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Delving into customer thoughts: TEXT ANALYTICS provides insights

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It is important to consider in advance what an organization will do with the information. “One insurance company analyzed incoming queries and realized that its website was not clear enough,” McNeill says. “The company then modified its site and substantially reduced the number of queries. However, if an organization is not prepared to make adjustments, then they might want to reconsider the role of their analysis.”

Fine-tuning for customer retention

The Medicare & Retirement division of UnitedHealthcare (UHC) serves nearly one in five Medicare beneficiaries. It offers a full range of products and services, and is the country’s largest provider of Medicare Advantage plans, Medicare Part D prescription drug plans and Medicare Supplement insurance. Many of its products carry the AARP name.

To determine whether UnitedHealthcare is meeting the needs of its customers, the company has used a variety of software tools to measure its success. “We use a mix of quantitative and qualitative data,” says Tom Allenburg, VP of data analytics and marketing solutions for UnitedHealthcare Medicare & Retirement. “However, we were underutilizing the information that is captured in free form text during call center interactions.” The information was being stored, but not analyzed.

UHC selected Mu Sigma to support a number of analytics initiatives. “Leveraging text was a new venture for us,” Allenburg says, “and we wanted to partner with an organization that had both the professional expertise and a set of powerful analytic tools.” Mu Sigma’s text mining engine, muText, analyzes text in structured and semi-structured content to evaluate customer comments.

One of UHC’s priorities was to identify customers who were likely to let their policies lapse. “The more we understand customers’ feelings and experiences, the better we can develop ways of retaining them as subscribers,” Allenburg adds. “If someone felt negatively about an interaction with our customer service area, we wanted to be able to find out why.” UHC had used both quantitative and qualitative data to derive a predictive model about policy lapses, but wanted to incorporate the call center notes to improve its accuracy.

In fact, UHC was able to show that bringing in the call center log notes provided lift, and improved its ability to identify those likely to lapse. “A model won’t lower the lapse rate, but since we are now able to better target the customers who are at risk, we can develop plans to reach out to those customers,” Allenburg explains. In addition, the analysis allows training activities with the call center representatives so they can be alert for certain key phrases. “If we know that certain phrases are associated with lapse, we can improve the customers’ interactions upfront and improve retention,” he adds.

Better communication

Over time, additional applications for text analytics have been developed. UHC is using voice recognition to convert audio to text and then analyze it. “This process allows us to ensure that certain statements required for compliance purposes are being made during the calls,” Allenburg says. In addition, UHC is using text analytics to improve interactions with customer service representatives during open selling season. “Some of the newer reps were not clearly conveying a sense of urgency during these limited time periods, to ensure that a prospective customer would make a decision by the deadline,” he notes. “We were able to analyze the conversations and make positive suggestions for better communication.”

The use of text analytics allows a much greater degree of personalization, which is a strong trend in marketing. “Traditional demographics and transactional data only get you so far,” Allenburg says. “Two people who are demographically similar might have very different reactions to a product, a service or an interaction. It all comes down to the individual, and personalizing our response to meet their needs.”

The desire to know what customers are saying in near real time is one of the most frequently mentioned drivers for implementing text analytics, whether the comments are coming from a CRM system or on the Internet through various social media channels. “Many large companies have years of focus group information that has not been analyzed,” says Mike Feldner, regional head of client services at Mu Sigma. “More broadly, everyone is contending with the explosion of data, and trying to decide what the right tools are. Since companies can now answer questions that they could not in the past, they are having to change their mindset about what is possible.”

Mu Sigma’s text mining application is integrated into its Decision Sciences workbench, muRx, which provides a guided method for data exploration, modeling, analysis and reporting. “We offer both a platform and professional services,” Feldner says, “but our services and products are transparent, so the clients know exactly how their data is being transformed and modeled.” The outcome of the decision process is a set of conclusions and recommendations on which the company can act.

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