By GARY CIARDIELLO, a principal in the Insurance and Actuarial Advisory Services practice of Ernst & Young's Financial Services Office, and BENNY YUEN, an executive in the practice
Insurance companies are looking at data-driven analytics approaches, such as customer analytics and predictive modeling, to improve performance across virtually all operational areas. Increasingly, insurers are using both internal and external data, with the objective of improving the effectiveness, cost-efficiency and quality of product design, product pricing, claims settlement and other core functions. Agency production and profitability are other important areas in which analytics can be a valuable driver.
Over the years, insurers have evolved their own agency management methodologies, and some are more rigorous than others. They include the tracking of performance metrics such as premium volume, compensation strategies, new-business and in-force counts, loss ratios and company retention ratios. In addition, there is always more than a little subjective evaluation based on relevant, but not always dependable, anecdotal feedback about one-on-one situations and interactions with agency personnel.
The most robust and effective management of agency networks almost always results when companies start to use a multidimensional analytics framework. Such a framework is geared to providing a holistic, objective view of all of the pertinent factors that interact with each other.
The objective is to provide insight into how each agency is performing relative to its peer agencies in a company's distribution system. Out of this analytics, or business intelligence, environment comes insight into agency performance and potential solutions for areas that achieve a low relative score.
Additionally, more insight and clarity can help direct an insurer's investment into those agencies and markets with greatest potential for increasing its brand strength and market share.
THE NEEDED DATA
The development and implementation of an analytics framework is not as daunting as it may sound. Companies already collect and store large amounts of internal data and use various analytics and modeling tools. The objective is to draw together existing internal data and acquire readily available external data to populate an initial modeling framework.
The current and historical internal data for the agency management framework include multiyear data such as production, profitability, compensation, agency profiles and back-office performance.
The addition of current and historical external data adds depth and breadth of perspective. Such external data include rate competitiveness, brand recognition, customer profile, customer satisfaction and market potential (i.e., local/regional demographic and socioeconomic projections).
Bringing together internal and external data and building a predictive model allow an insurer to understand an agency's performance in a market in the context of how the company's products have performed generally through its agencies in that market. Overlaying projected local and regional data will help the company evaluate an agency's potential and identify current barriers to continued success and growth.
THE OVERALL SCORE
Individual index scores, derived as output from the predictive model, are then combined into an overall score--or composite index--for each agency. The composite index is derived statistically through the assignment of weights to the individual indices in the model. The index will allow a company to compare and rank the agencies in its network in an objective way. Agencies with very high overall scores would typically have a stronger likelihood of continued success.
As important, the composite index will highlight the area(s) of investment that an insurer can make to improve the overall score of an agency. For example, if an agency's composite index is low due to an extremely poor score for brand recognition, the company can work on its marketing effectiveness. If the socioeconomic data indicate that a city or region is poised for significant economic growth or for growth in an attractive demographic segment (e.g., soon-to-retire affluent baby boomers), the company can look at adjustments in its product mix and marketing programs.
OPERATIONAL AND STRATEGIC BENEFITS
Arguably, the highest-level and most compelling benefit of taking an analytics-driven approach to agency management is to have a consistent, objective means to track, measure and evaluate agency performance over time. Having a consistent methodology for quantifying important factors allows a company to target its investments in its agencies and evaluate the improvement as a result of those investments.
Retrospective analysis of how a well-established agency has performed in terms of specific products and pricing strategies, as well as in different economic environments, can be strategically useful in setting future strategy with that agency.
The analytics framework also allows insurers to make judgments and decisions about how well its agencies are aligned with the company's long-term strategic direction in terms of product development, product mix, pricing and underwriting risk.
Some agencies are more willing and able to adapt to change than others. An analytics framework enables insurers to apply predictive modeling techniques, drawing on historical and current data, to predict how an agency is likely to respond to potential or emerging changes in its business environment.
For example, the potential impact of an increase in advertising on an agency's production can be measured objectively through the brand-recognition index and its effect on the composite index.
In a difficult economic environment, there are obvious potentially predictive correlations between agency performance and policyholder behavior.
Insurers need to identify various combinations of factors that are impacting, or could begin to impact, the dynamics of a particular market and its agencies in that market. It is critical to predict and act as early as possible to ensure agencies are positioned and prepared for greatest effectiveness.
As many insurance companies take a fresh look at their product strategies and pricing--their tiering structures and customer segmentation models, for example--their analytics frameworks and predictive modeling can help determine which of their agencies are best aligned to support evolving corporate strategic objectives.
Analytics does not negate or replace non-data-driven, conventional agency management techniques. Nonquantifiable experiential information, particularly with well-established agencies, can be invaluable in agency development and resolution of agency issues.
Analytics provides a critical added dimension of looking holistically and objectively at agency performance. An analytics approach provides rigor, consistency and objectivity in decision-making.
As companies create their analytics frameworks and populate them over time, the frameworks provide increasingly deeper understanding and perspective. The more mature an analytics framework becomes, the more accurate will be the predictive modeling that can be accomplished. A robust analytics framework will provide opportunities for rich self-learning and continuous improvement that can spur fresh thinking and innovative agency management approaches to improving agency production and profitability.
September 1, 2009
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