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Shaun Clowes

Episode #265

Chief Product Officer at Confluent

Confluent

📈Growth & Metrics🔍User Research🚀Career & Leadership

📝Full Transcript

16,594 words
Lenny Rachitsky (00:00:00): I love that you have very strong opinion about this, which is just the state of the product management career and how most PMs are not that great. Shaun Clowes (00:00:08): 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 unacceptable." Lenny Rachitsky (00:00:24): What's the answer, Shaun? How do we solve this problem? Shaun Clowes (00:00:26): In everything always talk from the customer's perspective, from the market's perspective, from the competitor's perspective, the 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. Lenny Rachitsky (00:00:40): You kind of have this hot take that the way AI will most impact product management is data management. Shaun Clowes (00:00:45): 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. Lenny Rachitsky (00:00:58): 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? Shaun Clowes (00:01:06): People really underestimate where the value is created in these applications and they just kind of get it completely wrong. Lenny Rachitsky (00:01:17): Today my guest is Shaun Clowes. Shaun 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 c...

💡 Key Takeaways

  • 1Most PMs fail because they focus internally on politics and delivery; great PMs spend 80% of their time focused on customers, competitors, and the market.
  • 2AI's biggest impact on product is not the model, but data management—feeding the right context, recent data, and specific business rules to the LLM.
  • 3In B2B SaaS, the moat is not the data model or UI ("forms on a database"), but the complex, customized business rules and workflows ingrained in the customer's operations.
  • 4Don't be purely data-driven; be data-informed. Data is a compass, not a GPS. If data contradicts intuition, investigate the data first.
  • 5Product-Led Growth (PLG) is essential even in enterprise sales because it forces the organization to care about the end-user experience, which sales-led motions often ignore.
  • 6To build a resilient career, treat it like a 'Bingo Card': intentionally seek roles that fill skill gaps (e.g., if you know Enterprise, go do Consumer; if you know Product, learn Sales).

📚Methodologies (3)

📈 Growth & Metrics

When analyzing a metric change or experiment result, never look at the data point in isolation. You must analyze the context surrounding the data—where users came from, where they went, and the higher-level business impact.

Core Principles

  • 1.Check Upstream: Look at what happened before the event. Is there selection bias? Does this only apply to 2% of traffic?
  • 2.Check Downstream: Look at what happens after. Do these users retain in week 3, or do they churn immediately after the 'success' event?
  • 3.Click Up (Macro View): Look at the business level. Did this increase users but lower the Average Sales Price (ASP) or revenue quality?

"If you look at data as a way of giving you the answer, you're always wrong... Data is more like a compass than a GPS."

#"upstream/downstream"#context#growth
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🔍 User Research

Instead of using AI to generate ideas, use it to process a continuous stream of customer feedback (interviews, tickets, calls). The key is to prompt the AI to find conflicts with your strategy, not confirmations.

Core Principles

  • 1.Surround yourself with the River: Aggregate all inputs (Gong calls, tickets, NPS) into a central flow.
  • 2.Provoke the 'Not': Ask the LLM, 'Where does my strategy NOT fit what these customers are saying?' or 'Where is the customer probing at the edges?'
  • 3.Comparative Strategy Analysis: Feed the LLM a competitor's public docs and ask, 'Does this customer feedback fit the competitor's strategy better than ours?'

"Ask ChatGPT 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."

#ai-powered#"feedback#river"
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🚀 Career & Leadership

View your career as a bingo card of experiences to collect. Instead of staying in one comfortable domain, intentionally jump to adjacent but different roles (e.g., Sales, B2C vs B2B) to build a 'scribble-shaped' skill set.

Core Principles

  • 1.Identify the Gaps: Look at what you haven't done. If you've only done B2B, try B2C. If you've only built product, try selling it.
  • 2.Seek Adjacency, Not Suicide: Jump to roles that are different enough to learn, but where you still have some relevant skills (don't jump out of a plane without a parachute).
  • 3.Value the Intersection: The goal is to eventually work at the intersection of what you are uniquely good at (due to variety) and what is valuable to the world.

"It's rarely regretted going deep in something that isn't quite my job... It turns out to be the tool that I need when I least suspect it."

#career#"bingo#card"
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