If you want to understand the gap between creativity and data science in marketing, look no further than the creative brief.
A good creative brief is two things: creative and brief. It’s the art of sacrifice. A primary audience. A customer insight. A single most-important message to communicate. And a handful of supporting facts, or reasons to believe.
This single-mindedness makes a lot of sense to focus creative energy, especially when your goal is to tell one audience one story at a time. In practice, however, marketing’s art of sacrifice limits a brand’s audience, message and reach.
Truth be told, it’s all a damn lie.
As every marketer knows, there isn’t just one great way to tell a product’s or brand’s story. As a creative director, I’ve personally killed thousands of brilliant, imaginative ways to tell a strategic story in order to get to the one, perfect campaign for the one idealistic audience segment. It made sense at the time.
The industry had to prioritize —or homogenize — audiences because we didn’t have a way to organize and process all the different motivations or triggers, let alone communicate with so many different groups of people efficiently.
Data science and ad tech change all that.
Marketers no longer have to decide on a “primary” audience when data science and creative teams can help identify dozens or even hundreds of audience groups who care about a brand through real-world message experimentation. And then systematically reach audiences and look-alikes through programmatic media and machine learning.
Doing it effectively means finding the relevant creative story for each of them.
So try this: Flip the brief upside down and start with the reasons to believe. Your brand has dozens of story angles someone may find value in. The product facts and benefits that didn’t make the “main message” cut when the brief was singularly focused need to be taken out of footnotes and allowed to help determine new brand stories.
Make some small bets in media to discover which ones work, and with whom.
One way to do this is to start with all of the possible factual reasons someone would want or need this product, then run experiments in the real world with inexpensive digital media to discover audiences who do indeed want or need such a product. Then segment them into groups of like motivations and affinities.
Now you know who’s interested and what exactly about the brand interests them. Once you do, strategic storytelling becomes far more effective and efficient.
Consider: Why prematurely limit the universe of potential audiences when data science could open up so many more viable options?
You’ll be surprised to learn about all the unlikely connections between audiences and product facts – with large groups you’ve never imagined. What do wrestling fans and lawn care have to do with one another? What do face care rituals and astrology devotees have in common? Why are video gamers so worried about bed bugs? And what is the common ground between baby baths and frozen pea buyers?
These are all real-world data signals from recent experiments — avenues that were opened up by re-imagining the creative process with the help of data science. By figuring out how to correctly monetize and scale up small bets to new audiences, marketers can turn their creative talents into many smaller, yet more personal and relevant ideas that collectively represent an even bigger idea for a brand.
It turns out, data science isn’t a threat to creativity. It’s a few hundred doorways.
This article was originally published by Media Post, April 12, 2019