Policyholder churn is fast becoming the bane of the U.S. insurance industry, but it doesn't have to be that way.
The auto, health and homeowners' insurance sectors have become revolving doors for customers.
Currently, the industrywide client retention rate stands at 84%, according to Nationwide. While the figure may not seem alarming at first glance, when compared to the stability the industry experienced for decades, it becomes a pressing concern. The 16% annual churn rate compounds over time, leading to thousands of dissatisfied customers and eroding hundreds of millions of dollars in margins for insurers.
So, what's driving policyholder churn? Let's take a close look.
In auto insurance, it's all about price. The market has become cutthroat, with the top 10 insurers holding about 72% of the market share. This competition drives the price of premiums, bringing about higher churn rates. In homeowners' insurance, volatile changes in American home values have driven policyholder churn over the past half-decade, with prices booming in rural work-from-home states only to deflate in the past year. In health insurance, policyholder churn results from shifting policies under the Affordable Care Act (ACA) and the fact that high job churn hasn't recovered from the pandemic.
Seeing a common thread in these three high-churn sectors, many insurers adopt the stance that the churn is out of their control. They've accepted it as a given and are, perhaps mistakenly, pouring the greater part of their resources into attracting new customers, rather than focusing on retaining the ones they already have. Acquiring a new customer can cost between twelve and twenty times more than keeping a current one.
But please don't just accept policyholder churn. There are ways to overcome it.
The first step in fighting policyholder churn is acknowledging that the insurance industry, especially the three sectors mentioned, are extremely price sensitive. A rival insurer offering auto insurance at a mere 20-dollar cheaper premium is often enough for a customer to consider switching, even if they've been with an insurer for almost a decade.
When customers switch for a $20 discount, some insurers often see only two options: either match the $20 discount, which many can't afford, or give up on the customer. However, there is a psychology to pricing and so a third option is in getting a better understanding of how a customer weighs the inconvenience of switching carriers over the more attractive cost. To that end, one of the business cases we're seeing for AI and Machine Learning, is using these tech advancements to better arrive at the optimal price that will retain the customer. In this case, a $12 discount could be that sweet spot, preserving both profit margins and customer loyalty.
There is an optimal price point for each customer, but finding it requires a blend of art and science. Advanced algorithms capable of analyzing producer performance, a customer's past purchases, policy usage, and communication history play a role in this, and gain much power when they cross-reference external policy renewal trends across a large data set. The new generation of AI tools departs from the traditional approach of grouping customers into broad categories. Instead, customers can be analyzed in a more granular way: for instance, determining the optimal price for a customer with two dogs, two cars, one cat, and three Rolexes can't be achieved through simple manual data analysis.
But price isn't everything. If an insurer can't afford to offer the optimal discount, they have other tools to retain customers. While price is often the main motivator for purchasing insurance, personalization can be a strong secondary factor. Many insurance buyers are older customers who feel alienated by technology. Personalized deals and retention strategies can provide the warm, human touch that's often lost in digital transactions. Personalization doesn't just appeal to older generations — millennials show a 28% increase in brand loyalty when they receive personalized communications.
By monitoring communication patterns, policy usage, and customer inquiries, insurers can implement proactive engagement strategies, such as targeted outreach campaigns and personalized retention offers. For these renewal strategies to be effective, they must be proactive rather than reactive. If AI-driven predictive modeling algorithms are provided with customer demographics, claims history, and satisfaction metrics, they can identify which customer-insurer relationships need strengthening before the customer even considers jumping ship.
The healthcare industry is already ahead of the curve in terms of personalization. Unlike its counterparts, this sector is less price sensitive. Many successful insurers have recognized this and have embraced wellness as a key retention strategy. Prompted by the pandemic, Highmark Health's decision to launch Living Health — a comprehensive program that uses data-driven insights to enhance nutritional, fitness, and mental health — has contributed to their core health plan and Blue Card membership boasting 98% retention rates.
The wellness industry also highlights the value of customer service, an often-underutilized tool that insurers can use to combat price sensitivity. Many insurers overlook customer service as a retention strategy, dismissing it because it involves only two touchpoints: when the plan is purchased and when a claim is handled.
However, these two points are crucial and are currently being neglected — 60% of customers change channels before making a purchase, and approximately one in six customers report that insurers do not follow up after an initial financial discussion. Among those who do receive follow-up, 40% interact with multiple representatives. The experience is generally disjointed, confusing, and impersonal.
AI and machine learning tools can address these significant gaps in the customer experience by analyzing extensive datasets of feedback from customers, producers, and internal stakeholders. Advanced algorithms can identify common themes, trends, and specific pain points, enabling insurers to achieve the greatest return on investment when overhauling the customer experience. Ultimately, this creates a data-driven feedback loop that facilitates continuous improvement.
In the coming years, the insurance industry may face increased volatility and more complex claims. In auto insurance, self-driving cars will pose significant challenges in accurately assessing risk. In health insurance, advancements in medical technology will complicate coverage and billing processes. In homeowners' insurance, the rising frequency of natural disasters will require more sophisticated risk assessment and response strategies.
However, insurers that successfully embed AI and machine learning will better position themselves to navigate this uncertain future.
Eric Bustos is General Manager of Vymo, a global insurance IT platform provider. He can be reached at eric.bustos@getvymo.com. Any opinions expressed here are the author's own.
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