In all 3 markets (UK, South Africa and India) we had proved our ability to acquire users with the “free forever” proposition on credit reports, even in markets where there was existing competition.
Monetisation (recommending loans and credit cards based on your score) had been the biggest challenge when entering a new market outside of our core UK business. Therefore, we’d need to be certain Australia had good potential from day one. From UK data and insights we already knew what has worked in this market and we were familiar with the problems to overcome from our experience with ZA & IN.
In addition to a speedy launch and the scaling of our user base, our main objective was to be NPV ( Net Present Value) positive. Results to target; sign up users at £5 CPA (Cost Per Acquisition), more than £1 average revenue per user in first 30 days and for users that have been using the product over 30 days, at least 30% return to the site.
Tim Chong (Product Owner) and I started with competitor and market analysis. Thankfully, with Tim being an Aussie, he was able to sign up to products that require users to have a financial presence there. We were also lucky to gain many useful insights from our Australian colleagues.
With the application process being lengthy for an ACL (Australian Credit Licence), we wouldn’t have one for launch. This would limit how we recommend products, for example, we would not be able to provide any sorting option or apply our own logic in recommendations as we do in the UK. From our competitor analysis, we learned that unlike our other markets, in Australia you can sign up without multi-factor authentication. If we did the same we’d need to monitor new users constantly to mitigate fraud.
The best part of this project came next; spending almost 3 weeks in Sydney! We flew the whole project team out with the intention of learning as much as possible to develop one of the more complicated parts on the site: registration.
Focussing on registration, we had meetings with Experian (credit bureau) and greenID (user verification) to talk about technical implementation as well as gather insights into the best way to optimise.
For the first week, I led daily user testing sessions. These involved an interview/questions, card sorting exercises and a run-through of a prototype.
With the insights from the testing sessions, I’d update the design/prototype for the next days testing. Each day, with collaboration from the dev team, our work became more real. The first day it was a Sketch prototype. The second day I added some timing and interaction animations in Invision Studio. The third day Jonny (front-end dev) coded the registration in a form and by the end of the week Andrew (back-end dev) had linked up the partner APIs returning actual data.
We met with offer partners to help deliver on our monetisation strategy. Upon returning to the UK, I designed the flow of our offers page based on these insights to help maximise conversion and help our users get the right types of information they needed.
The pace of delivery on the project was helped massively by our design system. I reused our components and contributed back to the system with new ones when I found a gap.
We have 5 score bands (6 when markets that we support have ’no credit file’ users) and each gets a specific photo background on the dashboard. After I sourced these photos I needed to create assets for our devs to use. These were required not only for web but also iOS and Android, each with their own specific image sizes and formats.
I created an asset library for these backgrounds so that when we uploaded new ones in other markets it would be a lot simpler than our current process. Designers could also use this library when mocking up screens for the dashboard.
Post-launch we heavily monitored the site and app, making interations and improvements where we could. We had an amazing data scientist in the team, Sofie, who made analysing all of this quant data very simple, with understandable dashboard widgets and daily alerts and updates.
A really strong localised sign up journey paid off. Putting in the extra attention to detail to ensure users know what information to give and how to input their identity information correctly really helped with conversion. In fact, we saw a 90% success rate for identity verification – this is at the very top achieved by Australian companies and will save ClearScore £200k in identity verification costs in 2020 alone.