Single White Female Seeks Better Marketing

Meet Emily.

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To you, this is Emily:  25-35 years old, female, from Georgia.

She is number 2,076 in the Excel Data List. She has a name, but she is actually only one of a number of Emilys on the file.

And unless this particular Emily responds directly to your campaign—with a positive action or a “take me off the list!”—you will probably never know anything more about this Emily, or any other Emily in your data segmentation.

But you should.

You should know much more about Emily.

The facelessness of marketing and sales has, according to some, become even more faceless since the online revolution. This trend bodes poorly for basic human connection and also for business. This is especially evident in the audience data points businesses choose. The common way that businesses cast their nets for data segmentation is often equal to a man with a boat boasting “I will be successful fisherman!” and simply motoring out to a random spot and dropping his net. This is the equivalent to your {Gender, Age} B2C segment. This is your {Title, State} B2B segment. What you as a “fisherman” pull back from the depths is an assorted population with no collective characteristics and debris data. With this kind of data, you cannot find a point of connection to better appeal to your target audience. Your lack of specificity will result in unread, unviewed campaigns.

This is why Emily matters. This is why the data that makes an individual is as important as the ones that make an audience collective.

Marketing Profs recently published an article emphasizing the need for a relationship to exist between data and storytelling. The idea is to transform information into a functioning narrative for the highest possible return. And one key element in narrative, as any storyteller can attest, is the careful development of believable and interesting characters. Yet businesses are often forgetting to add, well, character to the characters in their segmentation.

Imagine if Melville had written Moby Dick like these businesses segment data.

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It would read something like this: “Ishmael. 20- 35 years old. Male. Fishing Industry. Low-level.”  Pretty boring, and more importantly, useless. If businesses use information from social media, public records, and subscriptions, they could easily paint a more complete picture of their audience. Using this data, we might be able to create a character story more like this: “Ishmael. 20-35 years old. Male. Previous work: Merchant Sailing. Current work: Whaling Industry. Interests in cetology and world religions. Public testimony on the sinking of the ship Pequod.

The second set of data points not only tells us more about who Ismael is, but it tells us how we might reach to him as a potential customer. This is not a suggestion to granulate your segmentation so much that you barely get a workable number in return. Rather, think about what “open” categorization might help you find the type of customer you are looking for.

Let’s think about Emily again.

Let’s say you are a business seeking people who would be interested in arts and culture.  Your product is aimed at women within a certain age range. All right. You even choose to segment by the interest “arts&culture.” Well, Emily would have been a perfect customer, but you overlooked her.

How?

Though she appreciates the arts, Emily has never subscribed to any lists for this interest. But she did attend Savannah College of Art and Design. If you had considered the “type” of people interested in arts, rather than clicking an easy data point, you may have opened your data to include graduates of arts colleges and Universities. Then you would have snagged Emily.

Perhaps you are a bookstore looking to push a new shipment of popular and classic novels. You know your segment by heart. In fact, half of them have probably already subscribed to your business in one way or another. But let’s say you have considered customer acquisition in the B2B sphere. You have chosen to reach out to professionals in English education. Smart. But you still missed Emily.

How?

Like many readers, Emily is also a writer. But she chose to focus her writing efforts elsewhere rather than in English education. Instead, she chose marketing. She is reading and writing every day of her life, only her output is content development. And she is not alone. Many of those interested in reading and writing take jobs in the marketing field. But to know this requires a more open “characterization” of your data.

And these are just two of many complicated, multi-faceted Emilys. And Johns. And Lacys. And Matts. These are the characters in the story your marketing campaign is attempting to create. And knowing what makes them unique is as necessary as knowing what brings a segment together. Because, as Brian Solis recently pointed out in his blog, it can surprise you how a collective data point might fail, but a unique data point might bring a community of customers together.

So when you seek to “meet” your customer segmentation in order to market to them, try to turn the basic introductions into a real conversation.

And meet Emily.

About the author

Stirista began as an ambitious project from an apartment in San Francisco. But as office space expanded, so did our client base. After a few short years, we have worked with the largest healthcare insurance provider in the world, the biggest telecommunication company in the US, and some of the most prestigious universities in the country. We are on the preferred vendor list for a handful of Fortune 500 companies, with three of the ten largest companies in the world turning to us for...


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