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Customer Releationship Management  

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The Evolution of the Customer Database
Elements of a Customer Relationship Management Database
Taking a Lifetime View of the Customer
Integrated Infrastructure Supports Marketing Process
Typical Data Modeling Challenges

This white paper draws upon the lessons learned by C-Byte's in implementing large-scale technology platforms to support Customer Relationship Management (CRM) strategies within major organizations worldwide. It is assumed that the reader is familiar with the strategic direction of most customer-focused organizations and understands the cyclical and repeatable nature of a technology-driven marketing strategy. Interested readers are likely to be concerned with how they might model customer relationships in a way which will support the transition from present product-focused views of marketing intelligence to more useful (and profitable) customer-focused views. This paper provides the data architect or modeler with a generic template for modeling customer data. This approach is not product or technology specific but does provide a flexible data architecture for integrating the various technology components that use data to drive marketing. This paper also highlights commonly faced problems that occur when modeling customer data.

The strategic importance of managing customer relationships both drives and is driven by technology. In particular, this applies to data and the increasingly sophisticated and useful ways in which data is used to model relationships and to drive contact strategies. At the core of any technology enabler for CRM is the customer database. The customer database represents the data hub that integrates the various statistical modeling, campaign management, contact history and response tracking components of the marketing campaign lifecycle. This is true whether the database is used for the execution of marketing strategies (e.g., generates mailing lists), or whether it exists purely as an analysis engine that passes contact strategies and information to a separate customer interaction platform for execution (e.g., customer call centers).

The technology layer and its integration with emerging business processes is therefore key to the successful implementation of a data-driven Customer Relationship Management strategy. This paper describes, in a generic way, an approach that C-Byte has successfully used to ensure that the core data structures in the CRM technology layer support the integration of the various components of the marketing process and reduce the time required to design and execute a campaign. This approach enables the creation of a complete model of customer relationships over time.

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The Evolution of the Customer Database
Current approaches to the design of the customer database fall broadly into two camps. The first—the flat earth view of the world—hails from the glory days of target marketing in the late 1980s. This approach, which is popular with list providers and bureau operations, utilizes the concept of the customerfile or list. Such a list tends to offer a current snapshot of the customer or prospect base and is often the product of much tortuous cleansing, de-duplication and point-in-time segmentation. This approach makes it difficult to analyze the ups-and-downs of an organization's relationship with a customer over time due to its current snapshot view of the customer and prospect base. Further, it typically lacks the transaction-level detail and promotional history needed to model customer behavior.

The second approach has evolved from the data warehousing movement and C-Byte's experience in helping hundreds of organizations design and implement data warehouses. C-Byte has developed a mature methodology for delivering rapid business benefit by integrating sophisticated analytical tools with subject-oriented and time-consistent central databases. Such systems typically concentrate on the delivery of business intelligence and are generally not designed to plug directly into an organization's day-to-day operations. However, the modeling techniques employed by C-Byte for the delivery of successful data warehousing projects represent a radical shift in emphasis from both flat earth views of data and the microscopic views of data used in online transaction processing (OLTP) systems. C-Byte's dimensional view of data provides the optimum combination of analysis of facts over time and high system performance when dealing with large data volumes.

C-Byte's approach to successfully delivering large-scale technology platforms to support CRM strategies uses the best attributes from both of the previous approaches. This approach incorporates the maximum degree of analytical flexibility for the marketer and marketing analyst with the efficient scoring, segmentation and extraction of data to execute marketing campaigns or contact strategies. It also places the customer or prospect at the center of the model and seeks to model all facets of a relationship with that customer over the known lifetime of the relationship. C-Byte's approach to the design of customer databases is not list based and is not designed to simply support ad-hoc, point-in-time marketing solutions. Rather, the objective is to give the marketer true insight into the variability of his relationship with a customer or customer segment over time and to deliver seamless integration with the widest possible choice of campaign management and statistical modeling tools available.

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