How marketers can use data to shape beliefs and influence action

What if you are the way you are, and you take the actions you do because of strategically placed ads and articles online? What if your behaviour is being modified without your conscious knowledge?

We all know advertisers on the internet are stalking us, but Kirk Grogan shows us that marketers now have the power to change not just our buying behaviours, but our beliefs. The key is in understanding how aggregated user data can be used to group people into predictable stages in the persuasion journey.

We are all being stalked, and we know it.

You shop for a pair of shoes online and for the next several weeks, ads for brown tassel loafers follow you across the internet. Well, if you're my grandfather it’s the loafers... for me, it was a pair of Chuck Taylors.

We're sophisticated enough to know that this type of cyber stalking sneaker ad has something to do with ad tracking, big data and maybe even A.I., but what made you feel the need to buy a pair of brown tassel loafers in the first place? Just preference, right? You needed new shoes, you know what you like, what colours would go with your wardrobe - so you picked those.

Are you sure?

What if I told you, you were groomed step-by-step to prefer and purchase those exact shoes, from that exact website, and that those same grooming techniques could be used to make you commit atrocities.

I'm not paranoid. I'm a marketer.

Consumers don't fear that their data is being aggregated because consumers don't understand how it can be used to manipulate them, to groom them and to change their behaviour. The average consumer is likely to believe they are a unique individual with unchecked free will, and that there is nothing particularly special about them that would be worthy of tracking or collecting. From a digital marketing perspective, however, all of these assumptions are false.

As consumers, we are not unique. While each individual may have their own quirks, constant and pervasive collection of data has allowed us to place them into a group of thousands or millions of others with similar traits and beliefs.

It's these very similarities that allow marketers to review what worked on consumers in the past, and then guide new users onto that same path. No free will required. My job entails advising billion dollar corporations how to most effectively guide their customers through these steps, and despite this, I myself don't have the free will to resist. I literally do this for a living and I still buy products I see in online advertisements all the time.

Image: dirkcuys

Here's what's happening behind the scenes: Tracking, Prediction, and Behaviour Modification.

Tracking is constant, and honestly the easiest. When I say constant, I mean it. It isn't only what website you went to or came from…

It's how far into every video you watch. It's from what device It’s where you were when you opened every email. It's who you're around in real life.

If every person reading this gave me only their grocery list for the next 60 days, I can most likely give you scarily accurate information about you. Maybe I can tell you what your work schedule is or that you're prone to taking risks, or something as simple as you're attempting your third diet this year - a keto vegan diet perhaps.

We track until we can predict. While online data collection has made predicting easy, prediction itself is old news. Way back in 2010 using only shopper loyalty cards, retailers could track consumers purchases so well that they were able to determine the likelihood of a woman being pregnant - before that woman knew she was pregnant herself.

Think about that. Our data trail can spoil one of our most intimate and celebrated discoveries. But how do they do that? While pregnant women tend to develop a unique shopping pattern, their bodies begin to reject certain smells, driving them to scent free lotions and creams. They also crave certain vitamins and minerals to help the developing babies. The mother doesn't have to consciously shop for these products, human biology demands it. While individuals might not recognise these patterns, when millions of data points are grouped together, the conclusions become increasingly obvious.

Every major corporation that collects your data knows that secret. The more data you have, the better. The better you begin to understand everything your consumer does, the more accurately you can predict the most effective methods to sell them products.

If you're the first company to know that a woman is pregnant, you stand to gain a customer who, for 18 years, will now be shopping for a family.

Okay, so data can be used to track us understand us, maybe better than we would like but that's not an issue right? Companies know who I am and they serve me related products. Sounds nice, actually. It removes the burden of me having to find the products I might love to buy.

Here's the problem:

What if you are the way you are, and you take the actions you do because of strategically placed ads and articles online? What if your behaviour is being modified without your conscious knowledge?

You know the social media quizzes that determine what kind of dog you are, or find out what type of wine you would be? You know the type: Becky's going to share on social that she's a pinot noir because “she can doll herself up or dress casual, making her the perfect wine for any situation”. Or maybe “you're a rosé because you live for a Summer patio!”

To the marketer it doesn't matter what wine you are, it’s the 10 non wine-related questions you just answered that are being compared to the rest of your data to figure out what group of consumers you're most similar to.

You give off data every moment. It isn't only from the search engines and the social media platforms that you use; those are just the easiest methods to track you. Just by attending an event like, for example, a local TEDx, you're transmitting data. I can find out how much a ticket to the event costs and I know the opportunity cost - I know what else is going on in the surrounding area.

So I can begin making assumptions:

Most people in at a TEDx event are middle-class or above, have a predisposition to learning or disruptive thoughts. They're most likely an extrovert who enjoys mingling with large crowds (or they would have just watched the video on YouTube). They value being early adopters or the first to conform to a new idea or way of approaching issues. So, if I had a hypothetical client who was selling, say, an arm patch to reduce hangovers, I might create something similar to this.

A sales funnel:

It highlights the steps I have to take to guide or funnel the audience members here to my goal of buying my client’s patch. Group TEDx starts with the nearly 3000 people at the event, highly social, intelligent extroverts with disposable income, who like new things.

Step one is to qualify, so I'm going to filter by age first.

Then, I might hire influencers, who other TEDx groups follow on traditional or social media, to make you aware of my brand by posting or advocating for my hangover patch.

Then I'll compare everyone who engaged or clicked on that influencers post, and I'll pay your favourite bloggers to review my patch and link back to my website.

I'll track every person who came to my website and pay for a Facebook Ad to ask you for your email in exchange for my 10 guaranteed tips, to get a hangover ebook.

I'll compare everyone's email who subscribed to the upcoming public Facebook event called Seattle bar crawl. I'll schedule three emails to go out to you at intervals leading up to the event, each offering a larger discount on my hangover patch.

And voila, a few of these emails will lead to sales.

Now I'll go find a new event or demographic and I'll go through the whole process again.

Bonus points:

Due to the many apps on your phone that know where you are at all times, I have the ability to know where you visit frequently. I email my B2B sales team and they go sell 50 boxes to the local 711, knowing that all of you are likely to be hungover in that area. It can sound complicated and don't worry if you didn't follow all the steps, just know that these things are intern level tracking and marketing.

Consider this:

Using methods I've mentioned, researchers at Cambridge University were able to understand an individual’s personality better than his own family members could after analysing just 150 likes on Facebook. They could understand that subject’s personality better than his or her spouse could after just 300 likes.

More importantly, companies with this information know how to make you engage with different products and ideas. They know what makes you sad, what ignites the fire in you, what your vulnerabilities are.

Because that's what we do as marketers, isn't it? We manipulate. We take a product you most likely don't need and may never even use and we manipulate you emotionally to believe it's something you have to buy. This usually seems harmless, but what if these tactics aren't used to sell you shoes, but beliefs.

Let's look at that sales funnel again:

Here's a sales funnel I created after reviewing documents and first person accounts of western-educated ISIS recruits. The strategy is the same.

We look at our existing customers and review their data to find other groups online that qualify as our target group.

Then we expose them to our products and ideas through well-known individuals.

Next, we might share information with them through multiple sources they already engage with or trust.

Slowly, we drive them to echo chambers in the form of websites, forums or social media groups that other potential customers and recruits are in.

Finally, we're moving towards personalising a message to them, and we personalise this to make them feel like this idea was exactly what they were looking for, or they needed.

And they're ready to buy a product, or perhaps fly to Turkey so they can illegally cross into Syria. But every recruit was certain they made the choice to join — just like my Chuck Taylors.

Just like in 2016, when liberals and conservatives alike were targeted with millions of dollars of advertisements from a foreign nation. Just like today, as our political and racial divides grow wider.

A moment has quietly passed in society that is desperately important.

This moment was when a small number of humans realised that, by compiling massive amounts of data, they could proactively and intentionally shape our beliefs.

They discovered they could funnel consumers to a goal and mould them along the way to behave like the ideal customer, or activist, or citizen, or extremist.

Nothing I've mentioned is what's upcoming in the distant future or what might occur as a possible eventuality. Everything I've discussed are things I have personally done for clients and I guarantee I'm not the smartest marketer out there. Even the brightest people are not immune to digital manipulation, and peace can't be reached by deleting yourself from the digital world.

What we really need is open dialogue and a collective understanding of how these tactics have divided us - and how we can reconcile those differences.

It requires we all recognise that individuals who are neither elected, nor removable have the capability to alter and impact our daily lives. Most importantly, it requires we recognise that every single one of us, and our beliefs and thoughts, may not be as uniquely ours as we would like to think.

I want you to recognise one thing:

Your data is valuable. It is valuable as a consumer, as a voter and as a human. If you need proof, try to find a single person in your life has never seen an ad on Google or Facebook. If you can't, it's because companies find all of your decisions valuable.

Your data is quite literally your life story, and I sincerely hope that you are the only author crafting that narrative.

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Kirk Grogan is a marketing and sales strategist in Seattle. After witnessing how people in different countries receive drastically different news and information, Kirk began to see parallels with the world of data marketing. He now consults with Fortune 100 companies, where he coaches and leads marketing teams to develop conversion testing methods, and teaches them how to engage with potential customers in an organic environment. He has developed multiple unique strategies that are currently implemented across the business world, helping brands connect and build loyalty with consumers.

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This is an extract from a 2019 talk delivered by Kirk Grogan entitled “The dark side of our personal marketing data” delivered at TEDxSeattle, published under a Creative Commons Attribution License

The human insights missing from Big Data

By Tricia Wang

In ancient Greece, when anyone from slaves to soldiers, poets and politicians, needed to make a big decision on life's most important questions, like, "Should I get married?" or "Should we embark on this voyage?" or "Should our army advance into this territory?" they all consulted the oracle.

So this is how it worked: you would bring her a question and you would get on your knees, and then she would go into this trance. It would take a couple of days, and then eventually she would come out of it, giving you her predictions as your answer.

From the oracle bones of ancient China to ancient Greece to Mayan calendars, people have craved for prophecy in order to find out what's going to happen next. And that's because we all want to make the right decision. We don't want to miss something. The future is scary, so it's much nicer knowing that we can make a decision with some assurance of the outcome.

Photo credit: Merrill College of Journalism Press Releases on Visual Hunt / CC BY-NC

Well, we have a new oracle, and it's name is big data, or we call it "Watson" or "deep learning" or "neural net." And these are the kinds of questions we ask of our oracle now, like, "What's the most efficient way to ship these phones from China to Sweden?" Or, "What are the odds of my child being born with a genetic disorder?" Or, "What are the sales volume we can predict for this product?"

Now, despite the size of this industry, the returns are surprisingly low. Investing in big data is easy, but using it is hard. Over 73 percent of big data projects aren't even profitable, and I have executives coming up to me saying, "We're experiencing the same thing. We invested in some big data system, and our employees aren't making better decisions. And they're certainly not coming up with more breakthrough ideas."

This is all really interesting to me, because I'm a technology ethnographer. I study and I advise companies on the patterns of how people use technology, and one of my interest areas is data. So, why is having more data not helping us make better decisions, especially for companies who have all these resources to invest in these big data systems? Why isn't it getting any easier for them?

I've witnessed the struggle firsthand. In 2009, I started a research position with Nokia. And at the time, Nokia was one of the largest cell phone companies in the world, dominating emerging markets like China, Mexico and India -- all places where I had done a lot of research on how low-income people use technology. And I spent a lot of extra time in China getting to know the informal economy. I did things like working as a street vendor selling dumplings to construction workers. Or I did fieldwork, spending nights and days in internet cafés, hanging out with Chinese youth, so I could understand how they were using games and mobile phones and using it between moving from the rural areas to the cities.

Big Data Didn't Predict the Mass Adoption of the SmartPhone in China

I've witnessed the struggle firsthand. In 2009, I started a research position with Nokia. And at the time, Nokia was one of the largest cell phone companies in the world, dominating emerging markets like China, Mexico and India -- all places where I had done a lot of research on how low-income people use technology. And I spent a lot of extra time in China getting to know the informal economy. I did things like working as a street vendor selling dumplings to construction workers. Or I did fieldwork, spending nights and days in internet cafés, hanging out with Chinese youth, so I could understand how they were using games and mobile phones and using it between moving from the rural areas to the cities.

“So why is having more data not helping us make better decisions, especially for companies who have all these resources to invest in big data systems?”

Through all of this qualitative evidence that I was gathering, I was starting to see so clearly that a big change was about to happen among low-income Chinese people. Even though they were surrounded by advertisements for luxury products like fancy toilets -- who wouldn't want one? -- and apartments and cars, through my conversations with them, I found out that the ads the actually enticed them the most were the ones for iPhones, promising them this entry into this high-tech life. And even when I was living with them in urban slums, I saw people investing over half of their monthly income into buying a phone, and increasingly, they were "shanzhai," which are affordable knock-offs of iPhones and other brands. They're very usable. Does the job.

And after years of living with migrants and working with them and just really doing everything that they were doing, I started piecing all these data points together -- from the things that seem random, like me selling dumplings, to the things that were more obvious, like tracking how much they were spending on their cell phone bills. And I was able to create this much more holistic picture of what was happening. And that's when I started to realize that even the poorest in China would want a smartphone, and that they would do almost anything to get their hands on one.

You have to keep in mind, iPhones had just come out, it was 2009, so this was, like, eight years ago, and Androids had just started looking like iPhones. And a lot of very smart and realistic people said, "Those smartphones -- that's just a fad. Who wants to carry around these heavy things where batteries drain quickly and they break every time you drop them?" But I had a lot of data, and I was very confident about my insights, so I was very excited to share them with Nokia.

But Nokia was not convinced, because it wasn't big data. They said, "We have millions of data points, and we don't see any indicators of anyone wanting to buy a smartphone, and your data set of 100, as diverse as it is, is too weak for us to even take seriously." And I said, "Nokia, you're right. Of course you wouldn't see this, because you're sending out surveys assuming that people don't know what a smartphone is, so of course you're not going to get any data back about people wanting to buy a smartphone in two years. Your surveys, your methods have been designed to optimize an existing business model, and I'm looking at these emergent human dynamics that haven't happened yet. We're looking outside of market dynamics so that we can get ahead of it." Well, you know what happened to Nokia? Their business fell off a cliff. This -- this is the cost of missing something. It was unfathomable.

But Nokia's not alone. I see organizations throwing out data all the time because it didn't come from a quant model or it doesn't fit in one. But it's not big data's fault. It's the way we use big data; it's our responsibility. Big data's reputation for success comes from quantifying very specific environments, like electricity power grids or delivery logistics or genetic code, when we're quantifying in systems that are more or less contained.

But not all systems are as neatly contained. When you're quantifying and systems are more dynamic, especially systems that involve human beings, forces are complex and unpredictable, and these are things that we don't know how to model so well. Once you predict something about human behavior, new factors emerge, because conditions are constantly changing. That's why it's a never-ending cycle. You think you know something, and then something unknown enters the picture. And that's why just relying on big data alone increases the chance that we'll miss something, while giving us this illusion that we already know everything.

And what makes it really hard to see this paradox and even wrap our brains around it is that we have this thing that I call the quantification bias, which is the unconscious belief of valuing the measurable over the immeasurable. And we often experience this at our work. This is a very appealing message, because there's nothing wrong with quantifying; it's actually very satisfying. I get a great sense of comfort from looking at an Excel spreadsheet, even very simple ones.

image: Mark Smiciklas at Intersection Consulting

But the problem is that quantifying is addictive. And when we forget that and when we don't have something to kind of keep that in check, it's very easy to just throw out data because it can't be expressed as a numerical value. It's very easy just to slip into silver-bullet thinking, as if some simple solution existed. Because this is a great moment of danger for any organization, because oftentimes, the future we need to predict -- it isn't in that haystack, but it's that tornado that's bearing down on us outside of the barn. There is no greater risk than being blind to the unknown. It can cause you to make the wrong decisions. It can cause you to miss something big.

But we don't have to go down this path. It turns out that the oracle of ancient Greece holds the secret key that shows us the path forward. Now, recent geological research has shown that the Temple of Apollo, where the most famous oracle sat, was actually built over two earthquake faults. And these faults would release these petrochemical fumes from underneath the Earth's crust, and the oracle literally sat right above these faults, inhaling enormous amounts of ethylene gas, these fissures. 

It's all true, and that's what made her babble and hallucinate and go into this trance-like state. She was high as a kite!

The Oracle by Camillo Miola

So how did anyone -- How did anyone get any useful advice out of her in this state? Well, you see those people surrounding the oracle? You see those people holding her up, and you see that guy on your left-hand side holding the orange notebook? Well, those were the temple guides, and they worked hand in hand with the oracle. When inquisitors would come and get on their knees, that's when the temple guides would get to work, because after they asked her questions, they would observe their emotional state, and then they would ask them follow-up questions, like, "Why do you want to know this prophecy? Who are you? What are you going to do with this information?" And then the temple guides would take this more ethnographic, this more qualitative information, and interpret the oracle's babblings.

So the oracle didn't stand alone, and neither should our big data systems.

Now to be clear, I'm not saying that big data systems are huffing ethylene gas, or that they're even giving invalid predictions. The total opposite. But what I am saying is that in the same way that the oracle needed her temple guides, our big data systems need them, too. They need people like ethnographers and user researchers who can gather what I call thick data. This is precious data from humans, like stories, emotions and interactions that cannot be quantified. It's the kind of data that I collected for Nokia that comes in in the form of a very small sample size, but delivers incredible depth of meaning.

And what makes it so thick and meaty is the experience of understanding the human narrative. And that's what helps to see what's missing in our models. Thick data grounds our business questions in human questions, and that's why integrating big and thick data forms a more complete picture. Big data is able to offer insights at scale and leverage the best of machine intelligence, whereas thick data can help us rescue the context loss that comes from making big data usable, and leverage the best of human intelligence. And when you actually integrate the two, that's when things get really fun, because then you're no longer just working with data you've already collected. You get to also work with data that hasn't been collected. You get to ask questions about why: Why is this happening?

“Thick data grounds our business questions in human questions, and that’s why integrating big and thick data forms a more complete picture”

Now, when Netflix did this, they unlocked a whole new way to transform their business. Netflix is known for their really great recommendation algorithm, and they had this $1 million prize for anyone who could improve it. And there were winners. But Netflix discovered the improvements were only incremental. So to really find out what was going on, they hired an ethnographer, Grant McCracken, to gather thick data insights. And what he discovered was something that they hadn't seen initially in the quantitative data. He discovered that people loved to binge-watch. In fact, people didn't even feel guilty about it. They enjoyed it.

So Netflix was like, "Oh. This is a new insight." So they went to their data science team, and they were able to scale this big data insight in with their quantitative data. And once they verified it and validated it, Netflix decided to do something very simple but impactful. They said, "instead of offering the same show from different genres or more of the different shows from similar users, we'll just offer more of the same show. We'll make it easier for you to binge-watch." And they didn't stop there. They did all these things to redesign their entire viewer experience, to really encourage binge-watching. It's why people and friends disappear for whole weekends at a time, catching up on shows like "Master of None." By integrating big data and thick data, they not only improved their business, but they transformed how we consume media. And now their stocks are projected to double in the next few years.

But this isn't just about watching more videos or selling more smartphones. For some, integrating thick data insights into the algorithm could mean life or death, especially for the marginalized. All around the country, police departments are using big data for predictive policing, to set bond amounts and sentencing recommendations in ways that reinforce existing biases. NSA's Skynet machine learning algorithm has possibly aided in the deaths of thousands of civilians in Pakistan from misreading cellular device metadata. As all of our lives become more automated, from automobiles to health insurance or to employment, it is likely that all of us will be impacted by the quantification bias.

Now, the good news is that we've come a long way from huffing ethylene gas to make predictions. We have better tools, so let's just use them better. Let's integrate the big data with the thick data. Let's bring our temple guides with the oracles, and whether this work happens in companies or nonprofits or government or even in the software, all of it matters, because that means we're collectively committed to making better data, better algorithms, better outputs and better decisions. This is how we'll avoid missing that something.

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With astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers. She has taught global organizations how to identify new customers and markets hidden behind their data, amplified IDEO's design thinking practice as an expert-in-residence, researched the social evolution of the Chinese internet, and written about the "elastic self," an emergent form of interaction in a virtual world. Wang is the co-founder of Sudden Compass, a consulting firm that helps companies unlock new growth opportunities by putting customer obsession into practice.

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This is an extract from a 2016 talk delivered by Tricia Wang entitled "The Human Insights Missing from Big Data" delivered at TedxCambridge, published under a Creative Commons Attribution License

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Conducting a Customer Interview for Product and Marketing Insight

Doing a customer interview is one of the most efficient ways to gain insight to develop your product or the way you market it. 

You book an appointment to meet a customer for 30 - 60 minutes, preferably not someone you know already but who would represent a real potential buyer of your product or service. The purpose of the interview is to try and understand if the problem you are trying to solve with your product is a problem the customer actually has. That's the problem validation or problem finding part of the interview. You also want to gain useful insight into how the customer buys your kinds of products or services, and how you can fit in with that (or improve that experience for them), and then talking pricing. A bonus is if you actually get an order, but that certainly isn't the point. 

Following are two useful documents to help you get going on your first customer interviews. 

How to do Customer Development Interviews to Validate your Startup Idea from Jason Evanish

Download the homework sheet for this assignment. The questions from this