Signal to Noise

What Data Should Inform Media Investment Decisions?

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 are 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 e-mailing 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 are 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 is 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) and 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 are not exactly Nordstrom’s choicest customer. In fact, you are what we call “broke.” Maybe that was okay 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 am “considering a new car” is my price range “$19,000 – $25,500” or “35,000 – $50,000?” This type of social desirability bias is so common that reaearchers 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 those than shows which received far less social media attention.

Sorry, but maybe that hot social “meme” you are 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 are 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.

[This article was originally published in ClickZ on 12/2/2011]

Choosing between Performance and Branding in Digital Display?

Depending on how you are measuring success, maybe you don’t have to.

The New Data Ecosystem

According to Blue Kai, I am a tech savvy, social-media using bookworm in the New York DMA, currently in the market for “entertainment.” At least that’s what my cookie says about me. Simply by going to the Blue Kai data exchange’s registry page, you can find out what data companies and resellers know about you, and your online behavior and intent.

In this brave new world of data-supported audience buying, every individual with an addressable electronic device has been stripped down to an anonymous cookie, and is for sale. My cookie, when bounced off various data providers, also reveals that I am male (Axciom), have a competitive income (IXI), 3 children in my family (V12), a propensity for buying online (TARGUSinfo), and am in senior management of a small business (Bizo). I am also in-market for a car (Exelate), and considered to be a “Country Squire,” according to Nieslen’s PRIZM, which is essentially a boring white guy from the suburbs who “enjoys country sports like golf and tennis.” Well, I am horrible at tennis, but everything else seems to be accurate.

As a marketer, you now have an interesting choice. Instead of finding “Country Squires” or “Suburban Pioneers” on content-specific sites they are known to occupy (golfdigest.com, perhaps), now I can simply buy several million of these people, and find them wherever they may be lurking on the interconnected web. This explains why you suddenly see ads for Volkswagens above your Hotmail messages right after you looked at that nice Passat wagon on the VW website. Today’s real-time marketing ecosystem works fast, and works smart. But, what are the advantages of buying users versus the place where they are found?

Putting aside the somewhat “spooky” aspect of web targeting (such as using insurance claim data to target web visitors based on their medical conditions), I think every marketer agrees that these capabilities are where online media is going, and they present a powerful opportunity to both find and measure the audiences we buy. But, how do you decide whether to buy the cookie, or the site?

A Different Way to Measure Performance

Most marketers will insist that audience buying is meant for performance campaigns. This is largely a pricing consideration. Obviously, if I want to sell sneakers to young men that are well down the purchase funnel, it makes sense to buy data, and find 18-35 year old males who are “sneaker intenders” based on their online behavior and profile, and reach them at scale across the ad exchanges. Combined data and media will likely be under $4CPM, and probably less since both the data and media can be bid upon in real time. For most campaigns with a CPA south of $20, you need to buy “cheap and deep” to optimize into that type of performance.  It sounds pretty good on paper. There are a few problems with this, however:

What are they doing when you find them? Okay, so you found one of your carefully selected audience members, and you know he’s been shopping for shoes. Maybe you even retargeted him after he abandoned his shopping cart at footlocker.com, and dynamically presented him with an ad featuring the very sneakers he wanted to buy, and you did it all for a fraction of a cent.   The problem is that you reached him on Hotmail, and he’s engaged in composing an e-mail. What are the chances that he is going to break task, and get back into the mindset of purchasing a pair of sneakers? Also, what kind of e-mail is he composing? A work-related missive? A consolation note to a friend who has lost a loved one? Obviously, you don’t know.  Maybe you reached that user on a less than savory site, or perhaps on a social media site, where he is engaged in a live chat session with a friend. In any case, you have targeted that user perfectly…and at just the wrong time. This type of “interruption” marketing is exactly what digital advertising purports not to be. Perhaps a better conversion rate can be found on ESPN.com, or a content page about basketball, where that user is engaged in content more appropriate to your brand.

How do you know where the conversion came from? Depending on your level of sophistication and your digital analytics toolset, you may not be in the best position to understand exactly where your online sales are coming from. If you are depending on click-based metrics, that is even more true. As Comscore’s recent article points out, the click is somewhat of misleading metric. There are a lot of data that contribute to that notion but, put simply, clicks on display ads don’t take branding or other web behavior into account when measuring success. Personally, I haven’t clicked on a display ad in years, but seeing them still drives me to act. Comparing offline sales sales life over a four week period, Comscore reports that pure display advertising provides average lift of 16%, pure SEM provides lift of 82%–but search and display combined provide sales lift of 119%. That means you simply can’t look at display alone when judging performance—and you really have to question whether you are seeing  performance lift because you are targeting—or whether you are achieving it because your buyer has been exposed to a display ad multiple times. If it is the latter, you may be inclined to save the cost of data and go even more “cheap and deep” to get reach and frequency.

How do you value an impression? Obviously, the metric we all use is cost-per-thousand (CPM), but sometimes the $30 CPM impression on ESPN.com is less expensive than the $2 RTB impression from AdX. Naturally, your analytics tools will tell you which ad and publisher produced the most conversions. Additionally, deep conversion path analysis can also tell you that “last impression” conversion made at Hotmail, might have started on ESPN.com, so you know where to assign value. But, in the absence of meaningful data, how do we really know how effective our campaign has been? I really believe that display creates performance by driving brand value higher, and some good ways to measure that can now be found using rich media. When consumers engage within a creative unit, or spend time watching video content about your brand, they are making a personal choice to spend time with your message. There is nothing more powerful than that, and that activity not only drives sales, but helps create lifetime customers.

For today’s digital marketer, great campaigns happen when you understand your customer, find them both across the web and on the sites for which they have an affinity—and find them when they are engaged in content that is complimentary to your brand message. Hmmm…that kind of sounds like what we used to do with print advertising, and direct mail. And maybe it really is that simple after all.

[This article appeared 1/12/11 in AdWeek]