A Closer Look at Data Mechanics and Your Mobile Marketing

As more marketers begin to add mobile to their communication and customer engagement strategies, the standard questions prevail: who is my audience, and when and where can I reach them?

We tend to start with “who,” based on past experience, current customer base, research, or intuition. And, once we believe we have a grip on the “who,” we begin to evaluate and ascertain where or how we can reach this audience. Occasionally we jump right to “where,” most often because we believe we already know “who” we’re going to find when we get there.

But is this the right approach? Are who and where what we’re really after? Or is that just a step along the path to what we really want – action and behavior. Isn’t our real goal usually prompting or benefiting from a consumer behavior?

While most of us would agree the answer to the last question is yes, this significant and shared belief is nowhere near enough to throw away the decades-long concept that suggests that knowing “who” is still the strongest proxy or predictor for driving the end goal: the behavior.

I would suggest that if we’re going to continue to employ this demographic and lifestyle data targeting approach (which I’ll bet we do), we should at least arm ourselves with a better understanding of the data and mechanics involved. The more we know about the things that impact our tactics, the better we can design our tactical strategies.

The first two questions to answer are where does the data originate, and is it right?

The only globally consistent answer to where it originates is…from the consumer. After all, with no consumer to describe, there is no description to be had. But, of course, we all know the consumer is far, far away (often much to their chagrin) from the definition and capture of the data points we use as marketers. So data that indicates a certain type of consumer reads a certain publication on a certain device at a certain time of day is at best estimated, derived, compiled, and validated. These are not much beyond fancy words for a good guess.

Further, this process of generating presumably meaningful data is handed off and managed by multiple entities or companies, each with their own goals, limitations, and uses, each impacting the nature of the end product.

So the practical question is, once I’ve decided who my mobile target is, let’s say males 18 to 24, how or why do I get comfortable knowing I’ll reach males 18 to 24 on ESPN.com? Because that’s who ESPN says its reader or online customer is? But, how does ESPN know or determine?

Rewinding to the day of print-only publications, especially subscription magazines that arrived in the mail, identifying and defining the reader was fairly easy. The publication was sent to a physical address, for which we’ve long had highly validated sources of data, or it was purchased with a credit card with a name that could be matched to more information. But how is this determination, this attaching of data to digital inventory, accomplished today? And, how is it further refined or adjusted when we’re additionally talking about cookie-less mobile use?

Data providers and publishers conduct surveys, compile or purchase data from third parties, and employ data analysts to come up with rules for profiling or attaching characteristics to marketing moments. These are all valid and helpful approaches. But also, they are very academic ones, and often far from real-world validation or usefulness.

So, what is a marketer to do in this world of imperfect sources? Throw out the data? No. Rather, I suggest we embrace and exploit the academic nature of it all.

I mentioned above that the data we use is a product of all the hands that touched it along the way and their goals, capability, and limitations in building, buying, or selling. Marketers can and should approach their available data in the same way. Use the data with the knowledge that it’s only a theoretically correct proxy; information continually being tied to consumers and consumer engagement opportunities, whether specifically accurate or not, that can effectively help us consistently meet our particular goals: engagement, or certain targeted behaviors.

Does it matter if the reader is male 18 to 24 if they sign up or make a purchase? I’d suggest no. But, I’d also suggest that knowing someone such as the publisher labeled that, in hindsight successful, opportunity to communicate with that consumer as an opportunity to engage with an 18 to 24 male, is meaningful and important. It doesn’t matter whether or not they were really as the label described. What matters is there was a label or a data point that can be tracked and used again, to help find and engage the consumers demonstrating the behaviors that drive my business.

So, in other words, marketers need to be the source of their own data. Yes, it will be mapped to other players sources and labels, but mapped to your own “academic” version, which effectively becomes accurate because it helps you reach your end goals.

Yes, this requires discipline and tenacity, or a good partner who can do it on your behalf, especially as one embraces and grows a new channel. But in the end, data is only as good as what it accomplishes. And, when understood, it can accomplish a lot.

Mobile data is being defined and developed as we speak. So, mobile is a great place for marketers to begin to take hold of their own data destiny, across their customer base, across the media channels, and across the consumer landscape.

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