Setting the Right Expectations

Redesigning the 'Expectations Setting' page to minimize account opening drop-off

[ Company ]
Capital One
[ Role ]
Design Lead
Research Lead
[ Partners ]
1x Product Owner
[ Timeframe ]
2024 — Present

Overview

Through a data-driven, research-heavy process, I designed and delivered a new 'Expectations Setting' page to address a concerning level of user drop-off at the start of Capital One's account opening funnel.

Fig 1.1 —

Preview

Fig 1.2 —

Executive Summary

Of all pages within the account opening application, the highest drop-off rate (~33%) was on the 'Expectations Settings' page.

Jump to
Problem

In a large research initiative, I uncovered the exact expectations users found important to know for account opening.

Jump to
Research

The insights from research informed a redesign of the 'Expectations Settings' page that reduced drop-off by delivering the right information.

Jump to
Design

Context

When users apply for a Capital One bank account online, the 'Expectations Setting' page is their first point of contact.

About the Page

New applicants can feel anxious about the process of opening a bank account, unsure of what to expect or what's required of them. The 'Expectations Setting' page is intended to serve as a soft transition into the application for users, helping them understand the process and feel more comfortable before proceeding.

Fig 2.1 —

Current State Design

The design of the 'Expectations Settings' page has largely remained unchanged since the launch of Capital One's 'Flashcard' account opening application in 2018.

Fig 2.2 —

Account Opening Flow

The 'Expectations Setting' page is the first page of the 'Flashcard' account opening application.

Problem

The 'Expectations Setting' page was experiencing unusually high drop-off rates.

The Drop-Off Mystery

We noticed unexpected behavior on the 'Expectations Setting' page: many users were abandoning the application at this first step, despite already having expressed interest in the account.

Fig 3.1 —

User Journey

User explores the Capital One website to shop for a new account.

User clicks the 'Open Account' button on an account of interest.

User lands on the 'Expectations Settings' page, one of the first pages in the application, and drops off without opening the account.

    Business Impacts

    The drop-off on the 'Expectations Setting' page accounts for over a third of the total drop-off in the account opening flow, resulting in an annual loss of 400,000 potential customers from this one page alone. This is especially concerning, as each lost customer represents not only an immediate missed opportunity but also a significant negative impact on downstream business metrics.

    Fig 3.2 —

    Application Drop-Off Waterfall

    The 'Expectations Settings' page had the highest drop-off rate of all pages in the application. While a baseline level of drop-off is natural on the first page of any experience, an internal analysis revealed that more than half of this drop-off could be mitigated through experience improvements.

    An Early Signal

    Prior research suggested that the content of the 'Expectations Setting' page did not resonate with users, potentially driving the high drop-off rate. In order to validate this, a deeper investigation was needed.

    Fig 3.3 —

    User Feedback

    In a prior study, users expressed confusion and disatisfaction with the contents of the 'Expectations Setting' page.

    Research

    A large card sort study with 120 participants revealed user preference for clear, concise, and immediately relevant expectations content.

    Research Method

    To evaluate the content on the 'Expectations Setting' page, we conducted a card sort study. By asking participants to sort different expectations content by its importance, we could better understand what matters most to users and use that insight to redesign the 'Expectations Setting' page.

    Fig 4.1 —

    Card Sorting

    In a card sort, participants organize information/cards into categories that make the most sense to them, revealing their underlying mental models. This data-driven approach allows us to understand how users naturally process pure text-based information without influence from visuals or a UI.

    Segmentation Strategy

    We recruited 120 participants of diverse ages and financial backgrounds to ensure our findings captured a wide range of user needs. These participants were split into groups to test two key hypotheses we had about how certain user characteristics could influence what information they prioritized.

    Fig 4.2 —

    Hypotheses

    Customer Type

    We believe users' prior experience with Capital One ( New to Capital One, Existing Credit Card Customer, Existing Bank Account Customer) affects their familiarity with the bank's processes, and therefore what information they find important.

    Account Type

    We believe users have different mental models for different account types ( checking account, savings account), and therefore prioritize different information depending on the account being opened.

    Fig 4.3 —

    Participant Groups

    Segmenting our 120 participants by account type (2 variables) and customer type (3 variables), resulted in 6 groups of 20.

    Generating Relevant Cards

    To cover our blindspots, it was important for us to evaluate not only the expectations content we already had, but a comprehensive range of other expectations content as well. By conducting a competitive analysis of other 'Expectations Setting' pages, we were able to generate a list of 22 cards for participants to sort.

    Fig 4.4 —

    Card Generation Procedure

    First, we extracted the content competitors were providing to their users as expectations for account opening.

    This was done for range of competitive experiences at traditional banks, financial tech companies, and internally.

    Next, by grouping together related content, we identified themes in what expectations were being set across the industry.

    Finally, we distilled each theme down to carefully-worded representative statements. These statements serve as the cards for the card sort.

      Fig 4.5 —

      Final List of Cards

      The final 22 cards cover the range of possible expectations content we could put on a redesigned version of our 'Expectations Setting' page.

      Study Setup

      Generally, users apply for an account after exploring a 'Product Details' page on our website. To strengthen the validity of our results, we designed our study to closely replicate this user journey, allowing participants to mirror the head space of real customers.

      Fig 4.6 —

      Participant Procedure

      Explore

      Participants first explored a 'Product Details' page for their assigned account type. This gave participants foundational context about the account.

      Sort

      Participants then were tasked to categorize the subjective value of each card into four groups ( Critical, Important, Supplementary, Irrelevant). As they sorted, they also shared their thought process aloud, allowing us to capture qualitative insights alongside the quantitative sorting data.

      Key Findings

      The card sort results revealed clear patterns in user preferences, offering insight into what types of expectations content were most and least valuable:

      Fig 4.7 —

      Card Sort Results

      • Users consistently identified security-related information as critical for reassurance about safety.
      • Information that helped users feel prepared, such as required information or documents, was also seen as crucial.
      • Participants valued transparency into the application process, including key steps and estimated timeframes.
      • The recap of the account's benefits, even if repeated from the product page, served as a useful reminder and increased confidence.
      • Information about niche features, such as joint account setup and paperless statements, were seen as supplementary and appreciated, but not immediately relevant to applying for an account.
      • Participants were skeptical of vague claims like 'the best X account we offer,' as these statements felt promotional and lacked substance.
      • Information that felt obvious or well-known led to mixed responses—some participants found reiteration helpful, while others dismissed it as redundant.

      Revisiting Our Hypotheses

      Reflecting on our participant segmentation, we found very few meaningful differences between the segments, suggesting limited support for our initial hypotheses. This finding implies that a one-size-fits-all approach may be more suitable than initially expected, reducing the need for highly tailored expectations based on segment differences.

      Fig 4.8 —

      Hypotheses

      Customer Type

      Rejected

      The data did not indicate that users of different customer types ( New to Capital One, Existing Credit Card Customer, Existing Bank Account Customer) prioritized expectations differently.

      Account Type

      Rejected

      The data did not indicate that users of different account types ( checking account, savings account) prioritized expectations differently.

      Design

      Guided by our research results, we redesigned the 'Expectation Setting' page with a focus on the information users found most valuable.

      Redesign Framework

      We laid a foundation for the redesign by translating our card sort findings into 4 actionable recommendations.

      Fig 5.1 —

      Design Recommendations

      Display critical information prominently.

      Highlight security-related information, and information about what to have prepared.

      Maintain visibility of important information.

      Clarify the steps involved in the application process. Reiterate key product benefits.

      Reconsider the placement of supplementary information.

      Move helpful, but niche details to later stages in the application flow to reduce distractions during the initial steps.

      Remove irrelevant and obvious information.

      Eliminate unhelpful content to reduce cognitive load and maintain focus on more important information.

      Fig 5.2 —

      Card Mapping

      Each card can be mapped to a design recommendation based on the category it was sorted into.

      Destination State

      We envisioned a long-term destination state for the 'Expectations Setting' page that is unconstrained by the limitations of the existing application design.

      Fig 5.3 —

      Destination State Design

      The look and feel of this design aligns with the modern visual language of Capital One's planned brand refresh.

      Fig 5.4 —

      Highlights

      Optimized Page Layout

      We reclaimed dead space, allowing us to fit all the necessary information without it feeling cluttered or overwhelming.

      Prominent 'Critical' Content

      We placed all critical information in the main action container to ensure it is highly visible.

      Persistent Progress Tracking

      We introduced a persistent side panel with a progress bar to help users navigate the process.

      Situationally Relevant FAQs

      We moved situationally relevant content to FAQs, making it accessible when needed without distracting from the main content.

      MVP State

      To bridge the gap to the long-term destination state, we developed an MVP to address immediate needs while preparing for the larger redesign.

      Fig 5.5 —

      MVP Design

      We scaled back the destination state to create an MVP that fits within the existing application design and tech constraints.

      Fig 5.6 —

      Highlights

      Retains Existing Layout

      To minimize the development scope, we needed to work within the smaller footprint of the existing application layout, requiring careful prioritization of what content to include.

      Space-Saving Design

      We collapsed the product recap into an accordion, allowing us to retain that piece of 'important' information while minimizing its footprint to fit within the limited space.

      Impact

      As of writing, this project is still ongoing. At scale, we expect to make a sizable reduction in the addressable drop-off.

      Implementation Plan

      We are taking a parallel approach to release, with two main tracks running simultaneously.

      Fig 6.1 —

      Release Tracks

      MVP

      Rapidly develop the MVP in order to start making an impact to customers. Conduct an A/B test in production and monitor performance closely to make data-driven adjustments.

      Destination State

      Simultaneously, conduct user testing on the destination state, iterating based on feedback. Prepare for a synchronized launch with the larger Capital One brand refresh for a cohesive user experience across all products.

      Expected Impact

      The redesign is forecasted to reduce the relative drop-off rate of the 'Expectations Setting' page by 6 to 14 percentage points, yielding a $2.25M to $5.25M incremental present value (PV) per month.

      Fig 6.2 —

      Drop-Off vs New Accounts Present Value (PV)

      The drop-off improvement and business impact of this initiative was calculated for three scenarios: conservative, moderate, and aggressive.