How Uber Leverages Applied Behavioral Science at Scale

eng.uber.com

The article covers how Uber applies behavioral science research to a new product called Express Pool. I pulled the two most interesting paragraphs in line here:

...we dove into the behavioral science literature to gather insights about people’s perceptions of time and waiting. We identified three concepts that are important in presenting wait time: idleness aversion, operational transparency, and the goal gradient effect. [Tristan's note: explanations of each in the article itself!]
Given these insights, we recommended highlighting progress during wait times by explaining each granular step going on behind the scenes, like identifying other riders traveling the same way and finding a car for the trip. (...) The Express POOL team tested these ideas in an A/B experiment and observed an 11 percent reduction in the post-request cancellation rate.

Behavioral science is distinctly different from data science, but the two are quite complementary. It's unusual and fascinating to see a company applying behavioral science so formally—this is well beyond the tool kit of a traditional product manager.

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