written by Martin Lindstrom
2016 (St. Martin's Press)
Source: I bought it!
This amalgamation of gestures, habits, likes, dislikes, hesitations, speech patterns, decors, passwords, tweets, status updates and more is what I call small data.
I will never look at people in the same way after reading this book. When teaching science or writing to young children, we coach them to be observers, but do we really practice that in our own lives? Martin Lindstrom does to the tune of about 300 nights a year spent in other people's homes. What is he looking for? Lindstrom is a global branding consultant who is hired by entities to find out what people desire and how to make their product an object they want. He conducts interviews, searches through refrigerators, and generally sorts through volunteer's lives to piece together puzzles of what it is we really want in this life. Small Data is a series of vignettes that highlight his work. If you like Malcolm Gladwell's books, you'll like Lindstrom. In chapter 3, he is hired by a global cereal manufacturer to find ways to boost the sales of a sagging product. After decades of great sales in India, market share went down especially among younger female buyers. At the heart of this matter seems to be the relationship between mothers-in-law and daughters-in-law. In his research, the mummyji, or mothers-in-law, prefer vibrant colors in their products. These colors link to "concepts of luxury, rich, and aspirational." Lindstrom visits homes and discovers that younger Indian women are drawn more to greens that represent to them the idea of freshness. How to please both constituencies with cereal packaging is the challenge for him.
I am intrigued by the stories spun in this book. It has prompted me to think about how to observe the "small data" in classrooms. How does a teacher line up their desks and what does that say about them? Are charts/posters displayed and how are they used? How is the classroom library organized? You can probably learn quite a bit by unearthing small data as opposed to relying solely on big data such as standardized test scores.