In our data-rich world, today’s insurance customers are savvy. They are acutely aware of the value of their data yet are also increasingly willing to share their information - if it is used responsibly and in a way which benefits them.
Meanwhile, the availability of data is expanding exponentially along with the value company’s attribute to data, and the analytics tools at our disposal are becoming more and more sophisticated. However, the sheer volume of data available can make it hard to know where to start.
So how can insurers start to drive meaningful customer insights?
Breakdown data silos
One of the biggest barriers to data success is that data is often misunderstood, complex and filled with jargon or buzzwords, which can lead to it being siloed within data teams. In turn, other departments are less likely to see the benefits data science can bring the business as a whole, which can include informing new product development to making underwriting pricing recommendations and implementing personalised marketing campaigns.
In addition to breaking down the data siloes, there needs to be dedicated resource to interpret and analyse data. Data expertise is clearly fundamental to the success of any data initiatives. As well as expertise from a technical, data engineering and data science perspective, it’s vital to have sufficient expertise and knowledge within the insurance company’s core functions such as underwriting, commercial and marketing to be able to interpret what the data is actually telling you. Both are equally important to successfully leverage the data assets of an organisation.
Build a data-aware culture
It’s essential that all employees have at least a working understanding of the power of customer data and how to make best use of it. This is particularly important in the case of insurance companies who are custodians of a significant amount of customer data.
Understand ‘Big Data’
‘Big data’ is a field that treats ways to analyse, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.
Current usage of the term big data tends to refer to the use of predictive analytics, user behaviour analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyse it to find answers that enable:
- cost reductions
- time reductions
- new product development and optimised offerings
- smart decision making.
When you combine big data with techniques that allow you to extract maximum value from them such as advance analytics and data science, you can accomplish business-related tasks such as:
- Determining what went wrong or what to do different in real time.
- Generating promotions at the point of sale based on the customer’s buying habits
- Calculating risk profiles in seconds which allows you to underwrite in real time
- Detecting fraudulent behaviour on claims or at point of sale if the customer is using stolen data from the dark web
Data analytics and customer insights
Find out more about how our Data analytics and customer insights can help you make smarter decisions.
Read the other articles in the series
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