For modern marketers, the challenge that comes with big data is two-fold: knowing what data to collect, and finding ways to harness that data to drive smarter business decisions. The role of a marketer is certainly analytics driven – just think of all the metrics you track, from click-through rates to unique website visitors – but the reason there is so much buzz around big data is because it’s just that: big. It’s too complex and too diverse to be collected, stored, and analyzed using your everyday marketing tools.
As commerce has shifted to e-commerce and business transactions have gone from in-person point of sale to online shopping, marketers know so much more about their customers and can spot trends in buyer behavior. From social media interactions and geolocation to browsing history and device usage, we are all generating data en masse, and companies are competing for access to more data and the actionable insights that can come from it. Data these days is coming so quickly and with such vast heterogeneity, that we collectively experienced a moment akin to Roy Scheider’s first sighting of his humongous, fishy foe in JAWS.
In the face of so much information, we needed a bigger data boat (and a new term to go along with it). According to SAS, an analyst named Doug Laney gave shape to the modern meaning of big data in 2001, defining it by the “three V’s”: volume, velocity, and variety:
The introduction of “big data” as part of our lexicon was paralleled by the coining of a new type of analytics-driven field dedicated to making sense of all of this information: data science. The coining of the term “data scientist” is credited to D.J. Patil and Jeff Hammerbacher, the analytics leads at LinkedIn and Facebook, respectively. The Harvard Business Review (among others) have even dubbed “data scientist” the “Sexiest Job of the 21st Century.” Here’s how they explain the said sex appeal of a data scientist, in all its analytical glory:
“[A] high-ranking professional with the training and curiosity to make discoveries in the world of big data….Their sudden appearance on the business scene reflects the fact that companies are now wrestling with information that comes in varieties and volumes never encountered before. If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a ‘mashup’ of several analytical efforts, you’ve got a big data opportunity.”
We’ve even come up with fancier ways to make all of this data comprehensible – and often beautiful – with data visualizations. By processing data visually, we can often make the comprehension of key insights much more intuitive and relatable to real-world issues. Take a look at how these two visualizations outline patterns in the education of Nobel Prize winners, and roles that lead to Academy Awards:
So it’s the age of big data, and we have data scientists and data visualizations to help – but with big data comes big responsibility.
Insights from big data can help marketers target and engage their audiences with more relevant incentives, information, and advertisements. However, the delineation between data and private human behavior has grown increasingly murky. A key example of this blurring of humanity-as-data comes in journalist Charles Duhigg’s 2012 article for the The New York Times, “How Companies Learn Your Secrets.” Duhigg chronicles how Andrew Poole, a statistician employed by Target, analyzed customer profiles to determine when a shopper becomes pregnant. Yes, you read that right – a major retail store set out to sift through all of their customer data to predict when their shoppers are with child.
The business incentive for understanding this information is clear. Since pregnancy is a life event that triggers the type of out-of-necessity shopping that retailers crave, knowing when a customer is pregnant would provide the opportunity to build customer loyalty through targeted marketing efforts, including coupons and special offers on maternity-related items. But how can a retail giant know what’s going on in a customer’s womb from data alone?
According to Duhigg, Target’s customer data included “demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what Web sites you visit.”
Based on trends in the data, Poole was able to identify roughly 25 products that contributed to a customer’s so-called “pregnancy prediction score.” In fact, the pregnancy algorithm worked so well that Target was able to identify customers as highly-probable for pregnancy and send them maternity-related discounts, often before members of the women’s immediate family were aware of the pregnancy. As you can imagine, this led to a few strange encounters, and Target learned that there is a fine line between using big data to improve the customer experience, and alienating customers with eerily personal offerings.
While concern over big data meddling with consumer privacy will surely wage on in public debate, big data is inextricably linked to the future of proactive marketing strategies; our incredibly interconnected, online existence will only perpetuate the escalation of big data collection and analysis. For marketers, the responsibility is greater to not only understand the data in your company’s arsenal and implement new customer engagement strategies, but to respect and humanize the data at our disposal. So marketers, get to know your data – and maybe even a few data scientists – and learn to use your data for good.