H&R Block | Understanding Gig Worker
Onboarding Friction
A research-informed investigation into user identification, tax terminology, and onboarding navigation.
Project Snapshot
Company: H&R Block
Industry: Tax Technology / Financial Services
Project: Gig Worker Identification & Onboarding Experience
Role: Senior UX Designer — Research-Informed Product Design
Duration: 6 Weeks
Collaborators: Product Management, Content Strategy, Legal/Compliance, Engineering
Research Approach: Existing User Feedback Review, Workflow Analysis, Secondary Research, Information Architecture Evaluation, and Prototype Reviews
Project Contribution: Developed a revised onboarding experience using clearer self-identification options, contextual tax education, and targeted routing logic to address documented usability concerns.
Confidentiality: Project information and visuals have been generalized where appropriate to respect confidentiality.
Project at a glance
6
Weeks
Project duration
3
Key user moments
Identification, education, progression
4
Project restraints
Flow, technology, accessibility, compliance
These are documented project facts and framework counts—not claimed improvements in product performance.
Research Overview
Understanding why gig workers struggle to identify the right tax-filing experience
H&R Block serves users with a wide variety of employment and income situations. Among them are independent contractors, rideshare drivers, delivery workers, online sellers, and individuals earning supplemental income through digital platforms.
A key challenge within the onboarding experience was how users identified their employment situation.
Although gig workers could be categorized under self-employment, the existing onboarding experience relied on terminology that might not reflect how users describe their work.
Existing user feedback revealed confusion around onboarding terminology and pathway selection, creating an opportunity to investigate how clearer language and more contextual guidance could improve the experience.
The Research Problem
How might we help users accurately identify their income activities and navigate to the appropriate tax-filing experience without increasing onboarding complexity?
The investigation centered on three areas:
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Terminology comprehension: Do users recognize themselves as gig workers or independent contractors?
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Information clarity: What tax information do users need to understand before proceeding?
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Navigation and routing: How can the experience guide users to relevant information without introducing unnecessary steps?
The objective was to identify practical improvements that balanced user comprehension, business needs, and regulatory constraints.
Research Questions & Evaluation Criteria
The investigation focused on understanding users' mental models, information needs, and potential barriers within the existing onboarding experience.
Research Questions
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How do gig workers describe their employment or income activities?
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Which onboarding labels might create uncertainty or incorrect pathway selections?
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Where should contextual education appear to support users without interrupting their progress?
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How can routing decisions accommodate different employment platforms and self-employment categories?
Proposed Evaluation Metrics
Pathway Selection Accuracy: Percentage of study participants who select the appropriate onboarding path.
Terminology Comprehension:Percentage of participants who correctly interpret employment categories and related guidance.
Task Completion Rate:Percentage of participants who complete the onboarding task successfully.
Task Completion Time: Time required to identify the appropriate path and continue through onboarding.
Observed Usability Issues: Frequency and severity of hesitation, misinterpretation, navigation errors, or requests for clarification.
These metrics provide a framework for future validation. Historical numerical results were not available for this portfolio retrospective.
Baseline Experience Analysis - Existing Onboarding Flow
Baseline Experience Analysis
The original onboarding experience provided a way for gig workers to select a self-employment pathway. However, the available categories did not always clearly communicate how different income-generating activities fit within the broader self-employment classification.
This created three potential usability challenges:
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Recognition: Users might not identify with formal employment terminology.
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Decision-making: Broad classifications could make it difficult to determine the appropriate pathway.
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Confidence: Limited contextual guidance could leave users uncertain about what information they needed next.
These observations informed the research direction and subsequent design exploration.
Research Methodology
A combination of existing user feedback, secondary research, and workflow analysis was used to understand the onboarding problem and identify opportunities for improvement.
Method 1: Existing User Feedback Review
Reviewed available feedback regarding confusing employment classifications and onboarding pathways.
Objective: Identify where terminology or interaction patterns might prevent users from recognizing the appropriate tax-filing option.
Method 2: Secondary Research
Examined industry information on gig work, including commonly used classifications, income sources, and tax-reporting considerations.
Objective: Understand the variety of user situations that the onboarding system needed to accommodate.
Method 3: Workflow & Information Architecture Analysis
Evaluated the existing onboarding flow to understand where users encountered employment categories, how decisions were presented, and how those choices influenced routing.
Objective: Identify opportunities to improve pathway recognition and introduce relevant guidance.
Method 4: Prototype Evaluation
Used interactive prototypes, stakeholder walkthroughs, and comprehension checks to evaluate proposed labels, messaging, and navigation patterns.
Objective: Assess whether proposed changes were understandable, feasible, and consistent with compliance requirements.
Insights About GigWorkers
Terminology influences recognition
Formal classification labels might not match the everyday vocabulary people use to describe their work.
Employment categories overlap
Gig work encompasses delivery, rideshare, online selling, freelance services, and other independent income activities.
Contextual guidance can support decisions
Short explanations at important decision points may help users understand requirements without adding a separate educational flow.
Informational Architecture
Translating Research into Design Decisions
The research and workflow analysis highlighted opportunities to improve recognition, clarity, and progression within the onboarding experience.
Decision 1: Use Familiar, Activity-Based Language
Rather than relying exclusively on the term "gig worker," the proposed experience introduced options associated with familiar activities, such as rideshare driving, delivery services, and online selling.
Rationale: Support recognition by connecting system terminology to users' everyday activities.
Decision 2: Introduce Contextual Education
Designed lightweight interstitial content to explain relevant tax considerations at appropriate points in the onboarding journey.
Rationale: Provide information close to the moment when users need it, reducing dependence on prior tax knowledge.
Decision 3: Preserve Onboarding Momentum
Maintained a streamlined flow with focused calls to action and minimal navigation detours.
Rationale: Balance useful guidance with the need to keep onboarding efficient.
Decision 4: Support Multiple Income Scenarios
Created reusable content patterns that could accommodate specific employer or platform information alongside more general guidance.
Rationale: Support scalability without requiring entirely separate onboarding experiences for every income category.
Core UX strategy
Identification (Trigger Moment)
Help users recognize their situation with plain-language options (e.g., “delivery apps,” “rideshare,” “selling online”) vs forcing “gig worker” terminology.
Confidence (Education moment)
Give just enough guidance on what to expect (forms, deductions, what to gather) to reduce anxiety and prevent mistakes—without turning onboarding into a reading assignment.
Momentum (Next-step moment)
Use clear, single-action CTAs that keep the flow moving and prevent drop-off.
We focused on 3 core user moments:
Research, Design & Implementation Constraints
Flow Efficiency
Avoid unnecessary pages or additional decision steps
Technical Feasibility
Reuse patterns to limit engineering complexity
Accessibility
Consider readability, hierarchy, focus behavior, and interaction clarity
Regulatory Compliance
Ensure tax-related guidance aligns with legal and content requirements
We were able to gather insights on what would be under the self employed umbrella.
Design Execution
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Low → high fidelity wireframes in Figma
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Interactive prototypes for trigger/interstitial screens
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Dynamic content module for top employers + fallback messaging
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Validation through stakeholder reviews + comprehension checks (iterate copy/labels/CTAs)
Solution
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A clean, single-page interstitial that routed users into the correct tax path
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Employer-specific content (e.g., DoorDash) and general fallback content
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Triggered by simple user input based on business activity
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Clear CTAs with minimal friction for continued onboarding
Research Application — Final Onboarding Experience
Research Application — Final Onboarding Experience
The proposed onboarding experience translated the identified usability concerns into clearer pathway selections, contextual educational content, and more focused navigation.
The screens below illustrate how research-informed recommendations were incorporated into the interface through trigger points, activity-based options, employer-specific guidance, and reusable interstitial components.
These design artifacts demonstrate the proposed solution and its implementation approach.
Evaluation & Project Outcomes
Evaluation & Project Outcomes
What the Project Delivered:
Clearer Identification Options
Developed a revised entry experience using more recognizable income activities to help users navigate self-employment categories.
Contextual Guidance
Introduced lightweight educational content addressing relevant tax forms, deductions, and platform-specific scenarios.
Reusable Interaction Patterns
Designed interstitial components and messaging patterns that could support multiple employment platforms while maintaining a consistent onboarding structure.
Cross-Functional Validation
Collaborated with Product, Content, Legal/Compliance, and Engineering to evaluate wording, interaction feasibility, and implementation constraints.
Validation Activities
The project included stakeholder walkthroughs, prototype reviews, comprehension checks, and design-system evaluation. These activities informed revisions to content, labels, calls to action, and interaction patterns.
Measurement Limitations
Historical task-level analytics and participant-level testing results were not available for this retrospective. Consequently, changes in completion rates, pathway accuracy, and abandonment cannot be quantified.
Recommended Follow-Up Research
A controlled usability study or A/B test could compare the original and revised onboarding experiences using pathway-selection accuracy, task completion, terminology comprehension, and abandonment metrics.
Research Reflections & Key Takeaways
User language matters. Classification systems may be accurate from a business perspective but confusing when they do not reflect how people understand their own activities.
Context matters as much as content. Delivering information at the appropriate decision point can be more useful than adding extensive instructions early in the experience.
Research and design must account for constraints. Usability improvements must work within accessibility, regulatory, technical, and business requirements.
Measurement should be planned early. Future iterations would benefit from defined baseline metrics, participant-level task data, and post-release analytics to establish whether the revised experience improved user outcomes.
What I’d do next
Proposed Follow-Up Studies
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A/B test trigger-point wording (“delivery apps” vs “gig work”) to reduce misclassification
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Track interstitial engagement vs drop-off to confirm education is helping, not slowing
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Expand employer coverage with a rules-based content model to scale personalization