Many insurers have embraced analytics and have made great strides at leveraging data and analytics to solve pricing issues. However the industry has largely ignored other opportunities to increase customer engagement and loyalty and have missed lessons from other sectors that have successfully used analytics to drive the revenue side of the equation.
The insurance industry stands at an analytics crossroads. One path leads to the status quo, the other to improved decision making, more effective marketing strategies and the ability to better understand, predict and influence consumer behavior.
The reality of today's insurance marketplace resembles a Hobbesian, dog-eat-dog world where companies are competing for a finite pool of possible customers. It's a zero-sum game where growth strategies are largely built upon a company's ability to win business from competitors and defend against similar incursions into its own market share. The main weapon in this battle has traditionally been pricing – and this is precisely where those insurance companies that leverage Big Data and analytics focus their efforts, often to the exclusion of claims, products, agents and sales operations, among other areas.
However, while insurance companies historically spend a lot of time and effort on pricing, that doesn't mean the industry as a whole is where it wants to be. For example, if a 35-year-old married mother of two from a mid-sized American city asks for an auto policy quote from five different companies, her premium could vary from $600 to $2,000 for a six month period. All the underlying characteristics are the same – she's the same person, same age, living in the same city, with the same number of kids and the same driving record – but her price options can be all over the place.
One reason for this is that most companies look exclusively at their own data and experience for pricing. These are important indicators, but if you make a mistake, it's repeated over and over again. There is no outward focus, no benchmarking to other indicators in the industry.
Another reason for these price disparities is that business operations vary widely from company to company. Some companies sell through agents, for example, while others sell exclusively or primarily through the Internet. Still, these explanations fail to account for the chaotic price differentials from one carrier to the next. The use of analytics can help insurance executives learn from other industries. Mortgage lenders, for example, tend to set rates between plus or minus 75 basis points, and borrowers don't see rates swinging wildly between companies. Other industries have got it right so there's hope for us to do it with insurance rates, as well.
Ultimately, however, the insurance industry has to realize that pricing, in the modern world, is no silver bullet. For example, commoditization is a very real and on-going phenomenon in the industry and is an area ripe with opportunities for leveraging big data and analytics. Insurance is a high-involvement financial purchase and it used to be a nightmare to buy. People had to consult an agent and rely on specialists, but all that is changing. Americans are becoming better, more intelligent insurance consumers and this has led to the demystification of insurance products across the board. Consumers today understand what products they need, can purchase those products on their own and are no longer bound to professional advice-givers. Also, the process of buying insurance is much easier now than even five or 10 years ago, as phone and web-based sales skyrocketed.
At the same time, insurers are smart and they realize their products are being commoditized. This realization led, about ten years ago, to a sustained industry-wide advertising boom. Back then, the entire industry was spending roughly $2 billion on ads. In 2012, according to SNL Financial, the top two companies account for nearly that in combined ad spending. Not only is the industry as a whole spending more on advertising, but it is in many instances changing the way it advertises – primarily with ads that look and feel more like something a CPG or retail company would produce. Which is why that little British lizard is now nearly ubiquitous. And who can think of insurance these days without thinking of Flo's retro up-do?
[Lessons for insurers from Blockbuster's demise: Can auto insurance sell through kiosks?]
Quirky spokespeople aside, all this carries big implications for the use of analytics. Given that the products are more commoditized, how can companies better understand customers and resonate with them better? By deploying analytics solutions such as optimizing marketing spend to increase ROI, incentivizing agents / brokers to acquire profitable long-term customers rather than high volumes of unprofitable, high-churn customers or changing the pricing from a pure cost-plus to a more value-based pricing that captures customer value and their price sensitivity, executives can connect with the consumers better and improve their profitability in a much more powerful and meaningful way.
Another big industry trend that presents us with sophisticated and exhilarating analytics applications is the increasing use of pay-as- you-drive telematics devices. Instead of using age and gender as proxies for assessing risk, companies are using telematics to assess risk on an individual basis. These devices relay information about who is driving, how fast, at what time, from where to where, etc. In other words, companies are able to set prices based on actual drivers, rather than broad driver categories.
This is a remarkable, perhaps revolutionary, technological advancement. If these devices become universally accepted, however, the amount of personalized information that insurance companies will acquire, analyze and put to practical use is enormous. This will necessitate greater and more creative use of analytics solutions in order for companies to store this tidal wave of data, retrieve it and make sense of it.
The message for the insurance industry is clear: pricing is not the silver bullet. Big data and its benefits can help insurers become more informed, more nimble and better able to interact meaningfully with consumers.
About the author: Deepak Ramanathan is VP, Global Consulting, Financial Services and Insurance for Fractal Analytics.