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Small Data

The Tiny Clues That Uncover Huge Trends

By Martin Lindstrom
10-minute read
Audio available
Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom

Small Data (2016) is a guide to utilizing minor details about people’s lives to connect with them and sell them on your brand image. These blinks incorporate observations of cultures all over the world to point to the emotions and desires that help brands become household names.

  • Marketing professionals and students of human behavior.
  • Anyone working to establish a brand.

Martin Lindstrom is a world-renowned marketing and brand-building expert, and he has been an advisor to the likes of Walt Disney, Pepsi and Red Bull. A study of 30,000 marketers named him the number one brand-building expert of 2015, while Thinkers 50 named him the world’s 18th-best business thinker.

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Small Data

The Tiny Clues That Uncover Huge Trends

By Martin Lindstrom
  • Read in 10 minutes
  • Audio & text available
  • Contains 6 key ideas
Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom
Synopsis

Small Data (2016) is a guide to utilizing minor details about people’s lives to connect with them and sell them on your brand image. These blinks incorporate observations of cultures all over the world to point to the emotions and desires that help brands become household names.

Key idea 1 of 6

Big Data has a hard time predicting the emotions and desires of users.

Chances are you’ve heard of Big Data – the term has been a buzzword for years. As you might know, it refers to observable patterns in huge amounts of data that offer insights into consumer behavior on online shopping platforms like Amazon.

But despite the massive quantities of data users produce, the information this data offers is actually quite limited.

People search shopping sites, click on ads, stream videos or post on Facebook every day, in the process creating data about the products they like, their tastes and even the way they feel. However, this online behavior is a poor representation of who users really are, especially when it comes to emotions. When people are online, they tend to be less empathetic.

Just as being in a car can make you more rude toward other drivers, being online makes it easier to post a mean reply because you can’t see the other person’s reaction.

So, the information people leave online is insufficient to make judgments about them. While big data does allow for some emotional analysis, it doesn’t offer the specific data necessary to measure or build desire for a brand.

For instance, Google’s algorithms have a 70 percent chance of inferring a user’s emotional state just from the way they write and the typos they make. But it’s very difficult to determine whether a brand can appeal to a user’s emotions based solely on data gathered from web browsing.

This is important because such information is essential for great brands to stand out by making consumers desire their products. The makers of the BMW Mini, for example, know that the desire for their car is fuelled by customers’ urge to indulge in the joy of driving.

So, online behavior is insufficient to paint a complete picture of consumers. But where can you find the information you need to build a highly desirable brand?

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