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LESSON - Enhance Enterprise Business Intelligence to Drive Business Growth with Predictive Analytics

By Colin Shearer, Senior Vice President, Market Strategy, SPSS Inc.

Stories abound of the business improvements and impressive return on investment that organizations have achieved using predictive analytics, including dramatic reductions in customer churn, huge boosts in marketing response rates, and significant savings through better fraud detection and risk management. As C-level executives become aware of this opportunity to improve their businesses, many companies find themselves adopting predictive analytics.

Before rushing headlong into a predictive analytics project, however, you should consider a few key points to help you ensure success and maximize the value you can obtain from predictive analytics—both in your initial project and ultimately across the enterprise.

Choose your initial target. Look for areas where predictive analytics have been applied successfully in companies like yours. Across sectors, the most common initial areas for implementation are CRM—where it is used to optimize customer acquisition, value, and retention—and risk management. Seek out case studies and learn lessons that can apply to your business.

Treat it as a business project. Analysis may be a technical activity, but having Ph.D. statisticians drive your project is the wrong way to do it. Striving for technical improvements in accuracy of predictive models isn’t the same as maximizing the business benefit of your analytical results. Bring together analytical skills and business skills, and ensure the focus stays on business success.

Leverage all relevant data. Predictive analytics leverages your data assets. Many companies start with the most easily accessible data—typically, descriptive/demographic and behavioral/transactional data held in the customer warehouse. But the more you know about your customers, the closer you can come to a holistic view of them in your data—enabling you to better understand them and more accurately predict their behavior. Look for additional data that can add incremental value to your predictive analytics efforts. Consider text (e.g., transcripts of call center conversations), clickstream data from Web site visits, and attitudinal data from surveys.

Plan how to use the results. No matter how good your analysis or how accurate the predictive models you build, you only generate ROI when you do something with the results. Deployment of results is crucial and needs to be planned at the start of your project. Typically, deployment involves making intelligent model-based decisions at key decision points in the business processes you’re trying to improve. This might be deciding whether to include a specific customer in an outbound mailing campaign that makes a retention offer to high-value customers predicted to be at serious risk of defection; deciding, during a contact center conversation, to make a cross-sell offer based on a propensity model’s recommendation; or deciding whether to allow a transaction based on a model’s estimate of customer credit risk.

As you plan how these decisions will fit into your business processes, you also need to consider how they will integrate with the operational systems that support them. For example, will model scores be used to select and rank target lists that are sent to campaign management systems, or will models be invoked in real time during contact center conversations, where decisions will be made based on data that has just become available?

Focus on early success, but keep the enterprise vision. Although individual predictive analytics solutions can generate significant ROI, organizations that apply the technology across different areas of their business gain maximum returns by taking an infrastructure approach to implementing predictive analytics. While you should focus on the success of your initial project, keep in mind the long-term benefits of sharing a common analytical platform and data views across your future applications. Plan a roadmap that will support your evolution to a truly predictive enterprise.

This article originally appeared in the issue of .

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