£2.6m uplift for Ageas

Client: Ageas
Sector: Insurance
Website: Ageas.co.uk
Services: Conversion Services

Following an initial discovery and analysis of the Ageas quote and buy journey, we created a prioritised testing backlog that focused on increasing user conversion rate through to policy purchase. One of the leading concepts was improving brand awareness and showcasing the policy’s core benefits.

We worked with the Ageas’ design team and developed an A/B experiment that fulfilled the insight gained in the discovery phase to help increase conversion to sale. Here, we show how we achieved these strong results through A/B experimentation.

When partnering with Ageas, one of the key goals was to reignite the Conversion Rate Optimisation (CRO) programme and drive a 3% increase in conversion rate. The key strategic aims to achieve this were:

• Increase return
• Increase CRO maturity
• Increase innovation
• Increase velocity
• Increase visibility

To help achieve a +3% increase in conversion rate over the programme, we focussed initially on increasing return and increasing innovation.

To enable us to drive the targets set by Ageas, we ran an initial discovery to unearth insights across the journey. During this initial phase, the findings highlighted clear friction on the Premium page, where users land from a price comparison website (PCW). The insight showed a low conversion rate to purchasing the policy, with the next step conversion from the Premium page low. The data highlighted an opportunity to improve this metric and the 'quote to sale' conversion.

To help understand the why of the insight, our User Research team carried out moderated user research and found that brand recognition, quality, and reputation were issues, with all participants stating they did not recognise the Ageas brand.

We worked with the Ageas design team to design a mobile-first design on the Premium page that would improve the value proposition of Ageas car insurance and highlight key benefits of the core policy.

We then developed the design and then A/B tested it so that we could confirm with statistical significance whether the variation was better than the current version.