This piece is by Jon Jacobson, CEO and co-founder of Omnisient, privacy-preserving data collaboration platform provider.
The insurance industry has long relied on traditional data sources to assess risk, set premiums, and manage customer relationships. However, the growing use of privacy-preserving data collaboration (PPDC) to securely share alternative data can significantly enhance predictive modelling and risk assessment for insurers.
Grocery retail data is one such alternative source of data that provides rich insights into customer behaviour, lifestyle choices and financial health. By engaging in PPDC with a retail grocer, insurers can build predictive models to encourage healthy eating, lower premiums for policyholders, predict and prevent premium lapses, and acquire new lookalike audiences.
Encouraging healthy eating
One of the most promising applications of grocery retail data in the insurance industry is the ability to encourage healthier eating habits among policyholders. By analysing the types of foods a customer regularly purchases – such as fresh produce, whole grains, or processed snacks – insurers can gain insights into their dietary habits and overall lifestyle. Armed with this information, insurers can develop tailored wellness programmes that promote healthier choices.
For instance, if a policyholder frequently buys high-calorie snacks and sugary drinks, the insurer could offer personalised nutritional advice and incentives to encourage the purchase of healthier alternatives. These incentives might include discounts on health insurance premiums, cashback rewards, or access to virtual health coaching sessions.
This approach not only benefits the policyholders by improving their health but also helps insurers reduce their long-term risk exposure. Additionally, by providing tangible benefits and personalised advice, insurers can strengthen their relationship with customers, leading to higher satisfaction and retention rates.
Lowering premiums for policyholders
Traditional underwriting methods often rely on broad assumptions about a policyholder’s health and lifestyle, which can lead to overpricing. However, by incorporating grocery data into the risk assessment process, insurers can gain a more nuanced understanding of a policyholder’s lifestyle choices and adjust premiums accordingly.
For example, a policyholder who consistently purchases healthy foods, such as fruits, vegetables, and lean proteins, might be deemed a lower health risk than they were before, and could be rewarded with lower health insurance premiums, reflecting their healthier lifestyle.
Moreover, by offering the possibility of lower premiums as a reward for healthy shopping habits, insurers can incentivise policyholders to make better choices. This creates a positive feedback loop, where healthier behaviour leads to lower premiums, which in turn encourages even healthier behaviour.
Predicting and preventing premium lapse
Another valuable application of grocery retail data is its ability to predict and prevent premium lapses. Financial difficulties are a common reason for policyholders to miss payments or allow their policies to lapse. By analysing purchasing patterns, insurers can identify early signs of financial distress and take proactive steps to support at-risk customers.
For example, a sudden shift in a policyholder’s grocery purchases – from premium brands to cheaper alternatives, or an increase in the purchase of bulk or discounted items – could signal financial strain. This data allows insurers to reach out to these customers with supportive measures, such as temporary premium relief or flexible payment options. By addressing financial difficulties before they lead to missed payments or policy cancellations, insurers can maintain a stable customer base and reduce the costly churn associated with lapses.
By offering flexible solutions, insurers can also build trust and loyalty, making it more likely that customers will remain with the insurer long-term.
Acquiring new lookalike audiences
Grocery retail data is not only valuable for managing existing policyholders, but also for acquiring new customers. We have insurance clients who are using grocery retail data to flag and target lookalike audiences – potential customers who share similar shopping habits and lifestyle characteristics with existing low-risk policyholders.
For instance, by analysing the grocery purchasing patterns of their healthiest and most cost-effective customers, our insurance clients have developed a profile of the ideal policyholder. They then partner with the grocery retailer to target similar individuals through the retailer’s retail media network. This can involve digital advertising campaigns, personalised offers, or other marketing strategies designed to attract these desirable customers to the insurer’s products.
By acquiring new customers who are likely to be lower risk, insurers can improve the overall quality of their customer base, reduce claims costs, and enhance profitability. Additionally, by leveraging the retailer’s media network, insurers can reach potential customers in a highly targeted and cost-effective manner, increasing the efficiency of their marketing efforts.
By using PPDC with a grocer, insurers can leverage shopping behaviour to understand and predict policyholder behaviour and create more accurate risk profiles, offer tailored products, and ultimately build stronger, more resilient customer relationships. As the industry continues to evolve, those insurers who embrace the power of grocery data will be well-positioned to lead the way in innovation and customer satisfaction.
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