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Finding the needle in an online haystack – how machine learning helped Hiscox target some very specific customer needs

David Black, MD Branding & Consumer Markets, Google UK

Routines tend to be quite tricky to establish. But once they stick, they stick. My morning ritual has traditionally consisted of waking up, completing a spot of exercise before eating, showering and heading off to work.

It was only recently that I realised I had made an addition to this routine – I have developed a habit for logging into my banking app on my train journey to work.

Without even walking into my branch, I’m now engaging with my bank more than ever.

The digital era is changing banking and financial services brands irreversibly. This new interconnected landscape represents the next big evolution in banking as we move into an era in which people are able to enjoy real-time access and engage with banks and brands, wherever they are in the world.

Consequently, this new age has seen marketplaces, like financial services, grow increasingly competitive as more and more Fintech businesses emerge and enter the fray. With so many companies clamouring for consumer attention, financial services brands have to find ways of getting in front of the most interested customers – without breaking the bank.

But not all financial services companies have the same relationship with their customers as banks do. For insurance brands, finding a meaningful connection is a particular challenge. A combination of customer inertia and comparison sites make connecting directly problematic. Hiscox, which provides insurance for over 60,000 homes and tailored business cover for more than 150,000 small businesses (SMEs), professionals and consultants, had to overcome this issue. It needed to reach more of this target group and increase conversion rates, all while keeping its cost per action low.

“At Hiscox, we regularly see increases in competition across volume-driving head terms [popular keywords], which increases our average cost per click and cost per action,” explains Stuart Mahoney, Head of Acquisition Marketing at Hiscox.

The company enlisted the help of digital agency, Croud, international PPC and programmatic specialists, to navigate the paid search space. Together they used Dynamic Search Ads, Smart Lists and Smart Bidding – some of Google’s most powerful machine learning tools – to seek out new customers. “We’re exploring every opportunity for driving cheaper actions, from long-tail terms to ad copy and landing page testing,” Mahoney adds.

What do these tools do?

Dynamic Search Ads uses machine learning to pick up long tail [very specific phrases], low-volume search traffic that other ads might miss. They create ads bespoke to the audience and direct them automatically to the most relevant landing page, based on the search terms used.

Google Analytics Smart Lists use signals including location, device, browser, session duration and many others to identify users that are most likely to convert. Smart List then dynamically manages the audience with focused remarketing strategies, and utilising a constantly evolving model to add and remove users from this list.

Folding Google Ads Smart Bidding into the mix allows advertisers to optimise towards a particular goal, such increasing conversion or targeting a specific demographic, through leveraging data on keywords, device used, day of the week and location.

How Hiscox benefitted

Using a ‘test and learn’ approach, Hiscox and Croud initially rolled out Dynamic Search Ads over four months. Across the trial, the insurer saw an eight per cent increase in click-through rate compared to the rest of the account and drove down its cost per quote by £10, compared to non-brand search. In addition, the data generated by the campaign gave the insurer valuable consumer insights, such as how to target on an even more granular level – personal camera insurance, for example.

Smart Lists and Smart Bidding boosted Dynamic Search Ads’ performance still further. In using Smart Lists, Hiscox converted 62% better than standard search, while Smart Bidding increased conversion rates by 48% and reduced the cost per quote by 39%.

“Machine learning-based tools have proven particularly invaluable in keeping cost per action down, whilst also giving us great insights into our own users’ search behaviour. We’re excited to continue testing Google’s latest machine learning innovations,” Mahoney says.

Where competition for attention is high, the ability to target and engage quickly and easily with the consumer will always be what drives success. Being able to automate and deliver precision targeting at scale using machine learning is both cost efficient and effective for advertisers.