While business intelligence enables companies to measure customer behaviors, it is essentially a record of the past and usually reported at an aggregated level. But a more urgent and cutting-edge approach is competing on predictive analytics.

• Predictive analytics enable a business to look forward, and make educated decisions that anticipate the future needs of customers. It combines the known numbers with critical insight to solve problems, achieve business objectives, uncover hidden patterns in customer behavior, and then use the combined knowledge to inform activities that can improve performance.

• A 2008 IDC report studied dozens of companies and hundreds of predictive analytics projects. It found that the median ROI for the projects that incorporated predictive technologies was 145 percent, compared with a median ROI of 89 percent for those projects that employed only traditional analytics.

• Measuring and executing a predictive analytics initiative involves much more than just crunching numbers. Today, the analytics function has been transformed into the customer insight and retention group and is comprised of a full-time staff with a leader reporting directly to the CMO.

• Used properly, predictive analytics can serve both the customer's and the enterprise's long-term needs and build a deep and trusting relationship. The sub-prime mortgage crisis is a clear example of what can happen to companies that don't pay attention to those needs. It surprised the financial markets and wreaked havoc on banking balance sheets. Might it have been different if companies used predictive analytics?

Well-defined business goals will lead to higher ROI for predictive analytics because the scope is focused and more accurately measured. As an example, for a retailer, a broad goal would be to increase sales. A more targeted and actionable goal would be "drive sales from high-value customers." As the goal gets more specific, the tactics—from marketing materials to point of sale strategies—become more distinct and measurable.

The ROI Cycle leverages learning's across the entire customer lifecycle providing a more complete picture of the ROI.

Predictive analytics has led to new approaches for a variety of customer segments. They are now managed on a customer lifetime value model that predicts their behavior over a two- to five-year period, depending on asset profile as well as goals.

Getting Started with Your Predictive Analytics Program

1. What is my number one customer-based business goal?

   Predictive analytics will center a company on an achievable outcome that can improve profitability. Rallying around this goal will provide a starting point for the predictive analytics journey. "Attracting new customers" is a solid goal. But refining that goal to "understanding the behaviors of the high-value customers that churn" is more focused and more measurable.

2. What does my most valuable customer look like today and what will they look like tomorrow?

   A company that can answer this question can assign characteristics that include numbers and behaviors. Developing an ever-richer profile of your best customers will yield benefits throughout an organization.

3. Who has the potential to be my most valuable customer?

   To answer this question, the organization must understand the characteristics of their most valuable customers first. Then they need to understand what will motivate customers in the growable category to build a deeper relationship with the company. Is it a different product mix, customer experience, delivery method? What is the potential of these growable customers and what is the cost to meet these latent needs?

1. Create a customer persona.

   Build a model of the ultimate customer that considers total spend, profitability, attitudes, and behaviors.

2. Craft marketing campaigns that will grow the value of that persona.

   Here's where predictive analytics can define marketing activities. For example, a company that knows it is trying to grow a customer group that is young and technologically wired will want to consider mobile marketing. It will also immediately recognize that expensive in-store promotions or TV ads will not grow the value of this group.

3. Identify your analytics champion.

   A C-level executive should be accountable for analytics investment and results. A single sponsor, or evangelist, can ignite the passion for predictive analytics.

4. Rank five customer-based business goals.

   Make them specific to customer groups and make sure they have a financial outcome. "Grow sales by 10 percent" is too general. "Grow high-income new customer revenue by 10 percent" is a goal that can be supported by customer intelligence and informed by analytics.

5. Fine tune the definition of the numbers that will prove that they've been achieved.

   Focus on the ROI cycle and the incremental return on the predictive analytics initiatives. The "high income, new customer" initiative can be proved by return on marketing investment, revenue increases, and customer satisfaction surveys. Businesses need to know what to measure before goals are set.

The Business Imperative for Predictive Analytics

Competing on predictive analytics has never been more urgent. Wide-ranging changes in nearly every aspect of business makes customer behavior a critical "need-to-know" application.

Any company can generate simple descriptive statistics about aspects of its business—average revenue per employee, for example, or average order size. But analytics competitors look well beyond basic statistics.

These companies use predictive modeling to identify the most profitable customers."

Companies that break this need down to its most basic business applications and accurately monitor and measure ROI for predictive analytics will embrace change rather than being displaced and made irrelevant in the marketplace. They will not only be able to predict customer results, they will be able to predict the future.

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