Designing For Growth
Product
Bill pay is a white-label platform designed to manage everything from utility bills to mortgages in one place. It’s seamlessly embedded into the ecosystems of major financial institutions across the United States. Our largest client and investor was Capital One.
Problem
Capital One’s bill pay platform was facing an extremely high churn rate, with most new users abandoning the product within their first week. To make things more challenging, legal constraints prevented Capital One from sharing specific user data. This meant the product team and I had to identify and solve these pain points in the dark.
Methods
To bridge the data gap, I relied on internal walkthroughs and rapid usability testing via Maze to pinpoint friction. I also collaborated with our UX researcher, Dana, to conduct in-depth ethnographic studies. We went beyond the clicks and researched the actual psychology of debt and bill management.
To measure success, we used an iterative rollout strategy, using qualitative feedback from the Capital One team as our compass.
Results & Impact
While direct data from Capital One remained limited, our stakeholders reported significant progress in solving the churn issue. The impact was even clearer with our smaller regional partners: one bank saw a 100% decrease in user churn after implementing our updates.
Users
My starting hypothesis was simple: Money is stressful. Any amount of friction in the UX is a trigger for users to jump ship. Our research validated this completely.
By analyzing user behavior, we discovered that bill-paying patterns varied significantly by generation. This allowed us to move away from a one-size-fits-all approach and design for specific mental models.
Solutions
Most of the friction lived within the "Add Biller" flow. Let’s look at how a single screen change transformed the experience.
In the original design (left), users were prompted to search the BillGO directory or choose from a list when adding a biller to start a payment. Our walkthroughs showed that users often hesitated here; as new users, they weren't sure which billers were supported, and the popular list rarely felt relevant to their personal needs.
Our ethnographic research showed us exactly which bills the average American prioritizes. I updated the screen (right) to prioritize local utility companies based on the user's location, followed by auto loans and insurance.
Even if the suggestions aren't a perfect match, we are now communicating immediate value by showing the types of bills we support. In testing, this change completely eliminated hesitation, as users immediately understood to add their utilities. Changes like these significantly reduced overall friction in the onboarding process.