Qualitative Research Logistics

The research team started the study with a series of in-home interviews with riders in different locales. The study included 23 users across various neighborhoods in Chicago and Washington D.C. The cities were selected based on city density, rider diversity, product performance and business priority. Participants included a mix of prospective riders, new riders and tenured riders spread across specific predominant use cases. For example, the team categorized the main use-cases as: commute to or from work, airport or business travel, social outings and family errands. The team screened for participants exhibiting behaviors within these key uses cases to ensure that their travel experiences were within the realm of those that Uber supports.

Each participant session lasted 2.5 hours and was conducted at their house. The session was comprised of three main sections: a general travel-mapping exercise to understand the rider’s lifestyle and travel occasions; a job exploration section to understand the factors and decision-making process; and finally, a ride-along section to capture the context and nature that govern a top key job.

The interview portion of the study is focused on understanding participants’ feelings towards travel and a brief overview of their approach to travel. The team employed a travel mapping exercise as a grounding document to anchor and catalog all their travel occasions. This mapping exercise provides a systematic framework for soliciting discussion points. Each item on the travel map would indicate a particular occasion. For example, one participant mentioned taking their child to school every day as part of their daily routine. Therefore, dropping their child at school is one of their top travel occasions. As the discussion progresses, each occasion is built out with greater detail, uncovering details on the who, when, and what of that particular circumstance. This process of documentation provides an initial overview of the types of occasions that riders have as part of their travel.

The second portion of the research study is an in-depth discussion of each travel occasion to understand the complete breadth of jobs that are associated with each occasion. In this study, Ulwick’s eight fundamental process steps were utilized to guide the discussion of each occasion. The steps were to: define, locate, prepare, confirm, execute, monitor, modify and conclude (Ulwick, 2016). The first step, ‘Define,’ requires understanding the participant’s main objective. Each following step is a slight progression of their process, demonstrating how participants make calculated trade-offs between various needs when considering their transportation choices.

Qualitative Research Learnings

This research surfaced the breadth and interaction of the various functional, emotional and meaning-based factors that arise in users’ travel decisions. This summarization framework is a simplified adaptation from Maslow’s Hierarchy of Needs, a common framework used for organizing human motivations (Maslow, 2013). According to Ulwick, a ‘functional’ job is the core task that has to be accomplished. An ‘emotional’ job is defined as the way customers want to feel or want to avoid feeling during the process (Ulwick, 2017). And finally, a ‘meaning-based’ job is the self-actualization thought process of how the customer wants to be perceived by others. A crucial learning from this qualitative study was recognizing not only the magnitude of functional factors that govern users’ transportation decisions, but also the emotional factors that can play a significant role in a user’s decision process.

In this study, identified ‘functional’ jobs include factors such as price, efficiency and vehicle size. Riders are oftentimes much more vocal and aware of these functional factors because they are the core tasks that have to be accomplished in their particular travel circumstance. For example, a common rider task might be to ensure arriving on time at a particular destination. In this study, a participant noted their responsibilities as a mother, where “the school bus arrives at 7:20am, so at 6:45am I will need to drive [my child] down the street to the babysitter, where he will wait and then board the school bus.” As the participant shared this particular occasion, she explained how the current travel arrangement is ideal for her work schedule, but it would be beyond her budget if she had to continue this arrangement due to the cost of the school bus. In that instance, she described the functional goal of minimizing the cost expenditure of their travel option to stay within bounds of their budget. In this case, due to the participant’s price constraints, she has to trade convenience and efficiency for price.

Efficiency is a key component of uberPOOL and through this study, the team is able to understand how riders talked about this important concept. Riders mentioned topics such as route planning, trip duration, amount of waiting time, and arrival time variability – all of which are aspects of efficiency in riders’ travel choices. For example, a rider said “the way I approach travel…I don’t know if this is unique but I always make sure that I know more than one route that I can take in case there’s traffic.” In this case, the rider is concerned about traffic affecting her trip duration and, therefore, paid more attention to route planning. One of this rider’s functional goals is to identify the best possible route to her destination, but ‘emotionally’ she is also trying to increase confidence in her overall travel plan by creating a secondary plan. As such, this illustrated to the team how efficiency trade-offs do not live in isolation but are interconnected with other factors.

Moreover, efficiency factors, such as trip duration, can be interpreted as actual or perceived. For example, a rider described using other sources of travel information. The rider mentioned that by entering the “time you want to be there, [the app] will say ‘traffic is usually heavy around this time.’ So if you leave at this time, this is how long it’s going to take you. So I use [the app] to let me know so I can leave far enough in advance.” This would be a case where a rider has actual time predictions that inform them on the efficiency of their trip. In other cases, a rider might believe that a particular route will take longer based on past experience. This terminology around efficiency added a new way to frame of how riders evaluate perceived vs actual efficiency differences in their travel choices.

In summary, riders make trade-offs on trip attributes across all three ‘functional,’ ‘emotional’ and ‘meaning-based’ dimensions. In Figure 4, a participant walks through how his job as a police officer is mostly urgent, unstructured and unplanned – namely that “nothing is the same every day.” With his varying destinations and schedule, the participant emphasized the need for a more time-efficient but spontaneous travel arrangement. Meanwhile, he also noted strict values against drinking and driving, concerns around DUI and the need for travel decisions to include how to “help keep people more safe.” This example illustrates how ‘functionally’ time-efficiency is key; ‘emotionally’ the user needs control, and “meaning” where it supports his personal values, which is on individual and community safety. Considered together, this frames a rider’s decision making model for travel choices.

Figure 5. A rider explains the functional, emotional, and meaning-based trade-offs through the travel-mapping exercise (Left). A mother refers to her child’s school schedule to guide her travel-mapping exercise (Right).

Understanding Trade-offs in the Context of uberPOOL

The research team recognized how riders’ travel decisions cut across functional, emotional and meaning-based dimensions. But as first step in building the perfect POOL, the team had to narrow down the list of factors they had more direct influence and control over, such as the matching intelligence and efficiency attributes of uberPOOL. To do so, they proposed first utilizing a maxdiff survey and then a conjoint survey. After launching early algorithmic changes, the team would return to additional qualitative research assessments for a holistic re-evaluation of the tradeoffs, including an assessment of how users’ functional, emotional and meaning-based dimensions interact across the product. Accordingly, the team strove to understand the intricacies of efficiency attributes and how they should manifest in uberPOOL’s matching algorithm.

This also means that emotional needs, such as safety or concerns in sharing a car with strangers, are important factors but given that these factors are less controllable from a product perspective and more on a policy perspective, the research team proposed first focusing on understanding factors that the team has more direct influence over and supplement future work on understanding these emotional needs. This work was conducted post-launch but will not be discussed in this paper.

The uberPool algorithm that matches multiple riders with a driver introduces uncertainties to both user groups. When riders request an uberPOOL, they are only provided the bare essential information such as upfront price, approximate time of the driver’s arrival, and an estimated time of arrival at their destination. Even though they are told that uberPOOL is a carpooling product, they cannot be certain whether additional riders will actually join them. Riders also do not know ahead of time, when, where, and with whom this sharing will happen. This is because matching decisions happen in real-time. Meaning that users are only provided that information after the decision has been made. Even after the original matching has been made, the result may entail picking up two additional riders, adding 10-minutes to the original rider’s on-trip time. Hence, part of the uberPOOL experience involves coping with these uncertainties around the types of inconveniences they will experience during their ride.

An important distinction discovered from the qualitative study is how riders might have control over the types of inefficiencies that they will experience, compared to unknown inconveniences embedded in an uberPOOL experience. For example, a rider driving in their own vehicle but stuck in traffic, will have more control on what route planning to take. However, in the case of an uberPOOL, these routing decisions are decided by Uber, not the rider. As such, the rider will also not know the route that is designed before selecting uberPOOL. Therefore, the qualitative study sought to capture the added nuance of ‘ownership’ and ‘transparency’ of what inconveniences to be experienced by riders.

Illustrative Example – Sarah is a teacher at a school in the Hunters Point neighborhood of San Francisco; she commutes from her home 1-hour away. On a particular day, she needs to carry some class equipment and get to school by 8am. She has a transportation budget of around $10 per trip. She wakes up at 6:30am, considers her options, and then decides whether to request an uberPOOL. The Uber team understands that riders like Sarah have to evaluate transportation options against set requirements. Evaluating uberPOOL against these factors can be challenging for riders because they are not privy to complete information about the experience ahead of time, such as how many riders they will share their ride with, how much added time the additional matches will add to their on-trip time, and the overall quality of the route that may also affect the time of arrival.

As a result, the team questioned whether communicating some of these inconveniences that were traditionally unknown to them might be valuable to riders’ decisions. For example, riders do not know ahead of time the number of additional riders joining the trip or know where the defined route will take them. The team asks whether these efficiency factors need to be communicated upfront to riders. However, providing such information would come at a cost to Uber and can only be justified if riders deem it valuable in their travel choice. Therefore, the team conducted a maxdiff survey to validate top factors that shape riders’ decisions on uberPOOL and whether certain efficiency factors, if communicated upfront, would significantly alter their travel choice.