The author wishes to thank Jack Sumner for contributing to this column.
The concept of customer relationship management (CRM) in theory is simple: Listen to your customers and act on what they are saying to create a mutually beneficial relationship. To a customer, this translates into understanding the terms by which they prefer to do business, making the product to an acceptable level of quality and doing it all at a competitive price. No problem, right? The challenge for companies is to understand what customers are asking for in order to respond to or, better yet, anticipate their needs. Today, companies with current implementations of CRM technology can usually hear their customers; however, because they are only focusing on the operational and collaborative components of CRM, they cannot really understand them. They must also consider analytical CRM to optimize their customer relationships.
META Group has published many popular reports focused on the components of CRM technology. To date, the lion's share of the attention has fallen on operational and collaborative CRM. Operational CRM is the automation of customer-facing business processes. Traditional applications include sales force automation, customer service and marketing. Collaborative CRM services and infrastructure make interaction between a company and its channels possible. In the past, collaborative CRM has been limited to a single channel. However, evolving customer demands have created the need for a common user experience across all channels. The intent of CRM is to create a dynamic environment of continuously improving customer relationships. The component to enable this is analytical CRM. Analytical CRM is the use of customer data for analysis, modeling and evaluation to create a mutually beneficial relationship between the company and the customer. Collectively, the three components feed each other in a continuous cycle as depicted in Figure 1. Collaborative CRM provides the means for customers to contact the company, operational CRM handles the customer contact and processing, and analytical CRM enables the contact to be highly personalized through the company's knowledge of the user. In this scenario, each component is dependent on the others. While this scenario places equal value on each component, in practice companies have emphasized primarily the operational CRM and collaborative CRM components in their relationship management processes. This is changing rapidly as businesses begin to recognize the need for analytical CRM to drive strategic and tactical customer acquisition, retention and optimization decisions. Without the capability to analyze customer data, a company cannot effectively listen to its customers.
Figure 1: CRM Cycle
Analytical CRM enables companies to listen and become knowledgeable about customers and prospects. A company knowledgeable about its valuable customers can better retain them by offering preferred channels of interaction. In effect, analytical CRM drives decisions on the deployment of collaborative CRM. Customer knowledge also drives company decisions on operational CRM in the deployment of marketing, sales and customer service processes. Analytical CRM is essential for a company to be successful in CRM.
Analytical CRM Architecture
What does an analytical CRM capability include? At the center is a customer-centric repository (see Figure 2). Ideally, the repository is part of an enterprise data warehouse as the volume of customer data can be staggering. Many Web-based channels are available to consumers, and data storage can easily exceed several terabytes. Of course, storage requirements do not end with the Web data. Data from back- office systems is necessary to create a complete picture of customer value. Add third-party data such as household and demographic information, and it is easy to see how consolidation and management of the customer data requires a data warehouse.
Figure 2: Analytical CRM Architecture
Processes managed by an analytical CRM environment include data acquisition, cleansing/integration, loading/ unloading, aggregation and distribution.
Analysis of customer data essentially takes two forms predictive and retrospective. One form enables companies to predict or forecast future behaviors or values of customers while the other provides a multidimensional view of customer activity.
Predictive analysis uses historical customer data to uncover customer patterns, behavior and relationships. The use of data mining and predictive analysis enable a company to focus on high-value customers and create actions for cultivating potential high-value lifetime customers.
Retrospective analysis provides capabilities such as online analytical processing (OLAP), query and reporting. When companies need to understand existing customer data by transaction, location, product and time, retrospective analysis is a good solution.
Analytical CRM Capability
To date, most of the analytical CRM solutions have lacked deep integration with existing operational CRM solutions. Traditionally, organizations have built data warehouses to consolidate customer data across the enterprise. The deployment of operational CRM systems has merely introduced additional sources of customer data rather than enabled a unified view of the customer. This, however, appears to be changing. CRM solution providers such as Siebel have recently introduced customer-focused data warehouses as part of their offering. The question of whether it is a data mart, data warehouse or reporting data store is not as important as how the company chooses to integrate its customer data. One option is to adopt a vendor solution and modify it for the needs of the company. An alternative is to build a data warehouse that is fed by operational CRM solutions. The choice to build or buy an analytical CRM solution is dependent on the company; however, in general, the decision is a matter of tradeoffs (see Figure 3).
Figure 3: Build versus Buy Tradeoffs
First and foremost, CRM is a business strategy enabled through technology. To fully realize the value from a CRM solution, all components operational, collaborative and analytical are required to acquire, retain and optimize customers. While operational and collaborative solutions are important, analytical CRM is essential to maximize the value of your company to its customers and the value of customers to the enterprise.
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