Why great AI products are all about the data | Shaun Clowes (CPO Confluent, ex-Salesforce, Atlassian)｜Lenny's Podcast: Product | Career

I love that you have a very strong opinion about this, which is just the state of the product management career and how most PMs are not that great.

Why is it that product management is still such a relatively undeveloped discipline?

We're like 15 to 20 years into this.

And so there's something about the current state of product management that isn't getting at the truly important things, the truly value added things.

If we were doctors, you'd be like, that's totally what I said before.

What's the answer, Sean?

How do we solve this problem?

Everything always talk from the customer's perspective, from the market's perspective, from the vendor's perspective.

A very small number of PMs do that.

They get dragged into internal politics.

They get dragged into scrum management or scrum execution or product delivery.

And you just can't win that way.

You kind of have this hot take that the way AI will most impact product management is data management.

Well, you've got this synthesis machine, which is this LLM thing that's going to help you do synthesis.

But if it hasn't got all that data to do synthesis on top of, it's got nothing.

And so that means that LLMs can only be as good as the data they are given and how recent that data is in the future.

If you can easily clone a B2B SaaS app like Salesforce or Atlassian, what happens to these businesses long term?

Do they just become are they all in trouble?

People really underestimate where the value is created in these applications and they just kind of get it completely wrong.

Today, my guest is Sean Clouse.

Sean is chief product officer at Confluent.

Previously, he was chief product officer at MuleSoft, which is a billion dollar business within Salesforce.

Before that, he was chief product officer of Metro Mile, a public auto insurance technology company.

And prior to that, he spent six years at Atlassian, where he ran the Jira Agile and also built the first ever B2B growth team.

He also created two of the most popular reforge courses, one on retention and engagement and one on data for product managers.

Sean is awesome because he is both very tactical and execution oriented, while also being very philosophical and insightful about the craft of product and growth.

In our conversation, Sean shares why most PMs are not good, what it takes to become a good or great product manager, how he thinks about his career like a bingo card and why he indexes towards finding very different roles for every new job that he takes, why good data is the most important ingredient

in AI tools and for product managers working with AI.

Also, how to build a great B2B growth team, what he's learned about doing B2B growth, and his really interesting take on how AI will and won't disrupt SaaS tools out in the wild.

If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

It's the best way to avoid missing feature episodes and helps the podcast tremendously.

With that, I bring you Sean Klaus.

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Sean, thank you so much for being here and welcome to the podcast.

Thank you. My name is really awesome to be here.

I've had you on my radar for a long time and I am really excited to finally have you here.

And big bonus points for having a very beautiful, sultry Australian accent that always helps with the ratings, I think.

I don't know if it's causal, but it's correlative.

I'm glad to be a bit of a curiosity.

So I want to start with something I totally believe, and I love that you have a very strong opinion about this, which is just the state of the product management career and how most PMs are not that great and how there's a big opportunity to level up.

You just talk about what you've seen there and you're just thinking here.

Yeah, it's honestly a big conundrum for me.

I think it's actually part of...

It's grandiose to say, so a bit of my life's work.

Why is it that product management is still such a relatively undeveloped discipline?

We're 15 to 20 years into this thing, right?

You would have thought that it would be less random than it is.

The outcomes are random, the behaviors are random, individual performance is random, you know, seemingly, right?

And so there's something about the current state of product management that isn't getting at the truly important things, the truly value added things, the right way to think about problems, the right way to think through problems, the abstract reasoning that's needed.

There's something that isn't working about it.

I've spent a long time trying to put my finger on it and then be like, how do you reproducibly produce that?

Reproducibly produce people who can really be really great product managers.

The thing is that if you think all the way back to it, like I spent a long time as an engineer and people always talk about 10 times engineers, right?

And I wanted to be a 10 times engineer.

I'll leave it to others to decide, to tell you whether or not I was or I wasn't, but certainly I wanted to be, and I tried to be a really great engineer.

And it must be true that if there's 10 times engineers, and I would argue there definitely are, there must be 10 times product managers too.

But at the same time, those 10 times product managers, because product management is ultimately about leverage, so it's about helping other people have dramatically more impact than they would if they were unorganized, if they didn't have somebody to organize the goals more we're trying to achieve,

then that means that a 10 times product manager has a hundred times return, or more, because they're 10 times seeing the return on 10 times resources.

So their outcomes are so wild, like wildly distributed, and the benefits are so good that you would have thought that kind of it would have behooved us, there would have been a way that this had evolved and improved and really gotten way crisper than it has.

But here we are, I'm not saying that we haven't gotten better, we 100 % have.

But I think we could all say that we're not reliably producing 10 times product managers every day, every day of the week.

I love this point. And it's especially painful that when someone works with a PM, that's not great.

There's just this meme of why do I need PMs, PMs are useless, PMs suck.

And it just creates that.

No one's ever, engineers are useless, or designers are useless.

But there's so many people are like, I don't need product managers, our team never hire a PM.

And it just sets the whole profession back.

When I first started out in PM, somebody, it's obviously a chestnut, but he pointed out that realistically, when you're a product manager, your job is to say no to 90 % of things that get brought your way.

And so that kind of makes you the bad person, pretty much from the start.

And so you're saying no to 90%, so you can say yes to 10%.

And that kind of puts you behind the eight ball right at the very beginning.

And so you have to kind of very quickly get runs on the board, you have to prove to be to have the right insights, to have the right data to make the right decisions, or you don't get another go, you don't get another swing, you don't get another swing at it.

So it makes sense that, you know, product managers are the easiest to kind of single out and, and kind of criticize.

But that is also what makes it the funnest thing.

Like if you think about like, why do we do this?

You know, somebody wants to ask me, like, you know, would you retire?

Like, what do people do what they do?

Because certainly at some point, it isn't just about the money.

And at the end of the day, product management is so damn fun, because it's about trying to figure out an edge.

So trying to look at the world, find the portion of the of the chessboard that isn't occupied, but that is valuable, and find a way to get into it, invade it and destroy it.

I could say it's a really fun, like it's decisions under uncertainty.

And that makes it unbelievably fun, like really, really painful and very frustrating, and very hard to convince people, but very, very fun.

So you know, in equal measures.

What's the answer, Sean, how do we solve this problem?

I know you said it's your life's work.

What do you find actually helps most in helping PMs level up and become say 10x PMs?

I think the most important thing and that kind of the chestnut that I repeat to everybody is that at the end of the day, the time you spend looking inside the building doesn't really benefit you very much at all.

Right. And you know, Steve Blank and people used to talk about you should be spending 80 % of your time thinking about things going on outside the building, you might not be outside the building, you should spend 80 % of your time thinking outside the building.

And I would say a very small number of PMs do that.

They get dragged into internal politics, they get dragged into scrum management, or scrum execution, or product delivery, like elements of the delivery thing.

And you just can't win that way.

Like you just you just can't win that way.

You can never get an A, because you're fundamentally not solving the job.

Like the job is not about execution or anything.

It's about finding reliable, differentiated value, right?

That you can uniquely deliver into the market.

So I would say that if there's one thing, you know, two things, I would say actually that I generally guide product managers to do, one is to, they always start from the point of view outside the building in every document in everything, always talk from the customer's perspective, from the market's

perspective, from the competitors perspective, and the people who listen to me on that, I would say get better almost immediately, because they're starting from a place that's easier to understand.

And then secondarily, be data informed, like kind of use, use all of that view of the world, but don't just make up a bunch of statements, like support that statement with, you know, anecdotes and bits of data doesn't have to be a treatise.

But like kind of bring in to bring kind of convince everybody of what the world really looks like, and what the opportunities ahead of the company looks like.

And good, good things happen to you.

And all of a sudden, you go from a world where nobody wants to help you get anything done, to where everybody wants you to win, they want you to win and they may not give you everything you want, but they certainly will try because they're like, well, of all the bets we could make.

This is a good one.

I imagine many people listening to this are thinking, Oh, I am that person.

I talk to customers all the time.

I'm always interacting, looking at research, putting data together.

And what you're saying is, you're probably not doing that enough.

Is there anything that you could help someone recognize of, no, you're actually not doing this enough.

And you think you are, but you're not.

It's one thing to say you're spending a lot of time looking outside the building, it's a whole other thing to like, hear from the places you don't normally hear from.

So avoid availability or confirmation bias.

Like most of the time, people go talk to the people they always talk to, and they learn nothing particularly new.

They don't synthesize the results that they got from the conversation.

They don't seek out the counterfactual.

They don't seek out the proof that they're wrong.

They don't analyze what their competitors are doing and figure out what that must tell you about the market.

They don't bring back the data of how the product is actually being used versus how people say it's being used.

It's like, you know, kind of all data, no analysis is not very useful.

Like all kind of, you know, everyone can bring back an omnibus edition of like, you know, random stuff I heard on a Tuesday.

But the competitive advantage is extracted out of figuring out what other people don't see, figuring out what, you know, where we're wrong, figuring out where a well -placed bet could have dramatically outlandish returns.

And so people, you know, I think firstly, people often say that they do a lot of this stuff, but they actually don't, right?

Because they don't have any structured way of doing it.

So what they really mean is like, every now and then I get in, I give my customer a call.

Every now and then I get stuck into an escalation.

And so they're kind of conveniently bucketing it.

So firstly, they don't do it in a very structured way.

Then they don't bring back analysis.

They're true insights from that thing.

So they don't really gain very much at all.

It's just a more activity, no outcomes, activities, not people, people do far too much activity with not enough outcomes.

And there just is enough time in the day to do that, to be successful.

You as a product leader is at the Venn diagram center of the sweet spot of where this podcast has been going recently, which is product and growth and how AI helps you with all these things.

And so to follow a thread there, with synthesizing and understanding what hip are saying, because user research and surveys and all these things, have you found any tools that you and your team have found really useful to help you do this more efficiently versus traditionally just manually going through

all this stuff and finding patterns?

Yeah, so firstly, stepping back a little bit just into the motherhood and apple pie portion of qualitative research or whatever.

I find that most people don't even understand or don't start with the rigorous foundation in what they're going to need to do to get the answers that they want.

So for example, your listeners have probably heard about the Nielsen number before.

Basically, the idea is that once you interview between 7 and 14 people, you stop learning new things.

Less than 7, you don't learn enough, more than 14, you start learning anything new.

And so if you interview two people, you probably don't have enough data.

If you interview 22, you probably had too much.

So they don't even rightsize their efforts.

So that's a problem.

So they don't start that way.

Then they go into these conversations, asking leading questions, which really are designed to get the customer to say what they already want to be true.

So they haven't done enough research or they've done too much, and then they've blown up all of the results before they've even heard anything.

So if you don't rightsize your research and you don't set this up to learn, then you're going to lose.

No amounts of applying LLMs or any type of structured reasoning is going to help you because basically, you're reading back what you want to hear or some weird, summarized version of what you want to hear.

But stepping back from all of that, what I like to do, specifically getting to LLMs, is I think that we live in just the most amazing time for product managers right now in terms of being able to analyze vast quantities of information and see the common threads.

So let me give you a few examples of that.

One might be you can do a bunch of customer interviews, you can put a bunch of customer interviews into chat GPT and you can say, hey, chat GPT, this is my strategy.

Tell me where my strategy does not fit what these customers talked about.

It's all about the not, not where it does, where it does not.

People spend far too much time looking for what they're hoping to see, not for what they're not looking to see.

So you can literally ask chat GPT to help you find where the customer is probing at the edges of what you're trying to do, where it's wrong, where what you're saying is not what they believe.

And you can ask your questions like that.

You can ask it where you what your customers are saying would better fit what your competitors are saying.

So you can basically say, hey, you can copy and paste one of your competitors positioning documents into chat GPT and say, is this a better fit for what they have said than my thing, which is, which is you can summarize your own strategy.

Like you can take your competitors' public documents and you can ask it to summarize what their strategy probably is.

And it's actually surprisingly good at that because mostly your public documents are actually a summary, or at derivative of what your strategy is.

So it will give you crazy insights into what other people's literally the product strategy at times creepy, like, oh, they will probably do this, they will probably do that.

It's more likely they would do this that they're than they would do that.

And so like normally that type of insight was hard won.

Like, you know, it's a, it took a lot of sweat work.

You basically get to read a lot of stuff.

You kind of had to use your brain as like this big kind of summarization machine.

And eventually you knew what you felt about all the things you had read, but you couldn't summarize why LMS that you get to that really, really, really quickly in a very structured way.

But only if you push at the edges, provoke the provoke the answers, you don't want to hear provokes the problems, like try and try and, you know, prove to yourself that you're wrong, I think is the easiest way to start trying to use some of these tools.

I love that. And it sounds like in your experience, you're just using straight up open AI, Jadg, BT, Claude, not like any specific tool for user research for the specific use case?

No, mostly, I find that like the straight up LLMs themselves are good enough.

And, and we do have some internal tolling that we built around, you know, I don't know if you've ever had such an rookie on the show, you may have, he was a product leader, pretty well known in the gross community, and he was a leader at LinkedIn for a long time.

And he kind of, he used to, he used to call this Feedback River.

And he basically said that really smart product managers are constantly swimming in a feedback river, they set out to surround themselves by a feedback river.

And, and I really deeply believe in that, it's like, okay, how can I surround myself with, you know, user interview data, with direct customer feedback, with nps data, with competitor information, like I'm always kind of trying to wash myself off with information.

And where I'm going with this is that LLMs and tooling based on it can be exceptionally good for this.

So for example, we get a ton of a conference, we get a ton of inbound customer requests, as you can imagine, coming from the field or directly from customers, we use our rooms to take in those those asks, to summarize what they're about, to find other asks that are like that one, like really in a compelling

way, like a real way, like a semantic way, not a lot of other words, the exact exactly the same, are these the same concept, so that we can look across all of the inbound demand on us and say, well, the most popular ID is this one and is getting more popular.

The least popular idea is this one, it is getting less popular in a really deep, rich way, even across hundreds or thousands of pieces of inbound feedback.

I think you know, it's a really great time to be a product manager, if you can put these types of tools to work, but they would they don't do the job for you, they just help you do these things that are, you know, intricate in that job of finding the finding the gaps, finding the opportunities, finding

the common threads without, you know, necessarily having to do all of it just inside your wetware, just inside your brain.

I'm going to stay in this AI river that we're in right now and ask a couple more AI related questions.

And this may be what you just said, but I'm curious if there's more here, you kind of have this hot take that the way AI will most impact product management is is data management and data versus like models you're building or anything else.

Can you talk about what you've seen there?

Yeah, I mean, I think there's two implications for people as they're building products based on AI and as they're thinking about like AI in their workflow.

So let's start with the first one, because that's how product managers do product management things.

You just asked this question of like, should it be specific tools built for, you know, to make AI easier for product managers to use?

Or is it in fact, like more general models being put to work?

At the end of the day, like these models are very, very, very smart, but they're also like insanely dumb.

Like in everyone knows that right?

Insanely dumb. In other words, they really only know what they were trained on, or what you bring to them right at that moment, like in that millisecond, and then they will forget it immediately.

And so it's very easy to convince yourself that that isn't true.

But it's actually what really matters.

And let me add one extra piece that makes it really important.

At the end of the day, information has a decay rate.

So think about customer feedback, it has a decay rate, or what your competitors are doing has a decay rate.

So any new piece of data decays in its value to your decision making very, very quickly, very, very quickly, you can plot your decay chart if you want to.

But the answer is very, very quickly.

And so when you think about the job, which is synthesizing all of this very complicated information to make good decisions, what does that mean?

Well, you've got this synthesis machine, which is this LLM thing that's going to help you do help you do synthesis.

But if it hasn't got all that data to do synthesis on top of, it's got nothing.

And so that means that, like, LLMs can only be as good as the data they are given, and how recent that data is, they're ultimately like information shredders, like they are limitless information eaters, like they just can't be you can never have enough information to give to an LLM to truly gain its

value. The more things you give it, the better it gets.

Broadly speaking, that's, you know, kind of just not perfect, but that's close enough.

And so what that means is a as an internal product leader, or, you know, using putting LLMs to work, you need to figure out how to bring as much information about customers or their asks or your competitors, all of it, how can you find all of it and bring it together and give it to the LLM, either in your

tooling or even in just copying and pasting or whatever your flows is going to be.

That's one thing. But then if you take it beyond that, and you go, okay, well, now I'm a product leader, and I'm building an app, and I want to put AI in my app, what will make my AI experience really great?

It's definitely not going to be the models, because like these models are mostly going to be somewhat replaceable.

And you could say, Okay, well, is it going to be the prompts?

Maybe, but you know, so many good prompts are better than others.

And you certainly like that's kind of an ongoing investment, you probably want to make to ask better questions to get the LLM to deliver better answers.

But it's obvious that the real answer is the context, like all the context, you're going to give it all the data you're going to copy and paste.

And so if you think about, let's say I'm in I'm building a, you know, I have no relationship to this.

But let's say I was trying to build a human capital, like a HCM, a bot, like an AI bot, let's say I was working workday, and I was trying to bring it net an AI bot.

It's pretty obvious that the smarts of the bot would really be related to all of the employee information.

But not just that it would be the benefits information, it would be the legal situation in the country where that person is currently working, it would be the company's policies and procedures that apply to the suit, you got to mean by about like these, these kind of like the jumps of logic and the jumps

of data and the way data is all linked together.

If you want to have a smart AI experience, you'll convince yourself that all I really need to do is get a model and wire it in, and I'll build a little pipeline that will suck some data in them and it will whack it into the LN.

And if you think that way, you're going to be very sad, very, very sad for a very long time, because you're constantly going to be wrestling with how do I get data through this thing?

How do we get good data to this thing?

How do we get timely data to this thing?

How do we get well structured data to this thing?

And so, you know, it's a data management problem, like it's getting access to good data, getting access to high quality data, getting access to timely data, and getting it to the LLM to get the LLM to make a smart decision.

That's where 90 % of the calories go.

Maybe it's a bit like Einstein's thing, you know, it's 10 % inspiration, 90 % perspiration, nobody wants to hear it, everybody wants to just think about what these really cool models and how smart they are.

And the next one will be even smarter.

But really, it's just the hard work of getting really good data data to the LLMs to get them to do good things.

It sounds really obvious as you make this case.

It makes me think about at the at the name friends summit, Mike Krieger talked about how he had kind of the two types of PM groups within anthropic.

One was focusing on user experience product and the other was working on the model research side.

And they realized that all of the success came from the model research work, like making the model and the data they provided the model was where all the value came from, not just like optimizing the user experience.

And they're just putting more and more of the product team on just that versus like tweaking UX and buttons and things like that.

Yeah, exactly. Right.

Something sort of early I'm just gonna ask one more question.

I don't want every talk to end up being just all AI.

But something that's kind of been a meme recently, and I know you have a perspective on this, is that AI makes it really easy to build products.

So in the future, if you can easily clone, say, B2B SaaS app like Salesforce, or Atlassian, or whatever, whatever your favorite B2B SaaS app, what happens to these businesses long term?

Do they just become, are they all in trouble?

They're going to be 100 Salesforce competitors.

What's your sense and prediction?

What might happen there?

Yeah, I think it's really weird.

I think people really underestimate where the value is created in these applications.

And they just kind of get it completely wrong.

And I'm not sure why that is.

So if you think about it, so I spend a long time at Atlassian, so I worked a lot on Jira, which many people know, and I spent a long time at Salesforce.

So I spent a lot of time in the CRM ecosystem, the marketing ecosystem, and all the rest of it.

If you want it to be like not charitable, you'd step back and you'd look at all those applications, and you'd say they're all just forms on databases.

You'd say the Jira is a form on a database, Workday is a form on a database, so Salesforce, they're all forms on databases.

All vertical SaaS or business SaaS is ultimately forms on databases.

And you'd be like, well, how hard can that be to replicate?

And the answer is unbelievably hard, unbelievably hard.

And people just totally get it wrong, because it's not actually just about the data model.

So if you think about it, if it's formed some databases, it's these beautiful user experiences that sit on top of data models.

So whatever the object is, it might be a customer object or a campaign object or employee object.

You could say that, well, there's some element of lock -in in the object, like the object itself, like the interesting.

But maybe, certainly there's some value in being the system of record, like the default that everybody uses.

There's definitely some value in the UX, like the, well, I want to be the best HR facing applications for working employee data.

Yeah, there's some value there.

But the real thing, just staring at everybody in the face, is it's all about the business rules.

That is what drives the locking.

Because why do you buy Workday?

You don't buy Workday for its out of the box configuration.

You buy Workday because you want to configure it to be, you know, Lenny Inc's HR processes.

Like it becomes Lenny Inc's Workday.

It's not Shawn Inc's Workday, it's Lenny Inc's Workday.

And actually, the longer you have the software, the more it becomes that, the more it becomes less and less like Workday, more and more like your specific company, which makes sense because it was built to be configured to meet the needs of any specific company.

And every company's their own precious notebook.

And as that happens, those configuration pieces, the bit that makes the application native and a fit for your organization, makes it a fit for nobody else's organization, and also makes it a black box, to the point that you don't even understand how it works anymore.

Like, if you went to, for example, Salesforce, and you said, Hey, could you define all of the, you know, processes by which software was sold inside Salesforce, they couldn't tell you that without reading the code of their Salesforce instance.

I'm not, that's not a proprietary secret.

That's obviously true.

Because over time, that's that literally how sales happen, there is no other way to do a sale other than through their internal tooling.

And so what that means is a is that it's not the UI that matters.

And it's not the data model that matters, although those are both very useful.

It's the years and years and years of evolution of the underlying workloads of the product to support the customers, but also the customers evolving those workflows to make them work the way they do.

And so how does that impact AI companies, right?

You could say it's easier than ever to build on a forms on the forms on a database application.

And so I'm like, Yeah, okay, that presumably drives the incremental value of every new one of those to zero, right?

So probably leads to more power to the, to the existing winning systems of record, because it'll just be a gazillion competitors who are just more forms and databases.

So like, how would you ever choose between them?

You may as well just go with the winner, you know, nobody ever gets fired for buying Salesforce or whatever you may as well start from, from the kind of the premier vendor.

That's one element, you could go the other way.

And you could say, I've heard a few people mount this argument, which I think is really interesting that at the end of the day, agents are going to take away most of the use of that user interface.

So let's say, for example, your your Salesforce with Service Cloud, I've heard people say, well, you know, a lot of those service agents might end up getting being replaced with agentic workflows.

That will mean that, you know, there is no person operating the UI, if the UI doesn't even exist anymore, then why do you even need Salesforce?

I mean, you sort of have raw database tables and who even need forms of databases, you can literally just have databases.

But that also doesn't make any sense either, because the agents have to operate against the rules of the system.

And the rules are defined by the business processes.

So think about Salesforce without a head.

Imagine Salesforce had no UI, it would still have those business rules that I was talking about.

And those business rules are what define what the agent should do, they're almost telling the agent what it should do and how the world can operate, what is possible, what is allowed.

And so from my perspective, like this idea that this just completely destroys like the differentiation of these kind of business process SaaS applications, just seems like a fantasy, crazy fantasy.

The only way I could really believe it is if you said, well, like, you know, you could have a new startup that introspected all of the rules that are configured into a Salesforce to try and reverse engineer what your actual business processes are, and then kind of operate on top of that.

But the best place people to do that would be Salesforce themselves, or Atlassian in Atlassian's or Workday in Workday's case, you know, I don't, I just can't see a world in which this thing, I think one of two things could happen.

All this moving to AI makes those applications even better, like even more, even more amassailable, like they basically get stronger, it makes the strong stronger.

Or it could enable some new level of like applications that come from a more platform based thing, so less a domain specific thing like, you know, HCM or ERP, or, you know, engineering or, you know, less less of the domain specific stuff, it could enable a more platform like play where you have more

business objects and business objects have rules and you could imagine a world in which, like, there's kind of a whole evolution of new more platform like SaaS applications that do more than one business function worth of the business rules and the way things move around in the enterprise, but that doesn't

exist today. So you could say that could exist and it could say it could be way better than it would have ever thought of because of AI.

Or you could say that the rich are going to get richer, like the most likely outcome is that the currently dominant companies are going to get more dominant.

But I don't think this idea that it would just cause a spring up of a whole bunch of new apps that will more easily challenge the incumbents makes any particular, it's not straightforward to me how that would happen.

Wow. That was extremely fascinating.

And there's so much there, I can go in so many directions.

One is, I thought you would actually go in this direction, which is distribution advantage has become even more important.

If it's easy to like today, I could sit there and hire team clone Salesforce might take a while, but I could copy it.

But by the time I'm done, they've evolved, they're moving, they're adding features, they're ahead, right, you're skating to where the puck was.

And so if that's the case, one of the advantages, one of the ways to get anywhere is to have some kind of distribution advantage.

It's one thing to have Salesforce as a product clone.

Another is to get anyone to know about it, to adopt it, to sell it, procurement, all that stuff.

So do you have a sense of distribution advantages being even more valuable in that world?

Yeah, it certainly makes sense.

Ultimately, at the end of the day, distribution is always an advantage.

Because the hardest problem is to even be in the consideration set for any given problem.

The one is full of problems.

It's just when people have that problem, they firstly don't think they're going to solve it at all.

And when they do think of solving it, they don't think of you.

So distribution is always an incredible advantage.

But again, in the world of AI, it seems like distribution is more likely to get hard than easy.

So if you think about, for example, diminishing returns on cold email, because cold email is getting easier and easier to send even worse spam.

It sounds better, but it's effectively causing everybody to become desensitized to everything.

I don't know if you've noticed half the LinkedIn reach outs now are all basically clearly LLM generated spam.

To some degree, it's actually worsening the signal to noise ratio.

And so I think that a lot of the breakthrough distribution mechanisms that startups often use seem to be getting more crowded, just in general, and more expensive.

That doesn't bode well for, I'm the not as good sales force, I'm the not as good sales force.

That has to be something different.

You have to, there has to be some angle upon which you are materially better.

And what I saw happening and what I've been seeing happening, I think it's been really interesting, is a lot of modern, you know, next gen applications are bringing data as a first class citizen into the workflow.

And I think that that's pretty compelling, right?

So if you look at the next generation of applicant management products that deal with inbound job applicants, a lot of them now like the latest call ones, they include, you know, your time to fill data.

They include outcome data of like, who's got the best hiring outcomes?

Who over what period of time has the worst attrition?

You know, like, literally all the way back to the interviewers.

And that where the interviewers were in the interview cycle, so basically embeds data into the whole lifecycle.

And I think so I think that there are kind of ways in which startups can bring these experience benefits by just bringing a kind of different approach to the world, that doesn't enable them to capitalize on traditional disruptive innovation, like at the end of the day, this is just disruptive innovation,

it means that most companies have overshot their utility, like the average utility.

So you can win by meeting the average utility and being different, you know, meet the bar and be different meet the bar and be different is the way to cut through.

So that makes sense.

Like that's a half decent playbook.

But if, but if, you know, even for those companies, now they're going to have all these AI competitors who using AI to engineer faster, to build a competitor just like them, as quickly as possible, and start jamming it into the into the channel.

And it's going to be interesting to see how this whole thing evolves.

You know, it kind of got raised to the bottom, you know, characteristics around it, you're probably right, the distribution is still the hardest part in software, particularly getting started.

Right. So if you have some kind of clever, fair advantage, it feels like that becomes even more powerful, say have a platform of an audience or something like that.

You mentioned this ATS product they really like, is there one you want to give some love to you, they think is really cool, that you like, or you want to keep it anonymous?

Yeah, it's Ashby. It's the one all the cool kids are talking about now.

And it's funny, because people literally talk about it in comparison to even the, you know, the last generation of modern SAS ATS or whatever.

And they talk about in glowing ways, because of the way they put data in inside the actual workload.

So that the actions and its outcomes are directly tie able to each other in the application you're doing the work in.

I think that's a pretty compelling user experience.

So just to maybe close this thread before I move in a different direction, this point you're making about how valuable data is and how that's like at the core of being successful and differentiating in the future, especially with AI, tooling and products, any advice you'd give to someone that wants

to do that? Is it just make sure you have a is like half proprietary data?

Is it like make it a first class citizen?

Like what's the advice you'd give to founders who are trying to do, do this, what you're suggesting?

Yeah, I mean, I think at the end of the day, it's kind of all of those things, isn't it?

Like if you have first party data, but you can't bring it to bear, then it's not very much use.

If you have third party data, and you bring it to bearing interesting ways.

But the problem with data is like we're all surrounded by data all the time.

So data is everywhere.

What really matters is the right data at the right time in the right place, because we're all humans.

And so to me, like there are obviously data advantages and there are even data network effects if you can end up in a situation where you have very valuable first party data.

But in any case, it's still about being able to bring the right data at the right place at the right time for those users to for them to be able to get advantage from it.

You know, you know, like a little kind of segue, I guess, on that one is, is I spent and I certainly love my career, like, weirdly, actually, I mean, I've been a product person for a long time.

But weirdly, I've ended up inheriting data team.

So I've actually run data teams, and a lot of different companies, which is weird, because they because product managers don't normally own data teams.

I think it's because I have just like a really massive affinity for data was been really data use to call myself data driven.

It was kind of my kind of my jam.

And because I and in hindsight, I look back and I think, like, data is like data is the opposite.

Data is more like a compass than a GPS.

Right. Like, if you look at data as a way of like giving you the answer, you're always wrong, you're always wrong, or you're slow, wrong or slow, or sometimes both.

Because mostly data doesn't give you the answer.

It just tells you if what you just said is like ridiculous, or, you know, this potentially something there.

So it's more like disproving whatever you think.

And you end up being slow, because if you try and use data for everything, your brain is ultimately a data sifter or whatever.

So the reason your intuition tells you something is because you've seen a ton of data that tells you that this is the most like the answer.

And so, and so being like data driven, being data obsessed, is like, is something you can easily overdo very, very, very easily overdo.

So it's about right sizing data, having the right data at your fingertips, having the right kind of view on data, rather than kind of like trying to expect data to give you the answer, or trying to use data as a weapon, or trying to use data as a way to kind of force people to believe you or to go to

go in your direction.

But data is kind of at the center of everything and about how to influence and be successful in products you're building and arguments you're mounting internally and everything I love that you went there.

This is I definitely want to spend time on here.

You it's interesting you say that they used to be data driven.

Like I was Mr. Data driven.

You created the reforge course data for product managers.

And also retention engagement course and reforge.

And by the way, we'll link to these you're still you're still helping with these courses.

By the way, they're still running.

They're awesome. They love them.

Yeah. Great. So we'll point people to those.

I love that you're also saying you're like, I think the way you described it to me before this is your reform data driven PM.

A lot of people say this, they're like, don't, you know, don't just tell don't just do what data tells you to do, use your intuition, use it as a guide.

It's hard like on the ground and operationalize that advice.

What's your say, like, you know, it's your PMs and your teams when they have data telling them, hey, this experiment, this experiment is a huge success, or there's a huge onboarding opportunity, conversion opportunity here, I guess just like, what's your tactical advice to folks that have data telling

them one thing and, and maybe something else telling them something else.

I think the first thing I always encourage people to do is to like, look at a piece of data.

If you're looking at a piece of data, and the result tells you something that your intuition tells you is like insanely wrong, like probably not right.

First believe your intuition and go and prove yourself right.

Like don't just take it at first glance, because most of the time, it's like Occam's razor, the most likely explanation for something that is insanely not intuitive is that it's just wrong, that there's a problem somewhere.

Now, occasionally, sometimes you actually will be right.

Now those will be paid at moments.

Those are the moments that make it all worth it.

Like there are times when you do find the nugget of gold, like you're like just staring at it, and like, this is it, this was the problem, this was the thing we were looking for this whole time.

But you have to be very diligent about like following it through, like really understanding what you're looking at.

Is this data representative?

Is this data like a good sample of the audience we care about?

Is it already subject to some sort of selection bias?

Like oftentimes when I see analysis from different product data, or even data teams, you can drive a truck through it, literally drive a truck through it.

And if you present data with authority, and that data is like ridiculous, or the analysis is just full of holes, you don't just not get benefit for that.

Like you lose a whole bunch of brownie points.

Like it would be better not to show up with an analysis that isn't clear, than it would be to show up with an analysis that's dumb.

And I see people self -immolate on this, actually relatively regularly, because they just bring a knife to a gunfight or whatever, like they just bring in an analysis that is just not, it doesn't hold water, and they present it and then get shot down live, which is nobody's idea of a good time.

So kind of if I give you a little bit of additional tactical things about that, it'd be okay.

If I'm looking at a piece of data, what was upstream of this piece of data?

And does that look normal?

Like so this thing happened or whatever, which you're very, very excited about, what happened before that?

And does that match what you think should have been right?

Right? So what happened before this moment of situation?

And then okay, for that thing that you're looking at, what happened after?

If you have an idea of what happened before and after, that gives you some idea of whether or not this thing is at all worth interesting, interesting to talk about, and then go one click above the above this data that you're looking at.

So it's like, okay, these things, let's say it's, you know, looking at onboarding success, let's say I'm looking at onboarding success to second week retention, or some look at that, I'm like, I have found this thing that totally crashes it, this intervention crashes it.

If you go upstream, and you find out that this intervention only applies to 2 % of the inbound onboarding stream, it's meaningless.

It's most likely just a random aberration.

But even if it was not a random aberration, it's not a useful tool, right?

And so you can see you got to go up, and then you might go downstream, and you might find, yep, they last for two in the second week, but in the third week, they all churn.

So basically pointless.

Why are we even talking about this?

Or then you might step all the way back and go, okay, yes, those people do get retained for longer, but the average ASP is smaller.

Because what we really care about, we do care about engagement, and we care about more customers, but we want to keep the customers at a high ASP to reach a certain revenue goal.

The final goal is happy customers paying us money.

So that's what I mean about going a click up.

If you go a click to the left, a click to the right, so before and after, and then a click up, and you still see the thing that tells you the story that you want to tell, then now you've got something that's very compelling, because people want to hear about that.

They want to hear, well, what did happen before?

What did happen after?

And why is that outcome happening?

But you have to really do your homework and really be rigorous about it, to avoid fighting falls gold.

I love that advice.

ASP, what does that stand for, by the way?

Oh, average sale press, average sale press, MRR, or some other revenue entry.

Got it. At this point, you made about how a lot of time the experiments show positive, and then they end up not having anything.

I had the head of growth from Shopify on the podcast, and they do this really cool thing where they keep holdouts for years, of cohorts, and then it auto emails them, I think a year or two later, hey, check this and see if these cohorts are still, this is still high or not, and 40 % of the time turns

out neutral after a positive experiment long term.

Interesting. It's really funny because at a last time we did some experiments, we had a global holdover group actually, that was held out of all experiments.

The experiment platform couldn't target that group at all, so 10 % of all people never saw anything ever.

So that's really, really helpful, because you can always compare them against whatever the experience was for any of the same vintage of cohort, I agree with you.

But the other thing is, I don't really love some of that thinking process just in general.

It's like, hey, let's say an experiment does show a temporary benefit.

If an experiment shows a temporary benefit, but that benefit does not persist forever, does that mean the temporary benefit was never worth it?

Or does that just mean the temporary benefit was an opportunity to reach another level you just didn't capitalize on?

I don't think there's a perfect answer is what I'm trying to say.

It's like, I don't think that the fact that a benefit doesn't last forever means that you failed.

But I agree with you that not trying to understand what is the net benefit been, what is the net lift been, is also really important too.

It's why growth is so hard.

Growth as part of product is so especially hard.

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So you built the first B2B growth team when you're Atlassian.

Correct? Yes. Yeah, yeah, it was.

It makes me feel like an old person.

But yes, it was a very long time ago.

Slash it, maybe, you know, it's a new thing.

Yeah, it's either a long time ago, or it's just we've just recently figured out this is a thing that you could do in a B2B is like focus on growth.

Yeah, it is. It's like when I so it was around about 2012.

And at that time, kind of growth hacking was a thing.

And people don't really use that term anymore.

But in B2C, it was a very big deal.

Because people could see Facebook doing their 10 friends in seven days, and they could see this kind of thing that was working for people.

And they're like, man, that's amazing.

And at Atlassian, we set out to go, okay, well, do those techniques work in B2B?

And also they, you know, it's kind of obvious now that a lot of them do and that's worth doing.

But at the time, it wasn't that obvious, because it for a lot of B2B companies, I mean, you summarized it earlier than any distribution covers all faults.

Like almost all ills can be, you know, filled in by really great distribution, like you have a really good ground game, really good marketing, a really good ground game, and it kind of jamming your product into the channel, like you're jamming your product in front of people, and you're papering over

the ugly parts with, you know, customer success, people and services and consulting and whatever, then people will buy almost any software.

Or you can certainly be successful with a lot of different software.

But back in 2012, it wasn't clear of like, okay, which instead you went at this differently, and you've heard them in software that sell itself, is the juice worth the squeeze?

Right. And you know, now I would say that it's pretty clear that the juice is worth the squeeze to the point that people, lots of people think about this all the time.

But it was a bit of an interesting kind of time at that time.

And that was essentially the beginnings of product -led growth, is that a simple way to think about it?

Yeah, basically, it's now called PLJ.

But yeah, at that time, we didn't even know what to call it exactly.

Just growth. So kind of based on that experience, a lot of B2B companies now have growth teams that are investing in growth.

What makes a great growth team in B2B?

Any pitfalls you often find folks fall into that you think they should try to avoid?

Ultimately, a lot of these types of endeavors are a matter of balance.

So what I mean by that is, growth teams tend to go through a set of phases, right?

Their first phase is proving their value at all, right?

So call that the gold rush phase.

This thing's probably not worth even doing.

Why are we doing this?

Meriband of people out there trying to prove that there's some growth factor somewhere, right?

So that's the proof of phase.

And so, you know, the advantage of that phase is like life's good, because there's usually a lot of growth to be found, because nobody's gone looking before, so life's good.

But it's pretty random, because you're just literally searching across a random search base going, have we tried X, have we tried Y, have we tried Z?

Then kind of once you get that model going, then it starts to be, okay, you know, how do we scale this thing?

Like, is this just a flash in the pan?

Did we just find a little bit of, you know, lower hanging fruit, and there's nothing else here there?

Like, this is just a project we should have done rather than an ongoing thing?

So you have to kind of make it a system, like, you have to prove that it can be repeated, and then you have to scale it.

Like, it has to become a thing, it has to become part of your DNA.

You have to be taking a PRG lens to everything you do, all the way from, you know, paid acquisition to activation, retention, engagement, cross product expansion, upsell.

I mean, you name it, like all the different ways you can grow a product by revenue or engagement.

There's many different ways to go about that, and so you end up having to scale out and be able to do all of those different things.

And then you have to figure out how you fit in with the rest of the organization, because there's other people who build products all day every day.

There's other people who sell that product all day all day.

There's other people who market that product all day all day.

And so, you know, growth organizations are in this interesting space, they're in between everybody else.

They're kind of in everybody else's sandpit in a little bit, in a little way.

And they're kind of at the edge of everybody's kind of full -time job.

And they are very valuable, but they can be, you know, complicated because of all those relationships, and because of the way they sit amongst all the other parts of the organization.

So many organizations fail because they don't really find much that wins.

Or when they do find wins, it just seems totally random.

Or they do find a lot of wins, but they all can't understand them because they seem like they're just a random walk through a bunch of potential opportunities.

There's many different ways to kind of fail to fit as you go through your growth phase from trying the ideas to success, to scaling, to operationalizing.

One of the biggest memes along these lines is a lot of companies claim there's just PLG rarely ever works.

You always event either you try it and it just doesn't work or eventually just peters out.

I guess any thoughts on just like what are signs that your product has a chance to work PL, product led growth versus you're just just go straight to sales immediately.

And don't even worry about this.

First, let's examine the counterfactual.

So let's start with the opposite of your question and say, Hey, you know, how would the world be sadder if we all just gave up on PLG?

Like we just said, Hey, just there's no point in doing it in B2B sales.

The problem is that there is not a natural force that pulls companies towards thinking about the end users, enjoyment and success earlier in the early in their journey.

There is no natural force.

There's no natural countervailing force.

Why is that? I mean, what I want, the buyer is the most important person.

The economic buyer is the most economic person that their needs are the number one thing.

They're usually the person driving the RFP.

They're usually the person dealing with the sales organization.

So the needs of the person who you hear are usually all feature driven and they're not from the end users.

And so you're kind of sowing a seed of your own demise if you don't think about that end user, but it's one thing to say that you should think about the end user.

It's a whole other thing to have a system by which you do that because people pay lip service to all sorts of things.

But my, I'm sure you've heard this one before, but in economics, people only do what their incentives told them to do.

Like broadly speaking, that is what they do.

That is what happens.

You get what you set out to measure.

You get what you give people incentives to do.

If there is nobody in the organization whose true incentive is to measure that success, the end user success, their enjoyment, their happiness, their retention, their engagement early on, it will not happen or at best it will be a hobby.

And so then by extension, if I start from there, then I say, okay, let's say it doesn't exist, POD doesn't exist, and therefore it's a hobby.

And therefore there will be a bunch of hobby people who care about this.

Then you ask yourself, okay, will that mean that there will be many products for which like those experiences really suck?

And does that mean that that will be an opportunity for competitors of those products to be better at that?

And is that a differentiation, differentiated competitive advantage?

Yeah, I'd say it is.

And so they're kind of working.

I just work my way backwards.

And I go, okay, you can say that your POD investment might be too high.

You could be like, well, if I invest more, I won't get any more juice.

I can't spend my life just experimenting and onboarding.

That's not the only thing that matters.

And that's very, very true.

But it's very hard to argue it should be turned to zero.

And so to me, therefore, it's about the balance.

It's about, okay, how does POD fit with the other different ways that I grow my business.

So at Confluent, for example, we have a POD function, we do grow with self -serve signups, people who sign up, literally their credit card, lots of them sign up, and they're very successful, never speak twice.

We also have like an enterprise sales team that sells directly to very big companies, some of the biggest banks in the world, people you would definitely know of.

I don't think it has to be one or the other.

I think that it's about a balance.

It's about getting the motions to work.

And for really sophisticated companies, the people who really nail this, it's about making both motions work together.

Like if you can get a POG motion work to feed your sales team, and a sales team motion work to feed your POG funnel when the sales leads aren't ready yet, and you can get those motions into playing with each other, you can make a lot of money.

It can be an extremely successful way to build a very resilient business.

Why? Because you get a lot of customers, and you get a lot of revenue.

You can't be that successful as a company if you have a lot of revenue, but a small number of customers, because you're captive, everyone knows that.

You can't be that successful as a company if you have a lot of customers, but not enough revenue, because you shouldn't have enough money to sustain operations.

So the magic is in having both, a very large number of customers, and a very large amount of revenue, it's very hard to knock over a company like that.

If I look back on my time at us, and I think that they shared their most recent numbers, I kind of remember what it was, but it's in the public data or whatever, something 80 ,000 or that's a lot of customers.

Let's say you're going up against Jira, and you're like, yeah, man, I'm going to pick off 1 ,000 customers from Atlassian.

That's a lot, right?

That's a lot. Obviously, 1 ,000 customers is a lot.

You only have 19 ,000, sorry, it's going to be like 89 ,000 to go, or 79 ,000 to go, or however many it is to go.

I can't remember the exact number of customers.

But it's very hard to assail a company, which has a very large number of customers, and a very large amount of revenue.

And so that's why I think that POG as a mechanism is incredibly important for almost any type of company.

If you can make the motion work, obviously, there are companies for whom the motion just is irrelevant.

But for those where it does matter, it seems like the juice is worth the squeeze.

That was an awesome answer.

I looked up last year, and they have 300 ,000 customers.

Oh, man, I'm so far off when I left must have been 80 ,000 customers.

We've done good work since then.

Also, you're talking about incentives and how the power of incentives.

Charlie Munger has this great quote I looked up just to make sure I get it right.

Show me the incentive and I will show you the outcome.

Yeah, exactly right.

Exactly right. You know, I've seen cases where like a sales team was people trying to get a sales team to do like a POG motion.

And you can beat them over the head as much as you like, you can get into a meeting and tell them that you really, want them to do this.

But at the end of the day, like they're not going to do it.

And the same is true for every other kind of like function.

It's just the nature of things.

I have some newsletter posts around the stuff.

If folks want to dig deeper.

Also, Elena Verna had an awesome podcast episode talking about product let's sales and kind of the combination of these two things that will point to just like a whole other topic we can go deep, deep on, but we're not going to do that in this episode.

Maybe just one more question.

So you mentioned the companies you worked out.

So you've been at Salesforce G product officer, mule soft specifically, within Salesforce, Metro mile, last year, Confluent now.

A lot of really interesting and different roles.

How do you choose where to go work?

And how do you choose which opportunities to take?

I imagine you have many options.

I have to think of my career in hindsight looking at it this way, Lenny, so like, I'm just, I don't know if forward looking, it was obvious to me this way.

But looking back at my career has been a little bit like a bingo card.

Like I've always been looking for, to fill in boxes I didn't have filled.

Because I felt like that would make me a better professional.

Okay, it's like, if I didn't know anything about that specific type of sales model, or that type of marketing, or that type of product management, or that type of product, or that layer in the stack, or that kind of thing, is I was I learned about that thing, I will become more versatile.

So prove to yourself that you can do those new things.

And then it makes you more versatile, because it means that any given problem you go up against, you've seen something that pattern matches to it.

Like it kind of feels like you end up bringing your gun to a knife fight in a way.

Because every problem you look at, you're like, Oh, I have seen this from the other side, like I've seen this from some other angle.

And so I know that this is likely to work, and this is unlikely to work.

And so, you know, when I when I joined, you know, earlier on in my career, I was working for a big enterprise software company, sorry, small enterprise software company that sold to the fortune 100.

When I joined Atlassian and like I shared with you, we had no Salesforce at all, actually, at all, literally nobody to sell the software, sold itself or didn't get sold at all.

And we grew to have 80 ,000 customers, like it was just pure product that grossed and just an incredible company.

Then it was a metromar, which was a consumer company that got acquired, made an insurance product for end consumers.

So they got nothing to do with technology products, like literally a complicated internet of things device you installed in your car.

But ultimately, it's an insurance product that you'd sell to grandmothers in in Florida, as much as you would have in line also.

And then you also have to totally back end software that's used by IT organizations and a consular infrastructure that's used by developers everywhere to build really interesting data driven applications, data powered applications to all sorts of things in real time.

And you look across all that and but I didn't see it that way because I learned, I actually was in sales for a bit.

So I ran a pre -sales engineering group, went around the world selling software.

So when I joined Atlassian, I wanted to kind of understand what it was to sell software at massive scale with no sales team, can it even be done?

And so I learned a lot in my time at Atlassian.

When I went to metromar, I'm like, well, I've never built a consumer product before.

I can say that I've actually built a product that's touched many millions of people because Jira has, so I felt pretty good about that.

But I never built one that I could say, yeah, a consumer, your average consumer can use this thing.

It's so simple. Even my grandma can use it.

I've never built a product like that.

So I got that experience at metromar, which is really fun.

I'd never worked inside an organization as big as Salesforce, or an organization with as good a sales version like the, you know, you talked about distribution earlier.

Salesforce is an absolutely insane distribution machine, like just an incredible company within just an amazing distribution network and a fantastic marketing approach.

It's like a PhD in marketing when you spend your time at Salesforce.

You're like, this company is just one of a kind.

It's a one of kind.

And it's so outlandishly good at one specific thing.

And so looking back, you know, all of these jobs have been, when I say bingo card, I've just got an outlandish education in these areas that are not obvious at all.

And once you've seen them, they're like superpowers.

They're superpowers to be able to bring that same experience to bear on things.

And so one thing that I really, I'm trying to figure out is why often people don't do that.

And oftentimes people stay in a very specific domain, like they prefer to stay in a domain, or they prefer to stay in a specific kind of type of company or a role that works in a certain way, like companies that have the same operating model, or they plan the same way, or they kind of they try to stay

with things that are pretty similar.

But it seems obvious that the most likely way to kind of really grow is the opposite.

It's to constantly be choosing things that are outside that, not totally outside the lines, like don't jump out of a plane if you've never parachuted before, like, obviously, you want them to be in some way an adjacency, you know, that you want them to have something in common with what you know, but you

want them to stretch you and change you.

You know, I had like a really kind of transformative experience many, many years ago when I was at when I was at Atlassian, and a guy called Tom Kennedy, he was our general counsel, so like chief legal officer, basically, and a lifelong lawyer, very smart guy, I liked him very, very much.

But like just a lawyer, just a lawyer, you know, corporate lawyer, corporate counsel, make sure you know what they're like, and really great guy.

And I remember, so mostly in our meetings, like our meetings, he didn't talk that much except about legal things, right.

But I remember, in one meeting, we were having this vigorous debate about a product strategy question, about like what we should do, should we go?

Should we go left? Or should we go right?

And like, as usual, he's there, and he's mostly just staying silent.

And eventually, the conversation has been going on for 15 minutes.

And he's like, hey, everybody, like a year ago, we talked about x, y, and z.

And he proceeds to lay out our product strategy at that time.

And he's like, just recently, he said the following things, and that was a product strategy, whatever.

Now you're saying this, isn't it obvious that that isn't this, like what you guys are saying is not congruent with that.

And if you really met what you said back then, we should be doing x.

And then like the room went silent.

Everybody kind of turned to him, kind of nodded.

And then everyone went, yeah, okay, I guess we probably should be doing it differently.

And so like, the meeting stopped.

Like, when the GC randomly mentioned that he like deeply understood our product strategy, and he knew enough to like, be able to contribute in that way.

And so the life changing part for me about that was just this realization that if I'm going to be a really great professional, if I'm the type of professional I want to be, is that type of person, the type of person who can contribute to the whole company in all sorts of ways, like doesn't spend all

of their time in everybody else's business, but understands the business and has the mental horsepower and the experience to be dangerous in all sorts of and I mean, in a complicated way, I don't mean that in the negative way, but to be dangerous in all sorts of situations, I think that when you have kind

of like leaders like that behind you and with you, then you're just unstoppable, you're an unstoppable force in business when you kind of have that that motion happening.

Wow, that was an awesome story and an awesome perspective.

It's similar to the advice I always give PMs of people always wondering, should I like go deep on a specific subject?

Should I just try different things?

And I find just variety, especially early in your career is really powerful, not just to help you discover the thing you like, but also to your point just using insights from all these different parts of the product and like internal tools and trust and safety and platform and consumer product side

and growth and just core stuff, like the more that you have, the stronger you get.

And I think I feel like another benefit of your approach is if you if you work at just B2B SaaS companies, you're never gonna like if you have too many of that on your resume, it's very hard to get hired a consumer company.

And so just having a creates a huge optionality for you if you do what you did.

Yeah, it's interesting that people used to talk about people who are T shaped or whatever, I've never really loved the analogy.

Because it's more like people are scribble shaped.

Like, I mean, like there's the really best people you've worked with.

They want like scribbles than they are T shaped.

Because of course, you want to be horizontally capable.

So you want to be broad, and you do want to be deep, you actually want to be deep and way more than one thing.

Now, obviously, when I say deep, I don't mean like, like I'm not able to do the job of like, you know, our finance function all day every day.

But I am 100 % good enough to go like three clicks below, like the simple financial analysis, like like I can go reasonably deep in our financials, because I want to because it's partly like it matters.

Like it's important to be able to do that.

And so maybe a different way to think about that bingo card is like, I've really regretted going deep in something that isn't quite my job.

Like I've really regretted it.

Like the worst case scenario is I've learned something new that I will never use, which you know, I guess at least that made my brain slightly more agile.

Like I don't know, like there must be some potential benefit of that.

But the very best case scenario is that when I least suspect it, at some point in the future, it will turn out to be the thing that matters like it will be it will be the tool that I need.

But I'm facing some important problem.

And I will be like, Oh, my god, this was worth every cent.

And so like if you think about it on an ROI basis, doing things that aren't in your wheelhouse, like that aren't the things directly in front of you, that ROI can really be outlandish, like it can be off the charts, great.

But I guess it's speculative, because it's you know, you don't know you're going to need it tomorrow.

You don't know if it's going to be something that's going to be a regular tool you use.

It's interesting is the bingo card is the analogy.

What happened? Are you trying to?

Is there a bingo moment at the end of this?

Is there retirement?

Is there Oh, you mean like, you've got everything you've got?

Yeah, I was I was working with, you know, somebody at Salesforce.

And, you know, he was like a really he'd been there a long time.

Very, you know, very, very, very successful person, like honestly, you know, didn't need to work anymore.

And he said to me that I found really useful.

He's like, well, now I'm at the point of my life, where I want to work at the intersection of things that I am good at, and things that will be valuable to the company to do.

So basically, like, it feels like the reward of containing a bingo card is actually to just get to spend more time doing things that are leverage that you enjoy and that are high leverage.

And so that seems like a good outcome to me, like if if it's not as though you're gonna, I don't think most people are going to work and hopefully have some sort of great financial outcome, and then go well, that's it, picking up stumps, I'm retiring.

I think for most people, achieving some sort of financial outcome or some sort of, you know, independence or whatever, is really just another stage, it'll be at that point, it will be okay, well, now what do I do?

Like, what do I do with my life?

Like, why? And so that was what I said earlier that at the end of the day, product management is like, at times the worst job in the world, and at times easily the best.

And it's both, and it can be both.

And so, you know, it's hard for me to think about what, you know, if I think about the things that are the intersection of what I'm good at, and are valuable to the world, product management is a pretty fun one to do, and it's different every day.

So I think we're pretty privileged for those of you who listen, I mean, obviously, your podcast reaches a lot of product people, like I think we're pretty privileged to be able to operate at that intersection.

But it's not easy, because, you know, you got to show value, you know, it's like, it's not, it's a very complicated job to show value in and to demonstrate value to the world.

And it's constantly being attacked, like you mentioned, but it's still amazing.

Like when it all goes right, you know, when a product is very successful in the market, it's hard to describe the joy you get from that.

Kind of along those lines, to close out our conversation before a very exciting lightning round.

I want to take us to failure corner.

People here listen to these podcast episodes, and everyone's always just sharing all these wins.

Everything's always going great, the CPU of this CPU of that just moving on up.

And they people want to hear times when things didn't go right.

Because that's those are stories people don't share as often.

Can you share a story when some didn't go right when you maybe had a failure in the course of your career?

And if you learn something from that experience, what you learned, I mean, there's a lot of things that didn't go exactly to plan any, like very early on in my career, you know, I was a developer and I accidentally deleted like one of the core systems of the company that I was working at.

So that's that's gonna go down in infamy.

But luckily, that one's far in the review mirror, like that wasn't Atlassian.

That was pretty that was far prior Atlassian, but very bad.

Yeah, you know, the one I like to talk about, I wasn't I wasn't directly responsible for it.

But I feel like responsible for it was at a company.

And we launched a product that was one of those products that, you know, in hindsight should have been really obvious it was going to fail.

But for some reason, we were all blinded by the potential.

It was a it was a product that that was about it was basically for to measure the environmental impact of your company.

And to help you reduce the environmental impact of your company by doing think about as a power management, building power management, managing the power drawer of computers, managing the power drawer of, you know, AC and all of that stuff.

That was division, basically, it's like a kind of a manage your environmental impact of your business.

Kind of the idea was pretty cool at the time.

And also, it was the right time for that.

And still a thing. It's a lot of area of active research and investment or whatever.

But it was like one of those things, talk about the wrong company, wrong place, wrong time, wrong distribution, like we had literally no right to win, no right to play like just absolutely no business in hindsight being in that in that business.

And I feel really bad because I get a good idea wrong wrong company.

And and at the end of the day, we launched the product, we actually kept the product in in market for two years.

And the and the final, the final straw was weird.

The final straw was actually when a when a customer finally wanted to pay for it.

Like it had been a market for two years.

And we found ourselves with a customer who wanted to pay millions of dollars for it.

They were ready to sign on the dotted line.

And that was actually the moment we decided to kill the product.

Because really, if anybody if this person signs this piece of paper, we are stuck with this forever.

Like this, this one is one customer will be bound by contracts for long or whatever.

Like so we actually ended up killing it at the moment, after two years of a failure when kind of somebody wanted to pay money for it.

And I look back on that.

And I'm just like, man, like that was really big.

I feel really bad because like it should have been obvious.

It should have been, it was obvious.

And we should have been able to call a spade a spade and I guess speak truth to power.

But instead, it kind of got through to the keeper and turned out to be a real accidental drain on resources for years and just a big mistake.

So is the lesson there?

Just be real with yourself?

Just? Yeah, like I like to chat this forcing function of like, okay, let's get for real now.

Is it like I wish we had an earlier forcing function to force us to make a decision?

Yeah, yeah. I think if I could do it differently, like probably I might not have necessarily been able to 100 % change the decision, but I should have tried.

Like, I mean, it was pretty obvious after six months, like this thing was like a bit of a zombie product walking.

And it would, you know, it would at least I could have done is said like this thing is that like we could call it dead way earlier, but instead, we proceeded for another year and a half, investing in it.

And so that that's the bit that makes me kind of feel like a real bummer about it.

It reminds me of a recent episode of the ROZ, who is the CMO at Wizz.

And she joined as the first PM.

And a few weeks into it with doing tons of calls with customers, she is like, I think this I need a quick because I don't really understand what we were building, I don't get it.

And everyone's like, you know, like I don't even I don't either.

And just the con for the founders just had a vague idea what they're doing, but they didn't really have an idea.

And just sparked the okay, wait, your no one actually does, let's actually get more concrete.

And it helped them pivot.

And now I don't know if you know about Wizz, but they ended up being the fastest growing startup in history.

Yes, isn't that amazing?

Right. You know, it doesn't, it's not doesn't mean it's permanently fatal, but asking that question and kind of going through that reckoning turned out that came out through came out stronger.

Scary, but turns out it's for the best often.

Before we get to a very exciting letting around, is there anything else that you want to mention or leave listeners with maybe a last nugget, something that you think might be helpful before we wrap?

Maybe a couple of different things that I think sometimes well understood, but just repeating them, I guess because they're very valuable to me.

One is that, like, if you let your calendar rule you, then nothing good will happen.

Like, you know, I know people talk about that a lot, but it's surprisingly common in product management, in particular, the people end up ruled by their calendar.

And so it's related to that whole look at it, spend 80 % of your time thinking about things going on outside the business, sell the business.

Easy said, very hard to do.

And if you don't do it, no one's going to do it for you.

And so, like, it's really hard to be successful unless you find a way to force that to happen.

And so to repeat that, kind of also, like, somebody said this to me, I never actually looked up the quote.

But apparently, Colin Powell said that if you're making a decision with less than 30 % of the available decision, 30 % of the available data, you're making a big mistake.

If you're making a decision only after you have 70%, without the 70 % or 77%, I can't remember the exact number, when you have 77 % of all the available data, you have waited far too long.

Right? And that's, I've always found that very insightful, and it relates a little bit to what we're talking about about data earlier.

But at the end of the day, we get paid in product management to make decisions, good decisions, paid to make good decisions that will deliver business benefit.

And a decision with too little data is fatal.

A decision that takes too long and collects too much data is also fatal.

So like everything, it's about trying to find the balance of all of these different things to try and deliver business advantage.

A great way to circle back to all the things we've been talking about.

With that, we reached a very exciting lightning round.

Are you ready? Yes, let's do it.

Let's do it. What are two or three books that you have recommended most to other people?

Yeah, they're oldies, but goodies is probably going to be the main startup that I still find actually really good and kind of key lessons in there.

I still think are very applicable to a lot of people, particularly the cohort analysis bit, which for some reason, I still don't see people do anywhere near enough cohort analysis.

So there you go. That's my little tip.

And then inspired how to how to build products that people love by Marty Kagan and the Silicon Valley Product Group, that's an oldie, but a goodie think, you know, it's got a lot of the key lessons of product management in that even though it's been around for a long time.

Those are some classics.

Very cool. Do you have a favorite recent movie or TV show you really enjoyed?

I'm watching a program like it's just a I don't get to watch very much TV.

Mostly at night, I like to watch things that are extremely light that like just don't at all inspire any element of stress and that are very short.

So basically short and funny is basically my thing.

And there's a new program on Netflix, I think it's called Detroiters.

I've been watching that.

Yeah, it's really funny.

I really like that.

It's so ridiculous, but very funny.

So like that. That main guy is so funny.

I forget his name, his weenie or something like that.

Yeah, he's so good.

Good one. I've been watching that.

I'm loving it. It's like very quirky.

I think the New York Times quote on there is like very weird.

It's so weird. Like in the first episode, I'm like, what is this show?

It's not even clear what time it's set in.

And like, it's very weird.

It's really cool. Yes.

Well, good way to describe it.

Next question. Do you have a favorite product you've recently discovered that you really love?

Yeah, this one's like some of your some of your listeners might be using it.

But glean, like it's a pretty well known startup.

Now, they recently raised a ton of money.

We've been using Galina Confluent for a long time.

And it's just amazing.

It's just amazing. Okay, I can't describe how good it is.

And I don't say this lightly, because, you know, I think search, like a business search is probably one of the hardest problems in computing actually, getting it right is one of the hardest problems in computing amazing, it's not often I use a product and I'm like, this thing is like 10 times better

than anything that's come before it.

It's one of those for me.

What's the simplest way to understand what it does for you?

It searches all of our organization's knowledge.

So the thing you were just saying before you're like, you know, what does ASP mean, right?

If I had that in a meeting, I just opened my new tab, but automatically take over my new tab, well, just like what does ASP mean, and it will summarize back to me what ASP means, and it will give me a link to all the documents inside our company that just read what ASP means.

And then it will tell me who the expert in ASP and our company is.

It's a just, it's like having a second brain.

It's like an insanely cool kind of organization search.

Great tip. Okay, two more questions.

Do you have a favorite life motto that you come back to share with folks find useful and work during life?

I think about this one a lot.

You know, when I started off in my career, I was a, you know, an engineer's engineer, I used to very much about like, technical correctness and what computers were capable of, and kind of technical righteousness, you know, the right answer, rather than, you know, there is only one right answer in whatever.

It's a long -winded way of saying that I often think about this phrase, which is, people don't care what you know until they know that you care.

And, and so I've realized that really, being able to influence people, it doesn't matter about whether or not you're right or whether or not you're wrong.

And at the end of the day, it's first about trust, and about relationships, and caring about what each other's outcomes are, what their incentives are, and all good things sit on top of that.

Once you have those kind of foundations, then you can build like really good partnerships, and that's where good progress comes from.

Wow, that is so good.

It connects with like radical candor, similar, like in theory of just caring.

People need to feel like you care deeply about them before they take your advice.

And that also connects with this parenting book I'm reading called Listen, that a previous guest recommended, which is all about how your kids have problems when they feel like your connection to them is weak.

And so the solution is to build a stronger connection for them to know that you care deeply about them.

So this is really connected so much of what I've been reading.

Yeah, exactly. Great one.

Final question, you're born in Sydney, folks can maybe guess by your accent.

If someone were to visit Sydney, any tips, anything you think they should check out, favorite thing in Sydney?

Yeah, Sydney is a really beautiful city and like it's kind of famous for its beaches, and it's basically a metropolitan city.

People will probably be very surprised when you visit it.

It's a very big city, very metropolitan, a little bit like New York, but New York was really beautiful beaches.

If you want to think about it that way, it's kind of crazy.

But there's actually like a ton of really cool nature and beautiful things all around Sydney.

And so if you want to do something like off the beaten path, you can actually go to, there's an area called the Blue Mountains, which is like an hour and a half drive from Sydney.

And you can habsail down a waterfall, which is well, actually, first thing, you go canyoning through a canyon full of water.

And then you have sail of waterfall at the end.

And if you're really beautiful, fun kind of adventure like thing, an hour and a bit away from a massive metropolitan city, that's my sort of happy place, like really beautiful outdoors stuff, while also next to a beautiful city.

And you said you sail what sort of sail off a waterfall sail, you might think of it as repelling repelling, I think.

Yeah, lowering yourself down on a rope or got it.

Okay, because when I hear sail, I'm like thinking a boat just jumps through over the waterfall.

Oh, no, no, no, absolutely.

Absailing, which is also, I think in the States, you guys call it repelling repelling.

Yeah, wow. Yeah, very cool.

Sean, you're awesome.

This was extremely cool.

Thank you so much for being here to final questions.

Working folks finding online if they want to reach out also pointing folks to your reforge courses that you created.

And final question, how can listeners be useful to you?

Sure, yeah, so my reforge courses, you can check them all out at reforge .com.

As you mentioned, the retention engagement course and the data for product managers course, so you know, love to see folks get some value from that.

Lots of people have been through those courses already.

And I really get a lot of value from it because, like I said, one of my goals is to like, help all of us be better product people.

I think our leverage could be massive.

We can get in touch with me, obviously LinkedIn, but also Sean M.

Clowes on X, if you want to get in touch.

And in terms of, you know, being useful to me, I mean, broadly speaking, that I'm always open to new ideas, like if people have ideas about how to do better B2B, PRG, better B2B in a product led sales, for example, better better ways of going about distribution, and product led sales and product led

growth inside enterprise companies.

Hey, I'm, I'm open to learn myself, we're all, we're all in one big journey learning how to do this better.

So true. Sean, thank you so much for being here.

Awesome. Thank you very much, thank you so much for listening.

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Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast.

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See you in the next episode.

[제품 관리의 본질과 AI 시대의 기회: 션 클로즈(Sean Clouse)와의 대화]-[Why great AI products are all about the data | Shaun Clowes (CPO Confluent, ex-Salesforce, Atlassian)]

📋 Summary

제품 관리의 현주소와 10배(10x) 성장하는 PM의 조건

1. 외부 지향적 사고와 데이터 활용

2. AI 시대, 데이터 관리가 핵심이다

3. B2B SaaS의 미래와 '비즈니스 규칙'의 힘

4. 경력을 쌓는 법: '빙고 카드' 전략

🎯Key Sentences

📝Key Phrases

📖 Transcript