Data science: the revolution that is transforming insurance and advertising

21/11/2019
Read Time: 4 Min

If you’re unlucky enough to live in a “non-standard” home you might find it’s not so simple getting insurance for your home. Trying to get a quote on a price comparison website (“PCW”) might be the first time you realise that your home is deemed to be non-standard by insurers – it might simply be that an area of flat roof, a previous claim or using a room to work from home means that you are too difficult for insurers to quote. Your online foray will therefore likely be an exercise in frustration as the ‘computer says no’ and tells you to call a number instead. For digital consumers used to a seamless, speedy online experience this will come as a shock. Salvation, however, comes in the form of data science, which is transforming the world of insurance and providing further opportunities for nimble disruptors. 

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To be sure, digital disruption has already upended the UK’s £7.5 billion home insurance market. For most households it is easy enough to go online and switch providers but most PCWs cannot quote for so-called complex cases. Avantia is one of the few businesses that can help.

Data science is in Avantia’s very DNA. In order to be able to offer complex home insurance solutions for complex cases, a provider needs large quantities of data and the analytical power to unlock its value. As the premium becomes more difficult to quote for, non-standard cases mean more variables. During ECI’s investment period, Avantia has built a formidable, machine learning platform that can crunch enormous amounts of complex data, absorb these variables, and produce a quote inside of 700 milliseconds. 

Success begets success. Each quote that Avantia makes helps the company to improve its product offering. Every data point entered by customers is anonymised and used to analyse and enhance outcomes. The system becomes self-fulfilling. The Company digitally quotes for 98% of people requesting a quote, makes 1 million insurance quotes a month and to date has amassed 5 billion data points. 

The decisioning power of Avantia’s machine learning platform is now being unleashed on different parts of the business. The company is building new algorithms to help it find and attract customers with more complex needs and the same platform will be able to provide faster and better quality solutions when customers make a claim by choosing the best contractors for each specific type of work. 

MiQ: lending laser-precision to advertising campaigns 

Data science is also transforming the near-$600 billion advertising market. Think ‘Madmen’ meets Google. Traditionally, adverts have been a very blunt tool with which to target customers. ITV once would have commanded a princely sum for a 30 second spot on a Saturday night. However, the proliferation of social media, streaming services and smart phones have atomized and scattered audiences. What if a particular clothing brand wanted to target, say, sustainability-conscious millennials? How would it find them, how would it know how to reach them?

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MiQ uses data science to answer these questions and it’s called programmatic marketing intelligence. ECI invested in MiQ in 2017 and today the Company works with some of the biggest consumer brands across Europe and the US. MiQ’s army of data scientists and analysts connects data sets, applies data science, analytics and machine learning to these connected data sets, and produces actionable insight and analysis to help inform media buying decisions. 

For instance, MiQ’s team may help a car brand to better understand who is coming into their showrooms, and where they visited previously. Data analysis might show that 35-45-year olds visited a Peugeot or VW showroom beforehand, whilst 45-55-year olds visited Mercedes. The client can now better segment their target audience, understand who their real competitors are, and plan their digital advertising programme accordingly. 

Those data sets come from many different sources, including TV and social media such as Twitter feeds. ACR or automatic content recognition is also proving to be a game changer for digital marketing and advertising. ACR is basically software that identifies media content based on a sample of music or video. A simple example is Shazam, the smartphone app which allows you to identify a song title you might hear on the radio. The software is embedded in most internet-connected devices such as smartphones, tablets and smart TVs. 

Say you’re watching ‘Britain’s got Talent’ on TV. ACR technology can enhance the viewing experience for viewers by providing information and additional content such as programme and cast trivia, identify music featured in the programme, even direct viewers to relevant Twitter feeds so they can comment. You might even be able to look up and source your favourite judge’s outfit. It all makes for a more immersive viewer experience. 

For advertisers, ACR can provide valuable information such as audience measurement broadcast monitoring. MiQ can combine that ACR data with all its other data points such as online browsing behaviour and location data and translate that back into even more effective programmatic buying. 

MiQ’s army of data scientists and analysts in Bangalore provide the human overlay on these data sets, sense-checking the findings before using them to advise clients on how, where and when to spend their advertising dollars and pounds. A perfect example of man and machine working in harmony. 

Conclusion- making sense of a growing sea of data

The modern world is generating ever-more mountains of data. By 2020 this data is expected to reach over 40 zettabytes, or 40 sextillion bytes. This poses an enormous challenge for organisations drowning in this sea of data. How can they use it effectively? What should they focus on? The companies that can help them to answer these questions by developing and deploying the necessary analytical power to generate actionable insights will thrive in this data future. 

About the author

Paul McCreadie

"I have been a member of ECI’s Investment Team since 2008, working with management teams from our initial investment all the way through to when we sell our stake. That means I get to meet great founders and management teams and support them to successfully deliver their vision."

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