How to Use Voice Mining to Tune In to the Voice of the Customer - Page 3

Component No. 4: BI reporting and performance dashboards

Analyzing audio data to categorize customer segments and predict customer satisfaction levels is a multistep process-and one that our own human brains struggle to do in real time. An accurate predictive model, built from text mining and data mining algorithms, starts out by summarizing what the caller is saying now, and then goes a step further to inform you of what the caller is likely to say next.

With a BI dashboard, you can alert your staff to the recommended responses and actions to take. For example, let's say a delinquent customer calls in with a complaint. If this customer goes away, you may avoid future losses. However, if you have a very important caller (for example, one that your predictive model has identified as being a big spender or a representative of a large segment of customers), then your automated BI dashboard may prompt you to offer a special promotion or incentive to the caller-on the spot, in real time.

Voice mining technology is ready for prime time. The past decade has witnessed advances in each component. It's time to remove the "emerging" label and replace it with a market-proven tag. Not long from now, voice mining will be a "best practice" for successful organizations across the globe.

This best practice entails applying all four components in harmony. Voices are telling you how to increase satisfaction, build loyalty, reduce churn and make safer products. Technology is automating the process of giving ear to the voices that have long been muffled in mounds of data. When that information comes to life, true competitive advantage can follow.

/images/stories/heads/knowledge_center/crissey_MaryGrace70x70.jpg Mary Grace Crissey is the Analytics Marketing Manager at SAS, where she applies her Master's of Science degree in Operations Research to show how text analytics, optimization and data mining can solve real world challenges. After 20 years as a military scientific officer, she continues to serve in leadership roles with professional societies including Knowledge Discovery and Data Mining (ACM/KDD) and the Institute of Operations Research and the Management Sciences (INFORMS). She can be reached at