From Customer Intelligence to Customer Intimacy

Abstract

The theme of this year's Annual Meeting of the Industrial Research Institute, held in April in Seattle, was Extreme Customer Intimacy. Most of the presentations focused on the technology driving new forms of customer intimacy-such as additive manufacturing, which makes possible unprecedented levels of customization--or new ways of gathering the insights that power customer intimacy, such as modeling customer experience. But intimacy is not a technology; it's a relationship, and building that relationship requires more than amassing data, more than constructing smart algorithms or novel analytical approaches. It requires moving beyond customer intelligence, or even customer insight, to true engagement. Traditionally, this has been seen as a marketing problem. But the rise of servitization (see last issue's Resources for perspectives on that process) and a sharpening focus on design and mass customization mean that innovation is increasingly becoming less about technological development per se and more about providing unique and meaningful customer experiences. That means RD better analytics; and automated tools that allow companies to react to insights as they emerge from the data. Gaines's focus is on marketing, but her emphasis on the need to move thinking from demographies to individuals applies to R&D, as well. Like the marketing campaigns that support them, products and services must be designed to appeal to individual customers' needs, values, and emotions to be truly engaging. Joe Weinman, the subject of this issue's Conversations interview and author of Digital Disciplines, believes that big data can power a new kind of customer intimacy, "collective intimacy," which he defines in a 2013 Forbes post: "If customer intimacy entails multiple independent pairwise relationships between a firm and each of its customers, collective intimacy deepens each relationship via insights developed across all relationships." The primary example of this is the algorithm-driven recommendations engines of Amazon, Netflix, and Pandora, which make sometimes scarily accurate recommendations for individual customers based on data gleaned from across the user base. For Weinman, collective intimacy built on massive datasets, advanced data analysis and algorithm design skills, and hyperscale computing assets, "can form the foundation for market leadership." Rob Thomas, co-author of Big Data Revolution, suggests that big data tools can actually help businesses recreate an earlier era of individual customer intimacy. In a recent blog post, he focuses on the retail sector as an example, showing how big data tools are helping two clothing retailers build the kind of individualized experiences shoppers once had with their corner stores. …