Anthrosegmentation: Using Big Data to Approach the Customer Like a Human Being

The availability of Big Data is changing the way companies interact with the people who make up their customer base, and changing it rapidly. Some of these changes are ones we’ve seen in an embryonic form for many years as CRM systems try to better collect and share information about customers and web analytics provide new tools for excitedly trying to measure marketing metrics. Organizations that wanted to target women over thirty learned to place ads in magazines with readerships that reflected that intended audience. Toy companies learned to book ad space on the TV channels with the most colorful cartoons. The bright minds behind political campaigns learned to identify swing voters and target the publications they read, the channels they watched, and the radio stations they listened to. We constantly surge forward in the levels of sophistication we can apply to targeting the people we want to receive our message.

The problem with trying to do this with high levels of accuracy has always been us. Our brains aren’t capable of quickly sorting through huge amounts of information about people and then using little bits of knowledge to accurately categorize them. It just isn’t possible – not in any reasonable amount of time. The moment computers get involved, though, the process becomes a lot more feasible. First customer segmentation becomes possible: with what limited information a company can collect on their suspects, prospects and customers, they can group them up and market more effectively by tailoring their message and their offering to the general characteristics of that group. This isn’t too bad of a model when it comes to business-to-business, but when the end consumer comes into play and insists on being an individual, things get more complicated.

Our traditional understanding of the customer has always been incredibly limited by either quantity or quality. Hundreds of years ago a merchant might know his or her customers with intimate detail through personal interaction – and some small business owners still do. They could craft custom sales offers on the spot simply by knowing the customer well. “Hey, George, I haven’t seen you in a few weeks. New baby must really be sucking up your time. Hey, you know what I bet you need. Some good coffee. You look tired. Tell you what, I just got some new stock in of this really good coffee, strong delicious stuff. Let me throw in a little sample of it free with your usual order.”

That’s the most powerful kind of marketing, and what I would call “data-driven marketing” – it just so happens that all that intimate customer data is stored inside our shopkeep’s brain, and not in a database somewhere. The problem with this scenario is that the shopkeep can only remember this level of information about so many different people. With twenty or so regulars, that’s not a problem – as many as a hundred, if shopkeep is a smart guy. The more customers he has to try to remember, though, the less detail and intimacy he’s able to retain about them, and the harder it is to treat them as a friend and accurately anticipate what their needs and desires will be. You either have to compromise on the quantity of the data (remember only a few people in high detail) or on the quality of the data (remember lots of people without any meaningful detail).

These days, when a single organization may have billions of individual customers, companies have no choice but to lean towards quantity. They’ve started to grasp more at that ‘personal touch’ they once had in their humble roots as a Mr. Hooper-esque friend and advisor, but it isn’t easy. Even with computers to collect all this data, often the best they can do is to divide their ten million customers up into primitive groupings based on a high-level categorization like age or income bracket – which we’re starting to recognize aren’t really very meaningful classifiers for targeting marketing messages. Marketing departments often don’t have the manpower to do more than that very simple segmentation, though, because at the end of the day a human, not a computer, has to make the call about how to divide up these groups and define the various markets. As we’ve established, the capacity of the human brain is incredibly limited.

Computers, however, are getting smarter. With the steady advance of Machine Learning and Natural Language Processing technologies, our wonderful little robot assistants are becoming more and more adept at identifying significant patterns without our direct intervention and helping us to see pathways for smarter, more targeted marketing and sales efforts. Some of what is being accomplished with a handful of very clever algorithms and well-built platforms is beyond impressive – it’s stuff that seems pulled right out of a science fiction novel. Walmart figured out that people buy more Pop Tarts when they know a hurricane is coming and took advantage of this to drive dramatic sales boosts by having the right product in the right place at the right time. Target can use innocuous purchase data to deduce pregnancy. MIT has put together an analytics piece that can supposedly determine a person’s sexual orientation.

So having grappled in the last few decades with the sheer immensity of the number of customers they need to try to remember (nevermind trying to pick out important information about), organizations at last have the technology to deal with all of this data. With a “brain” capable of handling the three Vs (volume, variety, velocity), they can start working on getting back to that cheerful, familiar shopkeep status. The sooner they can say, “Hey George, been a while since you were last at Walmart, how’s the baby? Bet you’d like some coffee. How about a personalized coupon for half-off on this new coffee from your favorite coffee brand sent right to your phone?” the better.

Most sales and marketing people would call this practice “microsegmentation”, but the more I dive into the motivation behind using big data for sales and marketing purposes, the more I think this term is missing the point. The notion of “microsegmentation” just sounds like nitpicking over customer segmentation for the sole reason that we can do it. Technology gets smaller and more compact, so obviously standard segmentation will go the way of the SD card and get all micro on us, because that’s just how it goes.

What we seem to be forgetting is that the language we use shapes us and shapes the way we think about things, and this language is completely overlooking the whole point of what we’re trying to do. The end goal isn’t to make our defined markets smaller and more specific. The end goal is to get back to a point where we interact with our customers like valued individuals again. It isn’t segmentation for segmentation’s sake – there’s a reason for doing all this, and the reason is to have stronger customer relationships, deeper brand loyalty, more effective customer service, and more trustworthy and accurate recommendations to the customer. We want to group our customers in ways that give them what they actually want from us – and not what we assume they probably want based on something arbitrary like what year they were born or whether they use the public washroom door with the pants or the door with the dress.

For that reason, I’m rejecting the term “microsegmentation” for what we’re trying to do with Big Data analytics. Instead, I’m calling it “anthrosegmentation” (from Greek “anthropos”, meaning “man”): the principle of highly tailored sales and marketing campaigns with the ultimate goal of treating customers like individual human beings rather than faceless members of a crowd. Anthrosegmentation is about using technology to offer highly customized, individualized interactions with customers and patients, rather than painting them all with the same brush.

Anthrosegmentation is the confident and excited reply to that basic demand of every patient, customer, and citizen: treat me like a human being. This is why Big Data is a big deal to retailers, governments, financial institutes and every company that runs the gamut from corner store to corporation – they finally can get back to the highly personalized and tailored customer experience. We’re moving away from clunky, outdated modes of thought that were stunted in growth by the limitations of our data and our technology. As these limitations are rapidly overcome, we need to remember that keeping up with technology doesn’t just mean inventing new words – it means inventing new ways of thinking about what we’re doing, and never losing sight of why we’re doing it.

Big Data presents a multitude of opportunities for improving and innovating around how we do business, and customer segmentation is just a subsection of that opportunity. For an overview of some of the exciting use cases we’ve seen so far, stay tuned for our upcoming article.

Big Data, Big Data Analytics, Big Data Quality, CRM, Customer Loyalty, Customer Relationship Management, Machine Learning, Marketing, Natural Language Processing, Retail, social media

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