Marketplace lessons from Uber, Airbnb, Bumble, and more | Ramesh Johari (Stanford professor)
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- čas přidán 9. 06. 2024
- 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: www.lennyspodcast.com/marketp...
Where to find Ramesh Johari:
• LinkedIn: / rameshjohari
• Website: web.stanford.edu/~rjohari/
• X: / rameshjohari
Where to find Lenny:
• Newsletter: www.lennysnewsletter.com
• X: / lennysan
• LinkedIn: / lennyrachitsky
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
Referenced:
• Riley Newman on LinkedIn: / rileynewman
• Upwork (formerly Odesk): www.upwork.com/
• Ancient Agora: en.wikipedia.org/wiki/Ancient...
• Trajan’s Market: en.wikipedia.org/wiki/Trajan%...
• Kayak: www.kayak.com/
• UrbanSitter: www.urbansitter.com/
• Thumbtack: www.thumbtack.com/
• Substack: substack.com/
• Ebay: www.ebay.com/
• Coase: “The Nature of the Firm”: en.wikipedia.org/wiki/The_Nat...
• Stitch Fix: www.stitchfix.com/
• A/B Testing with Fat Tails: www.journals.uchicago.edu/doi...
• The ultimate guide to A/B testing | Ronny Kohavi (Airbnb, Microsoft, Amazon): www.lennyspodcast.com/the-ult...
• Servaes Tholen on LinkedIn: / servaestholen
• Bayesian A/B Testing: A More Calculated Approach to an A/B Test: blog.hubspot.com/marketing/ba...
• Designing Informative Rating Systems: Evidence from an Online Labor Market: arxiv.org/abs/1810.13028
• Reputation and Feedback Systems in Online Platform Markets: faculty.haas.berkeley.edu/sta...
• How to Lie with Statistics: www.amazon.com/How-Lie-Statis...
• David Freedman’s books on Amazon: www.amazon.com/stores/David-F...
• Four Thousand Weeks: Time Management for Mortals: www.amazon.com/Four-Thousand-...
• The Alpinist on Prime Video: www.amazon.com/Alpinist-Peter...
• Only Murders in the Building on Hulu: www.hulu.com/series/only-murd...
Production and marketing by penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.
Lenny may be an investor in the companies discussed.
Wow, this guy has a lot of wisdom to offer and not just for the data science/tech areas.
Currently applying for an AirBnB role, last round involves developing end-to-end ML experiments. This talk is invaluable to me!
Superb conversation! Loved the discussion on correlation vs. causation as it applies to business…prediction vs. making decisions to maximize value.
Fantastic speaker! I love so much the way how he explained usefulness of experiments and also bringing to the table the case from AirBnB about superhost badges.
Interesting topic and engaging guest speaker.
Just started watching your channel. Thanks for sharing :)
Keep up the good work , amazing conent
Very informative
Wow amazing content 🎉 thank you!!!
The timing of this video is great. Currently onboarding supply to my marketplace. Thank you Lenny
Doing exactly the same right now with my startup. Where u from?
19:56 Isn't it the same for other marketplaces too ? Haven't seen the full episode. But loving it so far. Excellent Pod👌 Also appreciate the honesty of the professor ( 12:18 ) 🙏
This is all common sense to a good entrepreneur.
just a few minutes in and this is the feeling I’m getting a tid bit
Great podcast, video. Comment from the creator of 2 marketplaces )
Experiments are one mechanism for learning. Thats it. Its not the only one, or the main one: just one of many. Experiments are more useful when you gravitate away from vision and strategy. Data does not predict the future, it only tells you what worked in the past.
Why dsnt cleaning or car washes work as marketplaces?
Hi my airbnb listings under google search is showing up in Spanish language! Any idea how i can fix this pls
cool