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Small Data

The Tiny Clues That Uncover Huge Trends

This book can be downloaded and read in Apple Books on your Mac or iOS device.

Description

Martin Lindstrom, a modern-day Sherlock Holmes, harnesses the power of “small data” in his quest to discover the next big thing

Hired by the world's leading brands to find out what makes their customers tick, Martin Lindstrom spends 300 nights a year in strangers’ homes, carefully observing every detail in order to uncover their hidden desires, and, ultimately, the clues to a multi-million dollar product.

Lindstrom connects the dots in this globetrotting narrative that will enthrall enterprising marketers, as well as anyone with a curiosity about the endless variations of human behavior. You’ll learn…

• How a noise reduction headset at 35,000 feet led to the creation of Pepsi’s new trademarked signature sound.
• How a worn down sneaker discovered in the home of an 11-year-old German boy led to LEGO’s incredible turnaround.
• How a magnet found on a fridge in Siberia resulted in a U.S. supermarket revolution.
• How a toy stuffed bear in a girl’s bedroom helped revolutionize a fashion retailer’s 1,000 stores in 20 different countries.
• How an ordinary bracelet helped Jenny Craig increase customer loyalty by 159% in less than a year.
• How the ergonomic layout of a car dashboard led to the redesign of the Roomba vacuum.

Customer Reviews

The World’s Best (and only) Meta-socio-anthropologist

Martin Lindstrom, in the age of big data, has taken a counter-intuitive approach to problem-solving: small data. Lindstrom believes traditional statistical models, like linear regression can only capture the average, forcing our nonlinear world to be mete and ruled by line. While the world looks to large data sets and algorithms to tame them, Lindstrom forages through small data, a forensic investigator of “emotional DNA.” From anecdotal observations, he extrapolates larger trends, undetected by big data and uncaptured by the market –untapped terra nova.

I’d call Lindstrom a meta-socio-anthropologist. From anecdotal observations, he inducts rules and extrapolates future trends no one else sees. He calls it sub-texting, looking at the emotional drivers behind decision-making, allowing him to predict non-linear effects; in other words, zigging when everyone else is zagging. Lindstrom believes, now more than ever, sub-texting will play a pivotal role in forecasting trends.

A dark haze obscures the online world. Even the cheeriest of social media posts –boasting a healthy meal, a steadfast workout regimen, or charitable endeavor– is filled with deception. Who actually eats that healthy? Works out that much? Donates their time and money to every charitable cause? On one end of the online deception spectrum, lies virtue-signaling; on the other: decadence and indulgence under the veil of anonymity (maskenfreihit, in German, the freedom conveyed by masks). Along this spectrum, where does the actual person lie? Their motivations, fears, and desires, and how they affect their purchase decision-making?

Business has a keen interest in unmasking consumers, aligning their fears and motivations with their purchase decision-making. In the opaque world of online consumerism, big data only gets you so far; or worse, leads to disastrously wrong inferences.

Misinformation flows in two directions, upstream, signaling a false virtue, and, downstream, masking our true desires. Lindstrom believes observations by the trained eye can parse through all this noise and infer consumers’ real motivators, allowing businesses to better target advertising, forecast trends, and reveal new markets.

Small Data
View in iTunes
  • $9.99
  • Available on iPhone, iPad, iPod touch, and Mac.
  • Category: Marketing & Sales
  • Published: Feb 23, 2016
  • Publisher: St. Martin's Press
  • Seller: Macmillan
  • Print Length: 256 Pages
  • Language: English
  • Requirements: This book can only be viewed on an iOS device with Apple Books on iOS 12 or later, iBooks 1.3.1 or later and iOS 4.3.3 or later, or a Mac with iBooks 1.0 or later and OS X 10.9 or later.

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