April 29, 2010

By Sabine Vanderlinden, Chordiant/Pegasystems

The objectives of customer retention and and maximizing value from the most profitable customers are top-of-mind for many insurance carriers, and we know that being able to reduce attrition even by even 1 percent could mean millions of dollars in protected premiums. This article will explore a few best practices that are very much linked to the current operational focus on customer satisfaction, loyalty and advocacy through a better understanding of a customer's persona and creating personalized experiences.First, let's take a look at the usage of the latest customer segmentation techniques to capture existing and emerging behavioral, buying and servicing patterns during the key touch points of the interaction lifecycle. Second, I will highlight how better profiling techniques can then drive the personalization of offers tailored around life-stages and life-events supported by real-time feedback, guidance and alerts to drive superior customer experiences.

A simple scenario will help illustrate the problem and potential solutions. Mr. Jones has a long-term, fully comprehensive auto insurance policy with ABC Insurance. He is calling in to change his address following a recent significant life event (e.g., a marriage, divorce, child birth, lost job, etc.), and we know that the minute Mr. Jones calls into the ABC Insurance call centre, the agent should be asking himself:

1) Will this customer terminate his policy? 2) When is he going to defect? 3) Can we predict the time of termination and prevent it? 4) Should we intervene in this case and, if so, when? 5) How much should we spend to avoid termination?

Using today's technology, however, the agent or self-service channel would go through the change of address process without trying to understand the root cause of the change or promote the best recommendations based on the policyholder's circumstances. The key problem with this is if Mr. Jones has a high risk of termination, it will most likely not be detected during the address change as all customers are treated the same.

With more innovative technology components, organizations can start to leverage advanced predictive analytical models able to predict the likelihood of someone defecting at the earliest moment and throughout the various stages of an interaction based on existing internal and external data, and then determine whether and why the customer is worthy of a retention campaign. Indeed, while the cost of retaining an existing customer is usually far less than the cost of acquiring a new one, all customers are not equal and different customers tend to have a different "cost of ownership."

Adaptive learning models can help with this. These models are able to learn on the fly while the agent is having the conversation with the policyholder - taking into consideration the policyholder's responses - or even when a customer is directly making the specific changes over a self-service channel. Considerations that must not be overlooked when using such capabilities include: 1) ease of use by the most novice business users, 2) outputs produced in days not weeks, and 3) they must be actionable at the touch of a button.

In addition to these well-predicted customer patterns, innovative next-generation technologies would then facilitate the proactive determination of the best sequence of retention strategies to deploy for Mr. Jones in real-time and would adapt each sequence of strategies based on Mr. Jones' profile and selected channel of preference. For instance, there are solutions that take the outputs from the advanced retention models that were developed and embed them within pre-defined, sophisticated, retention-focused decision blueprints. These customer-centric decision strategies are continuously re-evaluated, monitored, simulated and optimized in real-time to help ensure that the best option is presented and offered to a policyholder. These can be displayed through visual panels customized for the selected channel of use.

Focusing on just adaptive learning models and tailored retention strategies could help organizations achieve significant benefits through a more accurate understanding of which value-generating customers are "at risk" of defecting, so that scarce resources can be refocused where it matters using tools that facilitate relevant customer-centric conversations.

About the Author:Sabine Vanderlinden is a director focused on customer-driven insurance solutions at Chordiant, now a Pegasystems company, where she supports the unified customer experience management lifecycle.Focusing on just adaptive learning models and tailored retention strategies could help organizations achieve significant benefits through a more accurate understanding of which value-generating customers are "at risk" of defecting, so that scarce resources can be refocused where it matters using tools that facilitate relevant customer-centric conversations.

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