By David Dorf-Oracle on Jun 13, 2011
Zoologists and the like tag animals in order to track them over long periods and better understand their behavior. Marketers would like nothing better than to do the same to you and I, and in many ways it’s already happening. Luckily most companies take privacy very seriously, but the technology exists to build detailed profiles on us not unlike gathering data from Serengeti lions.
Retailers used to rely on focus groups to understand how best to merchandise stores and target their advertising. But as technology has advanced, it’s now possible to collect and analyze terabytes of real consumer-based data. And instead of segmenting customers into similar groups, it’s possible to build individual profiles that are very specific and detailed. All of this is an effort to understand which levers to pull to affect behavior and increase sales.
Websites have been tracking us for years using cookies, which are unique tags that are stored on our computers and mobile phones. While a cookie doesn’t know your name, it represents a list of websites you’ve visited. Between my PC and mobile phone, I am always “on the grid” and easily tracked. These cookies are used to tailor advertising to your implied interests, including “retargeting,” the practice of enticing you back to a website you’ve visited in the past.
There are three main types of data that can be collected about you: your social graph, interest graph, and location graph. Combined, they can help marketers choose the right levers to influence your behavior. Some call it targeting, others call it personalization; it all depends on your perspective.
Social Graph (“Who”)
Your social graph depicts your relationships with friends, co-workers, family, and other acquaintances. Each person is a node, and the lines between the nodes can be of varying thickness to represent the strength of the connection, depending on how often you interact with each person. The easiest way to build a social graph is the get the data from social sites like Facebook and Linkedin, but those sites typically don’t share the data. So marketers have to establish connections by other means such as working at the same company, living in the same neighborhood, attending the same school, or visiting the same websites.
The goal is to determine who wields the influence within a social graph, which is of course contextual to the topic. These influencers derive their power from different angles, but in the end they are able to influence sales. There are experts, like book reviewers and industry analysts, trendsetters, like celebrities and fashion designers, and advocates, like sports fans and zealots.
Interest Graph (“What”)
Built on top of the social graph is the interest graph, which associates interests with people. If the social graph is best represented by Facebook, then the interest graph is represented by Twitter or Quora. Many interests can be inferred from the websites you visit, the ads you click, and the products you purchase. Connected people with shared interest have influence over each other. Every time Bob buys music, three people in his graph buy the same music. They share music as an interest, and Bob is the influencer in the group for that product segment.
Sometimes retailers need only to ask for this information, as many consumers are willing to provide their interest in exchange for more relevant offers. (Remember, younger generations are less concerned about privacy.) Take Botiques.com for example. When you sign-up with the website, you are asked to select your preferred fashion outfits from a series. That information is used to discern the types of fashion that interest you, and personalize the website.
Location Graph (“Where”)
While it’s illegal to track a person’s movement via their mobile phone without proper authority, the data exists. As your phone switches between cell towers or WiFi routers, a pretty accurate picture of your movements can be captured. Mobile phone companies use this data in aggregate to understand where additional towers are needed, but tracking these masses can also help understand migration patterns like where people congregate, the paths they take, and how long they dwell. This information helps locate new stores, advertising billboards, and location-based marketing (like geo-fencing).
Leveraging Your Profile
With the advancements in processing so-called “big data,” it’s possible to analyze terabytes of data to assemble these three graphs a thus create accurate profiles of individuals. Then they can be used to personalize offers and experiences for individuals both online and in stores, which can be a win-win for both retailers and consumers.
I only hope I’m allowed to monitor and correct my own profile, so I’m not stuck constantly receiving offers for perfume and Legos after shopping for my lioness and cubs.