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Business Intelligence: The Missing Link in Your CRM Strategy

  • June 01 2004, 1:00am EDT
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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

The effective integration of information content and context drives analytic applications, which evaluate the relationship of encounters under various contextual circumstances to identify predictable trends in customer behaviors. The resulting information analysis can then be incorporated into your business intelligence process.

Analytical data includes customer-buying propensity by geography and other demographics, customer service preferences by channel and customer type, and customer profitability by longevity and other demographic categories.

Some CRM applications offer rudimentary analytics, focused primarily on the analysis of content and context that exist within the application's repository. However, truly analytical CRM practices must expand to accommodate cross-systems information resources for more robust contextual influencers such as payment, inventory and compensation systems.

Analytics are a necessary component of every business intelligence and CRM strategy. Next, we'll explore the role of analytics and its relationship to business intelligence within an overall strategy for the enhanced management of customer relationships.

CRM and the Role of Analytics

Analytical CRM has generated a lot of buzz lately within the industry. Vendors and customers alike have realized that the success of a CRM initiative depends on effectively leveraging the critical data collected and maintained by traditional CRM applications. These applications are now beginning to offer add-on tools to support some analysis of CRM data. There's also an entire segment of the application industry devoted to the delivery of tools for data analytics and data mining in support of independent business intelligence processes.

Analytical CRM is the process of evaluating customer contact and related contextual information to better understand the trends and conditions of customer behaviors, such as:

  • Do small companies in the Northeast generate higher profitability per unit than large companies?
  • Which customers prefer to contact us via the Web as opposed to a call center?
  • Do customers tend to access multiple contact channels for the same service request?
  • Do customers tend to repeatedly use the same service channel as when they first became customers, or do more mature customers migrate to a specific channel over time?
  • Based on third-party statistics, how do the behaviors of my customers (profitability, service calls, satisfaction, up-sell propensity) compare to their peers and my competitors?

Knowing the specifics of customer behaviors over time and in the proper context provides companies with the baseline information needed to adapt business decisions and operational behaviors to maximize results. This information also reinforces successful practices and defines the future customer interactions and behaviors likely to generate the desired business results.
Essentially, CRM analytics closes the technical information loop between "What happened?" and "What do you want to happen in the future?" That knowledge puts your company on a path to achieving the desired results.

However, it is the broader discipline of business intelligence that translates information into action - effectively leveraging information to manage and affect an organization's behavior for competitive advantage.

CRM Strategy: Business Intelligence and Execution

The process of business intelligence is a missing link in many CRM strategies and initiatives. The first step in a business intelligence strategy is to clearly define your business and performance objectives, establishing the foundation of a business intelligence road map. Your BI road map identifies key functional and process activities required to effectively integrate and leverage BI within your overall CRM strategy and execution.

Traditionally, CRM initiatives have focused on the collection of great quantities of customer behavior and encounter information. Some initiatives rely on basic activity reports or canned analytics provided by a CRM application for standard customer trends and demographic information. In most cases, these reports are of little value as they provide a simple rearward-looking view of what happened. They're not part of a comprehensive process to leverage information for behavioral change. These efforts fall short of delivering on the fundamental promise of CRM: the better you know your customers, the more effectively you can tailor your interaction behaviors, driving increased customer loyalty, sales and profitability.

A critical step in your BI strategy is to make sure you're analyzing results based on the most comprehensive information available. For every customer interaction and result, the more you know about the conditions surrounding the interaction, the better you'll be able to predict key influencing behaviors.

For example, customer demographics influence customer behavior, but other conditions, such as compensation and sales cycle, also influence sales behavior in each customer interaction. An effective BI strategy includes the evaluation of all possible conditions including information such as the status of sales representatives within the sales cycle, the influencing factors of commissions and management structures, time in territory, timing in relationship to fiscal year and sales representative performance evaluation deadlines. All of these factors are potential influencers that impact sales and profitability outcomes.

Once trends and influencing factors are identified, establish a business process that effectively communicates expected changes in behavior to reduce negative factors or conditions and to reinforce positive behaviors. The analysis of these changes must be a closed-loop process, one that continually evaluates the impact and rate of change as well as the results.

Remember, business intelligence is an ongoing process of evaluating business and behavior changes, of constantly refining organizational decision processes and execution strategies. Put another way, your information is only as good as the business decisions it facilitates and the behavior it changes - in the interest of better serving your customers. 

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