Understanding the Frequent Rider Experience

Using multiple methods in a research assignment

This case study illustrates my ability to combine multiple study methods in a single research assignment. The methods I used were: Competitive Analysis, Survey, Diary Study, and User Interviews.

This project was conducted at Lyft, a leading ride-sharing company known for its commitment to providing safe, affordable, and reliable transportation. The project focused on enhancing the user experience within the Lyft app for frequent riders.

Research methodology: Competitive Analysis, Survey, Diary Study, User Interviews

Research tools: Dscout, Qualtrics, Miro

Role

I directly collaborated with product and data science in the initial planning phase of the project. We discussed the target user behaviour found in the data and identified key areas for further research to provide context to the user behaviour patterns. Throughout the project, I worked closely with product and data science to make sure this project was meeting key business goals.

I took ownership of the overall project and decisions. This included the participant recruitment, screener survey design, diary study design, development of the interview scripts, participant payments, and led the data analysis.

Research Goal

We wanted to better understand a target user behaviour of frequent Lyft riders related to choice of transportation in their daily lives. We hypothesized that providing incentives to users who have the potential to exhibit this target user behaviour could increase incremental rides. In an experiment where users were offered an incentive to complete this target behaviour, the number of rides increased by approximately 2% in a month-long period of time.

We wanted to further explore how reminders could further incentivize this behaviour.

Design and Product generated a list of questions focused on understanding if this product direction had the potential to increase ridership and revenue.

How do users make decisions about transportation?
Why do users choose rideshare over other forms of transportation?
What are the key factors that influence their decisions?
How do users react to ride incentives?

Our goal was to understand how the overall journey of users who do and do not exhibit the target user behaviour. Additionaly, we wanted to see how users reacted to proposed incentives.

Research Methodology

I first conducted a competitive analysis of to better understand the opportunity gap that Lyft could fill in the market.

I conducted a diary study to better understand how real Lyft riders made decisions about transportation in the moment of making that decision. A diary study is a good research tool to understand the “in-the-moment” experience of decision-making that users exhibited by asking them multiple-choice questions and open-response questions via text and video responses. I chose to use dscout, an app-based diary study platform to facilitate the study.

I recruited participants from the Lyft rider database who did and did not exhibit the target user behaviour through a screening survey. I then followed up by calling participants for a quick chat to determine their eligibility.

Participants were asked to document their transportation choices in a diary for a week. They were asked to note the mode of transportation they used, the time of day, and the reason for their choice.

After the diary study, I conducted interviews with the participants to gain a deeper understanding of their decision-making process related to their documented experiences.

Timeline

This project was completed in 10 weeks to provide robust feedback to the product, data science, and design teams.

Planning: 2 weeks

Competitive Analysis: 1 week

Recruitment: 1 week

Diary Study: 3 weeks

Interviews: 1 week

Analysis: 3 weeks

Competitive Analysis

I conducted a competitive analysis of other rideshare and transportation solutions to understand how they incentivize users to use their services. This analysis helped us understand how Lyft could differentiate itself in the market. I looked at other rideshare services such as Uber, as well as carpooling, carshare, public transit, and traditional taxi services.

Survey Questions

The participants were asked to answer questions regarding their general demographics and target user behaviour. Through this, we collected premilinary data.

The survey included questions about:

How often they used Lyft
How often they used other forms of transportation
What factors influenced their decision to use Lyft
How they felt about incentives
How they felt about reminders

The survey was sent out to 5000+ Lyft riders in the United States. The survey was used to identify participants who exhibited the target user behaviour and those who did not. It was also used to identify participants who would be willing to participate in the diary study. We also ensured that we were able to recruit a diverse group of participants to ensure that we were able to capture a wide range of perspectives. This included participants from different age groups, ethicities, genders, jobs, household incomes, and geogeraphic locations. We also considered if they were able to drive or had access to a personal vehicle. Lastly, we considered membership in the Lyft loyalty subscription program, Lyft Pink.

Recruitment

I used the survey results to recruit 25 participants. The main criteria I used to choose the group of participants included:

Gender
Location
Access to a personal vehicle
Ability to drive
Household income
Lyft Pink membership

To ensure that participants had a clear understanding of what the diary study would entail, I scheduled called with each participant to explain the study, their responsibilities, and answer any questions they had about participation. I also took into account if they seemed like they would be a good participant by how talkative they were and interest in the study.

After calling all the participants, I narrowed down my participant group to 14 interested participants. Participants were split into two groups based on if they exhibited the target behaviour or not. More participants were recruited for each target user group (5 + 2) than necessary in order account for participants dropping out of the study. This subset of participants were chosen to represent a variety of demographics, in order to explore the interactions between those demographics and the target user behaviours.

I sent instructions to them via email on how to sign up with dscout, and provided them with a test task to have them become familiar with the platform.

Diary Study

Participants were asked to document their transportation choices in a diary for 10 days. They were asked to note the mode of transportation they used, the time of day, and the reason for their choice.

Day 1: Surveys

Survey about Transportaton Habits: How they typically get around in their daily life
Survey about Rideshare Habits: How and why the use rideshare
Survey about target user behaviour: What they think the target user behaviour is, when they tend to take them, what factors they consider when making a decision

Day 2-9: Trip Diary

Videos or text submissions for each leg of a round trip for a minimum of 3 trips (6 entries). We will learn:

Where they were going
What mode of transportation they are using for that leg of the trip
Why they chose that mode of transportation, and what factors influenced their decision

Day 10: Final Survey

Survey about their experience with the diary study, what they learned, and what they would like to see in the future.

By the end of the study, 10 (5 from each of the two behavioural groups) participants completed the entire diary study.

Post-Study Interviews

Interviews with participants who had completed the diary study to ask them more about their experience during the study or clarify any decisions they had made that were of interest. Each session was 30 minutes long. The first half of the interview focused on their diary study answers. The second half of the study focused on concept testing of potential Lyft incentives.

Concept Testing

Based on the insights from the diary study, I generated a set of values that users cared about when it came to exhibiting the target user behaviour:

Convenience
Price
Reliability

These values were used to create concepts of incentives that combines 2-3 of the values in different combinations, for a total of four incentives. The incentives were discussed with data science and product to ensure that they were feasible and could be implemented in the app.

Price + Convenience
Price + Reliability
Convenience + Reliability
Price + Convenience + Reliability

I created a low fidelity mock-up of the different placements of the incentives. I discussed these placements with Design to ensure they followed the Lyft design system.

Based on my ideation and discussions with design, I presented this incentive to participants as an in-app notification pop-up, appearing before they entered their destination.

We presented three of the scenarios with either the opportunity to lock in their price or save money in another way.

Pre-scheduled rides: Are participants willing to sacrifice time flexibility in order to save money? The goal of these incentives is to see how flexible participants can be with time, and what barriers may come up with pre-scheduled rides.

Pre-paid round trip bundle: Are participants willing to pre-purchase a bundle of round trips in advance in order to save money? The goal of this incentive is to increase incremental rides.

TTG: Are participants enticed by price discounts and more time flexibility? The goal of this incentive is to keep participants on the Lyft platform for both legs of the trip.

Lyft Pink trial: Can a free trial of Lyft Pink increase rider retention for the service? What are the perceptions of the perks provided by the membership program? The goal of this incentive is to increase Pink membership.

Analysis and Synthesis Process

Qualitative data from the interviews and diary study was analysed via thematic analysis in Miro. Quantitative data was analysed from the survey and existing data from data science.

The insights we uncovered focused on the following topics:

How users make decisions about transportation
Why users choose rideshare over other forms of transportation
What are the key factors that influence their decisions
How users react to ride incentives

Research Outcomes

Insights were shared with the design team in a collaborative session to discuss the findings, recomendations, and prioritize design changes.

Key Recommendations

Clearly define what the target behaviour is, and increase the amount of flexibility around the definition.
Target users without access to a personal vehicle and those who do not exhibit the target behaviour with Lyft with incentives/savings.
We have an opportunity to target low frequency riders and riders in less densely populated areas with target behaviour incentives
Loyalty rewards for riders who exhibit the target behaviour.

During the study share-out with product, data science, and design, we discussed the best way in which these recommendations could be incorporated into the rider experience. We also discussed potential experiments that could be conducted using my incentive designs.

Impact

I worked with data science to plan an experiment targeting the behaviour using app reminders, as well as additional financial incentives to better understand their impact on ridership frequency. This way, we would be able to validate the results of my study on a large scale.

In September 2024, the Price Lock feature was implemented. This was one of the options we explored as an incentive. This service offers the ability to lock in the price of their commutes for $2.99 a month, up to $40. This way, riders can easily take Lyft to and from where they need to go without worrying about variable pricing, and help them stick to their budgets.

Next Steps

I recommended further research with high frequency riders, due to the low sample size of this cohort study.

I also recommended research on Uber riders who exhibit the target user behaviour to see what incentives would work for them in converting them to Lyft.

Lastly, I recommended looking into promoting Lyft as a transit alternatives in transit deserts

Reflections

During this project, one important thing that I learned was the importance of creating a research timeline. There were many moving parts: the diary portion of the study took up quite a lot of time, and many tasks needed to be addressed simultaneously. Creating a timeline with the significant goals planned out at the beginning of the project helped me stay on track.

Another takeaway from this project was creating a contingency plan before things go wrong. As often happens, participants either dropped out of the study or did not attend the interview. By creating a contingency plan, I could make some leeway where I could still gain valuable insights despite problems that came up.