January 2002

BY DON MURPHY, PEOPLESOFT

Why does the early bird get the worm? The behavior of living organisms results in part from data gathered in past efforts for survival. Similarly, expertly leveraged data are a strong factor in successful initiatives within the corporate 'ecosystem.'

Customer relationship management (CRM) analytics are known for powerful synthesis of historical customer data. However, the data's potential often goes untapped. With the right tools and a little intuition, organizations can increase profitability by leveraging customer information to anticipate customers' needs and influence their behavior.

By consolidating customer information from within and beyond an enterprise, organizations establish the groundwork for modeling and predicting customer behavior. Quality information gathered from numerous customer-facing and third-party systems can help companies define and select customer populations for building predictive models. Companies can then use these models to segment and target customers for marketing campaigns, service programs and customer loyalty and churn analyses.

Descriptive And Predictive Analytics
Comprehensive CRM analytics include both descriptive analytics and predictive analytics. Descriptive analytics ' also known as performance analytics, operational analytics or effectiveness analytics ' can describe everything about a company's customers, activities and performance up to the most recent moment. Predictive analytics leverage this wealth of customer information to generate models targeting the likelihood of future behavior in a given segment. Proper interaction between descriptive and predictive analytics can provide practical tools to help organizations understand their customers, anticipate customers' needs and influence customer decision making through well-aligned programs.

The best analytic products draw information from systems throughout an enterprise, including front-office systems such as sales force automation, marketing automation, the customer interaction center, financials and others. Customer data, purchasing information, sales force contact histories, call records and all other customer-related information companywide should be included, as well as third-party demographics. Placed in a central data repository (data warehouse), the result is a very broad, dense range of consistent data.

Descriptive analytics use these data to take a historical look at organizational performance. From a customer relationship viewpoint, descriptive analytics reveal how effective the company's marketing efforts have been, how the sales force is performing and if support and services have been meeting client expectations.
Predictive analytics use customer data to provide a forward-looking perspective about an organization's consumers. Predictive analytics help identify customer segments so organizations can better understand customer behavior, gaining the ability to leverage opportunities and proactively control risks.

Three Steps To Predicting Customer Behavior
Customer behavior analytics use the predictive analytics process to identify customer segments, predict customer behavior and ultimately help make customer interactions more effective and profitable.

Predicting customer behavior involves three steps:
Profiling. Companies first build a profile of information about customers who have previously exhibited a targeted behavior. Profiling requires rich customer data, including enterprisewide transactional and behavioral data such as call center and purchase information. Other data sources include key performance indicators and third-party demographics. An example of profiling might be building a profile for customers who bought new homeowners' insurance policies in the past two years. The goal is to determine characteristics to look for in future buyers.

Modeling. By using data mining on the profile information, analysts can uncover the most relevant characteristics of the customer segment being analyzed. For example, the most significant attributes of customers who bought homeowners' insurance are gleaned from the profile via the data mining application. Such characteristics comprise the model of customers most likely to purchase homeowners' insurance in the future.

Scoring. Analysts use predictive analytics to score existing customers by comparing them to the model. Those most closely matching the characteristics included in the model are most likely to exhibit the targeted behavior. In the insurance example, a company can rate its customers numerically to indicate how closely they match the model of the person most likely to buy homeowners' insurance.

Once customers are scored and the analysis pinpoints customers most strongly correlating with the model, a company can address those customers, especially the top prospects. Customers scoring a nine or above might receive a special promotion for homeowners' insurance, while a separate, incentive-based offer might entice those scoring seven and above.

Targeting high-scoring customers increases the likelihood of correctly identifying needs, generating positive responses and achieving campaign goals. ROI is enhanced by eliminating efforts directed toward customers who are not part of the targeted segment defined by the predictive model.

Analytic Feedback Loop
It is best practice to test a model by scoring a sampling of existing customers. Once scoring results are analyzed, the model can be fine-tuned and tweaked to increase its accuracy.

When using predictive models for live campaigns and initiatives, it is critical to analyze the results with descriptive analytics for an understanding of the model's effectiveness and success with respect to campaign objectives. Descriptive and predictive analytics reinforce each other. An iterative loop results, helping companies identify accurate models in which they have confidence ' these are the models relied upon for reuse over time. Companies that fail to use descriptive analytics to measure results lose out on opportunities to clearly see how effective their efforts are.

Web Enablement For Time- And Cost-Savings
Ideally, a company will leverage a Web-enabled CRM analytics environment to automate and streamline all analyses. Speed, efficiency and convenience are maximized with integrated Internet applications providing secured access to front-office data, call center and support records, the data warehouse, customer profiles and models, scoring mechanisms, the campaign results repository and every other aspect of CRM analytics.

Close integration of customer analytic solutions with numerous data points makes predicting customer behavior an increasingly accurate, productive undertaking. Web enablement increases speed and reduces deployment efforts and costs.

A Foothold In The Future
According to META Group, 'Our research reveals that in the next 12 to 18 months, analytical CRM solutions will be among the fastest growing CRM technology segments. Indeed, executives believe that optimizing customer-related business processes, maximizing customer value and personalizing customer experiences is more than an art. While analytical CRM solutions come in many forms (e.g., general query and reporting, scorecards, data mining, packaged analytic applications), those that are most tightly integrated with multiple data sources and into operational and collaborative CRM business processes will yield the greatest returns.'

Analytics are becoming increasingly mainstream. It will be critical to establish platforms for consistent delivery and management of information across all analytic applications for collaboration within the extended enterprise. Analytics are supporting more users and more roles. Fragmented approaches can generate scalability and information consistency issues.

By modeling and predicting customer behavior, companies are beginning to use the wealth of information gathered by their front-office CRM systems to tap the potential of CRM analytics. The early bird gets the worm ' and the organization that leverages customer information by skillfully applying descriptive and predictive analytics reaps ongoing ROI from well-crafted campaigns and initiatives.

Don Murphy manages CRM Analytics product strategy for PeopleSoft Inc. He has extensive direct-response and database marketing experience, as well as experience with multichannel targeted marketing solutions.