TABLE OF CONTENTS Nov 2012 - 0 comments

Predictive Battle Lines

The use of predictive analytics is advancing into areas beyond the traditional rating and underwriting fronts. Organizations that opt not to venture into these new arenas - including customer service, sales and marketing - risk losing competitive positioning to other insurers.

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By: Roger Burkhardt, President and CEO, EagleEye Analytics

More and more insurers are realizing that the benefits of predictive analytics extend well beyond rating and underwriting. Use of analytics, once reserved for pricing actuaries, has moved into customer service, sales and marketing, as well as the executive suite. As carriers find that understanding, there is also more and recognition that making business decisions based on predictive patterns in all areas of the business generates significant positive results in growth, profitability and customer retention.

Carriers that get it are able to compete on a whole new level, capitalizing on important insights into their own customers and operations. But there is clearly a predictive analytics gap — one that separates future-focused carriers from those that will be left behind.

For the laggards, many using analytics solely for rating and underwriting, the risks are big, including missed opportunities and loss of competitive positioning to other insurers.


With customer loyalty decreasing and expectations increasing, customer churn rates in the insurance industry are at an all-time high, particularly when it comes to personal lines. In today's soft market, insurers can ill afford to watch their best customers slip away.

Carriers that use predictive analytics for rating are making progress in the battle against churn. They are able to identify the best risks, match them to the best rates, and improve not only retention, but overall profitability, too. Many carriers in Canada have used predictive analytics for rating for many years, working seamlessly with regulators, and improving their businesses.

That said, there is another part of the equation that these carriers are missing. Insurers typically review rating, and they measure retention. However, each function is examined independently. Measuring the intersection of profitability and retention is the key to even greater performance, one that will allow carriers to proactively shape their books of business.

Using predictive analytics, carriers can determine which customers are profitable, but difficult to retain. Most important, they can look forward, and understand how new customers will retain and how profitable they will be as the business is being written or renewed.

This use of real-time decision-making, across the lifecycle, is a definite game-changer. It enables carriers to accurately measure customer lifetime value, and develop proactive retention strategies. Leading carriers are moving beyond loss cost to include in their predictive models and optimization strategies other operational costs, including those related to acquisition and customer service.


Brokers are the closest direct link to the insured for carriers that rely on the broker distribution channel. While traditionally most carriers and brokers did not share data and analytics capabilities, predictive analytics wields such significant power to change the course of the business that it can provide a strong stimulant to break down the organizational divide.

The availability of strong analytics tools, combined with cloud technology, removes technology barriers for carriers looking to partner with their brokers on customer acquisition and retention initiatives. For example, one carrier is providing its brokers with individual prospect lists generated from analysis of its nationwide data, using predictive analytics to generate profitable business. It then manages and guides the broker’s prospecting by monitoring the profitability scores of the new business as it flows in.

Internally, carriers can measure current and predict future performance of brokers. Using modelling, carriers can identify the productivity and profitability of brokers by geography, line of business, underwriter or by individual broker. These models remove the random fluctuations in actual claims from an individual broker, which tend to lead to management by anecdote. These management tools provide unique opportunities to capitalize on growth strategies and change course quickly to minimize risk.


No carrier can afford to sit back when it comes to customer acquisition in the current market environment. But the degree to which insurers are leveraging predictive analytics to shape their sales and marketing strategies, and partner with brokers to execute on them, varies widely. Some are using predictive analytics, combined with big data and social media analytics, to target prospects.

These strategies are made possible through better modelling, providing insurers with an understanding of which segments — or even individual customers — are likely to be the most profitable. For example, each customer in a segment can be scored on the basis of proven predictors to profitability throughout the customer lifetime, as well as on the likelihood of attrition.

Carriers and their brokers can then devise plans to focus on customers who have a high customer lifetime value, but also a high likelihood of churning. These plans may include preferred customer service for customers with specified scores, multi-selling or retention discounts. Carriers can also use predictive analytics to establish correlations between different variables and policies to determine which customers would most likely be interested in additional products. Insureds often equate accurate multi-selling with good customer service, which increases customer satisfaction.


Using predictive analytics for customer retention and pricing has been a logical move in personal lines insurance because of the homogeneity of the risks and the high frequency and low severity of claims. In the United States, companies such as Progressive, GEICO and Intact have gained significant market share with a strategy built on superior predictive analytics.

Commercial lines carriers have been slower to follow suit. However, as carriers see the benefits and the technologies progress to meet their needs, the next wave of predictive analytics in commercial lines is now taking place. Traditional thinking in commercial lines has been that judgment is the key to underwriting; with low frequency/high severity claims, there was just not enough data to warrant the use of predictive analytics. That thinking is changing, as carriers in some markets have already seen big advantages of using predictive analytics.

Of course, the benefits of viewing variables and correlations in new ways are not limited to the workers' comp market. All commercial lines insurers can benefit from the segmentation and underwriting opportunities provided through predictive analytics. The idea that commercial lines databases are too small to justify the use of predictive analytics is outdated. Instead, there are advantages for all insurers — from personal lines to commercial lines — that may have only smaller amounts of data.

A large volume of data is no longer critical to utilizing predictive analytics effectively. Much can be determined from the patterns in small amounts of data, leveraging the strong signal gained from revealing the interactions of multiple variables. On the other hand, examining variables individually — whether a carrier has a large or small database — can lead to the creation of simplistic descriptions of customer segments that each have a wide range of profitability.

The interactions between variables are important for discriminating among customers who might otherwise be within the same segment, but who may have very different profitability. Moreover, carriers can add data sources from outside of the organization to improve predictive power.

Information such as census data, weather conditions, data from social media or consumer preferences, which can be incorporated into existing data, can improve the ability to predict the customers who will most likely contribute to the bottom line. Modern cloud-based predictive analytics offerings frequently have third-party data already available as part of the service. 


The time is now for carriers to identify creative new ways to use analytics to improve their businesses. Resource barriers — once a major issue — have been all but eliminated with the development of modern predictive analytics software. Modern predictive analytics tools have eliminated the need for carriers to hire large numbers of actuaries and spend months, if not years, working on manual analysis to get answers. Cloud-based software reduces the costs, and makes predictive analytics capabilities accessible to large and small carriers. And tools with insurance industry specific algorithms provide far greater speed and accuracy.

While the new capabilities provide a positive dynamic for many, others will be left behind. Adverse selection is real — good risks move to the leaders that will be most likely to offer lower premiums and better service, while bad risks are the ones that remain. It is a losing proposition. The availability of new tools removes the barriers for all carriers.

There is a gap in the insurance industry when it comes to predictive analytics. Some of the leading companies have been focused on pricing analytics for many years with great success, but others are catching up, sometimes managing to leapfrog them, by applying predictive analytics more broadly. Those advances are aid by not limiting their efforts to rating and underwriting, but extending the benefits of powerful analytics tools throughout the enterprise.


Roger Burkhardt, President and CEO, EagleEye Analytics
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Caption: Roger Burkhardt, President and CEO, EagleEye Analytics
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