For anyone who has ever sat through an English literature class, you know that stories are widely open to interpretation. I remember listening to professors speculate about complex symbols and imagery layered in stories, absolutely certain that the long-dead author had purposely intertwined these complex details.
“The mighty oak was chosen for its strength and resilience. It represents the protagonist’s will to keep going, to grow deep roots, to weather the storm,” they’d say with a self-important tone.
Yeah, I could buy that. Makes sense. But there really was no way to prove it. And I couldn’t help but feel that lots of times we were layering on our own interpretation to someone else’s story. I mean who’s to say the author didn’t pick an oak just because it was his favorite tree?
English class felt like art to me – open to wide interpretation. And that’s probably why I loved it, and why I majored in it. It felt like freedom.
But girls with English degrees need to pay their bills, too, so I started my professional journey in the corporate world. As all good journeys do, it took me on an unexpected adventure and led me from print to web content to information architecture to web analytics to personalization to data to analytics. Math and science were joining my English party, whether I liked it or not.
In the Amercian school system, the way we teach these subjects is as if they are completely separate, with the exception of the occasional mind-bending SAT word problem. (You know the ones, e.g. if a train going south is traveling at 65 miles an hour, and has to make three stops before reaching its destination 432 miles away, how many people are on the train?) But newsflash – more than ever these things go together. Language, story, math, science, data. They are not topics to be tackled separately.
We see this in the data and analytics world, as terms like data storytelling and data literacy are taking root and gaining momentum. It’s an acknowledgement that data and math are great, but you have to know what is important. And stories are great, but you have to know why and how they matter.
Marketing activities are a wonderful example of the power and beauty that comes out of bringing together what can seem like disparate disciplines. Let’s walk through a simple use case.
The problem: Your business is doing well, but you’d like it to grow. You have a few options: you can sell more to your customers, you can add new customers, or both.
The exploration: But who are those potential new customers? What do they care about? How do you reach them? Are you having to win them away from a competitor or introduce them to your business value from scratch? Do these new clients look like your existing clients?
The solution: Take your questions, your ideas, and your customer stories and dig deep with data to help you find answers. Measure past campaigns to understand who converted, their channel preferences, the messages that drove their response. Overlay third-party data onto your best customers’ files to see if there are common attributes or indicators about those customers that could help you find more like them, and give you a hint where they might be and how to message them. Create models of your best customers. Build stories and experiences for them with what you have learned.
More than ever, consumers expect brands to build relationships with them. They want to believe in the brand, interact with the brand, and be a part of something. That means it’s time for us to stop separating the story from the data and the math. It’s all intrinsically linked.
We are going to have to encourage individuals with a wide range of skills to come together, respect what each brings to the table, and equally value the math and the words. After all, our stories are what make us human. Data helps us prove it.