Increasing efficiency and effectiveness of marketing spend in Insurance

The problem:

Direct Line insurance is one of the largest personal line insurers in the UK. The insurance industry was disrupted a few years ago with the introduction of Price Comparison Websites – and their large marketing budgets – which drove huge amounts of traffic to the sites. They have become the leading destination to buy insurance for your car and home.

Direct Line is known for its comprehensive cover. They knew that if their price point was compared with other providers, without taking into account quality or level of cover, they would lose out. Also, their brand mantra from back in the 80’s when they first launched was to ‘cut out the middleman,’ passing on the commission savings to their customers. Being on a price comparison site, which essentially functioned as the commission-taking middleman, seemed to act against the very ethos of the brand.

Direct Line had a problem. If they were not going to go on price comparison websites, such as comparethemarket.com, they needed to find a way to reach new customers without breaking the bank and wasting marketing spend on people who would never convert, and most likely just use a price comparison website anyway.

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Using customer data:

They held a lot of customer data, from being a large insurance provider for over 25 years, and decided to use customer analytics to best assess who and how they should target. They developed multiple predictive models to identify who of their existing customers were the most valuable, what were the characteristics of these most valued customers that they could look for to attract new customers, and who within their customer base would be responsive to cross sell and upsell tactics.

Not only did they look at their most valuable customers, but they also looked at their least valuable customers – to identify who not to target, and even who was losing them money.

All this information was used in a variety of ways:

– Developed predictive modeling to assess who they could directly target in one to one communication – such as direct mail and email. This was used for both acquisition of new customers and for cross sell and upsell activity for existing customers. As it was a personal interaction, direct responses could be monitored and this information constantly fed back into the model so the model learned and developed with each campaign. Each campaign relied on personalization to improve responsiveness.

– Created look-a-like profiles that highlighted which characteristic their ideal customers over-indexed on. These profiles were then used to purchase data, and for online advertising and retargeting.

– Target segmentation groups were developed and to used and identify wider audiences in broader media such as banner displays and TV. Equally, segment groups were identified that looked as though they were price sensitive and were most likely to purchase via a price comparison website. These groups were actively avoided.

– Messaging hierarchy was developed used A/B tests to see which messages resonated the best with a corresponding target segment. The winner was rolled out and then a small volume/ group was constantly challenged at each campaign to see if it beat the reigning champion. This ensured the marketing team was constantly learning and the messages and even the operating model to which the insurance was delivered, and the features it included, best suited their exact customer needs.

Result: 

The result was a more effective and efficient marketing process – even changing the companies operating model to be customer centric. From a value capture perspective Direct Line’s customers were better suited to their brand, and more valuable, staying with the brand for more years – and acquisition costs declined, improving customer metrics across the department. From a value creation perspective, from the messaging test learnings, Direct Line developed propositions and better products to best suit their customer need and differentiate themselves from competitors.