Article discussing key trends in technology and considerations for leaders looking for strategies to achieve competitive advantage without taking years and millions of dollars. Offers up the question of legacy system replacement tradeoffs and alternatives.

1. Property Casualty 360
The Silver Lining
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.
BY STEVEN M. CALLAHAN
From the February 2012 issue of Tech Decisions
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2. 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.
THE FOCUS
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.
ANALYTICS INVESTMENT
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.
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3. 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.
HOLISTIC VIEW
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.
MORE RIGOR NEEDED
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4. 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.
BEYOND P&C
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.
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5. 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.
LIFETIME ECONOMIC VALUE
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.
NO WINDFALLS
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
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6. Like
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 steve_callahan@renolan.com.
ABOUT THE AUTHOR
Steven M. Callahan
Steven Callahan is a senior consultant and practice director for the Robert E. Nolan
Company, a management consulting firm specializing in the insurance, health care,
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 steve_callahan@renolan.com.
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