From the February 2012 issue of Tech Decisions • Subscribe!

Analytics Provides the Silver Lining for New Policy Systems

Economic conditions for 2012 do not look particularly promising, challenging companies to find unique ways to generate sustainable growth and profitability. Continued high unemployment, near zero interest rates, limited growth in the economy, increased consumer diversity, extended low investment returns, intensified service expectations, expanded competition, and a probability of additional natural disasters converge to create yet another challenging year for the insurance industry in general.

Constrained by the combination of these profit-compressing variables with little room to maneuver, earnings are likely to remain low throughout 2012. Despite the low profit projections, strategic plans and a longer-term horizon demand that leadership take action now to be positioned for growth and viability in the future.

The systemic challenge facing the industry hidden amid all these critically relevant and immediate economic, regulatory, and competitive distractions can no longer be found in simplistic product variations and reduced cycle times. Market diversity, increased options, advances in consumer awareness with easy access to information, and ubiquitous and affordable technology has changed the competitive landscape.

Brand is important but not determinate as consumers have watched even the greatest fall; pricing is relevant but within a wider acceptable variance as convenience, serviceability, social responsibility, and individualization of product design take precedence; distribution is complicated by generational and cultural differences bringing new demands for diverse method, as-needed access; and service has become the true competitive differentiator as consumer expectations of treatment, competence, responsiveness, and individual relevance have become the determinant of loyalty.

The convergence of changes combined with the diversity of new tools, technologies, and talent bring the industry to the tipping point at which the ultimate solution rests with true innovation. Refined status quo will be drastically insufficient to meet the demands of the new market. Gradual transition will equally fail, as the degree of patience for change is minimal at best.

Leadership within the industry must focus on innovative integration and leveraging of technology to significantly alter the mechanics of how insurance is designed, communicated, distributed, and serviced. Unfortunately, barriers abound, in particular the chaotic tactical challenges that command the bulk of leadership’s bandwidth to address, leaving naught for the contemplation of strategic directions or innovation.

Despite these challenges, sufficient awareness of the need and willingness to invest in moving to the next level exists, even in these difficult times. In fact, from a technological perspective—the foundation for near term innovative solutions—four different year-end 2011 surveys by reputable firms regarding IT investments in 2012 indicated a likely increase in expenditure in the two to five percent range, with the top priority focusing on replacing policy administration, or core processing, systems.

More specifically, according to a well-known industry analyst, the level of activity directed at installing modern, flexible systems has never before been as high as it is currently running, validating recognition of the critically important competitive advantage that will be provided by those who successfully navigate the complex renovation and replacement path.


Granted, with a nod to the skeptics, legacy system replacement has been around for quite a while, and has been touted intermittently as “the” focus of a given year. Yet the older systems persist, in various forms of enhanced, wrapped, blended, or hybrid form as the complete transition to a new platform has proven both expensive and, in the eyes of many C-suite executives, of questionable ROI compared to other market growth strategies.

In fact, after engaging with a number of different companies in the RFP / ROI process, it has become apparent that the initial selling point of legacy replacement—operational efficiency and reduced maintenance cost—is in truth inconsequential on a relative basis compared to the creation of previously unforeseen revenue-leveraging opportunities. The true benefits of legacy system revitalization or replacement, clearly in combination with other synergetic technological investments, rest with:

• The capacity to nimbly adapt to customized product designs that are granular, modularized, and targeted;

• Extensive servicing options that easily integrate with social networks, portable devices, and 24/7 access;

• Analytically informed pricing, distribution, underwriting, servicing, claims payment, and retention strategies; and,

• Consolidated and incremented customer data providing a holistic view of economic value across lines and time.

At the core of each of these advantages is an understanding of the market and customer as opposed to the more traditional focus on process improvement or efficiency. Brand name, product features, even pricing differences within a given margin, no longer provide the differentiation needed to successfully gain market share. Instead, competitiveness and differentiation are based on finely-priced products, an effective pairing of distribution with target market, and an intimate knowledge of both prospects and customers.


This introduces the second critical area of investment for 2012 and forward, one that has gained prevalent coverage in more recent years and that is analytics. While the industry has traditionally been data intensive, the data has tended to exist within distinct and separate silos and has resisted translation into coherent information that was proactively actionable; for the most part, it was a lagging indicator of performance that could be used to make retrospective adjustments in pricing, product mix, target market, or channel compensation.

In order to achieve the necessary foundation for integrating the wealth of silo data within a carrier with the calculating abilities of predictive analytics is to be able to holistically look at all of the available data on a given customer, channel, location, demographic, and psychographic in order to construct the ideal combination of appeal and profitability. A number of approaches can be used, including:

• Integrating all of the relevant variables regarding customer information into the policy administration system, similar to creating a complex CRM built around the transactional system;

• Develop an external BI layer that rests across the various silos of data, including the separate creation of a “behavioral database” that consists of externally purchased data and caller/non-transactional data collected;

• Building disparate models for the various scenarios that take extracts from the different data silos and assemble those extracts into a coherent view of the target situation to determine optimal offering; or,

• Create a hub-and-spoke model with a data warehouse in the center with spokes of informational exchange connecting outward to each of the operational and informational systems including the policy admin system.


Whichever method is selected, the end goal has to be to take a holistic look at the data across systems, not only at a point-in-time basis, but also over periods of time within the context of what is occurring to best inform the model. The key takeaway is that analytics will only provide complete value when data is no longer segmented into discrete silos within a company; it must be viewed as a complete dataset, which will require cultural, technological, and intellectual changes from the current mindsets.

The traditional functional views of data have a great deal of momentum, and will take change management effort to divert into the new direction of consolidated modeling. Despite the growing prevalence and apparent acceptance of the importance of data analytics, recent senior executive level research indicated several barriers to success, including:

• Nearly two-thirds of today’s senior executive decisions are still made based on experience, collaborative consensus, group dynamics, or intuition, with only one-third stating they use analytics;

• Limited investments in analytical tools or techniques are likely over the next two years according to those surveyed, as historical trends, benchmarks, and traditional unit measures and ratios remain prevalent;

• Agency management and actuarial (pricing) remain less likely to use analytics than underwriting or finance, with claims in the middle of the pack; yet they all represent significant profit-improving opportunities;

• Those organizations using analytics tend to have fragmented unities within individual departments, several separate systems versus a single integrated one, and/or rely upon finance or IT to do their analytics for them; and,

• Of most concern were two of the top reasons that surveyed companies were not further leveraging analytics, the fact that the benefits were not viewed as outweighing the cost and a general lack of the executive sponsorship needed to ensure an integrated solution is pursued.


After surmounting these obstacles, expertise will have to be established and formal roles assigned at the enterprise level in order to maintain consistent data use definitions (how many different ways can “premium” be interpreted within a company), ensure effective data management across the silos, sustain persistent consistency of data etymology via formalized governance over new applications and developed systems, and monitor the tools, techniques, and practices used by various departments in modeling actionable strategies from the analysis of the now-common data.

The additional rigor is needed not only to offset the newfound complexity, but to allow for the fact that today’s analytics are no longer retrospective, but are forward-looking (a/k/a, predictive) as companies seek to learn, integrate, and act at a granular level of customer attributes and expectations regarding product features, method of sale, service delivery, and lifetime touch points. Here is where one finds the source of innovation, as companies take the vast amount of discrete data items, consolidate them with externally acquired behavioral and lifestyle information, and generate individualized solutions presented in the preferred manner at the appropriate time from initial prospect through customer lifecycle.

It is no longer a matter of an offering of predetermined options by a trusted advisor in response to a call, lead or referral, it has become a well-defined approach via a singularly specific method (advisor, Internet, telephone, mail, email, online chat, Webcam, podcast) to present a custom-designed solution. The method, timing, and features are the result of extensive multivariate or Generalized Linear Model (GLM) analysis working with tremendous amounts of variables operating within the context of existing company channels, product components, risk appetite, growth strategies, and profitability goals, all constructed for the specific offering.

This is where the true innovative use of analytics comes into play—the bridging of the vast amount of operational and transactional information that exists within policy processing and core systems, first eliminating the silos and then supplementing the data with externally acquired relevant information. Property & casualty lines have been expanding their use of GLMs as a source of pricing for a number of years, combining age, gender, driving record, credit score, ZIP code, and other variables into a single rate. The key complexity of GLMs resolved by predictive analytics involves coming up with the answer to two key questions:

• What rating variables to include as relevant and material in calculating the rates; and,

• What relative weight to give each of the variables included.

Fine tuning the variables down to the materially impactful subset and then determining the appropriate relativity of each variable requires processing an extensive amount of data through iterations of models that are validated against real world results before adopting.


Looking beyond P&C business, at a recent life insurance product development conference, the discussion centered on the use of predictive analytics for pricing purposes. Here, the variable set would be expanded beyond the typical age, sex, risk class, face, and duration to incorporate additional variables like marital status, geographic location, occupation, income, type of car, type of housing, and even number of children.

In both instances—p&c and life—there was discussion about how additional generally-available sources of information could be used to supplement the models in the future, further enhancing the granularity of the pricing, by including magazine subscriptions (weight loss versus runners world); foods purchased (its logged by your “frequent buyer” card); time in front of television; credit card purchasing patterns; and a variety of other already publicly available information that could be integrated into the GLMs for pricing purposes.

Needless to say, both explaining and perhaps even justifying the inclusion of many of these variables may prove a challenge. Still, they exist, and they could be used to model out casualty risk or morbidity. In fact, as it turns out, there are examples offered where this information is already being used in the healthcare field for similar determination of group ratings, treatment programs, and other individualized solutions to specific needs.


The value of analytics extends beyond the pricing, distribution, and acquisition of a customer, and serves a purpose prior to even the fraud detection opportunities at point of claim. A fully integrated analytics program looks at customers on an ongoing basis in order to determine levels of service provided, potentially call/answer rates and field access, or even support options. The goal is to balance the needs of the customer with the profitability in terms of Lifecycle Economic Value (LEV).

LEV measures a given customer’s total contribution to the company’s bottom line from the perspective of across product lines, across points in time, and through the expected future interactions and premiums. In determining this LEV, or lifetime profitability, customers that may look like small contributors from one line are seen for their total economic value when looked at across the enterprise and family-line connections, which in turn could drive different rating actions, retention efforts, and pricing considerations.

A substandard policy on a low-risk line might be adjusted, for example, if the LEV of a customer is seen to be high when considering all other business and associated business. Absent the ability to have a holistic view, decisions are made that sub-optimize the full value of that hard-earned relationship. Given the upside potential of revenue over expense reduction, the extra care to fully understand the implications of each customer will prove well worth the effort over time.


The economy is not going to provide a windfall break to the industry; survivability and profitability rests with leadership to find near-term solutions. Given the rapid rate of change in diversity, globalization, technological advances, and competitive landscape, time is of the essence for those wishing to achieve competitive advantage. Neither analysis paralysis, a patient “wait it out” perspective, nor a continued focus on expense reduction at the staff level—that front line of talent that represents your most critical customer touch points—will provide the necessary solution to sustainable growth and profit. Sustainable profitability will come directly from:

• Investing in your front line staff, ensuring that they are empowered to provide world class differentiating service;

• Investing in a policy administrative system that is nimble, adaptable, cost effective, and customer friendly;

• Investing in a consolidated collection of business data across all systems that is externally supplemented;

• Investing in a comprehensive, granular understanding of your market, distribution partners, and customers; and,

• Driving decisions and strategies from a well-governed dynamic pool of reliable facts, projections, and models.

For the strategic enterprise, the long-sought alliance between information, technology, business, and the intellectual capital of the work force has arrived. As Victor Hugo once said, “There is only one thing stronger than all the armies of the world: and that is an idea whose time has come.”

The convergence of economic challenge, technological advancements, insurance market dynamics, and consumer expectations brings with it a tremendous opportunity for those willing to rise to the occasion. Similar to buying houses in a down market (perhaps a controversial example), the best time to invest and excel is now.

Steven Callahan is a senior consultant and practice director for the Robert E. Nolan Company, a management consulting firm specializing in the insurance, healthcare, and banking industries. For over 38 years, Nolan has helped companies achieve measureable improvements in service, quality, productivity, and costs through process innovation and effective use of technology. Steven can be reached for further comment via e-mail at

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