AI and Advanced Analytics Enhance Engagement in Insurance

Awareness is rising among insurers and their customers of the potential of advanced analytics to enhance engagement and, ultimately, sales. Recent research has shown customers identify artificial intelligence (AI) as a key driver of better experience, yet it is, as yet, often less clear how to apply AI in a way that delivers on this promise.

In the second installment of a three-part series of reports on the subject, Swiss Re discussed the value of using AI to personalize customer interactions. How can insurers produce the best results from AI-powered tools in terms of retaining customers and improving the quality of interactions?

Most insurers use AI primarily to identify the customers most likely to let their policies lapse, Daniel Levy, principal risk consultant and author of the report, says, explaining that "single-purpose propensity models are highly effective when it comes to identifying a specific subset of customers at risk of being lost."

Applying such a targeted approach makes sense when customer interactions are relatively costly. If, however, the cost of outreach is low and the subset of customers identified is a significant proportion of the total customer base, the impact of a propensity model becomes less meaningful. "After all, why zero in on a subset of customers only to end up interacting with a majority of customers anyway?" Mr. Levy asks. "Conversely, why ignore most of your customers if communication is cheap?"

Propensity models may also be of less relevance when it comes to responding to situations such as inbound inquiries. For all these reasons, Mr. Levy says, it's important that instead of relying on the results of a single solution, insurers leverage multiple AI models to realise a greater return on investment.

Behavioral segmentation models have emerged as a particularly powerful tool. "These divide customers according to behavioral patterns and formulate insights accordingly as opposed to traditional marketing approaches that assign customer groups personae based on demographic characteristics," Mr. Levy reports.

In general, the company has found that demographic-based approaches underperform behavioral models in terms of customer response rates. By analyzing customer behaviors, behavioral models provide visibility into motivations and allow insurers to deliver messages that speak to these directly.

"Behavioural models can reveal stark differences within the customer base that may not be apparent along demographic lines," Mr. Levy says. "For one of our clients, customers in a behavioral segment indicating high activity had a 33-times greater likelihood [of taking] action than customers in the low activity segment. Insights like these have clear implications for where and when outreach should be targeted and the most relevant content to deliver."

Behavioral analysis can also highlight cases where customers take the same action but for different reasons. For example, new homeowners considering a competitor's offering and customers who are simply price-sensitive might both have high lapse propensity, but different motivations require distinct approaches, even though a propensity model might categorize both customer groups the same way. Interactions that recognize behavioral and motivational differences and are tailored accordingly tend to deliver superior results.

A different type of propensity model can make personalization possible, primarily by selecting the optimal message to send each customer from a menu of prepared messages. "Using this method for SMS renewal messages led to a 0.8 percent increase in retained premiums for one of our clients," says Mr. Levy. "These models can also incorporate reinforcement learning: with ongoing testing, the AI program can learn which content is most effective for each customer as well as the ideal channels and times of day for interactions to maximize their commercial impact."

These models raise possible ethical issues that need to be factored into any responsible company's strategy. Unlike with behavioral segmentation, it is not always clear why a propensity model chooses a particular message, and the difficulty of explaining results can raise questions. For this reason, their usage needs to be monitored carefully.

Another important consideration is the frequency of communication. "A 'pestering' approach that insists on touching base every day demonstrates a lack of understanding of customers and a lack of respect for their time," Mr. Levy says. "In insurance, where ongoing relationships are vital, it can cause premium-paying customers to stop engaging or even cancel their policies. These considerations need to be front of mind regardless of what a propensity model may recommend."

Propensity and behavioral segmentation models should ideally play complementary roles. For example, an insurer could use a propensity model to determine the optimal channels for different customer groups and a behavioral segmentation model to optimize the content sent to each customer. "This [would] ensure coverage of all customers, maximize the return on investment in more expensive communication channels, and responsibly position personalization to deliver the best possible outcomes," Mr. Levy reports.

Another flaw of commonly deployed AI models is the embedded assumption that customers will either have a consistent propensity to act throughout the year or only take action once a year (e.g., policy renewal). "In contrast, we've observed that customer propensity to act frequently changes," Levy says. "Customers have many possible triggers, and it is important to understand what each means."

One approach that has been shown to be successful in the past is to use models to understand what individual customers may do in, say, the next 3 months. "By applying behavioral models to analyze past patterns of behavior for each customer, we can understand the most likely next action each customer may take," Mr. Levy notes. "Studying the behavior of similar customers following often complex patterns of trigger events can provide insights into where a customer on the brink of a life change will go next in their journey."