Swoop Funding
A rebuild of the onboarding journey to respond to user intent and context. Offering user control, shortening paths, reducing errors, and guiding to tailored outcomes utilising AI-support.
Client
Swoop Funding
Role
Lead Product Designer
The Solution
20 Steps down to 5
An onboarding journey that learns what users need, removes unnecessary steps, and guides you to the best possible outcome.
Prototype of the updated onboarding journey's for a business owner looking for funding or seeking insights on their business and their savings opportunities.
Forgetful onboarding
Onboarding is often underestimated, yet it’s a user’s first real interaction with a product and the point of highest drop-off. The goal is simple: make it disappear. Great UX is intuitive enough not to be noticed, with just enough delight to leave a positive, lasting impression. The challenge is that no one wants to “do onboarding”, they just want to get where they came for, fast.
"It's less steps than what I would have expected - it's really quick" - Business owner
Winning Approach
By following UX best practices and listening to the users who mentioned they struggled with understanding the language and breath of funding. The change needed to go from "What funding do you want" to "What are you trying to achieve". This allowed the user to drive the journey, feel heard and incorporate machine learning to provide the most suitable personal results.
Key Features and business impact
Smarter Handling of Low-Quality Leads:
With the updated onboarding we could check if they're a business that will be eligible for funding straight away or not, or further data was needed.
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LQ leads are provided with solutions instead of being forced into an application to inevitably be denied.
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Users feel informed, not pushed.
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Fewer applications, higher success rates: Reduce noise of funding managers who previously had to handle all cases that came through.
AI Funding assistant:
User reassured through out with access to support, feedback on business instantly and returning users have a family "face" and tone to return to on their next entry.
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Pulling data from companies house and credit safe for registered companies to generate a company overview and Swoop score.
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Take on user feedback that it can be overwhelming to navigate the platform: Reduce noise and simply have the user direct the journey through conversation.
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Opportunity to update the design system to suit an AI focused platform.
Micro interactions and accessibility
Rebuilding onboarding open an opportunity to rebuild the design system foundations for Swoop and to test for passing accessibility marks. For a successful UX experience we need to consider the micro interactions such as page focus position, keyboard layout and style on mobile.
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Clear area of focus and next action with input focus and CTA hierarchy.
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Saves steps by having the keyboard ready and a sentence started for them to quickly finish and move on.
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Check for accessibility in colour, spacing, padding and copy.
The Challenge
Turn a long confusing onboarding into a seamless journey with high quality leads
The original onboarding forced every user into a funding application flow, regardless of:
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Their intent
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Eligibility
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Readiness
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Understanding of funding types
Key problems:
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Users didn’t understand funding categories and often chose the wrong path.
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Low-quality (LQ) leads clogged the system.
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Users looking for insights or savings hit unnecessary friction.
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Onboarding optimised for data capture, not user confidence or outcomes.
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The business needed better matching, not more applications.
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Designs not following UX best practice or following the design system.
The How
Redesigning onboarding as a system, not a 'form'. Balancing user intent with the operational needs of funding managers and lenders.
Discovery
Usability testing with business owners across multiple regions to understand differences in data availability, expectations, and tolerance for effort, while keeping the experience consistent.
Designed for outcomes, not questions
Mapped end-to-end funding journeys by working backwards from what funding managers and lenders actually need to make decisions. This ensured we still captured high-quality matching data, without asking for it all upfront.
AI-assisted matching
Removed manual funding-type selection, which users often misunderstood. Instead, we infer the true funding need and match users to the best solutions using machine learning—reducing misrouting and increasing success rates.
Expanded product value beyond funding
Opened the platform to users not yet ready for funding by surfacing business insights and cost-saving opportunities; creating value earlier and expanding long-term engagement.
