The Problem

Our Houston Runner population is ‘weird’ and exhibited differentiating behaviors from other comparable markets in TX. The marketplace was unbalanced, meaning we had an imbalance of Runners versus orders in certain neighborhoods. To accommodate this problem, Favor placed new Runners on waitlists and ‘locked’ neighborhoods that were too full.

I knew I needed to educate myself on current operations in the company related to the Houston market. My first goal was to gather data and analyze trends since the best predictor of future user behavior is analyzing current user behavior.

Background Research of Quant/Qual Trend Data

Quantitative Data

After digging on my own in Google Looker on my own, I reached out to our support, operations, and data science teams to better understand the trends I was seeing in the data I pulled.

Using my background in GIS, I visualized some of the data on maps to present to stakeholders. While I have some background in SQL and spatial statistical analysis, I knew others at the company could better analyze the data. I worked with data scientists to understand acceptance rates and types of preferred orders Houston Runners wanted.

Qualitative Data

Here, I got a little creative. Before creating surveys and moderator guides specifically for Houston Runners, I decided to dig into Facebook groups for people who fit into this market segment. Here, I found qualitative sentiments regarding the problems and “weird behaviors” we saw. I even contacted my network to see if anyone in the Houston area who tried Running experienced these same issues and received feedback from them.

To corroborate my initial findings, I surveyed Houston Runners to understand trends on a larger population scale. I also decided there is great value in speaking with individuals and hearing their stories to understand their ‘why’ rather than just seeing overall trends.

Research Goals & Focus Areas

Understand Houston Runner Behavior

• Why do they sit idle for long periods of time even if they are being offered orders?

• Do they prefer certain types of orders? Why?

• Do they have strategies/goals for running?

• How long have they been running for Favor?

• How do they start a session?

• What are their behaviors while running a session?

• What happens when their session is ‘slow?’

  • How do they know it is slow?

  •  What do they do while waiting for orders?

• Do they use competitor apps/which ones?

• What are their goals for running with Favor?

Houston Runner

Exploratory Research @ Favor

Analysis & Presentation

Survey

Using AirTable, I organized and tagged the qualitative data from the survey to determine what themes were consistent throughout the responses. After tagging, I used AirTable’s embedded tools to create graphs and quantify the themes.

Moderated Interviews

I pulled quotes and video clips to showcase common themes. I used affinity mapping in Miro to analyze the notes taken during the interview and grouped common problems associated with Runners in HTX. I also noticed insights that applied to certain individuals that could transform the way we understand our Runners. Almost all of our interviewed Runners were people of color, and they described issues related to DEI and geographic challenges that I highlighted in my final presentation.

Presentation

Once the analysis was complete, I generated a comprehensive slide deck that I presented to our product team, including designers and product managers, other stakeholders in operations, and C-suite level leadership that included practical next steps for solving the marketplace imbalance.

Personal Reflections

This project proved to be a great success. However, I recognize there is always room for growth, especially as I have gained more experience since conducting this study.

I believe the methods used in this study worked well for generative research. Moving forward, if the design team wanted to test specific flows, I would suggest running tasks in UserTesting to compare flows for efficiency and preference.

I also believe in the concept of ‘eating your own dog food’ when it comes to this type of work. Doing a diary study or a live drive-in with HTX Runners would have been extremely valuable for this research. Because we were located in Austin, and I was only working as an intern, I had limited time to complete the study and could not incorporate this idea. For future studies in specific problem neighborhoods, I believe this type of research would bring a lot of additional context and value.

Study setup

Survey

I collaborated with our CRM team to create an email campaign to embed our Typeform survey. We sent the survey to 23,000 HTX Runners and received approximately 390 completed surveys for statistically significant results with 95% CL and 5% ME.

Moderated Interviews

In addition to surveying HTX Runners, I also moderated 10 live virtual user interviews with the following criteria: HTX Runners; varying acceptance rates; varying #s of completed orders; and a mix of seasoned/new Runners.

For recruitment, I worked with the data science team to pull a list of Runners who fit this description and sent emails using Google Sheets and Mail Merge with a link to our Calendly to set up a time for an interview, making sure to schedule more sessions than I needed knowing some people would . I created the moderator guide and questions for the Runners to understand their experiences.