Uber Technologies
Uber Technologies
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Case Study—One of Uber’s company missions is to make carpooling more affordable and reliable for riders, and effortless for drivers. In 2014 the company launched uberPOOL to make it easy for riders to share their trip with others heading in the same direction. Fundamental to the mechanics of uberPOOL is the intelligence that matches riders for a trip, which can introduce various uncertainties into the user experience. Core to the business objective is understanding how to deliver a ‘Perfect POOL’—an ideal situation where 3 people in the vehicle are able to get in and out at the same time and location allowing for a more predictable and affordable experience. This case study argues that, for a reduced fare and a more direct route, riders are willing to forego the convenience of getting picked up at their door in exchange for waiting and walking a set amount to meet their driver.
This case study explores the integration of qualitative and quantitative research to understand user trade-offs. Methods utilized were in-person interviews and two large-scale surveys: a maxdiff and a conjoint, each with a different purpose. The study started with a multi-city qualitative research study designed to understand how users make trade-offs among their transportation options, suggesting key characteristics of a ‘Perfect POOL.’ The team followed up with a maxdiff survey to validate these characteristics and identify the factors most important for riders’ decisions. A customized conjoint survey was then built to study what values each product feature contributes to maximize rider opt-in to the ‘Perfect POOL’ product. The team subsequently explored ways to translate the trade-offs revealed by the conjoint survey back into the product experience. This case study will discuss the conjoint survey’s outcomes and implications that directly confirmed the hypothesis that riders are willing to make experiential trade-offs. Learnings from this multi-phase research led to the initial Beta-launch of Express POOL in November 2017.
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INTRODUCTION
Uber started with a simple concept—press a button and request a ride to your destination. Founded in 2009, Uber is an on-demand transportation platform that enables individuals to get a ride using their mobile phone. What started as a luxury ride service quickly became a global logistics platform changing how people move around. Today, Uber is available in more than 600 cities around the world, transporting riders in hundreds of languages across dozens of countries. Uber is committed to making transportation safer and more accessible, reducing the congestion impact of urban transportation by getting more people into fewer cars, and creating economic opportunities for people to work on their own terms. Core to realizing this mission is promoting carpooling at scale to enable everyone to afford the experience of Uber.
UberPOOL was originally launched in August 2014 as a service that makes it easy for people headed in the same direction to share their journey. The overall benefit was lower costs for riders and a higher volume of paying passengers for drivers. Since its launch, uberPOOL has become a popular carpooling service for riders worldwide and has served over one billion rides. Moreover, uberPOOL constitutes a large portion of the company’s overall business. As such, the service satisfies customer needs, strategically grows Uber’s business, and benefits cities by improving the usage of each car on the road.
That said, prior user research identified some critical rider experience pain points on uberPOOL, particularly around routing and affordability. Poor matches cause significant routing detours and prices are not affordable enough. Combined with the goal of delivering a more efficient and enjoyable service, the team focused on creating the ‘Perfect uberPOOL,’ an ideal situation where all 3 people in a vehicle are able to get in and out at the same time, leading to a more predictable and affordable experience.
The research team, comprised of user researchers and data-scientists, devised a multi-phase research study approach to investigate what and how to create the ‘Perfect uberPOOL’ from the riders’ point of view. The research started with an in-depth qualitative study across multiple cities to understand how riders make trade-off decisions when choosing transportation. Findings from this study informed the core characteristics of the ‘Perfect uberPOOL.’ As a result, the Product, Engineering and Research teams formulated a set of refined hypotheses:
- Riders are willing to wait for a short period and walk to an optimal pickup location in exchange for a cheaper price and a faster, more direct route to their destination;
- Riders are willing to trade a predictable pre-trip experience for a lower price and a higher quality on-trip experience with fewer detours.
The dual purpose of this paper is to demonstrate collaborative research processes and to assist readers in executing similar research methods. Each stage of the research is discussed, detailing how the team collaborated in continuous and interdependent knowledge building. Details on the methodological approach and execution are intentionally included to support similar types of inquiry.
Section one introduces the mechanics of the original uberPOOL, beginning with an overview of how the product works and the basic unit economics underlying it. Included is a walk-through of the current interface design of uberPOOL. Section two discusses the motivations to improve uberPOOL and the existing concerns across the three main actors: Uber as a company, riders and drivers. Section three focuses on the in-depth qualitative study conducted to understand how riders make trade-off decisions when choosing their transportation. The research suggested key characteristics in creating a ‘Perfect POOL.’ Section four is devoted to the design of a maximum differentiation (“maxdiff”) analysis survey that was constructed to validate the most important factors for a rider when choosing uberPOOL. Outcomes from the maxdiff survey helped the team focus on a core set of features. Section five discusses the conjoint survey that was then built to understand the value of each product feature in order to maximize rider opt-in. The conjoint survey design with its implementation and analysis will be discussed in detail. Section six is an overall summary of the research outcomes and business learnings that led to the launch of the resulting product named Express POOL in November 2017.