Companies have a lot of data, as I’ve discussed before. The New York Times reported on Sunday that my alma mater, Acxiom, keeps 1,500 or more data elements on just about every person in the United States.
Let me set the scene. At my first-ever advertising job, I worked at Kirshenbaum & Bond (as it was called then, now Kirshenbaum Bond Senecal + Partners), doing pretty much whatever anyone asked of me. When we pitched the Subaru account, other junior employees visited dealers of competitive brands, posing as recent grads buying our first cars. The irony that we couldn’t have afforded a used Yugo, given what they paid, us was not lost on us.
When I visited the Nissan shop on a quiet afternoon, a silver-haired gentleman rose from his desk to meet me. Even before he shook my hand, he focused on my shirt, my watch and my shoes in quick succession. Even as a soaking-wet-behind-the-ears account planner, I recognized that the silver fox shaking my hand knew everything he needed to know about me. Together, my wardrobe gave him a good idea of how much money I had to spend and how I liked to spend it. He could then guess what kind of car I wanted, what kind of car I really wanted and how I’d fare in asking for financing.
As I asked him about the Sentra, he knew I pictured myself hot-shoeing around in a 300ZX.
These days, when I first sit down to suss out a client’s data needs, I ask myself what the silver fox would want to know and what would take the place of glances at shirts, watches and shoes.
In the digital realm, where the marketer cannot eyeball the consumer, different cues exist. For instance, a website owner can know what site a user came from or, if he came from a search engine, what search terms got him there. That information offers many opportunities to a marketer with any up-to-date web analytics package.
Using Nissan’s website as an example, an incoming visitor from a performance-oriented site such as Car & Driver could see an image of performance cars on the home page while a visior who got there via a Google search of “fuel economy” might see MPG numbers.
Similarly, if Nissan thought to use different SMS short codes to sign up consumers at events, it could dynamically populate mobile marketing based on the location or nature of that event. For instance, addresses associated with a rock concert might feature youth-oriented cars while addresses associated with county fairs might feature pickup trucks.
Do these micro-bites of information beat detailed data portraits? Of course not. Well-managed data deployed strategically offer up a much more nuanced approach towards driving conversion.
However, the silver fox probably made a good living by looking at shirts, watches and shoes and so can you.