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CRM analytics–an array of options

Hallmark has also been pleased with the usability of SAS’s BI solution. “Our power users can build predictive models more easily, and our less technical users are able to access information that has been processed for their queries,” Dittmann says. “So far we have not encountered any challenge that the SAS BI solution has not been able to handle.”

Data management is often the most difficult problem in achieving a complete view of the customer. “Being able to pull information in from various silos involves not only technical issues but also political ones,” says Larry Mosiman, product marketing manager for SAS customer intelligence. “But it pays great dividends if you can do it.” Once the data is integrated, customer behavior can be analyzed and behavioral models built. Predictive analytics used to build the model can drive interactions that increase customer satisfaction and optimize use of resources.

Technology is an enabler, not a replacement for an effective CRM strategy. “CRM is a business strategy first,” says Gartner’s Collins, “and at the highest level there must be a vision. After that, the organization needs to discover the best way to support the vision.” One big challenge in coming years will be how to carry out analytics on the many social web channels. With more qualitative data coming in, it’s important to listen to all the channels and combine them with quantitative data. (For another view of the customer experience, see “Customer Experience and Sentiment Analysis,” KMWorld February 2010, kmworld.com/Articles/Editorial/Feature/Customer-experience-and-sentiment-analysis-60764.aspx.


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