Feature: Service Advantage - InformationWeek

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Software // Information Management

Feature: Service Advantage

To improve satisfaction, service organizations are analyzing customer interactions to identify and correct problems before they escalate. And to stay in tune with changing needs, firms are scoring customers by the minute, applying analytics to the data stream from call centers, retails stores, Web sites and more to customize on the fly. Find out how you can use analytics to ace customer service.

Banks, brokerages, telcos and insurance companies are no strangers to analyzing customer data to reduce risk and conceive marketing campaigns, crunching the numbers to detect fraud, evaluate creditworthiness, cross-sell and more. But most organizations are just beginning to direct their considerable data mining and analytic prowess to serve customers better.

"Instead of using analytics to predict what customers are going to buy next, we're using them to improve the customer experience, which indirectly boosts revenue, too," says Forrester Research analyst John Ragsdale.

To retain customers in markets prone to churn, such as financial services, firms are using predictive analytics to spot behaviors that foreshadow customer departures and to develop the right inducements to make them stay. To improve customer satisfaction, service organizations are analyzing customer interactions to spot and correct problems. To stay in tune with changing consumer needs, companies are scoring customers by the minute, applying analytics to a steady stream of data from call centers, retail stores, Web sites, e-mails and text messages to customize products and services on the fly.

Making all these efforts possible are emerging predictive capabilities that can examine customer interactions across channels, new analytic tools for inbound call centers and visual analysis tools that give service reps insight into customers at a glance. Read on to learn how the latest technologies are being applied to customer service challenges at mutual fund firm Dreyfus, at U.K.-based mobile phone provider O2, at a leading regional AAA organization and at online insurance provider eSurance.

Predicting Defections

One point at which predictive analytics and customer service logically intersect is customer retention. By applying predictive models to customer behavior, companies can identify patterns that lead to defections and then figure out how to solidify shaky relationships. SAS and SPSS are the leading providers of predictive analytics software for customer data analysis; other specialists include Chordiant Software and Intelligent Results. These products can analyze multiple data sources using several standard data-mining algorithms, often in combination, then provide reports to different groups of decision makers.

When Dreyfus realized its biggest business challenge was redemption--customers cashing out their investments--the mutual-fund company began building predictive models in SAS Customer Relationship Management Solutions, so it could forecast which investors would leave over the next five to nine months. Dreyfus used a "survival analysis" technique that originated in the biomedical industry and was used determine how long patients would be likely to survive untreated or under various treatment regimes.

In financial services, warning signs can include a change in almost any behavior: more calls to the customer-service center, more transactions, fewer transactions, higher or lower balances. At one point, for instance, customers in Dreyfus' Golden Years segment were found to be taking more money out of their accounts than they were putting in; further investigation confirmed these customers were transferring their wealth to younger generations. Dreyfus responded by offering these "majority redeemer" customers trust services and education savings plans. "Once we do that, we extend the customer's life with Dreyfus," says Prasanna Dhore, executive vice president. The overall effort paid off handsomely, bringing the redemption rate down from 25 percent to 7 percent.

Dreyfus's challenge is to keep that defection rate below 10 percent while the industry average hovers around 22 percent. To do so, statisticians run the predictive models regularly to troll for customers with a high probability of redeeming their funds, then test hypotheses as to why customers might leave and what alternative products might keep them in the fold. "We have to keep doing that because people do change," Dhore says. "New customers come in and the segment behavior changes, so models and segmentations become stale and have to be rebuilt."

Dreyfus focuses primarily on what Dhore calls "money in motion." The biggest challenge over the next five to ten years will be predicting how to retain retiring Baby Boomers. "They're looking for ways to invest their money so they don't [outlive] their investment," he says. His team of eight analysts is creating predictive models to determine which customers are on the cusp of retiring, how long each customer is likely to keep working, how long customers will keep their multiple 401(k) plans and how to break the large segment of pre-retirement customers into five to seven smaller clusters that can be served in a more customized way. Surveys and focus groups are being conducted to better understand what these customers want, and service reps are being trained on retirement issues. The company also plans to feed some of the results of its predictive models to partners such as Merrill Lynch, Wachovia and brokerages that sell its funds.

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