Article

Executives Speak out on Analytics

By Steve Callahan
Practice Development Director

The importance of analytics has become increasingly apparent over the last few years as the complexity of the market, demands for customer differentiation, available information, and technological advancements have converged. While many companies have used a variety of business intelligence and analytical devices extensively to manage their business, the recent convergence has shifted the focus from a predominantly historical perspective to one incorporating multivariate modeling predicting the outcome of complex scenarios. To what extent are today’s companies integrating the various forms of analytics available into their decision-making process? The answer can be found, at least in part, in a recent survey conducted by Nolan and Tech Decisions.

Figure 1. Basis for Decisions

Not surprisingly, the most common element in the decision-making process across more than 200 companies surveyed was experience, outranking any form of analytics or group dynamics. While there is no replacement for practical experience, the risk organizations take in using this approach rests in assuming that the future can be based upon a subset of data used to make past decisions. Experience provides an excellent benchmark against which to measure progress but might not be the ideal source of directional information to use in determining future actions. The 87% of respondents whose use of analytics consists primarily of trends, scorecards, performance reports, ratios, and comparisons are missing the opportunity to find relevant patterns in a vast array of interdependent variables. And it is within these patterns that future outcomes can be more accurately determined.

Figure 2. Relative Frequency of Use by Analytics Type

Unfortunately, according to the respondents, investments in these capabilities will be limited to a range of “not at all” to “limited” over the next two years, even though the opportunity for effective differentiation and profitability would probably require significant investment. It could be argued that for the sake of a penny’s savings, a pound of opportunity is being lost.

We found that while the reliance upon experience and historical data provide some comfort in the decision-making process, several other common barriers stand in the way of the industry expanding the use of analytics. The number-one identified barrier is inaccurate, fragmented, and insufficient data spread across multiple disparate and often antiquated systems. Without quality data, the ability to leverage analytics is clearly limited, requiring companies to first overcome a broad range of challenges associated with cleaning up and keeping clean the relevant data. Related to the data quality challenge is the reality of inadequate technology resources, compounded by the fact that these same resources are required to clean up the data, maintain transactional systems, and assist in the implementation of analytical models. Without the necessary resources, companies are somewhat trapped by their current processes and information, a problem clearly identified by the survey participants. Still, it is worth recognizing that several vendors and service providers have specialized resources available to assist with data cleansing and toolset implementation. Assuming an appropriate priority and willingness to invest, companies have options to leverage these resources as part of a quick-start program that incorporates knowledge transfer and training with resource supplementation.

This brings to the forefront some of the less explicit but more ingrained barriers to implementing advanced analytical systems and solutions. In particular, a perception that the costs of these solutions actually outweigh the expected benefits persists. Whether the result of a previously failed project or the inability to effectively project the business value, a good portion of management still questions the returns that could result from investing in the expansion of analytics beyond the historical models currently used.

Figure 3. Likelihood to Invest

Given our industry’s current expense sensitivity, failure to be able to project a satisfactory ROI is often the death knell for proposals. Not surprisingly, this barrier is often paired with the next most common one identified by survey respondents—a lack of executive sponsorship for the projects. Absent sufficient returns and executive support, rolling out new analytical toolsets and models proves difficult to impossible. Compounding these barriers are the final two identified: a lack of business expertise and the consistently present cultural barriers to data sharing and ownership. Taken in total, these organizational constraints represent a much more difficult field of hurdles to surpass than the more technical resource or data ones. These issues represent the need for understanding, collaboration, education, and trust that are integral to any cross-organizational process that leads to change.

Recognizing and directly addressing these hurdles should be a foremost consideration in any implementation planning.

Where does this leave us? Despite the challenges that surround the expansion of analytics within our industry, the opportunities for increased profitability are making more companies take a closer look. Over 35% of those surveyed described the growth in use of analytics over the next two years as:

Recent meetings involving the Society of Actuaries have looked at the use of external data elements in multivariate models for underwriting and mortality management, recognizing that for most companies, a mortality improvement of 8% would represent tens of millions in saved claims. Other industry associations working in conjunction with companies and analysts have begun to measure the potential impact of applied analytics in marketing, agency management, and customer segmentation as the drive for greater profitability by customer shifts the industry’s focus from broad-based to finer levels of differentiation.

Although the PC industry has proven the value of analytics in the specific areas of pricing—through credit scoring models, claims, better management of exposure to fraud, underwriting, and more complex risk rating profiles—there remain ways to expand into new areas for PC and, in general, for Life and Annuities. As best put by one of the survey respondents, “Those companies that do not embrace technology and analytics will be left behind in the dust by those that do.”

Interested in learning more about the role of analytics in the insurance market, and especially recent advances in the application of these powerful tools to specific needs