Why the Auto Insurance Industry is Like a 3-D Printed Dress

March 15, 2017

Author: John Hoffman

In an increasingly consumer-centric business environment, data and analytics are helping companies tailor products and services to suit specific customer needs. The fashion industry, for instance, is utilizing data, analytics and 3-D printing to collect information about a customer’s body dynamics and movement and then create designs that are especially for them. While Insurance may not be as avant-garde as fashion, the use of big data and analytics to create better customer experiences and boost the bottom line is no different.

Insurance is an inherently risk-averse industry. This tendency to play it safe contributes, in large part, to the slow adoption of the innovative “insuretech” that has emerged in recent years. Reluctance to change with the times has left many established companies with a steadily decreasing market share as digitally-based industry disruptors continue to offer consumers cutting-edge services.

The underlying driver of this trend is the ability to leverage big data and the Internet of Things to assess risk, determine policy terms and settle claims more effectively. Despite the head-start insurtech newcomers have on traditional organizations, incumbent insurance companies can use big data and analytics to increase personalization and decrease risk.

Consumer-driven Auto Insurance Trends

Satisfaction among auto insurance customers is among the lowest of any industry. Much of this discontent revolves around the idea that customers don’t feel they’re getting significant value for their money. An insurance industry study conducted by J.D. Power revealed that many customers reported premium increases without a corresponding claims incident or life change. The most significant factors in insurance customer dissatisfaction, however, are the interactions consumers have with their insurance companies and the sparse selection of products that are available to them.

This gap between auto insurance offerings and consumer expectations is actually a simple fix. It requires establishing a new business model that caters to consumers while reducing risk and increasing revenue for insurers.

Harnessing Big Data

Auto insurers are no strangers to big data. These companies are actually among the first to utilize millions of pieces of population-wide, aggregate information and averages as well as personal habits and behaviors to assess the riskiness of potential and current customers. While this data has historically benefitted the insurance company itself, it has also served a number of customer-side uses, including premium discounts for less risky behavior and automated claims processing.

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The difference today is the exponentially increased volume of information that’s available in the form of past transactions, information gleaned from connected devices, app data and social media. This data allows insurance companies to better assess individual risk and tailor their products and services to each customer. Most millennial consumers, for example, are “very or extremely likely” to share data from wearable activity tracking devices with life insurance companies if it means more personalized, lower cost services. Similarly, pay-per-mile insurance pioneer Metromile relies on an in-vehicle dongle collect accurate mileage data and charge accordingly.

Advancing Analytics

Despite the myriad of benefits access to this information provides, data is only as good as it’s interpretation. Big data need analytics to sharpen and amplify raw information into granular, actionable insights that create targeted products and services, facilitate better risk assessment and improve operations:

  • Predictive analytics can recognize trends in a customer life, such as when they start looking for a new car or when they have a baby on the way, and offer personalized, timely products.
  • Analyzing current claims, claim histories and individual customer data can help companies optimize the settlement process and provide more accurate assessments of both the merit and amount of a claim.
  • Claims forecasting can predict the likelihood of large-scale claims being filed, such as in instances of natural disasters, allowing insurance organizations to plan accordingly.

Establishing more personal interactions with customers while better protecting against risk has become even more important in recent years. To remain competitive in this new insurtech environment, conventional insurers must revamp their business models to more fully embrace big data and the analytic tools that are used to hone it.


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