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Peter Caputa made some great points yesterday on this LI post. He said:

“The old way of inbound marketing was about proving your own expertise.

…

This new way is about curating the expertise of your market.”

Yes! Stop proving, start curating.

What if?

• TEDx talks are commoditized and mass produced
• Even “real” TED speakers are smug and hard to watch
• Long, in-depth essays are rarely full of insight end-to-end; they are SEO plays
• People read web content even less than in 2022, let alone 2012 (per data Peter cites)
• TikTok-style short videos are now where most people seek “brain-on-fire”

Exceptions prove the rule, of course, but the point is that it’s getting harder to achieve brain-on-fire by reading web content by experts. Traditional expert-content is oversupplied and misaligned with our desire for equal exchanges.

Meanwhile, GPT lets us converse with collective essays of millions.

I think that’s where the brain-on-fire effect is now mostly relegated to – conversations:

• in public on social media, mostly in comment threads
• on conversation-based podcasts, especially when comments are enabled
• on personal newsletters, if replied to
• on cozy web discussions (Slack, Discord)
• in DMs and emails

And it happens between people who don’t necessarily need anything from one another – other than to exchange ideas.

It makes you think about where to take your message – and how.

When we’re young, we believe words have fixed meanings. But word meanings are fluid and change over time. Noam Chomsky, the father of modern linguistics, said,

“What we call definitions are not definitions … they’re just hints that a person who already knows the concept can use to understand what’s really going on.”

This is obviously true in the b2b tech ecosystem, with its new jargon, buzzwords, platforms, and frameworks. At some point in many conversations, you hear, “Ok but what do you mean by that _____”?

Maybe this has happened to you, where a customer asks a question that forces you to define a key part of what you’re selling:

• ‘What does your “customer success” program actually mean – what is that?’
• ‘Ok for the purposes of this conversation, what do you mean by “digital transformation” – you mean just putting our data in the cloud?’
• ‘What do you mean by single-source of truth? Isn’t our Avectra instance already that?’

It’s easier to field questions like this if you have written down your definitions. It’s also a lot easier to work these definitions into your product messaging. (And you don’t need that many.)

The alternative is letting someone else write your definition for you. Some other company, pundit, authority, tech journalist, whoever. And your customer uses their definition instead of yours.

What sounds like the better option to you?

Many well-known frameworks aid us in product development, messaging, ads, and marketing. Without exception, they are interesting and, for given purposes, useful. Otherwise they’d be unknown.

Examples:

• Lean Startup for quick product iteration
• AIDA for persuasive ads and landing pages
• Jobs-to-be-Done for pinpointing customer needs
• StoryBrand for recasting your solution into narrative

Interesting stuff! To you at least – and a few of your fellow framework fans. And again, useful, on a case-by-case basis, thus firms rightly exploit them.

But if you know about a framework, so do at least 100,000 other people using it to create.

And if you rely on any one framework too much in your messaging, it might dilute down your original thinking; it won’t feel different.

Different is better than better.

Just as in baking: if you use the same framework (the baker’s percentage, the creamingg method), ingredients, and equipment as other bakeries.. how will your products taste different?

And if there’s an “art of message”, it’s the art of tasting different.

“The Business of Expertise” addresses service providers – agencies, consultants, etc. What’s nice is that you can guess the premise of the book from the title and the working definition of expertise is very clearly laid out: “deep and specialized knowledge or skill in a particular field or area.”

Interestingly, by this definition, many product businesses are also expertise businesses.

I once had a consulting client whose SaaS sold for $14,000/month because there was so much expertise around it. It helped people making big bets (8 and 9-figure medical real estate) discover opportunities and vastly reduce long-term risk. And it did this in days, rather than waiting months for consultants to do it.

But the principle holds even when the product falls in, let’s say, the $100-200/month range, like SEMRush, Hubspot, Freshbooks, or DocuSign.

Each of those products was designed and is serviced with expertise. It’s no secret that expertise goes into the design of software.

What’s more of a secret is that expertise adds value as you deliver it; B2B software is:

1. a relationship business. It’s active income, not the other way around, and it relies on EQ
2. a customer success game – intelligent people enable the customer – teaching the product, of course, but also helping them think strategically.

For example, say a customer success rep at Hubspot teaches a customer with many experts on staff about “segmented thought leadership,” melding marketing automation, personalization, and inbound marketing. They explain not just the technology – but make a business case for why this kind of thought leadership is attractive.​

The more expertise you build into B2B software relationships, the better the customer does with the product; in short: expertise can play a big part in the value creation & capture cycle of products.

​This is sort of like a short detective story – who authored this messaging? In what decade did they do this work?

Well, it might have been in the 40s – the 2,600,040s BC, that is. Give or take 100,000 years.

The first technology “products” were made in the Olduvai Gorge, in Africa, about 2.6 million years ago by homo habilis – our ancestors.

Homo habilis were the first technology innovators; just as the IPhone was a  three-in-one , they designed multi-purpose tools: plant-processing, woodworking, hunting.

To do this, they had to select and experiment with the right “tech stack”- the existing technology used to make new technology.

This mean chert and flint over basalt, quartz and other rocks. Especially certain kinds of flint that were easy to shape through precision chipping but also partially smoothed, providing handle surfaces for better UX. But it also meant choosing the right “hammerstones”, the hard rocks (like quartz) used to shape flint.

Another strategic capacity was knowing where to source materials. Firstly, there had to be a sufficient supply to make it worth the calories to get you there. Eroded hillsides exposed large quantities of flint that had been naturally pre-eroded by wind. So did riverbeds – and water erosion provided even smoother surfaces. But then you had to worry more about lions and other predators animals.

Where and how to source the tech stack, how to design and produce the products – this knowledge was refined through careful observation, collaboration, and learning. And through consideration of the various requirements these products had to fulfill, from butchering to tailoring.

That’s where our leader emerged, with vision, knowledge, and powerful product messaging:

“Let’s go there”.

Here’s the thing – product strategy and messaging is nothing new. It’s part of being human.

Technology at its essence is a cognitive process by which an animal transforms its natural environment into a tool, material, or resource by leveraging accumulated group knowledge and skills.

• Examples of animals: homo sapien, homo erectus, raven, chimpanzee
• Example of natural environment: sticks, basalt stones, silicon
• Examples of tool, material or resource: stone axe, silicon chips, chopped meat

What do these business products  have in common?

• big tech companies such as Google, Microsoft
• 2nd-tier big tech companies such as Salesforce
• 100s of major technology platforms (eg. Notion, Canva, Wix)
• 1000s of startups
• major open-source tools, especially WordPress

They’re all incorporating user-facing AI features into their products.

As a result, AI technology has suddenly become ubiquitous in the workplace, affecting almost every area of work.

So why is it then, according to an interesting survey in the news, that 91% of companies hiring are looking for workers *skilled* in ChatGPT. And why are they hiring for this *skill* across the board: customer service, data entry, sales, marketing, HR, and software engineering.

I mean, if GPT-style technology already exists in their products, why insist on ChatGPT for new hires?

Partly this is because ChatGPT has, relatively speaking, great UX. The infrastructure may have problems delivering service, but as for the SaaS app itself (the web app you interact with), what few features it has  work well. To be fair, so do some other GPT-enabled products, but most employers still have no idea what they are. So of course market-share and brand recognition are at play as well – and the UX is part of that.

But the core reason businesses need ChatGPT-savvy employees is that, despite its tendencies toward filtration of results, it’s a “programmable” product.

What I mean is, rather than a product designer dictating how you use this kind of AI technology, you, as a worker, decide how to use it.

And for me, that’s the product design takeaway—give users freedom in how to use it, instead of trying to force it into the box you call a product feature.

 

Seth wrote about the  placebo effect today  so this post is a sort of a plugin to that one. Here’s the definition he uses for placebo: “a prompt for our subconscious to do the hard work of healing our body, increasing our satisfaction or maximizing our performance.“

I like this: a prompt.

But is the prompt transferable? I mean, can someone else provide a prompt your subconscious – or only you? And if someone else can, who?

In the 1960s, Harvard psychologist Robert Rosenthal and San Francisco elementary school principal Lenore Jacobson found something surprising about the placebo effect.

They found that if a teacher was led to believe that a given student had enormous potential, that this student greatly outperformed their peers over the course of the year. Even when in reality, all students selected for the experiment were of average academic potential (whatever the f*ck that means).

Belief in their potential changed that.

But let’s be honest, was it belief by itself? Or was it 100s of little concrete actions that proceeded from that belief- more attention in reviewing work, more tolerance of missteps, more personalized assignments, etc? Beliefs and actions.

Here’s my takeaway, how do I make my product, which requires users to complete an extremely complex, multi-step form, believe in them? What actions can it take to express that belief?

And your takeaway?

Where to get product advice?

…

From your customers.

Why is that? Because they’re the ones that will live with the consequences of your decisions.

They are gold mines of advice.

This is Anand Sanwal’s tip #2 of 100 here (speaking of gold, how about tip #1?).

It’s a good tip. But how many gold mines of advice do you need? Consider the following:

• 30. In Statistics, a sample size of 30 is a rule of thumb when research concerns people. Of course, that’s just a made-up heuristic and can be adjusted according to context.
• 11. Drip founder Rob Walling got a constant stream of advice from 11 paying beta customers over a 5-month period before officially launching Drip. This number is no more or less scientific than 30 – and definitely worked fine for Drip.
• 5. In UX research, Jakob Nielsen’s “five-user” rule says you only need to talk to five people; again, that’s made up but it appears to work well for Nielsen.

The throughline here is we’re going on gut; do you think 5-30 is a reasonable range in which to do so?

 

 

I can’t forget a story I heard Malcolm Gladwell tell in his masterclass.com course

It was about a lifelong friend from New York who was very smart and knowledgeable and knew a lot about many things. Whenever someone brought up almost any subject, her response was, “Yep – I know about that already”, followed by an impressive display of her vast knowledge.

That behavior itself isn’t inherently “wrong”. But Gladwell made the point that it’s basically the exact opposite of the right way to interview someone as you do research.

The right way, in sum: listen actively, ask open-ended questions, solicit stories, be curious about everything, create a rapport, be patient with silences, minimize your own input, and cherish anything unexpected.

The last point hit hardest because it’s where he got the most intense. To paraphrase, he said:

“Don’t be the person that jumps to the conclusion that you already know, even if you do. Don’t ever say, ‘Oh I know everything about that’. Because you don’t, not fully. Even if you’re a world-renowned expert, there’s always something you don’t know – an odd detail, a turn of phrase, a personal anecdote, or a perspective you hadn’t heard before. Your job as an interviewer to fight like hell to find it.

To return to this question – how can a product ask the right question?

Maybe assume that customers know something – anything – about the product’s purpose that the product owner doesn’t. Not just about the product itself but about the category, industry, or market it lives in.

Products often attempt the low-cost approach – having an anonymous form, chatbot, support rep, etc, automatically ask something like, “Anything we missed?”.

Automated or not, perhaps there’s a better question and a better way of asking it.