Lenny’s Podcast96 min
Ronny Kohavi · Jul 27, 2023
Key learnings- Test every code change, since even small bug fixes or tiny UI tweaks can have surprising, large impact on key metrics.
- Expect most ideas to fail: roughly two-thirds to over 90% of experiments in his experience did not move the target metric, so…
- Allocate a portion of experiments to high-risk, high-reward bets, accepting that about 80% will fail, while keeping most work…
Lenny’s Newsletter · Free post♥ 512
Jan 16, 2024 · 13 min
Lenny Rachitsky and Dan Hockenmaier argue that every business can be reduced to a simple equation, and that you don't fully understand your business until you can write one. Building the equation forces teams to name…
Lenny’s Newsletter · Free post♥ 172
Aug 30, 2022 · 18 min
Olga Berezovsky, guest-writing for Lenny Rachitsky, argues that cohort retention is widely misunderstood and costly to get wrong, and offers a practical guide to measuring it. She recommends defining "active" by the…
Lenny’s Newsletter · Free post♥ 1,022
Feb 28, 2023 · 21 min
Jorge Mazal, former CPO of Duolingo, recounts how the company reversed a slide in daily active user growth and eventually grew DAU about 4.5x over four years. The journey began with failed attempts: a…
Lenny’s Podcast173 min
Jeff Weinstein · Jul 11, 2024
Key learnings- Treat a customer who takes time to complain as a rare gift; respond quickly and personally, since speed signals that you care.
- Before investing in craft or polish, confirm the product solves a burning problem customers would pause their day for; polish…
- Discount feedback from friends and casual users; focus on the specific target customer who has the problem and would pay to solve…
Lenny’s Podcast131 min
Hamel Husain & Shreya Shankar · Sep 25, 2025
Key learnings- Evals are systematic ways to measure and improve an AI application, essentially data analytics on LLM behavior, replacing…
- Start with error analysis by manually reviewing around 100 sampled traces and writing short notes on the first upstream failure…
- Product people with domain expertise should lead open coding; appoint one 'benevolent dictator' whose judgment you trust instead…
Lenny’s Newsletter · Free post♥ 108
Jun 16, 2021
“This is just quick email to let you know I wrote a guest post for Future (a16z’s new media site) that I’m pretty excited about. It’s a guide for helping you choose your North Star Metric(s), based on a survey of current and past employees at over 40 of today’s most successful growth-stage companies. Check out the table below for a glimpse of what you’ll find. Your North Star Metric is your strategy, and your strategy is your North Star…”
Lenny’s Newsletter · Free post♥ 193
Jan 28, 2020 · 8 min
Lenny Rachitsky compiles the most concrete and actionable definitions of product-market fit (PMF) from leading investors, founders, and operators, organized into signals before a product exists and signals after launch…
Lenny’s Podcast108 min
Ramesh Johari · Nov 9, 2023
Key learnings- Marketplaces sell the removal of friction (transaction costs) rather than the underlying goods, and both sides are customers who…
- Don't build a marketplace before you have one: early on, find a bespoke value proposition that solves a real problem (e.g…
- Apply a scaled-liquidity smell test: if you don't have a lot of buyers and sellers, you're not yet a marketplace; if one side is…
Lenny’s Podcast91 min
Jess Lachs · Jul 14, 2024
Key learnings- Treat analytics as a business-impact function with a seat at the table, answering 'so what do we do now?' rather than only…
- A central analytics org with pods mapped to partner teams keeps talent bars consistent, creates growth paths, and avoids…
- Carve out intentional time for self-directed deep dives, such as hackathons, because exploratory work is the first thing to slip…
Lenny’s Newsletter♥ 13
Dec 1, 2020 · 7 min
Subscriber post — summary onlyThe post argues that experiments are usually the right default for product changes, but describes when to skip them. It covers the benefits and downsides of A/B testing, then three cases to ship without testing: when…
Product Talk · Teresa TorresDec 13, 2023
Dec 13, 2023 · 12 min
Teresa Torres profiles Thomas Groendal, a senior PM at Bluestone Analytics, whose intelligence-analysis products (DarkBlue and DarkPursuit) operate on the dark web with few users, privacy restrictions, and very long…
Product Talk · Teresa TorresMar 24, 2014
Mar 24, 2014 · 5 min
Teresa Torres argues that product teams should start by defining what success looks like, since a clear goal is the ultimate measure that makes alignment and decisions easier. She warns against drowning in dozens of…
Lenny’s Podcast74 min
Crystal W · Jul 31, 2022
Key learnings- Run Wizard of Oz style experiments before building: use WhatsApp groups, screenshots, or manual processes to validate demand…
- Experiments are worth running even with around 30 users; the trends stay similar, and precision improves with more data, while…
- Define retention concretely as cohort retention; for a free weekly product, expect roughly 60% of the prior week's users to…
Lenny’s Podcast76 min
Kristen Berman · Oct 2, 2022
Key learnings- Pick an uncomfortably specific behavior to change rather than a vague outcome like 'engagement'; teams should align on the exact…
- Map every step required to perform the target behavior (a behavioral diagnosis) and attach the psychological barriers at each…
- Reduce logistical and cognitive barriers to make desired behaviors easier, since people default to the path of least resistance…
SVPG · Marty CaganFeb 15, 2016
Feb 15, 2016 · 6 min
Cagan argues that product optimization, typically A/B testing and iterative tuning, is often confused with product discovery, and that optimization only makes sense after a team has found product/market fit. He…
Lenny’s Newsletter♥ 90
Nov 23, 2021 · 5 min
Subscriber post — summary onlyLenny Rachitsky answers a reader's question about which metrics matter for a consumer app, arguing the right metrics depend on how the business makes money. He walks through five consumer models (trial-based…
Product Talk · Teresa TorresDec 3, 2014
Dec 3, 2014 · 5 min
Teresa Torres argues that product teams should treat every idea as a testable hypothesis, which requires explaining why a change matters and how much impact it should produce. Ideas from design, sales, and product…
Product Talk · Teresa TorresAug 9, 2017
Aug 9, 2017 · 9 min
Teresa Torres argues that the popular "We believe... will result in... we will have confidence when..." hypothesis format is quick but insufficient for sound experiment design. She explains that teams should test the…
Product Talk · Teresa TorresMar 19, 2013
Mar 19, 2013 · 3 min
Teresa Torres argues that a single A/B test rarely settles a question; instead, teams should string related hypotheses together around one underlying insight. Using an email subject-line example about urgency, she shows…
Product Talk · Teresa TorresSep 5, 2014
Sep 5, 2014 · 9 min
Teresa Torres argues that as product teams adopt experimentation, their tests are only as good as their hypotheses and experiment design, so poor setup wastes money, time, and sprints. She lists fourteen common pitfalls…
Product Talk · Teresa TorresSep 18, 2012
Sep 18, 2012 · 3 min
Teresa Torres argues that choosing product themes is not enough; teams also need a way to predict and measure how each user story moves the product goal. Using an event-site example with the goal of increasing the…
Lenny’s Podcast114 min
Austin Hay · Aug 13, 2023
Key learnings- MarTech treats the marketing systems and platforms, both third-party and first-party, as products, and centers on people and…
- A dedicated systems or MarTech owner usually becomes necessary somewhere around 100 to 150 people, when a village approach to…
- Pick tools to solve specific problems, and combine buying a tool for most of the solution with building the remaining custom…
Lenny’s Newsletter · Free post♥ 203
May 2, 2023 · 16 min
Guest author Olga Berezovsky explains the difference between correlation and linear regression analysis for product teams. Correlation measures how strongly two variables move together on a scale from -1 to 1, and is a…