14 Kellogg Insight of marketing wisdom that Reader’s Digest hit upon decades ago: loyal customers—people who bought more recently, buy more frequently, and spend more on purchases—are more likely to buy again when they are targeted. So rather than the number of emails driving the amount of sales, the causality actually works the other way: the more purchases cus- tomers make, the more emails they receive. Which means that the data are effectively useless for determining whether email drives revenue. Use Domain Knowledge In addition to making sure that data are generated with analytics in mind, managers should use their knowledge of the business to account for strange results. Zettelmeyer recommends asking the question: “Knowing what you know about your business, is there a plausible explanation for that result?” Analytics, after all, is not simply a mat - ter of crunching numbers in a vacuum. Data scientists do not have all the domain expertise managers have, and analytics is no substitute for understanding the business. Consider an auto dealership that runs a promotion in February. Based on a rise in sales for that month, the dealer assumes the promotion worked. “But,” Zettelmeyer says, “let’s say what they were trying to sell is a Subaru station wagon with four-wheel drive, and they completely ignored the fact that there was a giant blizzard in February, which caused more peo - ple to buy station wagons with four-wheel drive.” In cases like these, he says, having the data is not enough. This step becomes even more critical as an organization’s analytics capabilities increase, and analyses become more powerful—and opaque. Algorithms that rely on neural networks, for instance, are powerful Based on insights from Florian Zettelmeyer