Signal to Noise

The other day, I was updating my Spotify app on my Android device. When it finally loaded, I was asked to log in again. I immediately loaded up a new playlist that I had been building – a real deep dive into the 1980s hardcore music I loved back in my early youth. I’m not sure if you’re familiar with the type of music that was happening on New York City’s Lower East Side between 1977 and 1986, but it was some pretty raw stuff…bands like the Beastie Boys (before they went rap), False Prophets, The Dead Boys, Minor Threat, the Bad Brains, etc. They had some very aggressive songs, with the lyrics and titles to match.

Well, I put my headphones in, and started walking from my office on 6th Avenue and 36th street across to Penn Station to catch the 6:30 train home to Long Island…all the while broadcasting every single song I was listening to on Facebook. Among the least offensive tunes that showed up within my Facebook stream was a Dead Kennedys song with the F-word featured prominently in the song title. A classic, to be sure, but probably not something all of my wife’s friends wanted to know about.

As you can imagine, my wife (online at the time) was frantically emailing me, trying to tell me to stop the offensive social media madness that was seemingly putting a lie to my carefully cultivated, clean, preppy, suburban image.

So why, as a digital marketer, would you care about my Spotify Facebook horror story?

Because my listening habits (and everything else you and I do online, for that matter) are considered invaluable social data “signals” that you’re mining to discover my demographic profile, buying habits, shoe size, and (ultimately) what banner ad to serve me in real time. The only problem is that, although I love hardcore music, it doesn’t really define who I am, what I buy, or anything else about me. It’s just a sliver of time, captured digitally, sitting alongside billions of pieces of atomic-level data, captured somewhere in a massive columnar database.

Here are some other examples of data that are commonly available to marketers, and why they may not offer the insights we think they might:

• Zip code. Generally, Zip codes are considered a decent proxy for income, especially in areas like Alpine, New Jersey, which is small and exclusive. But how about Huntington, Long Island, with an average home value of $516,000? That Zip code contains the village of Lloyd Harbor (average home value of $1,300,000). Or waterside areas in Huntington Bay like Wincoma, where people with lots of disposable income live?
• Income. In the same vein, income is certainly important and there are a variety of reliable sources that can get close to a consumer’s income profile, but isn’t disposable income a better metric? If you earn $250,000 per year, and your expenses are $260,000, then you’re not exactly Nordstrom’s choicest customer. In fact, you’re what we call “broke.” Maybe that was OK back in the good old days of government-style deficit spending, but these days luxury marketers need a sharper scalpel to separate the truly wealthy from the paper tigers.
• Self-declared data. We all like to put a lot of emphasis on the answers real consumers give us on surveys, but who hasn’t told a little fib from time to time? If I’m “considering a new car,” is my price range “$19,000 to $25,500” or “35,000 to $50,000”? This type of social desirability bias is so common that researchers have sought other ways of inferring income and purchase behavior. When people lie about themselves, to themselves (in private, no less), you must take a good deal of self-declared data with a hearty grain of salt.
• Automobile ownership. Want to know how much dough a person has? Don’t bother looking at his home or Zip code. Look at his car. A person who has $1,800 a month to burn on a Land Rover is probably the same person liable to blow $120 on mail order steaks, or book that Easter condo at Steamboat. Auto ownership, among other things, is a great proxy for disposable income.

It would be overly didactic to rehearse all of the possible iterations of false data signals that are being used by marketers right now to make real-time bidding decisions in digital media. There are literally thousands – and social “listening” is starting to make traditional segmentation errors look tame. Take a recent Wall Street Journal article that reported that the three most widely socially-touted television shows fared worse than shows that received far less social media attention.

Sorry, but maybe that hot social “meme” you’re trying to connect with just isn’t that valuable as a “signal.” We all hear the fire truck going by on 7th Avenue. The problem is that the only people who turn to look at it are the tourists. So what is the savvy marketer to do?

Remember that all data signals are just that: signals. Small pieces of a very complicated data puzzle that you must weave together to create a profile. Unless you’re leveraging reliable first-party data, second-party data, and third-party data, and stitching that data together, you cannot get a true view of the consumer.

In my next column, we’ll look at how stitching together disparate data sources can reveal new, more reliable “signals” of consumer interest and intent.