A Wholesale Mortgage Lender Builds a Comprehensive View of the Customer

At the outset of a systems reengineering effort, the client understood that large-scale process transformation must include a deep focus on the customer.  The client had collected customer information from multiple sources over a number of years.  These included customer satisfaction surveys in multiple channels, brand identity studies from its advertising agency, call-center data, syndicated cross-company surveys, interviews with subject matter experts, and industry-wide regulatory data.  However, the data was fragmented—sourced from multiple departments and difficult to consolidate into a comprehensive view.  Senior management engaged West Pole to put the puzzle pieces together and provide a synthesis of the organization’s customer knowledge assets. 

A Finance-Based Framework Links Disparate Customer Knowledge

West Pole’s approach included the architecting and development of a knowledge base that served to consolidate customer data of different types with different formats from different sources.  The data was then linked to financial measures in order to rank the importance of product and service attributes.  Further, West Pole developed and deployed a stability measure to provide the client with its first view of the dynamics of customer satisfaction attributes.  Finally, the data was mined and key findings presented with specific recommendations for optimizing the customer satisfaction / cost-to-serve trade-off. 

Discovering the One Thing You Need to Know (by Channel)

West Pole provided the client with its first comprehensive view of customer data.  The analysis of the consolidated knowledge base unearthed significant findings that critically informed process redesign, as well as Sales and Marketing efforts.  The results clearly identified the primary product and service attribute for each delivery channel and served to focus employee understanding of the customer being served (the one thing you need to know).  Moreover, the West Pole analysis of customer attribute stability drove a re-thinking of how to prioritize customer demands (e.g., customers are much more consistent in identifying attributes that are not important to them than in identifying attributes that are).

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