Finance quote accuracy
We regularly see that the closer a finance quote is to a customer's finance offer, the better that customer converts. Both these features were aimed at increasing the accuracy of the finance quote a customer took into their application.
Monthly price search filters
Assisting customers in shopping budget-first
Our research into cinch's target demographic showed us that ~79% shop budget-first. For those looking to pay monthly for their car, shopping in this way wasn't easy, as monthly prices within our search are only based on average finance examples.
Solution
Rather than monthly prices in search being based on average deposits, credit scores or term lengths we set out to allow customers to tailor these to their needs.
A number of factors impact a customer's monthly price, such as their deposit, chosen term length, estimated mileage, preferred finance type, and credit score.
We set out to allow customers to search based on personalising each one of those variables.
Design and delivery
Our first designs took just finance type and term length, allowing customers to select their preferences between the two within our search filters. We A/B tested these and found them to have a positive impact both on onward progression, and car selection rate.
Allowing customers to select their deposit and credit preferences led us to consider a standalone tool, rather than these be listed as independent filters.
Lots of testing and iterations took place on the presentation and content within this flow, to ensure customers knew the impact of each choice they were making when they opened the tool.
Outcomes
Progression from search > product page, and onwards into checkout is an important metric by which all features are measured, and we consistently see that customers who interact with our pricing filters are more likely to progress.
We also monitor much longer-tail metrics, such as what % of customers who interact with price filters go on to get a finance offer which is close to their estimated quote.
We consistently see that customers who interact with our pricing filters go onto receive offers which are much closer to their quote than customers who do not. These customers are therefore far more likely to take up these offers and purchase the car.
Finance eligibility checker
Offering customers pre-approved finance rates
An opportunity arose to rebuild our finance eligibility (soft search) tool. However, the soft search tool only showed a rough score, and didn't do much to reassure customers ahead of applying.
Solution
To encourage more customers to apply, we built much more reassurance into the soft search results, including a new pre-approval feature for customers with the highest credit scores.
Design and delivery
Lots of technical discovery was required with third-party providers - we needed to understand what scores would come back, and what APR these would map to.
We tested these for customer comprehension and tone, making sure that each score we shared with customers would make sense. We tested and iterated on the results screen until we were confident that it was widely understood.
For customers who were pre-approved, we additionally included a "pre-approved" tag that was visible to them throughout their application. This, we believe, gives confidence and reassurance to customers within the finance journey.
Outcomes
Customers who use soft search are ~20% more likely to go on to apply for finance. Customers who are pre-approved see very little variance in the monthly payments they are offered, and are therefore highly likely to both apply and convert.
