If you learned your marketing trade any time in the past 60 years, there’s a very good chance a large part of what you learned was related to demographic profiling, the statistical art of putting people into behaviour buckets. These were clusters created to define what people believe and how they’re likely to behave so that they could be ‘targeted’ with financial efficiency. It was the marketing diet I was brought up on, and I believed it to be accurate in most ways, until I realised that everything I saw in the ‘real world’ flew in the face of demographics. It’s not surprising, when we have a close look at what makes up our demographics, to see that it’s no longer an accurate measure. Once marketers start to dig deep, it’s apparent that demographics is a tool that’s past its marketing ‘use by’ date and that there are better ways to engage with an audience that actually wants to hear from us. Rather than mind-spamming a large group of people who may be interested in what we offer because some demographer has said they behave a certain way. Actually it’s even a bit more sinister than that. The truth is that the large majority of demographics were self-perpetuating. Choice in the market was limited, self-expression was limited, the ability to connect was largely geographically based and culture was defined by gatekeepers and tastemakers. The way demographics ‘got fed’ shaped the beast itself, rather than the other way around. And now that we choose what to feed ourselves, the shape is turning out to be vastly different.

Business methods have limitations, and we could only use what was available at the time. The tools weren’t great, but it was probably the best option we had to market with during the post–World War II consumption explosion — and it worked. But when something better arrives, it’s time for the responsible people making decisions in business to adapt and move on. And that time is now.

How to get profiled

Demographics have typically included the following measures for creating clusters or profiles to market to: gender, age, income, education, ethnicity, location, language spoken, mobility, home ownership and employment status. These are all classic weapons in the demographer’s and marketer’s arsenal. But today, many of them are less relevant than ever.

On closer observation, they’re more like forms of human discrimination than decent marketing tools. Many of these measures go beyond political incorrectness and are closer to downright rude or even illegal ways of discriminating. I’m relieved we’re also graduating from this nonsense. Yet most boardrooms and marketing strategies are yet to evolve.

Why does it take so long for old, ineffective tools to disappear from strategy documents? The reason is that senior management believes in them, and they’re too busy managing the balance sheet to notice their tools need replacing.

It’s not that some of these on their own are not predictable measures of what people do; it’s when they get used at a layered psychographic level that we start to get false positives. This is a test result which wrongly indicates that a particular condition or attribute is present. When it comes to behaviour prediction in an omniconnected, choice-driven world, demographic profiling is starting to deliver a barrage of false positives. Predictive data lies; real data is what we need because it’s much more reliable.

The price of pop culture

In 1968, famous late artist Andy Warhol claimed that in the future everyone would have 15 minutes of fame. It was a nice idea based on what we knew about the world at the time. Limited access to the tools of fame (mainstream media) and the need to be selected by a tastemaker along the way meant we could only ever hope for a slither of fame. It turns out, however, that fame is now permanent for those who choose to be selected. It turns out it’s not that hard to be famous within a selected sub group who care about what we care about. It’s a very different picture from when Andy was in town.

The price of pop-culture success is that you had to be prepared to roll the financial dice if you wanted to play. That price was the cost of super-expensive infrastructure, or renting out expensive parts of the infrastructure — not just the factories and the systems, but the price of renting eyeballs seconds at a time (in the form of expensive television commercials) and renting space on a mass retailer’s shelf. The incredible cost of these two end points in the business supply chain meant they were only available to the well-financed few.

The best average

Mass retail by definition needed mass media to pull products from the shelf. It meant producers had to make the ‘best average’ (oxymoronic, I know) products they possibly could — average products for average people to support the price of the system. Pop culture could simply not support niche because niche is invisible and niche has no voice in a world defined by mass-media monologue. Big brands were created more by big budgets than by big ideas and amazing products. As marketers, the desire was for conformity of the masses. It made things easier and helped the balance sheet work.

The weapon of choice

The financial cost of failure was high, so risk had to be mitigated. It’s the way any program in an organisation gets approved: not by the potential upside, but by understanding and minimising the downside risk. The weapon of choice for reducing the risk was demographics. Companies would project who the audience ‘might be’ based on mashing up a set of parameters. They would then select a set of media programs to find the agreed demographic and ‘shoot to kill’ with a good dose of target marketing, all the time knowing that the majority of the investment would be wasted. In fact, it would often be regarded as a success if 30 per cent of the audience fell into the profile. It’s a pretty weird definition of success to have a 70-per-cent failure rate. While any media buyer could argue that the media-purchasing company buys based on a cost–benefit ratio, the reality is they’re also paying for the people who aren’t the desired audience. Worse still, it doesn’t mean that those in the desired demographic who actually are exposed to the message aren’t annoyed by the interruption. The reality is, demographic media buying is a very expensive guessing game.

Don’t fence me in

The curious thing about demographics is that they were actually shaped by the media, rather than the media reacting to what the demographics liked and believed in. The media loved the idea of demographics so much that they invented pet names for different groups to define them in neat, saleable clusters: the baby boomers, Generation X, Generation Y, the sea changers. They were all designed to simplify the selling process. The media shaped the demographics itself according to what it chose to expose the demographic to. Did young families really like watching sitcom television shows between 1950 and 1995, or did young families watch and learn to like sitcoms between 1950 and 1995 because that’s what was on every television channel from 7.00 to 10.30 pm? Did teenagers in the US like American top-40 music, or did teenagers like music and that’s what was on the radio for the last 50 years of the twentieth century?

The probability of aggregating an audience was higher at that time because the media was part of the shaping process itself. The choice of what any demographic group would see was determined by a very thin line-up of media. They all aired the same types of program at roughly the same time. They all ran the same advertisements for the products they thought a demographic may buy, and the retailers would choose to only put those products with national advertising support on their shelves. The limited choice created the profile more than the attitude and desires of the people inside the profiles.

It was also hard to escape the ‘norm’. We all worked and went to school at normal and predictable hours. It was hard work just escaping the message. For want of a better term, people got brainwashed into fitting into their pre-defined demographic behavioural pattern. But this system is breaking down, and beyond age and a few limited geographic constraints, demographics is totally finished as a useful marketing tool.

How do you define a teenager?

How would you define a teenager today? What do teenagers like in a world of infinite choice, global connection, pricing parity and high disposable incomes? How is their behaviour different today from 1985? Defining a teenager and their behaviour is no easy task, but the reality is that teenagers would associate themselves with interest groups that fly in the face of geographic boundaries and even the actual age bracket they fall into. So the question marketers need to ask themselves is this:

How similar are teenagers who live in the same suburb, go to the same school, with the same ethnic origin profile, with the same average income who are also these different things: goths, punks, surfers, skaters, geeks, ravers, jocks, musicians, hipsters, preps, emos, gamers [insert your preferred teenager genre here]?

Would any of these groups listen to the same music, wear the same clothes, hang out in the same places, eat the same food, read the same books or watch the same movies … let alone like the same brands?

Probably the only thing we can reliably contend is that they all purchase the technology that keeps them connected with their peer groups.

Stealing music or connecting?

One of my favourite examples of this shift — that is, how kids connect across interests rather than demographics — is the story of two of the co-founders of Napster, Shawn Fanning and Sean Parker. They were online friends for three years without ever meeting each other. They ‘met’ via an internet chat room called IRC (Internet Relay Chat) and only met in real life when they went to pitch to investors after Napster took off and gained traction. They built Napster as a way for people to connect over music.

Fanning claims that the connection of like minds was one of the key things that made Napster work in the early days. It wasn’t about stealing music. It was about accessing the music you liked and couldn’t find in the physical world, and finding other people who liked that music too. It facilitated what teenagers have been doing with music since the days when they listened to 45s and shared them with their friends. Digital files simply made the process ridiculously more efficient and enabled micro-interest groups to form around the music. All of a sudden, punk rockers could find punk music and goths could find their favourite goth bands that weren’t ranged in record stores. It was like border hopping digitally. It spawned the idea of sharing things online: first music, then interests, and now most of life.

Marketing 1.0

The marketing process during the mass-media era was linear and it had a deep predictive orientation that could be followed relatively easily as long as the company behind it had the courage to make the large and risky financial investments required to play. It went something like this:

1. Define the target demographic.
2. Research the concept with said demographic.
3. Design the mass product.
4. Tweak the mass product with qualitative research.
5. Gain mass distribution.
6. Buy mass media.
7. Rinse and repeat.
8. Innovate incrementally using existing infrastructure.

But the linear process just doesn’t work anymore. The environment and resulting go-to-market methodology has fragmented into non-linear, unpredictable pieces. We now operate in a world where a smartphone game designed by an independent game manufacturer can end up being a major motion picture with global licensing that can compete with the likes of Disney (think Angry Birds). Or where a crowdfunding campaign can result in enough financial backing for a new wearable computing device — such as the Pebble — to be launched before Apple or Google enter the smartwatch market space.

Marketing revised

A simplified view of the old marketing world compared to the new marketing world could be defined by making this comparison in table 6.1.

The feedback cycle in business today isn’t a segmented part of the process or a period of time for interaction after which no more questions or input are allowed. Now it’s a fluid and never-ending process that involves the brand stewards — the audience — and they’re the people who feed it. Technology facilitates a fragmented process that’s hard to define and requires constant experimentation and iteration. It requires a process where it doesn’t really start and end like it used to. The needed approach from marketers today is to remove the launch mentality.

The new intersection

If we’re not going to use demographics as a marketing tool, then how do we make a connection with a potential audience? How does business go about doing business if we throw out the old methods of going to the market and marketing to people? (Mind you, marketing isn’t evil. It’s beautiful and powerful, it’s just that it needs to be used in a more human way.) If marketers embrace this philosophy, we’ll all end up better off after the interaction.

The way we replace demographics is with social and interest graphs.

Social + interests = intention

It gets interesting where these two ideas intersect. The overlaying of the social graph and the interest graph tells us much about a person’s intentions. When people develop relationships based on a connection of interests facilitated by social-media connections we can see the true predictive persona. It’s actually how the best and most enduring relationships have always been formed; it’s just that now we can form them more quickly, develop larger cohorts and there’s less luck involved in finding similar souls. And if marketers are nice, collaborative and helpful, most people will welcome them into their cohort. Modern-day marketing just has to have good manners, something all good relationships do well with.

The story of cities

The social and interest graphs are redefining cities, not just social cohorts. In a type of paradox, cities are becoming more alike and more different at the same time. Let me explain.

Globalisation and the collective mind have facilitated a shift where things are not as different as they used to be in cities. Cities, or more accurately the behaviours that occur within them, are becoming more and more alike. New York City, Shanghai and Warsaw are more similar than they have been at any time in history. And I’m not talking about the availability of McDonald’s and Coca-Cola. Cities are experiencing a move to niche, yet global, trends — interest by interest, social group by social group — into a massive subset of connected communities that exists in most geographies. People are self-organising themselves into groups around passions. They can do this now because they can connect easily and find each other, but more importantly because the culture vultures (mass-market tastemakers) have finally left the building. It’s a fragmentation into subcultures that are replicated on a global scale, facilitated by the network connections people have. All cities now have a local startup community, a clear subculture that was the domain of San Francisco’s Silicon Valley.

There are now more niches in every city maintained by a group itself, not by mass marketers looking for the next pop-culture hit. In fact, niches can now build themselves sustainable micro-economies around their interest. The community itself becomes the designer, producer, promoter and end user. They can do this because the barriers to entry are inconsequential. Chances are we can find that niche — whether it’s the local break-dancing scene or drone flying club — wherever we go. All cities are fragmenting to have it all. But at the same time each city has never been more fragmented and differentiated within its walls. Figure 6.1 depicts this fragmentation of cities.

No city is immune. Whereas cities would once have had their own macro identity driven by geographic realities or history and localised influencers, whether human or media based, now their key influencers and influences are interest based. They follow the output of the interest group. And they listen to the personal micro celebrities from those groups. My economic and political philosophy is more influenced by Seth Godin and Tim O’Reilly than it is by my local members of parliament. The people we revere within our interests shape us, and it’s we who choose who can be part of our shaping process.

Do I know you?

Apart from my family, I didn’t know any of the people I spend time with today 10 years ago. I didn’t know 80 per cent of my social group just five years ago. We found each other digitally. By following certain interest groups that popped up in my city I met people at events and through discussing issues of importance to us online first. Simple metadata search terms helped me find an entirely new cohort: people I didn’t work with, people I didn’t live near and people who have a different education profile from me. One powerful tool for doing this was simple hashtag surfing.

A hashtag is a word with # before it to denote a topic thread. For example, the topic ‘startups’ would be represented as ‘#startup’. Then anyone posting things of interest on social forums could add the hashtag to their post so others could follow the topic. These new relationships blossomed quickly because we already had something in common. Unlike for pre-digital connections, we didn’t have to work out whether we had something in common after serendipity brought us together. What is significant is how my life stage says that this shouldn’t be true. I’m a 40-year-old married man living what should be the stable part of my life, a life filled with old friends and colleagues who play golf on Saturday mornings. And yet, I’m meeting new people and pursuing the interests that weren’t available to me during the first part of my adult life.

The interest graph in action

If I think about the things that matter to anyone these days, there’s a very strong chance they’re unexpected and unpredictable demographically. We’re all progressively heading towards having what might be regarded as weird interests. The social network Pinterest is a place that provides the perfect example of this. Pinterest is a visual-based network. In its own words, it’s ‘a tool for collecting and organizing the things that inspire you’ — a tool where you can ‘pin’ only pictures and videos to separate, topic-based boards. It’s the stuff we really care about, desire and aspire towards. My Pinterest page says much about me (my reality) and nothing about my demographic profile (the ‘target market’ I belong to).

Here are some of my current boards:

• Aviation: I can’t fly a plane, I don’t have an engineering degree and there is no other evidence that I like planes. Yet, I’ve watched every documentary that exists on how to fly a plane, even though I have no intention of ever flying one.
• Surfing: I live a one-hour drive away from the nearest surf beach. I live in one of the coldest cities in Australia. I don’t read surfing magazines, but I do check the surf every morning on my phone at daybreak. I refuse to wear surf clothing (everyone knows that real surfers don’t wear surf brands unless they’re paid to) and yet I go surfing multiple times a week. Billabong, Quiksilver and Rip Curl can’t find me because they’re fishing in the wrong pond. And I guarantee I spend more on surfing every year than the teenage girls these companies chase with their clothing ranges, a chase which also damages their brand in the eyes of real surfers.
• BMX bikes: I’m not 12 years old and I don’t own a BMX bike. However, I’m about to invest about two thousand dollars on the bike I could never afford as a working-class kid. Demographically, I’m invisible.
• House stuff: This is the Pinterest board where I’m statistically and demographically predictable. It’s the board that I fit the demographic profile for. It has all the items I need for my house renovation and fantasy garden in it, the stuff brands try to sell on free-to-air television gardening programs and magazines. They’re the programs I don’t watch because my free-time attention is elsewhere. I’ve fragmented away from the mainstream media even when I’m undergoing my own so-called predictable behaviour.

The anti-demographic recommendation engine

A lot of e-commerce platforms and social-media engines seem to be able to do what mainstream marketers could never quite pull off. Every day, I’m exposed to products and services that I have zero interest in ever purchasing, mainly due to the laziness of the marketers who allocate the budget behind them. But occasionally I’m utterly inspired and thankful when great marketers (with permission) introduce me to things that are just perfectly suited. Twitter is terrific at this with its who-to-follow recommendations. But the best example has to be Amazon’s ‘Recommended for you’ books. It’s always spot on, sitting perfectly in the centre of my personal interest graph, based on the simplicity of what I’ve bought, looked at, wish listed and what others have in their list when there are overlaps. For me personally, it’s very accurate indeed. What’s interesting is that this recommendation engine is what I’d coin an ‘anti-demographic’ profiler:

• It doesn’t care what sex I am.
• It doesn’t care where I live.
• It doesn’t care or know how much I earn.
• It doesn’t care if I finished school.

None of this matters. What matters is the direct connection and the reality of my interests based on my digital footprint. It’s the type of efficiency that mass can never achieve. The smart marketing money now lives in a node-by-node approach.

Not only did the industrial-era mass marketers try to fence us in, but they also tried to trick us into buying today, saying, ‘Tomorrow is too risky. Prices might go up’. The reality is just the opposite because everything is getting cheaper.