Virtual Card Numbers
Streamlining digital spend management
This was a generative research project I led for the Virtual Card Numbers (VCN) product at Capital One, informing product strategy on how VCNs could be the top choice for spending online. We explored customers’ digital spend experience through qualitative interviews, diary studies, and concept testing. We identified high-leverage customer problems and recommended feature concepts to product, design, and tech leadership.
Some information has been removed or modified due to confidentiality.
Client
Capital One
Duration
6 months
(Oct. ‘21 - Mar. ‘22)
My Role
Generative Research
Product Strategy
Team
1 UX Researcher (me)
1 Designer
1 Product Manager
Table of Contents
Problem Space • Research Planning • Generative Research: Phase 1 • Generative Research: Phase 2 & 3 • Impact
Problem Space
Figure 1: Virtual card number experience on Capital One’s ENO browser extension
I came in as the new lead researcher for the VCN team to specifically to work on this project to inform a product strategy for their 2nd generation features.
Business Objective
The 1st generation experience highlighted the security value of using VCNs to customers. However, customers were not using VCNs as much as leadership expected for their digital spending. We needed to find a value proposition that resonated more with customer needs.
UX Research Objective
I had to understand Capital One customers’ digital spending experience to identify needs and pain points and recommend the biggest opportunity for VCN features to address these. I collaborated closely with my product and design partners throughout UX research so they could hear customer perspectives firsthand.
What is a virtual card number (VCN)? A temporary credit card (CC) number associated with a customer’s physical CC that can be used while shopping online. These disposable card numbers are designed to protect the customer’s actual CC number from falling into the wrong hands.
Research Planning
I created an initial research roadmap to set expectations with stakeholders. These were the steps needed to get to the final deliverable of feature recommendations. After securing sign-off from leadership, I started phase 1 of generative research.
In this case study, I will go over Phase 1 and the impacts of the research on product strategy.
Generative Research: Phase 1
Understanding digital spend behaviors
Research Approach
Research Objectives:
Understand customer needs and pain points in their spend management journey
Gather customer feedback on prior feature use case concepts
Methodology: Semi-structured interviews + lightweight concept testing
Platform: DScout
Participants: I recruited customers who had used VCNs before, and those who have not. Additionally, I recruited participants with diversity of age, gender, ethnicity, and income to ensure our research was conducted equitably.
Research Synthesis
As this project took place during the pandemic, our note-taking and synthesis took place online. I created an interview note-taking board on Mural. My design partner and I took turns moderating video interviews and taking notes. Our product manager partner joined to take notes on several of the interviews.
After interviews were complete, I created a Mural synthesis board and facilitated affinitization over Zoom.
Figure 2: Screenshots of the Mural note-taking and affinitization boards.
Research Findings
I presented the findings to product, design, and tech leadership. Of the findings, my team agreed that there was a big product opportunity within shared spending. This finding is detailed below:
Customers need to share their spend with others, but lack the control to share with peace of mind.
Customers would like the flexibility of sharing their credit card information, as long as they can be sure it won’t be misused. Customers share their spend with a variety of individuals - from high-trust (spouse) to low-trust (babysitter) - and need ways to control how much and where others can spend to be sure their money is being used appropriately.
For additional findings and learnings, please contact allison.h.ngo@gmail.com
Generative Research: Phase 2 & 3
Unpacking shared spending & testing feature concepts
I ran a diary study next to understand how customers shared money with their personal network on a daily basis. In the third and last phase of research, I ran prototype testing of initial feature concepts based on the findings of our of research. This gave us a solid footing on feature recommendations. A visual representation of the research roadmap can be found below.
Figure 3: Full strategic research roadmap, detailing phases and objective of each.
Due to confidentiality of the feature concept screens, I cannot share details on phase 3 of the research. For additional information on the diary study, please reach out via email.
Impact
Final feature recommendations
As a result of our research, my team created a finalized list of VCN features for implementation. We provided a series of artifacts to leadership to qualify our prioritization and reference for future product strategy conversations.
Feature 2x2 Matrix
I created a 2x2 matrix to evaluate all product feature ideas from prototype testing. We found the most opportunity in the bottom left quadrant and highlighted sample customer scenarios for the top 5 feature concepts.
Feature Ranking
We ranked our top 5 feature recommendations. I quantified customer value based on findings from our research. My product partner worked with his tech team and legal partners to quantify the other factors. This enabled leadership to quickly evaluate and approve features for implementation.
Research Strategy Artifact
I created a persistent UXR artifact for stakeholders and future team members to easily understand the core customer need that VCNs are positioned to address. This proved to be useful as my product manager partner and product VP left the team shortly after the conclusion of this project.
What I Learned:
Timeline Expectations: When starting this project, my product partners were requesting an aggressive timeline to complete research. I initially agreed, but found that the timeline was not sufficient. I presented a case to leadership to extend our timelines by a couple of months to ensure we could deliver product recommendations by the end of our project. In the future, I would like to set timeline expectations ahead of time as the researcher conducting the work. I will need to influence stakeholders to understand that research may require multiple phases to translate customer needs into recommendations that inform business strategy.