Building the Customer-Centric Enterprise, Part 3
Business Intelligence and the Customer Life Cycle
Part 1 of this series described the customer life cycle concept and presented a generic model from which to start defining your organization's customer life cycle. Part 2 described two of the three major business functions found in the Corporate Information Factory business operations and business management and mapped those functions to the customer life cycle. This last installment of the series explains how the business intelligence components of the Corporate Infor-mation Factory support the customer life cycle.
A key success factor of any CRM strategy is the ability to utilize the available information on customers to understand the characteristics of the customer base and to influence the ways in which customers and the organization interact. To accomplish this, data must be converted into information by adding context and facts to the data. Information becomes knowledge when decision- makers are educated about the information and begin to use it. Finally, knowledge becomes corporate wisdom when the business community takes action by consistently using and enhancing this knowledge (see Figure 1).
Figure 1: Moving from Data to Wisdom
The primary technology systems and processes within the Corporate Information Factory that facilitate this conversion from data to wisdom are the business intelligence components. These components consist of the data warehouse, data marts, exploration warehouses, mining warehouses, the decision support interface and the processes to get information into the Corporate Information Factory (data acquisition) and then out into the hands of the business community (data delivery).
Data Warehouses and the Customer Life Cycle
The data warehouse is the starting point for all analytical CRM capabilities. This component contains the detailed, static, enterprise-wide and integrated source of historical data for usage by all subsequent data marts and is a significant contributor to the overall intelligence in your CRM initiative. It acts as the collection point for the detailed, historical data garnered from both the operational systems as well as the operational data store. Figure 2 demonstrates some of the collection points found in the customer life cycle.
Figure 2: The Customer Life Cycle and the Data Warehouse
The data is extracted from the various systems, run through the data acquisition processes and loaded into the data warehouse for usage by the data marts. Obviously, for CRM the collection points and the quality of the data garnered from these points will greatly influence the success of your initiative. Unlike the operational systems, the operational data store and the data marts, the data warehouse is primarily mapped to the customer life cycle in terms of information, not business functions:
- Prospect information is integrated with customer information so that correlations can be found in the downstream data marts.
- Customer interaction history is captured.
- Competitor information is collected and integrated.
- Point-of-sale information is stored.
- Product history is captured.
- Key performance indicators that reflect the health of customer relationships are retained.
Data Marts and the Customer Life Cycle
From the data warehouse we can build a variety of analytical capabilities such as OLAP data marts, exploration warehouses and data mining warehouses. Each of these forms of analytical capability requires its own data, its own data design and its own set of access tools specific for the business problem at hand. (See "If the Star Fits" Parts 1 and 2, DM Review, April and May 2000, for more on the design of various data marts.) Choosing a physical database solution without first understanding the business problem may result in a mismatch. By including a variety of physical database solutions in your toolbox (star schemas, snowflake schemas, third normal form, raw transaction files, and so on), you'll have the flexibility to efficiently satisfy a wider variety of business needs.
There are also two different orientations for data marts. Departmental data marts usually satisfy requirements for the departments who pay for them. Application data marts usually satisfy requirements for multiple departments or the organization as a whole and are funded within enterprise level budgets. Neither type of data mart is necessarily better than the other, although each has its own ramifications. Each organization has its own unique cultural, political, technological, budgetary and human resource issues. In practice, you may end up with a combination of both types of marts because you need to tailor your solution to your situation. For more on this topic, please see "Goin' with the Flow," DM Review, June 2000.
The primary role of the data mart in the customer life cycle is as a source of strategic analysis that will arm the enterprise with the necessary information to influence consumer behavior. Figure 3 lists a sample set of the many types of analytical capabilities your organization may need for its CRM initiative.
Figure 3: The Customer Life Cycle and the Data Marts
Each data mart yields the information you will need to fully understand the history of your customers, their value to your organization, their preferences and patterns of behavior. Without this critical information, you will have little chance of successfully moving your organization toward a true CRM environment. It is applications like these that supply the real "business intelligence" to your organization.
We hope that this series describing the customer life cycle and how the components of the Corporate Information Factory support the various functions in the life cycle have helped you understand the role each technological piece plays in your CRM initiative. By fully comprehending the role these pieces have, you can design and implement the appropriate parts in the appropriate order, thus ensuring your overall CRM initiative a higher probability of success.
This series is an excerpt from the authors' upcoming book, Building the Customer- Centric Enterprise Data Warehousing Techniques for Supporting Customer Relationship Management, published by John Wiley & Sons, 2001.