On the podcast we talk with Ora about LinkedIn’s value-driven growth philosophy, how they personalize experiences and plan offerings based on user intent, and the complexity of running over a thousand experiments a year.

Top Takeaways:

🌱 Growth follows value

The surest path to long-term growth is adding features and benefits that genuinely help people achieve their goals. Growth tactics may bring a spike, but sustainable revenue comes from a product that keeps evolving so members find new reasons to return. When value creation is continuous, acquisition and retention become self-reinforcing.

🎯 Personalize by intent

Not all users are looking for the same outcome. Job seekers, small business owners, and learners need different experiences. Matching plans, features, and paywalls to their specific intent—whether expressed directly or inferred from behavior—makes the product feel relevant and worth paying for. The alternative is irrelevance, which guarantees churn.

📊 Test like a scientist

Scaling experimentation changes the culture: debates give way to data. By running over a thousand tests a year, teams learn faster, spot what actually resonates, and avoid relying on intuition alone. The goal isn’t just to optimize pricing or layouts—it’s to build a habit of constant learning that compounds into growth.

🔄 Retention isn’t linear

Churn doesn’t always mean goodbye. Many users return months or years later when their needs change—“boomerang” behavior that can become a meaningful revenue stream. Win-back offers, refreshed trials, and simply continuing to add new value all help capture these returning customers and turn them into long-term loyalists.

🤖 AI is a tool, not the story

Artificial intelligence should quietly power better outcomes, not become the headline. Helping users write a stronger profile, find the right lead, or save time drafting a job description creates tangible value. Positioning AI as a behind-the-scenes helper keeps the focus where it belongs: solving the user’s problem.

About Ora Levit:

👨‍💻 Vice President of Product Management at LinkedIn.

📈 Ora manages LinkedIn’s billion-dollar online subscription businesses, growing both the free weekly active user base and adding value for LinkedIn Premium subscribers.

💡“Our offering changes over time, and as I mentioned, we believe in value-driven growth. We add a lot of value. And so the Premium that you've seen if you subscribed two years ago is not the Premium of today. It's a very different product, and I want you to try it out.”

👋 LinkedIn

Follow us on X:

• David Barnard - @drbarnard
• Jacob Eiting - @jeiting
• RevenueCat - @RevenueCat
• SubClub - @SubClubHQ

Episode Highlights:

[0:51] Value add: How LinkedIn centers value-driven growth in their product development.

[8:40] The long game: The importance of optimizing for and measuring long-term revenue.

[9:57] Pay to play: Where to draw the line between free and paid features.

[17:59] Put it to the test: Ora and her team prioritize A/B testing and user feedback over internal debates about feature ideas.

[23:32] Take it personally: The role of AI and LLMs in personalizing in-app experiences.

[27:47] Here today (and tomorrow): Strategies for retaining users in the long term and winning back churned users.

[34:52] The AI touch: LinkedIn’s philosophy on incorporating AI features to add value to their product.

[39:44] Two (or three) for one: Leveraging strategic partnerships to add bundled perks to a premium subscription offering.

[41:43] Pulse check: Monitoring earnings calls, reports, books, and podcasts to stay in step with the current state of the subscription app industry.

On the podcast I talk with Eric about how measurement dysfunction paralyzes growth, why diversifying channels for the sake of diversification actually hurts performance, and the futility of trying to interpret why ads win.

Top Takeaways:

📊 Broken measurement kills growth

The biggest pitfall isn’t creative or channel choice—it’s disorganized measurement. When finance, product, and UA each use different models, growth stalls. The fix isn’t another dashboard; it’s alignment. Build one coherent, incrementality-aware framework everyone trusts, with clear definitions of success and outputs that meet each team’s needs.

🌊 Don’t diversify just to diversify

Spreading budget across more channels feels safer but often reduces performance after integration, creative, and reporting overhead. Start with a waterfall method: max out your primary channel until ROAS hits your threshold, then move to the next. Diversify for scale or cross-channel effects—not optics.

🎲 Stop asking why an ad worked

Winners often defy tidy explanations. Treat individual ad outcomes as stochastic and largely uninterpretable. Put your energy into the system: feed diverse concepts, automate prospecting/synthesis, and measure whether your process is increasing the rate of wins over time. Learn from inputs and process—not post-hoc stories about outputs.

⚡ Ship speed over certainty early

You won’t have fully baked LTV or incrementality in week one. Push spend methodically: kill obvious losers immediately, let plausible winners age, track cohort ROAS at day-7/30/60, and widen budgets as curves support it. Iterative frontier-pushing beats premature “terminal LTV” guesswork.

🧩 Engineer better signals

Algorithms optimize to the signals you send. Create intentional, high-intent events (light “hurdles” that correlate with LTV) and send those back to platforms. Better signals shift spend toward durable users and compound efficiency, especially as automation on major platforms accelerates.

About Eric Seufert:

👨‍💻 Quantitative marketer, media strategist, investor, and author.

📈 Eric shares expert advice on the Mobile Dev Memo blog and is an investor at Heracles Capital.

💡 “The way I approach creative testing is trying to identify losers as quickly as possible. The winners take time to prove out, but the losers are pretty quick to prove out.”

👋 LinkedIn

Follow us on X:

• David Barnard - @drbarnard
• Jacob Eiting - @jeiting
• RevenueCat - @RevenueCat
• SubClub - @SubClubHQ

Episode Highlights:

[1:00] Intelligent design: How to effectively incorporate AI into your business strategy.

[4:52] I, Robot: Machine learning =/= generative AI.

[8:36] AI Pitfalls: AI works best for automating tasks and coming up with ideas — not generating brilliant creative assets.

[17:29] Predictive AI: Brand-specific, full-fidelity video ads generated by AI could be a reality within 18 months.

[33:25] Risky business: How to effectively diversify across advertising channels to optimize ROAS-adjusted spend.

[37:43] Measure of success: Above all, make sure your measurement system is coherent and has cross-team alignment.

[42:04] Tortoise vs. hare: To balance speed and efficiency, identify your ad “losers” as quickly as possible.

[44:43] Missed opportunity: Good marketing comes down to embracing some uncertainty and minimizing the rest.

[49:23] Human touch: Why generative AI creative tools probably aren’t a worthwhile investment right now.