Predictive Price Technology
One of the hardest questions for anyone selling a product is 'how much should I charge?' This question becomes even harder when you're selling highly unique items: rare collectables, time, tasks, journeys, space. For disruptive companies that enable people to sell previously unavailable products and services in an open marketplace, the challenge is magnified as little historical data or comparative models exist.
At Airbnb we discovered one of the most nerve-wracking and confusing moments for a new host is setting their price. How much should they charge? When should they change it? Putting a price on your home and experience is challenging! Last year we set out to develop a predictive pricing model to help our hosts confidently set the best price for their space, and know when to change it. We integrated dozens of signals, some obvious and some unexpected and new. The result? 3X the number of reservations.
This talk will cover:
Why is price so hard to determine for a user?
How do you to determine the best signals to include in your model? Especially when little historical data exists and the inventory is highly unique.
How do you communicate a price suggestion to a user? People don't always want to hear their asset is worth less than they thought. How can you calculate price in real-time?
How can you adapt a price for seasonality or market changes?
How do you measure the effectiveness of a pricing model?
Dan was the co-founder and CTO of Crashpadder, a London based competitor to Airbnb. After Airbnb acquired Crashpadder in 2012, Dan joined the engineering team at Airbnb. He is now Product Lead, working with Growth, Tools, Payments, Trust and Risk.
Dinner, prepared by Airbnb's Executive Chef, will served at 6pm.
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