In the world of consumer research, many major retailers have proven their skill at gathering customer data and designing relevant offers to grow sales. And insurers may want to forge partnerships with these leading retailers with the aim of enriching their own data and predictive capabilities.
How did retailers cash in on the big data bonanza? It’s partly thanks to their steady investments over the years - by grocery chains, convenience and liquor stores - to create loyalty programs that pull in grocery carts full of customer data.
“By doing so, retailers have developed very deep profiles of their customers based on their shopping habits,” explains George Svinos, Partner with KPMG in Australia. “These data help them understand trends in consumer behavior, which can shape future campaigns, new products or incentives.”
George observes that retailers have another advantage: “They have access to more dynamic data, since their customers shop frequently, which provides much timelier, granular detail. Insurers only have access to historical information based on fewer client interactions or broader demographic trends.”
In light of retailers’ bounty of data, George proposes that, “Insurers could find ways to marry retail data with their own data sources to build much clearer customer profiles. This high value, real-time data could enable insurers to develop algorithms based on more relevant information than using history as a predictor of customer behavior and risk.”
For example, imagine if customer data showed that an individual bought diapers and formula at the grocery store, indicating the potential to offer life insurance to protect their young family. Or, perhaps a client’s shopping data reveals that they often buy petrol and energy drinks at 3am. This might help predict that individual’s claims risk.
George notes that a UK credit card firm currently analyzes shopping basket data to determine the connection between consumer purchases and account default. The same type of analysis could help insurers use client behavior to offer more tailored pricing.
To access this wealth of consumer data, an insurer might establish a joint venture with a prominent retailer, enabling the chain to sell its insurance products on a white label basis. Or, an insurer may join the retailer’s existing loyalty program, to share data among program sponsors and provide loyalty benefits to members.
The key is choosing the right partner retailer, with a customer base that complements the insurer’s target market and a loyalty program with frequent and dynamic data.
Like any groundbreaking approach, there are challenges, including the need to overcome consumer concern about misuse of their behavioral data.
George suggests that insurers must obtain the appropriate permissions from customers, which is easier to do with the promise of loyalty points or rewards: “If explained clearly, a customer may like the idea that their insurer is trying to get to know them better, in order to price their policy based on their real behavior, rather than on the postal zone in which they live.”
The bigger challenge may be convincing insurers to rethink how they price risk. “The financial industry tends to believe their data are best, so they may be hesitant to explore new approaches,” says George. “It does require an investment to have your statisticians review vast quantities of transactional data and develop new algorithms around the relationships between behavior and risk. You can have all the data in the world, but the hard part is getting value from it.”
Despite the obstacles, George affirms that big data represents a revolution in how insurers can assess risk. “The ultimate goal is getting to know your customer better and assess their risk accordingly. In a marketplace where consumers are buying largely on price, if you can find ways to price more accurately, then you have the advantage.”
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