Demystifying data science

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ITIJ 223 | August 2019

Jean Ortiz-Perez, Head of Analytics at Collinson, gives a unique insight on how best to harness the power of consumer data to enable a personalised customer experience and enhance customer loyalty

 

In today’s data-rich world, insurance customers are savvy. They are acutely aware of the value of their data yet are willing to share their information – if it is used responsibly and in a manner that benefits them. Furthermore, the availability of data is expanding exponentially, along with the value that companies attribute to it, while analytics tools are becoming more accessible and sophisticated. 
So, why have insurance companies been so slow to respond to this opportunity? 
In a sector that has historically suffered from high customer attrition, poor levels of loyalty and the rise of aggregators, customer data should be the holy grail for insurers. After all, so many have expressed a desire to get closer to customers and drive up lifetime value; skilfully mining customer data to gain meaningful insights must hold the key to this. 
One of the biggest barriers to data success is that data is often misunderstood, perceived to be 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 to the business as a whole, from informing new product development to implementing personalised marketing campaigns. 
While there are some elements around data science that are incredibly sophisticated and technical – particularly when it comes to deep learning or artificial intelligence –
you don’t need a computer science degree to understand the basic principles. In fact, by understanding some basic principles, insurers are taking an important step to improving data use and business success. 
Here we seek to demystify data, to enable insurers to look beyond the buzzwords to the potential customer data has to transform their businesses.
 
The value of customer data
Before making any investment in data, it’s vital to understand its importance, as leveraging data is no longer best practice, but rather the difference between success and failure. Insurers should search deep within the data to find those key customer insights that give a competitive edge. As we have seen in our Winning customer experiences report, 81 per cent of UK consumers are willing to share their information and personal data in return for a better shopping experience. Only those organisations that are astute enough to extract real value from customer data will be able to deliver this. 
Despite the importance of data, many companies are still not maximising its value, as was highlighted in a recent study commissioned by Collinson and conducted by Forrester Consulting. Decision makers at financial services and insurance institutions with revenues exceeding $300 million were surveyed. On average, only 20 per cent of respondents had plans to increase their investment in analytics by five per cent or more.
Our research amongst consumers also found that 21 per cent will not engage with insurers that fail to personalise their customer experience or communications. Without a deep understanding of customers, their needs, motivations and behaviours, truly recognising and rewarding advocates will remain an elusive goal.
 
Driving meaningful customer insights
In addition to breaking down the data silos, there are two key strands to data success. The first is investment in a dedicated resource to interpret and analyse data; the second is instilling a data-aware culture across the whole organisation. 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.
 
Without a deep understanding of customers, their needs, motivations and behaviours, truly recognising and rewarding advocates will remain an elusive goal.
 
This is particularly important in the case of insurance companies that are custodians of a significant amount of customer information, and there are some key considerations for data teams when building and managing a data lake.
 
  • Data infrastructure
    In order to maximise the value from data, it is key to have the right resources to store, organise, clean, manipulate and process it with the necessary care, security and agility. This is of even greater importance when it comes to ‘big data’ and is defined by the three V’s: volume, variety and velocity. 
    Volume. The data architecture needs to be set up in such a way that it can handle huge volumes of data streaming in near real time. 
    Variety. The appropriate mining techniques need to be in place to handle the wide variety of structured and unstructured sources of data such as images, voice and feeds.
    Velocity. It’s important to have adequate velocity to react to market changes with speed, and to anticipate and predict behaviour – even prescribe actions. For example, a health insurance company might stream Internet of Things (IoT) data from wearable health monitors in near real time, to predict when a customer will benefit from a health check, and subsequently encourage positive behaviours by giving incentives for a healthy lifestyle, heart monitoring and physical activity. This can eventually reduce the customer’s risk profile and give the insurer a healthier book.
  • Data quality
    Data quality can only be achieved if data is organised appropriately. But, in order to have a full end-to-end quality and governance function, cultural and organisational change is needed – changes that focus on data ownership, responsibilities and a clear data stewardship model.
  • Data expertise 
    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 expertise and knowledge within the insurance company’s core functions, such as underwriting, commercial and marketing to interpret what the data is telling you. 
 
Conclusion
One of the reasons data is often overcomplicated and underutilised is that the expertise and knowledge tends to sit in silos within insurance companies, contributing to this sense of mystery that permeates data in insurance. 
By encouraging all employees to have a basic understanding of how data is collected and used, it is possible to decentralise data expertise and create an army of data advocates across the business. Once we strip things back to why data is so important, rather than focusing on the intricacies of technology, success will be so much easier to achieve.
With the right investment and organisational culture, insurers will be primed to improve processes and utilise data more effectively. By harnessing customer data, they will build a more detailed view of the individual customer to provide a highly personalised experience. In doing so, insurers will be well-poised to foster loyalty with customers, building a more emotional connection – and ultimately a higher lifetime value. 

 

Jean Ortiz-Perez, Head of Analytics, Collinson Group

Jean is responsible for building and maintaining a data-driven culture and driving the growth of Collinson from an analytics perspective. Her responsibilities at the firm include advising management on decision making based on data modelling, as well as using predictive models, AI applications and big data management to maximise the use of Collinson data assets. She is a keen public speaker and a contributor to multiple advanced analytics magazines.