Here, global CSO and award-winning strategy leader Leif Stromnes, explains why hurricane donation patterns and your underwhelming Spotify Discover Weekly playlist have more in common than you might think. And why the common thread has significant implications for marketers’ personalisation efforts.
In August 2005, Hurricane Katrina devastated New Orleans, leaving more than 1,800 people dead and causing $US125 billion in damage. As images of the flooded city filled television screens worldwide, charitable organisations launched urgent appeals for donations.
What happened next revealed something peculiar about human behaviour.
Psychologist Adam Alter noticed a pattern in the donation data where people whose names began with “K” were significantly more likely to donate to Katrina relief efforts than those whose names began with other letters.
Katherine, Kevin, Kenneth, and Kelly were all overrepresented among donors.
Even more remarkably, people with names that rhymed with Katrina – Sabrina, Marina, and Trina – donated at higher rates than the general population. The hurricane’s name was unconsciously drawing contributions from people who shared even the faintest linguistic connection with the disaster.
This wasn’t isolated to Katrina. When Hurricane Rita struck later that year, people named Rita, Anita, and Margarita donated disproportionately to relief efforts. The pattern repeated with subsequent natural disasters: people named Sandy gave more during Hurricane Sandy, and those named Harvey contributed more during Hurricane Harvey.
The effect was so pronounced that researchers could predict donation patterns simply by looking at the first letter or sound of a hurricane’s name and cross-referencing it with census data on name popularity in different regions.
What Alter had discovered was a powerful manifestation of what psychologists call “similarity attraction theory” – our deep, unconscious bias toward people, places, and things that remind us of ourselves, no matter how trivial the connection.
The theory suggests that similarity breeds liking and liking breeds action. But what’s remarkable is how minimal these similarities can be while still influencing our behaviour.
In controlled laboratory experiments, researchers have found that people are more likely to help strangers who share their birthday, even when they’ve never met and know nothing else about each other. Participants were more generous toward people who had the same last digit in their student ID number. They even showed a preference for individuals who shared their favourite colour or preferred the same type of vegetables.
These aren’t meaningful similarities. Horoscopes aside, sharing a birthday with someone tells you nothing about their character, values, or worthiness of assistance. Yet our brains treat these coincidences as signals of kinship and connection.
From an evolutionary perspective, this makes sense. For most of human history, the people most similar to us were our tribe, our clan or our genetic family. Similarity was a reliable indicator of shared interests and mutual benefit. Those who looked like us, sounded like us, and behaved like us were more likely to reciprocate cooperation and less likely to pose a threat.
Natural selection favoured humans who could quickly identify in-group members and extend preferential treatment to them. This hair-trigger similarity detection system helped our ancestors survive in a world where distinguishing friend from foe could mean the difference between life and death.
But in the digital age, this ancient psychological mechanism has become the foundation for some of the world’s most sophisticated marketing systems.
Spotify’s recommendation engine doesn’t just analyse your listening history, it creates an intimate portrait of your musical identity. The algorithm notices that you listened to The Smiths on rainy Tuesday afternoons, that you skip ballads during your morning runs, and that you replay certain songs when you’re feeling nostalgic.
But here’s where similarity attraction theory becomes powerful: Spotify doesn’t just recommend music you might like. It creates playlists with names that feel personally crafted for you. Your “Happy Mix” suggests songs for when you’re feeling up and “Moody Mix” for when you’re feeling down. “Discover Weekly” implies these are discoveries meant specifically for you. “Made for Kate” (if your name happens to be Kate) creates an even stronger sense of personal connection.
The genius lies in the micro-details. When Spotify suggests a new artist, it doesn’t just say “people who like The National also enjoy Phoebe Bridgers”. Instead, it might create a playlist called “Melancholy Monday” featuring both artists, tapping into your specific emotional and temporal listening patterns. The similarity isn’t just musical, it’s contextual, emotional, and deeply personal.
Netflix operates on similar principles, but with an additional psychological twist. The thumbnails you see for movies are personalised based on your viewing history. If you frequently watch films with strong female protagonists, you’ll see a thumbnail featuring the female lead. If you prefer action movies, the same film will display an image emphasising explosions or fight scenes.
This isn’t just personalisation, it’s emotional priming based on similarity attraction theory. Netflix is essentially saying, “This content is like other content you’ve enjoyed, featuring people who look like the people you typically watch, in situations that mirror your viewing preferences.”
Amazon has perhaps the most sophisticated similarity detection system of all. The e-commerce giant tracks not just what you buy, but how you buy it. It knows you research electronics extensively, but make impulsive purchases for books. It observes that you buy organic food on Sundays but order junk food late at night. It notices you comparison-shop for expensive items but trust recommendations for cheaper ones.
Amazon’s “Customers who bought this item also bought” feature seems simple, but it’s incredibly nuanced. The algorithm doesn’t just look for broad purchasing patterns; it identifies customers whose buying behaviour mirrors yours across multiple dimensions. Age, location, browsing patterns, price sensitivity, brand preferences, and seasonal purchasing habits all factor into the similarity calculations.
When Amazon suggests a product, it’s not just saying other people bought it. It’s implying that people like you, people who share your specific combination of preferences and behaviours, found this item valuable. The recommendation feels less like marketing and more like advice from a friend who really understands you.
The power of these systems lies in their ability to identify and exploit incredibly granular similarities. When Spotify plays exactly the right song for your mood, when Netflix suggests a show that becomes your new obsession, or when Amazon recommends a product that solves a problem you didn’t even know you had, it feels like magic.
But similarity attraction theory cuts both ways for marketers. Through millions of years of evolution, humans have become incredibly sensitive to authenticity. When the data is poor or the personalisation feels even slightly off, the results can backfire spectacularly.
According to McKinsey & Company, whilst effective personalisation can generate revenue lifts of 10-15 per cent or more, poorly executed personalisation turns off 50 per cent of shoppers, with up to 44 per cent of customers becoming less likely to buy from the brand again. The insight is that successful personalised communications don’t just need to get the big things right, they need to nail the tiniest details that make customers feel truly seen. For instance, they need to understand that you prefer emails in the morning but text notifications in the evening. They need to know you respond to humorous subject lines but ignore urgent calls-to-action. They need to recognise that you engage with video content on mobile but prefer articles on desktop.
Spotify, Netflix, and Amazon succeed because they’ve learned to speak to the part of our brain that says, “This is for someone exactly like me”. In a world of infinite choice and constant noise, that feeling of personal recognition has become the ultimate competitive advantage.
Just as people named Kate felt compelled to help Hurricane Katrina victims, we’re all unconsciously drawn to brands that mirror our identity back to us, no matter how trivial the similarity might seem.
The companies that master similarity attraction theory don’t just sell products or services, they sell the intoxicating feeling of shared connection and alike-ness.
