How I designed and optimised Bionic’s business insurance onboarding flow to improve lead quality, reduce customer frustration, and provide better call experiences.
Introduction
Role: UX Designer
Timeline: 9 months
Tools: GA, Usertesting.com, Hotjar, Miro, Figma
Bionic needed its own insurance customer flow to generate high-quality leads and reduce time wasted on unqualified customers. I designed a user flow that efficiently qualified and routed leads while ensuring regulatory compliance - collaborating closely with stakeholders across compliance, product, engineering, and sales.
Using GA funnels, Hotjar dashboards, and usability testing, I iterated on the flow - introducing expectation-setting changes that reduced friction and improved engagement.
INDUSTRY
B2B, Insurance (Regulated)
KEY CHALLENGES
Lead qualifications, Multiple branching requirements, User frustration with call requirement
STAKEHOLDERS
Compliance, Sales Agents, Product, Marketing, Engineering
METHODS USED
Competitor analysis, GA funnels, Hotjar dashboards, User testing
RESULTS
Improved lead quality, Reduced friction points, Introduced user features
Understanding the Brief
Business Challenges
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Bionic relied on partner-generated leads, which were often low quality and required agents to spend time disqualifying users.
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No internal customer flow existed - Bionic needed its own lead generation system to provide higher-quality leads and better user experience.
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A new onboarding flow was needed to both qualify/disqualify leads and route them to the right sales journey.
UX Challenges
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The flow had to be compliant with strict industry regulations, ensuring customers were not misled.
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Users needed to be qualified or disqualified quickly while also providing the right level of data for agents.
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There was no clear expectation setting - users assumed they would get an online quote but were required to have a mandatory call.
The Process
Discover
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Understanding the Problem
Initial Discovery: Establishing the Right Data for Lead Qualification
To achieve this, I used a mix of qualitative research methods:
Competitor Benchmarking – Analysed how industry leaders handled lead qualification.
Agent Shadowing & Call Listening
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Observed agents handling partner-generated leads to understand their decision-making process.
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Documented how agents routed leads to brokers
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Identified which qualification steps could (and couldn’t) be automated.
Agent Workshops – Ran short collaborative sessions to:
- Pinpoint common reasons why partner-generated leads were lower quality.
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Validate the essential data points needed for a high-quality lead.
Before designing the flow, I conducted a deep discovery phase to understand:
How competitors structured their insurance onboarding flows.
How agents manually branched leads on the sales floor and what challenges existed in automating this process
Where existing partner-generated leads were failing.
Define
Defining a Solution
Defining the Scope of the Onboarding Flow
Through research and collaboration with agents, product managers, and compliance, I crystalised the scope of the onboarding flow, ensuring it was:
Focused on core objectives:
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Qualifies leads effectively - ensuring only relevant customers proceed.
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Branches users appropriately - directing them to the right next step.
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Builds user trust - by setting clear expectations upfront.
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Creates better-quality leads for agents - reducing time spent on disqualifications.
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Minimises double keying for agents - ensuring collected data is reusable.
Avoiding unnecessary complexity:
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Not an underwriting journey (that happens later in the process).
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No trade-specific questions beyond initial qualification.
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Not handling renewals.
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Not closing sales, taking payments, or issuing policies.
By defining these boundaries early, I ensured the flow remained user-friendly, aligned with business goals, and scalable for future needs. This clarity also streamlined cross-team collaboration, helping product and engineering teams focus on building the right functionality.
DELIVER
Designing the Onboarding Flow
Phase 1: Initial Flow Design
With the scope and the data requirements gathered, I delivered a high-fidelity flow, using the establish design system and design patterns
One-question-per-screen format (following company design standards).
Icons, supporting text & error states to reduce user confusion
Disqualification screen with alternative next steps (redirect to homepage or other products).
MVP Flow
Phase 2: Iterating Based on Analytics & User Research
REFINE
Learn, Refine & Iterate
After Phase 1, I conducted data analysis and user research to identify friction points and opportunities for improvement. This led to targeted design changes in Phase 2, improving user experience, lead quality, and agent efficiency.
2: User Research Insights – Frustration With the Call Requirement
Key Insight
The lack of transparency and control over the call led to user drop-off and dissatisfaction - even among users who would have ultimately engaged with an agent.
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Through post-release user interviews, a consistent frustration emerged - users disliked being forced into an immediate call.
Users with experience using insurance brokers were less surprised by the call but still preferred scheduling options.
3: Iterating in Phase 2 – Design Changes Based on Insights
Set Expectations Earlier → Revised Landing Page copy & CTAs
Reordered Questions to qualify/disqualify sooner
Gave Users Control Over Call Timing → Call Scheduling Feature
Improved end screen → UI redesign
Customised Flow by trade groupings
Improved Progress Visibility → Progress Bar
1: Identifying Issues Through Analytics & User Behavior
Where Users Abandoned the Flow (GA Funnels)
Step 1 (Business Name) → Highest drop-off rate.
Final Screen (Call Confirmation) → Major drop-off
Users expected an instant quote, not an immediate call.
Business Type Screen → High Drop-off
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Users hesitated when selecting between mobile, home-based, or premises-based options.
Trade Screen → High drop-off
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Users struggled to find their trade from a long list, causing frustration.
What Hotjar Revealed (User Behavior Insights)
Users struggled with trade search (confusion when searching for their business type).
On the final screen, users clicked on non-clickable UI elements (false affordances), indicating a misalignment between UI and expectations.
UI Friction - Non-clickable causing confusion
4: Measuring Impact - What Changed in Phase 2?
Landing Page CTA Change:
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Users still overwhelmingly chose the main "Start Online Quote" CTA, showing that framing the call differently didn’t drive them away.
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Choice improved user trust while maintaining business goals.
Call Scheduling Impact:
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Call-Answer rate reached 70% with agents confirming that those who scheduled a call showed higher intent and had higher conversation rates
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60% chose ASAP call (validating that forcing a call wasn’t the issue - lack of control was).
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40% scheduled a call → Higher intent leads that agents could prioritize, improving conversion rates.
Reduction in Drop-offs:
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Trade search improvements + progress bar improved Step 1 completion rate.
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Personal data consolidation + browser autofill reduced form fatigue
Retrospective & Next Steps
What went well
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Combining quant & qual data (GA + Hotjar + user testing) led to clear problem identification & targeted improvements.
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Small UI/UX tweaks (LP CTA changes, call scheduling options) had a substantial impact on user perception & experience.
Challenges & Areas for Improvement
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Further A/B Testing: Explore alternative CTAs & messaging strategies to improve engagement.
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Trade Search UX: Improve search experience with better semantic matching.
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Progress Bar: Deliver an updated progress bar from a continuous progression to a step progression
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