Marketplace lessons from Uber, Airbnb, Bumble, and more | Ramesh Johari (Stanford professor, startup advisor‪)‬ Lenny's Podcast: Product | Growth | Career

Ramesh Johari is a professor at Stanford University focusing on data science methods and practice, as well as the design and operation of online markets and platforms. Beyond academia, Ramesh has advised some incredible startups, including Airbnb, Uber, Bumble, and Stitch Fix. Today we discuss:
• What exactly a marketplace is, if you boil it down
• What you need to get right to build a successful marketplace
• How to optimize any marketplace
• An easy litmus test to see if there’s an opportunity to build a marketplace in the space
• The role of data science in successful marketplaces
• Ramesh’s philosophy on experimentation and AI
• Advice on implementing rating systems
• Why learning isn’t free
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Find the full transcript at: https://www.lennyspodcast.com/marketplace-lessons-from-uber-airbnb-bumble-and-more-ramesh-johari-stanford-professor-startup/
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Where to find Ramesh Johari:
• LinkedIn: https://www.linkedin.com/in/rameshjohari/
• Website: https://web.stanford.edu/~rjohari/
• X: https://twitter.com/rameshjohari
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Where to find Lenny:
• Newsletter: https://www.lennysnewsletter.com
• X: https://twitter.com/lennysan
• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/
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In this episode, we cover:
(00:00) Ramesh’s background
(04:31) A brief overview of what a marketplace is
(08:10) The role of data science in marketplaces
(11:21) Common flaws of marketplaces
(16:43) Why every founder is a marketplace founder
(20:26) How Substack increased value to creators by driving demand
(20:58) An example of overcommitting at eBay
(22:24) An easy litmus test for marketplaces 
(25:52) Thoughts on employees vs. contractors
(28:02) How to leverage data scientists to improve your marketplace
(34:10) Correlation vs. causation
(35:27) Decisions that should be made using data
(39:29) Ramesh’s philosophy on experimentation
(41:06) How to find a balance between running experiments and finding new opportunities
(44:11) Badging in marketplaces
(46:04) The “superhost” badge at Airbnb
(49:59) How marketplaces are like a game of Whac-A-Mole
(52:41) How to shift an organization’s focus from impact to learning
(55:43) Frequentist vs. Bayesian A/B testing 
(57:50) The idea that learning is costly
(1:01:55) The basics of rating systems
(1:04:41) The problem with averaging
(1:07:14) Double-blind reviews at Airbnb
(1:08:55) How large language models are affecting data science
(1:11:27) Lightning round
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Referenced:
• Riley Newman on LinkedIn: https://www.linkedin.com/in/rileynewman/
• Upwork (formerly Odesk): https://www.upwork.com/
• Ancient Agora: https://en.wikipedia.org/wiki/Ancient_Agora_of_Athens
• Trajan’s Market: https://en.wikipedia.org/wiki/Trajan%27s_Market
• Kayak: https://www.kayak.com/
• UrbanSitter: https://www.urbansitter.com/
• Thumbtack: https://www.thumbtack.com/
• Substack: https://substack.com/
• Ebay: https://www.ebay.com/
• Coase: “The Nature of the Firm”: https://en.wikipedia.org/wiki/The_Nature_of_the_Firm
• Stitch Fix: https://www.stitchfix.com/
• A/B Testing with Fat Tails: https://www.journals.uchicago.edu/doi/abs/10.1086/710607
• The ultimate guide to A/B testing | Ronny Kohavi (Airbnb, Microsoft, Amazon): https://www.lennyspodcast.com/the-ultimate-guide-to-ab-testing-ronny-kohavi-airbnb-microsoft-amazon/
• Servaes Tholen on LinkedIn: https://www.linkedin.com/in/servaestholen/
• Bayesian A/B Testing: A More Calculated Approach to an A/B Test: https://blog.hubspot.com/marketing/bayesian-ab-testing
• Designing Informative Rating Systems: Evidence from an Online Labor Market: https://arxiv.org/abs/1810.13028
• Reputation and Feedback Systems in Online Platform Markets: https://faculty.haas.berkeley.edu/stadelis/Annual_Review_T