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What's in a name?

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Aug 5 2009

Customer Data Management. Customer Intelligence Management.  Data Intelligence Management.  The notion of using customer data to drive investment decisions has propagated many organizational names and acronyms over the last few years.  Customer Intelligence Officers were crowned, integrated task forces were formed, and dedicated publications multiplied.  Why?  Because data = $.

Customer data, specifically, transactional or behavioral data, has proven to be predictive in ways that transcend gender, zip code and income. There is a reason credit card companies have always been the most successful direct marketers in the industry; buying behavior and life stage patterns are easy to model, aggregate and wrap marketing process around.  But today, marketers are focused on more than just marketing.  They are accountabe for extracting value - both direct and indirect - at every customer touch point and through every channel. And although "customer-centricity" is positioned to be in the best interest of the customer, the application of customer information and execution of associated programs is most often self-serving.  One reason why, today's educated consumer is not buying the idea they should opt-in to online behavioral tracking so they can be served more relevant ads.

But all is not lost. Those companies that truly understand how to collect and use this information in a meaningful way do prevail.  Tom Boyles, senior vice president, global customer managed relationships for the Walt Disney Company, recently said in his keynote at NCDM, "It's all about the data."  But he emphasized, "The data has to be accurate, real-time, and multi-channel."  Easy, huh? 

Well, not really.  But marketers can take a few basic steps to improve their use of customer data:

  1. Data Value Analysis --- an analytical approach that benchmarks the value of the data sources and provides a business case for prioritizing their acquisition and incorporation into your CRM environment. This analysis helps you filter down mountains of data to only the most actionable bits of customer information.
  1. Customer Value Model --- a customer value segmentation model that looks not only at historical value, but relative potential value to help you focus your marketing resources.  This will estimate any additional revenue to be captured from an individual customer based on a comparison between the value of that customer to your company and their peer group.
  1. Go-to-market workflow analysis --- an in-depth analysis of your current processes and the supporting technologies for presenting offers and handling customer service inquiries.  From such an analysis you can determine what changes will have the most impact on the customer experience and your bottom line. 

Most importantly, do not allow the customer to get lost in the vernacular of technology platforms, business processes or value extraction.  Remember, you're supposed to be managing the customer, not just their data. 

 

 

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