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Data & Analytics

03:15 PM
Dave Kaytes, Managing Director, Novantas; and Matthew Josefowicz, Director, Insurance Practice, Nova
Dave Kaytes, Managing Director, Novantas; and Matthew Josefowicz, Director, Insurance Practice, Nova
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Technology and the Science of Customer Retention

Data accessibility, analytics, rules-driven workflow and process visibility can help insurers retain profitable customers, according to Dave Kaytes, Managing Director, Novantas; and Matthew Josefowicz, Director, Insurance Practice, Novarica.

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As the insurance market continues to contract and becomes ever more competitive, carriers are discovering that retaining profitable customers has become even more important than acquiring new ones. New customers have high acquisition expense, high initial cost of service and lower persistency, all of which result in lower profits than serving and retaining existing customers.

Fortunately, key new technology capabilities have emerged to help insurers retain their best customers. The challenge today is to define the right strategy that deploys these technologies to realize superior levels of customer retention.

Winning organizations will capitalize on some combination of four core technologies to achieve this strategic objective:

  1. Enterprise data accessibility, made possible by advances in enterprise data warehousing and service-oriented architectures (SOA) that enable data to be gathered and called more easily in a transactional environment.
  2. Predictive modeling, using advanced analytic tools and made possible by the consolidation of enterprise data.
  3. Modern, rules-driven workflow systems, including business process management (BPM) platforms as well as the new generation of service, underwriting and claims platforms.
  4. Process visibility and iteration, which subjects the information created by an automated working environment to optimization analysis and allows for data-driven organizational learning.


Leading competitors are seeking to accelerate progress in the long march from product-focused files to client-focused data. This transformation implies redeveloping, replacing or augmenting legacy systems with an end-to-end consolidated view of the customer relationship across the institution. While many organizations have set out on this path, few have moved beyond the basic cross-reference data in each product system silo to a true consolidated customer database across their lines of business. Some of the more advanced examples that will be familiar to I&T readers include Nationwide and CUNA Mutual.


Winners are developing and deploying predictive analytics to identify current and potentially high-profit customers. A number of leading insurers have begun to use customer and agency profitability measurement, which identifies the probable lifetime value and future value of the relationship for each individual. Using this measure, the insurer is able to allocate resources and management attention to the most profitable current and future customers and agents, rather than the noisiest. The profiles of these high-value customers and agents are flagged at the point of service to ensure that claims handling, renewal efforts and other servicing go beyond the standard level of service. A major mutual fund company takes this a step further by monitoring in real time the flow of transactions and contacts of the best customers, identifying those patterns that indicate the likelihood of attrition and initiating proactive steps to influence the customer before its too late.


Many insurers have recently invested in improved work environments for key knowledge workers (i.e., service people, underwriters and adjusters), including smart workflow, BPM, VOIP-based integrated telephony, and modern claims and underwriting systems. These improvements allow insurers to deploy predictive models to drive better decisioning.

Process Iteration and Optimization

This enhanced degree of automation makes it possible for insurers to track effectiveness of individual knowledge workers more than ever. Leading competitors now profile their highest performing staff, learn details about what makes them so effective, and then inculcate or replace low performing staff to generate significant improvements.

In the contact center, winners are using real-time "test and learn" techniques to improve point-of-service interaction with customers. These tools capture the interaction between customer and phone agent (typically through recordings) and use advanced technology to analyze the words, tone, pacing, sequencing and other events of the call. This analysis is then correlated to the outcome of the call, isolating the best from the worst approaches. These tools allow the phone service channel to fundamentally upgrade the quality of servicing by determining what styles and types of interactions result in positive (and negative) outcomes. The result is that the service center institutionalizes the best practices of its few best agents, more quickly trains underperformers, improves handling time, and delivers a more uniform, upgraded customer experience.

Insurers that can leverage these advances in technology to build effective models based on solid data and then use those models to drive decision making and service provision will have a significant advantage over their peers in the battle for customer retention.

Dave Kaytes ([email protected]) is a managing director at consulting firm Novantas. Matthew Josefowicz ([email protected]) is the director of the insurance practice at Novarica, a division of Novantas focused on insurance technology strategy.

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