cinch
Gave car buyers financial confidence
A large percentage of cinch customers were declined or rejected amended finance offers. To address this, I designed a soft credit check tool that provides early eligibility insights and personalised quotes, helping customers make more informed car finance decisions.
Key results
Increased acceptance rate by 6.9% and take-up rate by 15.4%.
Extrapolated to extra £8m revenue per year.
My role & timeline
Led product design
Worked with Product Manager, Data Analyst, Content Designer and Engineering team.
February 2022 - June 2022
The challenge
Previously, cinch customers could only determine their finance eligibility by submitting an application and consenting to up to three hard credit searches.
A high proportion of applications were declined, while many medium-risk customers received unsatisfactory offers they chose not to accept, leading to customer experience issues and lost sales.
Getting to the root of the problem
With ~8,500 monthly applications, the 90% rejection rate for amended offers highlights a major issue. I recognised that addressing this could significantly boost conversion rates and customer satisfaction by better aligning offers with customer needs.
To understand why car buyers were dissatisfied with their offers, I conducted a survey at the point of offer, revealing that 50% were unhappy due to higher-than-expected costs.
Testing the hypothesis
Our hypothesis: “50% of customers unhappy with their offers cited high APR or monthly payments. We believe that enabling customers to understand their finance eligibility and credit costs upfront will increase acceptance and take-up rates.”
To test this, I proposed a low-effort experiment, introducing an estimated credit score field in the calculator using data from previous finance customers. This provided users with more accurate finance quotes.
The A/B test results showed a 7.6% increase in take-up rates and a significant 78% increase in finance order conversions, driven by improved customer confidence in the process.
Iterative approach
Using the result from the A/B test, I decided on a soft credit check tool to enhance the accuracy of quotes and further boost customer confidence. Throughout the design process, I explored various solutions and have highlighted some key decisions below.
Entry point
I tested several placement options, including the car finance landing page, search results, and car detail page.
On the car finance landing and search results pages, users experienced friction when asked to provide personal information early in their journey.
The product detail page performed best, as it was closer to the purchase decision and tied to a specific car, which increased user confidence in the accuracy of the quotes.
Drawer vs. full screen
Initially, I designed the tool using a drawer component, but usability issues arose due to the multi-step information capture process and users frequently “pogo-sticking” between the product detail page and the drawer.
Switching to a full-screen version resolved these issues, reducing distractions and helping users stay focused on completing the required tasks.
Data visualisation
In the first iteration, users lacked context for the score and questioned its accuracy.
The second iteration performed better, but testing with diverse customer groups revealed that the use of colours influenced perceptions—e.g., yellow was perceived as negative, even when the chance of acceptance was high.
This insight informed the final iteration, where I adopted neutral colours to ensure clarity and reduce bias.
Final prototype and testing
I conducted three usability tests and a preference test with support from the research team. The testing focused on different customer profiles, considering their credit scores and understanding of finance, to ensure the solution met diverse user needs.
Responsive designs
Over 70% of users accessed cinch on their phones during the car search process, so I prioritised optimising the mobile experience.
After completing mobile testing, I developed fully responsive designs for tablet and desktop to ensure a seamless experience across all devices.
The outcome
Quantitative
Increased acceptance rate by 6.9%.
Increased take-up rate by 15.4%
Extrapolated to extra £8m revenue per year.
Qualitative
Increased customer confidence whilst applying for car finance.
