Here are dragons. What is your data map not telling you?

By Nigel Hollis

People love to claim that their marketing is data-driven. It makes them sound smart and fiscally responsible. But do we really have all the data we need for a brand building campaign? And could bigger opportunities be hiding off the data map?

Here are dragons

“Here are dragons” dates from around 1504, when the Latin phrase “hic sunt dracones” was inscribed on what is now known as the Ostrich Egg Globe. The phrase came to mean uncharted territory, the white space on the map. The person who created the Ostrich Egg Globe clearly believed that the unknown was potentially dangerous and sought to one-up the Romans who had previously used “Here are lions” to refer to unexplored territory. Maybe the danger was real – the phrase is inscribed near where one might find Komodo Island – but the white space could equally have hidden riches. To find out, someone had to explore the territory.

White spaces? What white spaces?

White spaces are not limited to medieval cartography, they also exist in the data maps we use to market brands and products. But the essential difference between the medieval cartographer and today’s data-driven marketer is that the former imagined the unknown white spaces held dangers, the latter imagines there are no white spaces. The data-driven marketer assumes that they have all the data they need to identify new growth opportunities for their brand.

What could possibly be missing?

Underpinning this belief is that there is just so much data available to us. When you have access to first-, second-, and third-party data it is hard to believe that anything could be missing. And the data is so granular, allowing us to leverage machine learning to build audience profiles, map connections, and analyze every action taken on a platform or web site. But, setting aside the issue that much of that data is inaccurate, incomplete, or outdated, do marketers really have all the data they need to construct a brand building campaign?

Brand building defined

To explore this question, I guess that I better describe what I mean by a brand building. To my mind, brand building involves the creation of motivating brand impressions that encourage future buyers to consider the advertised brand for purchase. The process starts by ensuring that future buyers know your brand exists, understand what it has to offer, and regards the company favorably when the time comes for them to buy. Effective brand building expands the number of future buyers primed to respond to the brand’s sales activation.

Future buyers may have no idea they will need a brand

So far so good, but here comes the critical point that I think escapes many data-driven marketers. Most buyers are not ready to make a purchase right now. Their need lies in the future, and they may have no idea that they will have a need for a specific product or service. As a result, they will not perceive advertising to be relevant to them now, which means they will not click, check out the landing page, or follow the brand.

No obvious behavioral response

The dilemma is not that the marketer cannot identify future buyers – look alike modeling should at least identify potential buyers with a similar profile to existing customers – and, failing that, the marketer might simply assume that someone will have a need because they work in a specific industry, own a specific type of car, or enjoy a particular sport. The dilemma is that there will be no behavioral evidence of whether the campaign is working or not. The only way to do that is to survey potential buyers to identify if the desired impressions are being established.

Targeting the easy to find

Besides, there is a risk with data-driven targeting that every competitor ends up targeting the same people. I have not been on Facebook so much recently, but I have made a few posts from the ski hill. Those posts probably account for all the ads I see from Outdoor Research, REI, Oros, Duer, and Stio. When every brand targets the same group of people because they are easy to find, no one is going to win big. Maybe all those outdoor gear companies should be looking for what their data is missing and go explore the white spaces?

Which is more expensive, targeting or reach?

I have little doubt that with enough data and machine learning a data scientist could come up with an algorithm to target potential buyers for many of the more deliberative product categories – the ones where people do research their purchases and leave an obvious data trail – but how accurate would that targeting be and would it be worth it? Or would the cost of identifying and targeting those people become so high that you might just as well reach everyone and have done with it?

The benefits of reaching everyone

If you did reach everyone, of course, your brand building campaign would come with the benefit that everyone would know your brand. And that is important…

• Because when everyone knows a brand and understands what it is good for, they are much more likely to talk about it. Word of mouth matters in more considered product and service categories, and it helps if positive word of mouth confirms a pre-existing, positive impression.
• Because, when everyone knows your brand, it does not matter when they transfer industry, take up a new management position, or unexpectedly adopt a new sport or pastime. They are already primed to consider your brand.
• Because, when everyone knows your brand, the other stakeholders in the purchase decision understand why it makes sense to choose your brand (well, your partner may not understand why you bought that Flylow ski jacket, but the CEO might understand why you recommended Adobe).

And, of course, if you did spread your marketing net wide then you would not miss influencing those people on which you had no data or had misidentified as being unlikely to need your brand.

Reaching people is easy, influencing them is hard

Irrespective of whether we really have enough data or budget to reach all potential buyers in a category, there is still one major unknow lurking out there. The human values, instincts, and emotions that lie behind the data and which will motivate someone to pay attention to your ads and buy the brand. Reaching people is the easy task, influencing people is the hard task. And that all comes back to understanding humans, not analyzing data.