Business Intelligence: The Missing Link in Your CRM Strategy

Information Management Magazine, June 2004

Julie Hall

If you're like many companies that have invested in a customer relationship management (CRM) initiative, you have integrated information, applications and services to drive customer-focused behaviors across the enterprise.

However, if you're among the 65 to 80 percent of companies who feel your CRM investment hasn't delivered on its promised potential, you're likely missing a critical piece of the solution: a business intelligence process that drives behavioral change.

Each day, business decisions increase in number and complexity. Customers demand higher levels of service while interacting with companies through multiple channels, posing technical and business challenges throughout the enterprise. As a result, analytics and business intelligence (BI) play a pivotal role in a comprehensive CRM strategy.

Some companies think they have a business intelligence strategy once they purchase an analytics or business intelligence application. However, there's a flaw in that logic: applications are tools that can help execute a strategy - they're not a strategy in and of themselves.

An often-overlooked component of CRM is the process of applying lessons learned from customer information to enhance business and customer relationship behaviors. Even after purchasing BI software, it's important that you invest both time and money in defining how the information will be used for business advantage.

Business intelligence is a process - a process of leveraging customer information to enhance corporate behaviors and improve your relationship with current and target customers for enhanced profitability and competitive advantage.

Business Intelligence Advantages

The discipline of business intelligence addresses a broad range of functional activities from data mining and statistical analysis to predictive modeling and reporting. Within the context of CRM, business intelligence is the process of leveraging detailed customer-behavior information to best manage relationships for maximum customer satisfaction, loyalty, retention and profitability.

Across industries from retail sales to healthcare, companies focused on excellence in managing customer relationships have demonstrated the significant competitive advantage through an integrated strategy for business intelligence and CRM.

CRM Strategy and Data Resources

The foundation of a CRM strategy is the capture and leveraging of the right information to enhance your customer relationships. Relationships by their very nature are a reflection of human interaction or behaviors. Information comes in different forms that require different tools and methods for effective collection, analysis and dissemination.

Similarly, business intelligence requires the right tools - data mining, decision support and analytical technologies - to collect and analyze the right information about customer behaviors. The process of BI involves using these tools and information resources to understand related behaviors and outcomes so you can make the necessary changes to your business to achieve the desired results.

There are three fundamentally different types of CRM information resources - content data, contextual data and analytical data - and each requires different tools and methods for the appropriate management and use within your CRM strategy (see Figure 1). It is the effective integration of information across these resources that will drive your CRM strategy development and related business intelligence processes. As a result, it is important to understand the fundamental differences in information resources and their roles within an overall CRM strategy.

Figure 1: Business Intelligence for CRM

Content Data

Your content data consists of all information captured about individual events and customer encounters. Content information essentially records the details, or facts, of customer encounters - who, what, when and where. This fact-based information reflects an activity that has occurred. For example, content-oriented information includes direct sales encounters, customer calls to your contact center and Web service interaction.

Because fact-based data doesn't change, its technical structure and data management and maintenance needs are fundamentally different from data that changes over time, such as contextual data outlined in the following section. Analytical data is yet another construct, reflecting the relationship of fact data to contextual data for a specific point in time. Your information management strategy needs to accommodate all of these different data types with the appropriate methods and data management techniques unique to each form. Otherwise, over time, you lose your flexibility to effectively use and understand the data.

Traditional contact management applications were created to record customer contact information and basic encounter details, forming the foundation of your customer information repository - or content data.

Contextual Data

Contextual data refers to the conditions under which an individual event or customer encounter occurrs. Contextual information enhances your knowledge of basic encounter content data by providing a more comprehensive view of the conditions of an encounter.

In addition to a customer encounter (or fact), contextual data includes a broader representation of information that might have influenced the customer's behavior during the encounter. Remember, contextual information often changes over time. Thus, it is important to maintain records of customer contextual information that reflect both the current context, such as a customer address or buying propensity, as well as the historical context, such as prior demographics. This combination of current and historical data enables effective analysis of customer relationships and trends over time.

Contextual information involves leveraging such internal and third-party information as customer demographics, related marketing and event campaign details, and customer historical behaviors such as buying trends and customer service interactions.

CRM applications have focused on extending traditional contact management applications through the collection and maintenance of more robust contextual customer information. While this is a great improvement in CRM to date, most of these applications are limited in their ability to manage data content and context independently. This limits your ability to accurately view data and relationships over time, a process necessary for critical analysis of hypothetical situations to evaluate the potential impact of future behaviors given a variety of new conditions.

Analytical Data

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