ve got.

Eventually, however, categories mature, and it’s far more incumbent on the startup to come up with a new narrative to explain why their technology is necessary.

Sometimes these folks get referred to as category creators, but that’s not always quite accurate. We’ll look at classic category creators in a moment, but first let’s think about concept creators who take a bite out of an existing category, like CRM, with our new concept = new change + new playbook + new tool formula.

Let’s run through Salesforce, HubSpot, and Drift, who all used an incredibly strong story 1 narrative to put their new concept on prospects’ radars in a big way.

Salesforce

The OG of SaaS rode the trend of SaaS itself — well before we called it “Software as a Service” — and started out as an ‘inexpensive web-based solution.’ Salesforce, who in the dot-com era once famously ‘protested’ a competitor’s conference with their “No software” campaign (as in, no on-prem software), rode the SaaS wave all the way to their current $200B+ market cap.

To use our new concept = new change + new playbook + new tool formula, Salesforce new concept was:

• Ride the new change of the internet itself.
• Launch a new playbook for software adoption — no more going through IT, you can sign up on the website (crazy!).
• Pave the way with a new tool that enabled all of this — remember, there’s little precedent for this at the time, so they had to be the ones to create it.

Salesforce’s journey is indicative of enterprise SaaS as a whole — it started as a plucky underdog with a freemium offer in a ‘crowded’ category, caught a wave, and was able to grow and grow and grow by selling and selling and selling. That growth included moves into adjacencies (customer service, marketing, commerce), acquisitions (Tableau, Mulesoft, Slack), and attempts to catch new waves, betting heavily — with everyone else — on AI. That little wave we call SaaS has taken them a long way, and it started with a vision for buying and using software in a way that seemed completely contrary to the wisdom of the time. I mean, who in their right mind would hand over their precious customer data to some random company on the internet?

Everyone, it turns out.

HubSpot

HubSpot actually started as LegalSpot, a niche solution for legal firms, but they pivoted, went horizontal, and rode the organic digital marketing wave with their “inbound marketing” concept. The actual category they would pioneer was marketing automation, before pivoting to CRM. Their market cap, as of writing, is about $30B.

To use our new concept = new change + new playbook + new tool formula for HubSpot, HubSpot’s new concept of inbound marketing was based on:

• The new change of organic digital marketing — initially blogs, SEO, and social.
• The new playbook for adapting to this change was to adopt an “inbound” marketing approach as opposed to the (supposedly) old “interruptive” approach of advertising and sales outreach.
• The suite of tools HubSpot built helped unify and automate much of the jobs-to-be-done around this work, hence they were seen as a ‘marketing automation’ platform.

HubSpot gets credited as a category creator, which is true in a sense, and is now an incumbent CRM player. But their early success was a product of their vision — their ability to see the organic digital marketing wave, wrap up the change in a catchy narrative and playbook (“inbound marketing”), and repeatedly prove their value with their customers through several pivots and expansions while truly owning their message in the broader market.

In the 2010s, HubSpot was inbound marketing, inbound marketing was HubSpot, and their innovative, partner-driven GTM approach ensured that everyone knew it.

Drift

Founded by ex-HubSpot exec David Cancel in 2015, Drift took an incredibly strategic approach to the CRM market, riding the messaging wave with their “conversational marketing” approach to the use of live chat. They were acquired at a valuation of $1B despite competing in a category where Salesforce had a 16-year head start.

In terms of our new concept = new change + new playbook + new tool formula, in Drift’s case:

• The new change was messaging. We’re messaging our friends and family all the time. We’re using chat for support. Why not chat with businesses when you want to buy, too?
• Drift’s playbook for this new era was “conversational marketing” which Drift evangelized far and wide.
• Naturally, Drift provided the freemium tools to get started with this approach, and those tools had to be good because competitors quickly started labeling their own stuff “conversational marketing,” too. (Narrative windows can close quickly.)

Cancel was, like HubSpot, incredibly deliberate with his strategy. He had the vision — he saw the impact the messaging was having in society at large. He had the narrative — he wrapped that change up in a “conversational marketing” narrative Drift could own. And he had the product and execution chops, leading an incredibly effective team to execute on a product (website chat) that had seemingly already been done, and used that as a wedge to try and get into the bigger CRM market.

(As an aside, Sierra, an AI startup co-founded by ex-Salesforce CEO Bret Taylor, is running a similar narrative play as they try and brand themselves “The conversational AI platform.” They’re targeting support, not sales and marketing, but the messaging wave remains an enticing one.)

These three examples — Salesforce, HubSpot, and Drift —were all ultimately CRM plays, which goes to show you that wave riding and category creation aren’t the exact same thing, given CRM was already an existing category. You don’t have to invent a category — or even be first in it — to achieve an incredible exit, and indeed most startups don’t.

Instead, one option is to ride a wave and take a very healthy bite out of an existing category. Salesforce thought they were entering a crowded market when they launched in 1999. HubSpot must have thought the same when they pivoted to CRM. And Drift entered the CRM market precisely because it was a crowded market, in a swim-for-the-red-ocean sense.

Big markets signal big demand, after all, and the CRM market continues to see innovative new players emerge and shake things up. The Notion-esque Attio, for example, which has raised $56M over 2023 and 2024, is aiming to disrupt Salesforce, once the disruptee, as the startup circle of life of incumbents and disruptors continues unabated.

Category creators

AI-first startups have the luxury of insane demand for AI solutions — businesses know they need to put AI to work.

Concept creators riding a wave within a category can also rely on the fact that there’s strong, established demand for the parent category — businesses know they need a CRM, for example.

Some startups, however, need to create — or at least pioneer — the new category itself. This is still about a new concept targeting right-brain attention per our change + playbook + tool formula, but the result is a new category of software. This is a high-risk, high-reward strategy — you have to cultivate demand from scratch, but that blue ocean (so to speak) of opportunity can prove very lucrative indeed.

Let’s take a brief look at the classic category creation examples of Gainsight and customer success, Yammer and enterprise social networking, and Qualtrics and experience management, before we switch modes of attention to finding value with our left-brain laser beam.

Gainsight

Gainsight hitched their wagon to the emergence of customer success as a discipline and are credited as the customer success ‘category creators.’ In reality, they didn’t create the “customer success” category from scratch, but they did start riding the wave early and rode it all the way to a juicy $1.1B acquisition.

In change + playbook + tool terms, it was the SaaS wave itself that brought about the need for ‘customer success’. (Automation creates new jobs!) Mere customer support was no longer enough — customers needed active help to succeed with the tool, not just be reactively supported if and when they asked for it.

SaaS companies themselves needed customers to succeed so they wouldn’t churn. (Again, an important B2B lesson is that customers aren’t buying tools, they’re buying outcomes, and they often need, and will pay for, a lot of handholding to achieve those outcomes.)

The playbook, then, was a focus on churn. Having a specific metric to target is a very powerful thing, and Gainsight’s tools could, as their 2013 homepage describes it “Reduce Churn, Increase Up-Sell and Drive Customer Success.” The change, playbook, and tool all went hand-in-hand.

Yammer

The OG category creator, Yammer, created the category “enterprise social networking,” or ESN. This was a true category in the literal category-of-software sense, a category that’s still with us today. But it wasn’t the category name that particularly mattered — they just put “enterprise” in front of “social networking” — it was the wave that mattered.

Founded by David Sacks back in the 2000s, Yammer built social networking-style tools for the enterprise and, crucially, combined it with a ‘bottoms up’ (i.e., product-led) GTM motion and a sales force that eventually outcompeted their main competitor, Jive.

Founders can get hung up on the name for the new category they’re trying to pioneer, but what’s interesting about Yammer is that “enterprise social networking” was self-explanatory. It provided instant clarity on how Yammer was different from the competition, and that clarity is worth striving for.

In the change + playbook + tool sense, the new change was of course B2C social networking (with Facebook and Twitter blowing up at the time), the playbook was bringing this new UX to the enterprise, and the tool was building a platform to match. That might sound like an obvious opportunity in hindsight, but the dominant social paradigm at the time was, as Sacks tells it, blogs and wikis, and Yammer had to forge their own new category to distinguish themselves from the ‘old school’ players they were competing against.

(The other lesson from Yammer is that PLG is great to build bottoms up-momentum, but you still need a sales team to close enterprise deals, a lesson startup founders will keep relearning until, one imagines, the end of time.)

Qualtrics

Qualtrics coined the term “XM,” or experience management, to help them transcend their software category of survey software. And it worked! But it happened when they were a very mature company, having spent more than a decade and a half riding the digital transformation wave, which put them in a position to launch a new concept, playbook, and brand.

The change was the “experience economy,” the playbook was experience management (XM), and the tool was their mature survey and analysis platform rebranded for a new era.

What’s remarkable about Qualtrics is the extent to which they were able to sell their own narrative of being a category creator. There was no AI-like wave to take them there; they did it themselves, and they started in an incredibly narrow niche, which brings us to the next SBP strategy, niche positioning.

Vision questions

These examples are all just little bits of inspiration for your own wave-riding vision and story 1 sales narrative. It’s never been easier to ‘mind meld’ with past founders, understand their strategy, and see what made their vision tick. In terms of what can make your right-brain vision stronger, however, let’s consider the following questions:

• What wave (if any) is driving demand in the segment you’re targeting? (If you answered “AI” — and I know 9 out of 10 of you did — think about what the wave is about. Is it specifically about automation, analysis, conversation, acting as a co-pilot, or something else?)
• Are you trying to ride that wave to create a new category, transcend a category, or carve out a new position in an existing parent category (like CRM)?
• Are you going for the horizontal platform play (Hebbia), the strict vertical play (Harvey), or the 10x point solution for a specific ICP (Spellbook)?
• What role does external change play in your current narrative or messaging? What role could it play? What does your concept = change + playbook + tool formula look like?
• Finally, how are you proving out the new value for a given set of jobs-to-be-done that this wave creates? Your ability to prove that value is what will make or break your startup.

First published: September 2024 · Last updated: November 14, 2024