Driven with Business Expertise, Analytics Produces Actionable Predictions

There is an often overlooked, but critical, bit of good and bad news about CRM analytics. Adhering to tradition, let's start with the bad. The bad news: CRM analytics is a business activity, not an IT activity.
Other data-intensive initiatives, such as deploying a data warehouse or OLAP solution, can be handed off to IT and revisited later to receive the results. But CRM analytics and data mining require a wholly collaborative process driven by business needs and marketing expertise--you need to meet with your analytics folks a few times a week. The good news: CRM analytics is a business activity, not an IT activity. That's right, the bad news is also the good. With CRM analytics run as a business activity, you can guide the process. This ensures that the results are actionable within your company's operational framework, and that they have the greatest impact within your company's business model. Why are business and marketing expertise required throughout the CRM analytics process? Let's turn to the mechanics to see how analytics produce a business impact. Customer Prediction Produces Actionable Results A central capability of CRM analytics is to predict customer actions, that is, to forecast the individual behavior of each existing or prospective customer under certain conditions. Naturally, such customer predictions are key to allocating marketing and sales resources. For example, by predicting which product features each customer will respond to, you can target each customer accordingly. There are three steps where business expertise is needed to direct predictive analytics: defining your prediction goal; evaluating the prediction results and redirecting; and deploying your prediction model.
1. Define Your Prediction Goal Business and analytics experts must collaboratively define the prediction goal. Although there are many aspects of customer behavior that can be predicted with analytics, only a fraction will be business-actionable. It takes an expert in your business to know which predicted behaviors have this business value. Likewise, conceivable prediction goals are limited only by the imagination of your marketing staff, and only a fraction can be achieved with analytics. Each customer prediction goal must be defined with a great degree of detail, for example: Response: Which customers will respond to a certain brochure mailing within 13 business days with a purchase value of $125 after shipping? Repeat customers: Which first-time customers will make five or more additional purchases totaling at least $1,250 within six months? Cross-sell and upsell: Which customers will purchase product C or D, given a purchase of A or B, within four months, given a mail solicitation? Attrition: Which customers of more than four months will decrease their monthly usage by 80 percent in the next three months, if no marketing contact is made? 2. Evaluate Prediction Results and Redirect Your business expertise is needed to choose between certain pragmatic trade-offs in prediction capability. Generally, predictive analytics provides a range of customer segments with varying prediction performance from which you can select. For example, you may need to choose between a group of 100,000 customers with four times greater likelihood of making a purchase than average, versus a group twice as large with only three times the likelihood. These kinds of trade-offs can also be examined in terms of profit, market penetration, or a measure of loyalty like one-year customer value. In some cases prediction results are not strong enough, requiring that the defined prediction goal be altered, under business supervision. For example, it may be discovered that the targeted customer behavior has not occurred frequently enough historically to train the prediction model (e.g., customers very rarely up-sell from product A to product B). In other cases prediction results reveal errors or misguided business assumptions. For example, even when attrition prediction is successful, it may then become clear from the business side that predictions must be made two months earlier to be actionable. Certain data problems are discovered only after initial prediction models are generated. These include misunderstood variable meanings, heavy reliance on expensive data sources, accidental biases in data sampling, or mundane data errors that require a data cleansing process. In the best cases unexpected results inspire the collaborative definition of new prediction goals. Sometimes, variables that prove important for prediction come as a surprise, inspiring the addition of related data sources. 3. Deploy Your Prediction Model How customer predictions are used as part of business operations is up to your marketing creativity and business philosophy. For example, if repeat customers are predicted, marketing resources could be directed toward non-repeaters to improve their value, or toward repeaters to leverage their existing value. Predictions can trigger targeted market campaigns or guide sales activities, such as personalized up-sales efforts at unsolicited touch points. With business expertise directing each step, your analytics process will produce actionable customer predictions with a business impact. About the Author Eric Siegel, Ph.D., is a senior consultant at Prediction Impact. A former professor and award-winning graduate-level teacher at Columbia University, Siegel is an expert in analytics and data mining. Siegel cofounded two software companies for customer/user profiling and data mining. Contact him at eric@predictionimpact.com.
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