Predictive analytics: Harnessing big data to boost customer satisfaction and reduce churn
In the late 19th century, Philadelphia department store pioneer John Wanamaker quipped, “Half the money I spend on advertising is wasted. The trouble is I don’t know which half.” For the century and a half that followed, Wanamaker’s admission served as a widely accepted advertising and marketing maxim. Despite increasingly sophisticated segmentation, messaging, testing and tracking, intelligent guesswork and uncertain outcomes remained all-too-common components of advertising and marketing.
But in the modern business environment, organizations enjoy precise fact-based insights from customer data that help them retain customers and boost their sales by connecting with customers at the right times with the right messages—all thanks to predictive analytics.
Tap the potential of predictive analytics
Predictive analytics, easily one of the most transformative technology trends at work within modern businesses, has gained a foothold among marketers. Indeed, a study conducted by Ventana Research found that 48 percent of marketing departments are now applying some form of predictive analytics.
Yet although marketing tops the list of business functions that have begun employing predictive analytics, the vast potential of this powerful capability remains largely untapped. Too often, the sophistication of predictive analytics technology, combined with the quantum leap from the capabilities offered by spreadsheet-based analytics, creates the impression that adopting predictive analytics is a daunting technological undertaking beyond the reach of all but the most advanced data scientists.
Thus business executives and line of business leaders remain on the sidelines, unconvinced of either predictive analytics’ impact or its accessibility—and the IBM SPSS predictive analytics team has an unequivocal message for them: You can do this. Indeed, in a competitive environment marked by empowered, hyperconnected customers, predictive analytics is no luxury but rather an essential tool for raising a company to new heights of agility and effectiveness. Only consider, for example, how a next-best-action modeling framework could help telephone customer service representatives identify effective offers and recommendations based on a given customer’s profile, transaction history and known concerns—or how geolocation capabilities could help a store identify its customers as they draw near to its entrance, texting them offers tailored to their interests and desires.
Lay the groundwork for insight
IBM SPSS predictive analytics solutions lay the groundwork for these and even deeper levels of insight, offering high-level statistical analysis and modeling without requiring programming or deciphering of algorithms. Another maxim of marketing holds that retaining current customers is easier than acquiring new ones. But actually doing so requires a deep understanding of customer behavior such as that delivered by IBM SPSS predictive analytics solutions. Such understanding can help reverse the marketing mindset, turning a business’s focus away from finding the right customers for its products to finding the right products for its customers.
Begin your move from reaction to proactivity by learning how to adapt business models that create value at the point of impact. When you use the power of IBM SPSS predictive analytics to reimagine your customer engagements, you can gain a competitive edge that helps you provide the levels of insight and service that modern customers have come to expect.