August 15, 2012 –  Customer analytics should be ready to take an enterprise-level leap into real-time operations, social metrics and advanced data crunching in the next few years, according to analyst insight and a new market assessment from Forrester Research.

The Forrester report, “The State of Customer Analytics 2012,” involved an extensive survey of 90 enterprise customer analytics practitioners in banking, finance, utilities and professional services. In it, the majority of customer analytics practitioners in the report noted mature use of reporting and BI (69 percent), descriptive analytics (81 percent) and predictive models (73 percent) for customer metrics. That level of adoption and use puts analytic prowess in marketing and sales above most other enterprise departments. Now, these departments plan to take their next forays into returns and develop their customer data resources, with a somewhat mixed bag of plans, says Srividya Sridharan, a customer intelligence professionals analyst at Forrester and an author on the report.

When asked about the top customer analytics program they planned to pilot in the next two years, “social analytics” led the way, though 30 percent of respondents pegged social analytics returns as a long-term goal. This is not be surprising in terms of social media data interest, but definitely “indicates that social data is still a largely unexplored data source” at the present, says Sridharan.

Sridharan says she expects businesses that have already made investments in better customer data management and measurements to take a serious look at predictive analytics as a next step. However, the Forrester expert cautioned that enterprises need to evaluate what returns they would expect from a predictive platform before diving in to an implementation, as well as take an inward look at the information at hand and how it would be managed moving forward.

“One of the prerequisites for predictive analytics is to have the right type of customer-level data available and accessible at the appropriate granularity in order to build predictive models. While firms can pilot or build models based on predictive analytics techniques, where they need to focus more is in putting these models and scores to work through marketing execution systems that actually manage customer interactions,” Sridharan says.

The top two inhibitors to further development of analytic methods and models cited in the report were the lack of capabilities to integrate data from multiple sources (54 percent) and assurances of data quality (50 percent). Also, 42 percent stated they lack the analytic resources, and 37 percent noted they don’t have the staffing to drive customer analytics. These problems are not new to analytics and are expected to grow alongside the intake of more data, Sridharan says.

Sridharan recommended enlisting help from internal or external experts in data governance, who may not be domain experts in marketing or customer intelligence, but are adept at defining processes around data definitions. The analyst says she’s also seen enterprises overcome integration challenges by adopting a “quick-win approach”: tackling manageable projects like marketing ROI measurement that have a restricted approach, limited data streams and readily identifiable outcomes.

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