The Power of Habit

by

Charles Duhigg

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The Power of Habit: Chapter 7 Summary & Analysis

Summary
Analysis
Just after the eclectic statistician Andrew Pole started working at Target, he was tasked with building a model to figure out if customers are pregnant. He had spent six years analyzing greeting card sales for Hallmark before working for Target, which collected vast amounts of data about their consumers’ buying habits. From their purchases, Pole learned about shoppers’ lifestyles, families, and interests. Pregnant women were great customers because they subsequently turned into new parents, who would often buy everything they needed for their children in one place. Trying to figure out if customers were pregnant was an interesting challenge for Pole, but it eventually showed how surveilling customers can be dangerous.
Pole’s assignment again shows how habits are absolutely central to people’s lives. In fact, shopping habits reveal so much intimate information about people that it’s possible to build a complex profile of them based simply on what they buy at Target. Thus, Pole’s job also points to another important side of habit change: companies can profit by understanding and appealing to—or sometimes even manipulating—consumers’ habits on a mass scale. While transformative, this possibility also raises important ethical questions about how much control and responsibility people have over their own habits.
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For a long time, retailers like Target were more likely to hire psychologists than data analysts. By learning about consumers’ psychology, they learned to change the placement of products and increase sales. These days, though, they try to target individual shoppers. Studies have shown that people’s unique habits drive the way they shop. In fact, habit determines what people buy even more than what’s on their grocery list.
Target’s switch from psychologists to data analysts again shows how new technology transforms our ability to understand and change our habits. Just like scientists discovered the cue-routine-reward habit loop through brain scans, Target has been able to study and target individual shoppers’ habits through its mass data collection.
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To understand these habits, Target started collecting data about its consumers and linking their activity to a Guest ID number. It also began purchasing other data about them, including ages, addresses, ethnicities, job histories, and brand preferences. Target started using this data to guess what a customer buys habitually, then send them ads and coupons for those items at Target. Virtually all major American retailers use the same tactics, but Target is one of the most successful—largely due to its talented analysts, like Andrew Pole.
Since shopping primarily depends on automatic, habitual decisions, modifying consumer habits can be incredibly profitable for corporations like Target. However, whereas individuals and companies modify their own habits intentionally, Target wanted to modify consumers’ habits without them realizing it. Therefore, it had to combine key tactics for habit change—like the Golden Rule—with other tactics that would prevent consumers from understanding what was happening to them.
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In the 1980s, the professor Alan Andreason discovered that shoppers tend to change their habits (like by switching brands) in response to major life events like moving, getting a new job, or—most importantly—having a baby. This is why “pregnant women are gold mines” for Target. Their shopping habits are flexible, they have to buy thousands of dollars of items for their babies, and once they go to Target, it’s easy for them to start purchasing everything else there, too. Companies even market to new mothers by giving them free samples in hospitals.
Target’s strategy was possible because of how habits persist over time and tend to repeat themselves throughout society. First, marketers didn’t just want pregnant women to buy certain items at Target—they wanted them to make shopping at Target a sustainable habit, because they knew that people tend to keep returning to the same stores over time. Second, Andreason’s research showed that consumer habits are relatively consistent across any given society. As a result, Target could be confident that people with similar shopping habits were in similar life stages.
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But Target wanted to push things even further by getting to couples before they had babies. That’s where Andrew Pole came in. He analyzed data from the Target baby-shower registry to determine what women bought during pregnancy. For instance, he found that women tended to buy lots of lotion during their second trimester and vitamins during the first half of their pregnancy. He put together a list of 25 products that allowed Target to predict if a woman was pregnant and, if so, when she was due. Based on this list, he found several hundred thousand Target customers who were probably pregnant. Sending them ads could be hugely lucrative.
Target knew that habits, once established, tend to reinforce themselves. Therefore, it wanted to be the first place where pregnant women went to shop. Pole’s analysis confirmed the results of Andreason’s research. Although they probably didn’t realize it, pregnant women develop strikingly consistent shopping habits across the U.S. Having learned about people’s lives from the habits that underpin them, Target’s next challenge was to change those habits for its own benefit.
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But advertising to these women could also be dangerous, because they didn’t know that Target knew so much about them. One Minnesota man angrily complained that Target was sending his teenage daughter coupons for baby items—only to find out a couple days later that she was pregnant. Therefore, Andrew Pole had to figure out how to get ads to pregnant women without them knowing they were being targeted.
Target’s methods didn’t just anger a few customers—they also raised a series of ethical questions that have become more and more important in the digital age. Namely, is it ethical to automate habit change? Should corporations be allowed to manipulate millions of people’s habits through algorithms? Who is responsible for those algorithms, if the people who they target do not even know that they are being manipulated? While Duhigg understands these issues, he also seems to view Target’s practices as justified and relatively innocuous. But his readers may or may not agree.
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In 2003, Arista Records started promoting Outkast’s genre-bending song “Hey Ya!” The company was using a new algorithm that forecasted a song’s popularity based on factors like its tempo and melody. These algorithms were more reliable than industry experts’ predictions, and they suggested that “Hey Ya!” would be a massive hit. But radio listeners hated it—a third of them immediately changed the station when it came on. Arista Records wondered if they could do anything to make the song into a hit.
Duhigg presents a puzzle: why would so many radio listeners turn off a song that the algorithm says they should love? Perhaps the algorithm was faulty, or perhaps the reasons people love a certain song are very different from the reasons they keep listening to it on the radio. Of course, the difference is really that listening to a favorite song is a choice, while listening to the radio is a habit.
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The radio station manager Rich Meyer has been analyzing the most popular radio songs around the country and publishing his findings in a newsletter since the 1980s. In the early 2000s, he started wondering what made some songs “sticky”—meaning that listeners usually listened to them all the way through. Some sticky songs were by popular artists like Beyoncé. But others were bland and forgettable, and others were by artists that many listeners disliked, like Celine Dion.
Rich Meyer hit on the difference between good songs that people actually liked and “sticky” songs that were popular on the radio. However, unlike for pregnant shoppers, there was no obvious formula for “sticky” songs. Therefore, Meyer had to dig deeper to figure out what they all had in common. Specifically, he had to figure out why they all fit in with listeners’ habits.
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Rich Meyer concluded that sticky songs sound more familiar than other songs. In other words, they are closer to the average of their genre, so they sound like what the brain expects to hear on the radio. In fact, the parts of the brain that process music tend to seek out patterns and familiarity. This prevents music from overwhelming the brain or distracting the listener. This explains why people habitually listen through familiar-sounding Celine Dion songs on the radio, while they turn off unfamiliar-sounding ones like “Hey Ya!” Arista Records getting listeners to like “Hey Ya!” is like Target trying to send pregnant women ads without them knowing that Target is spying on them. The key is to “mak[e] the unfamiliar seem familiar.”
A new stimulus has to be familiar—or meet the brain’s expectations—in order to become part of a new habit. This fits with the neuroscience research Duhigg cited at the beginning of his book, which characterizes habits as a way for the brain to save energy. Clearly, familiar patterns are easier to process than totally new ones, like the beat in “Hey Ya!” Since listening to the radio is usually a habit, it makes sense that people will gravitate to familiar-sounding songs and avoid unfamiliar ones. As Duhigg hints here, this principle has important implications for habit change. Namely, change is easier when the end result feels familiar. This is why the Golden Rule of habit change states that people should keep the cues and rewards for their habits the same. These consistent cues and rewards make the new habit familiar for the brain.
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During World War II, the U.S. exported so much meat to support the war effort that it started facing meat shortages at home. The Department of Defense led a successful campaign to popularize organ meat in the U.S. by making it more familiar—for instance, by telling Americans to add it to dishes like meatloaf.
By linking something totally new (organ meat) to something familiar (meatloaf), the government tapped into Americans’ existing culinary habits. In a way, it used the Golden Rule of habit change: it kept most of the habit the same, while just slightly altering the routine by changing one ingredient in the recipe.
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Radio DJs did something similar with “Hey Ya!”—they played it between two popular, sticky hits. The portion of listeners who turned off “Hey Ya!” fell from 26.6 percent to 5.7 percent. And as it kept playing on the radio, it became more and more popular. Now, it’s remembered as a hit.
Sandwiching “Hey Ya!” between popular songs was an effective way of “making the unfamiliar seem familiar.” Over time, this naturally made “Hey Ya!” familiar to radio listeners and gave them an opportunity to finally appreciate the song’s merits.
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Andrew Pole also followed the same formula with his algorithm to predict pregnancy among Target customers. His department learned that pregnant women wouldn’t get offended if Target mixed together coupons for baby items and random coupons for items that pregnant women wouldn’t buy. Essentially, Target disguised what it knew about its customers. And its sales skyrocketed in the “Mom and Baby” section. Organ meat, “Hey Ya!,” and Target’s ads show that, “if you dress a new something in old habits, it’s easier for the public to accept it.”
If pregnant women received coupons for nothing but baby gear, they would certainly notice—the new ads would be too unfamiliar. Instead, by mixing targeted coupons with random ones, Target made its ads feel more familiar. In fact, Target hoped that its customers wouldn’t even know that they were being targeted. It wanted to modify the customers’ habit loops, while tricking them into thinking that their habits never changed.
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This lesson also applies to lifestyle change. For instance, the YMCA hired researchers to boost membership retention rates by analyzing customer satisfaction surveys. The researchers found that customers didn’t keep coming back because of high-quality facilities, but rather because of their emotional connection with the gym. So, the YMCA trained its employees to remember customers’ names.
The YMCA might seem very different from “Hey Ya!” and Target, because it doesn’t involve “dress[ing] a new something in old habits.” But the basic principle behind this guidance is still the same as the principle behind the YMCA’s success: habits have to feel familiar in order to stick. “Hey Ya!” feels more familiar when it’s surrounded by popular songs. Coupons for baby items feel more familiar when they’re surrounded by a variety of other unrelated coupons. And exercising at the YMCA feels more familiar when one develops a personal relationship with the staff.
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Soon, Duhigg concludes, companies will often know more about their customers than those customers know about themselves. But to get customers to actually take on new habits, companies must make new things seem “familiar.” Duhigg notes that his wife is about to have a baby, and he’s already noticing new Target coupons for diapers quietly arriving in the mail.
Duhigg again returns to the principle that the truth is no match for habits. People’s unconscious habit loops control their lives much more than their knowledge or conscious decisions do. Similarly, knowledge about people isn’t enough to change their lives unless it’s paired with effective habit change strategies. On the other hand, by understanding the habit change strategies that corporations use to manipulate them, consumers can also resist this manipulation. (For instance, Duhigg understands why Target is sending him coupons for diapers.)
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