Salary Finance Loan Calculator
Introduction
At Salary Finance, each Product tribe works toward a company OKR (Objectives and Key Results). In the Borrow tribe, our OKR is to increase our monthly originations to over £20m a month.
As a result of this metric, I undertook a project aimed at improving the user experience in the borrowing landscape, with the hope of increasing monthly loan originations.
This case study dives into the process of conducting a comprehensive discovery survey involving over 1,200 users, subsequently influencing a redesign of the loan calculator page.
Objective
The primary goal of the project was to gain a deeper understanding of our users' borrowing habits and behaviours, and use these insights to refine and optimise the Borrow product to meet our OKR.
Research and Discovery
Conducting the survey
To kickstart the project, a discovery survey was created and sent to over 1,200 users. The objective of the survey was to understand various aspects of borrowing behaviours, preferences, pain points, and expectations, ensuring a comprehensive understanding of our user base.
Image of how the survey was structured
Analysing the insights
The collected data was analysed, uncovering valuable patterns, trends, and user sentiments.
The survey analysis was shared across the business and utilised by various departments. For example, Commercial teams applied the insights in B2B discussions with clients, Credit Risk incorporated the analysis into their loan repricing process, and Marketing used it to refine brand marketing strategies.
Within the Product team, the survey served as the basis for making design decisions and adapting the product to meet the specific needs of the target audience. Furthermore, the survey findings significantly influenced the product roadmap.
Designs
Refining the Loan Calculator page
Some of the findings which influenced the product roadmap were that 14% of our customers opted for smaller loans due to fear of rejection and potential credit score damage. In contrast, 47.1% of users that took out a larger loan were influenced by the monthly repayment figure.
Given our existing quantitative data, we recognised the need to delve into qualitative insights to understand the underlying reasons behind these trends.
After speaking to users we discovered that the existing loan calculator page posed challenges. Users struggled to understand the benefits of opting for a salary-linked loan, faced difficulties in assessing loan costs due to a lack of hierarchy and high cognitive load. Additionally, users were deterred by the early-stage agreement indicating potential credit score impact if payments were missed.
Image of the previous loan calculator page.
Looking to the competition
Competitors structure their loan calculators differently. Their content is easy to scan, allowing users to quickly grasp the benefits of their product. They also organise complex information in a user-friendly way, using hierarchy and emphasis to highlight important elements on the page.
Some of the competitors screens used for design inspiration
Design changes
As a result, we hypothesised that redesigning the page by improving our value proposition and improving the hierarchy would increase the average loan size (helping us meet our OKR). However, we had to be careful that we were maintaining good customer outcomes as set out by the FCA’s Consumer Duty and not promoting or encouraging our users to over-borrow.
To do this, we made a few changes to the page:
Clearly explained the benefits of a Salary Finance loan.
Introduction of our Trustpilot rating to boost trust and utilise social proof.
Overhaul of page hierarchy, structure and general UI.
Restructured loan quote to emphasise the monthly repayment figure.
Added copy assuring users that proceeding to the next page wouldn't impact their credit score.
In addition, we made a deliberate choice to not add any copy or design elements which would encourage over-borrowing. We firmly believe that empowering users to make their own informed decisions is best.
Outcome
Since we already had qualitative information, we chose to implement the new loan calculator through A/B testing to gather quantitative data. This approach was apt for capturing subtle differences in behaviour due to the relatively minor changes made.
Following the implementation of the changes, we observed:
A 2.5% increase in click-to-submit applications, which equates to ~4,000 more applications per year.
An increase in the average loan size by around £200
Finally, we conducted a subsequent survey among users who had taken out a loan and interacted with the updated loan calculator page. The results showed a 4% decrease in customers opting for smaller loans than originally intended, compared to the previous survey.