How Applied Behavioral Science at Uber looks like?

On the Uber Labs team, the mission is to leverage insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for customers, exploring why and how partner with product and marketing teams to build better products and experiences for its customers. 

What is the behavioral Science?

Behavioral science is a generic term that, similar to the term data science, encompasses a broad spectrum of potential roles and may be applied differently across companies. At Uber Labs, it defines behavioral science as the study of how people think and behave. They include many fields in our concept of behavioral science, such as psychology, behavioral economics, sociology, political science, and neuroscience. 

Researchers within these fields have spent decades conducting rigorous empirical research to discover principles around how and why people behave the way that they do. People are complex, so behavioral scientists have developed diverse toolkits; they include novel methodological and statistical approaches to handling complex issues, like not always being able to run randomized controlled experiments. They can utilize these theoretical insights and methods in industry settings.  

Different from traditional behavioral science industry, Uber Lab's end-to-end approach stems in part from the origins as the experimentation team for the operations side of the business, thus quantifying the impact of business. 

How it delivers insights and methods?


Many of its consultations involve applying theoretical insights to problems. Additionally, as behavioral data scientists we take a quantitative approach to problem solving. Its data work focuses on questions about user behavior and falls into three categories: quantifying psychological constructs and processes, applying behavioral science methods, and conducting experiment analysis.

First, it uses Uber data to quantify latent psychological constructs and processes that drive behavior. 

To do so, it develops novel techniques or adapt existing methods from the social and behavioral sciences, such as factor analysis

Next, it applies methods less commonly used in data science to solve otherwise intractable problems. 

Examples include mediation modeling and causal inference approaches like interrupted time series analysis

Finally, it analyzes data from experiments, ranging from standard A/B tests to approaches used when A/B tests are not possible or recommended, like randomized encouragement designs.

Successful Use Case: Express POOL


Unlike uberPOOL, uberX, and other ridesharing products, Express POOL includes extra wait time between requesting and matching with a car and a short walk to their pick-up location; these changes create straighter, more efficient routes, in turn leading to a more affordable trip.

Problems: The team noticed that there was room for improvement: riders were cancelling their trips in the time between request and matching at a high rate relative to other products.

Having learned the context for the problem at hand, the first step was to conduct a literature review, or in other words, a behavioral science deep dive, to identify the relevant theories and insights to address a given problem. In the case they dove into the behavioral science literature to gather insights about people’s perceptions of time and waiting.

Methods: It identified three concepts that are important in presenting wait time: idleness aversionoperational transparency, and the goal gradient effect. The concept of idleness aversion is face valid: people dread idleness and want to be busy. Operational transparency, or revealing to consumers what is going on under the hood, so to speak, has been found to increase consumer’s valuation of products. Finally, the goal gradient effect describes the greater motivation and effort exerted in goal pursuit when people feel like they are advancing well towards their objective.

Solutions: Given these insights, it recommended highlighting progress during wait times by explaining each granular step going on behind the scenes. Additional information—for example, explaining the arrival time estimate calculation—could be provided by clicking an info icon. 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 adds value

It accounts for the specialized skill set and how it can add value when they select and scope out partnerships and projects. 

At a high level, they consider the priorities for the year set by product leadership in terms of business impact. 

At a more granular level, when prioritizing the projects that they will do within a partnership, the product team provides input into which areas have the strongest business need. 

Importantly, their team views these areas with a behavioral science lens, determining whether or not our domain knowledge and quantitative expertise will be utilized. In some cases, this could mean deprioritizing analysis for experiments which lack a strong theoretical basis or purely qualitative research which does not require our methodological skills. 

Maximizing for both business impact and behavioral science relevance allows our team to have the biggest impact.

 

 

 

 

 

 

Created and Edited by AI Analytics;

Source: Uber Lab Engineering