‘Twas brillig, and the slithy toves
Did gyre and gimble in the wabe
All mimsy were the borogoves,
And the mome raths outgrabe.
– Lewis Carroll, Through the Looking-Glass, and What Alice Found There (1871)
If you’re familiar with slithy toves and borogoves, then that paragraph goes down nice and easy. But what if you’re not? And what if toves are a crucial part of running an efficient business or breaking through with some kind of innovation?
Then you’re missing out on game-changing knowledge. All because Lewis Carroll used language that made sense to him instead of language that makes sense to you.
Carroll had his reasons for being obtuse. In PR and marketing, there’s never a good reason.
Our work for clients often puts us in contact with scientists, technologists and academics who really know their stuff. How do we translate their expertise and technical language into a digestible, credible narrative for reporters and readers?
Rather than gyre and gimble in the wabe, we do things like …
Bringing data to life: Making the quantitative, qualitative
In a groundbreaking study we did with SONOS last year, we conducted a live beta test to measure people’s physical and social well-being with and without music. This meant 30 homes and families around the globe outfitted with biometric trackers, motion-sensing cameras, Apple Watches with a custom-built app, and iBeacons. Plus a survey of 30,000 people about their music and relationship habits.
It added up to 151 million individual data points, many of which made their way into the video shorts, social media posts and other assets we created for the project. But what really made the data sing (pun intended) was combining it with anecdotes, evocative snapshots and insights from participants and experts.
For example, the fact that households that listen to music together are 15 percent more likely to laugh together means a lot more when it’s paired with something like this.
Contextualization and simplification: Deep learning algorithms
Machine learning is one of today’s buzziest technology phrases. Broadly speaking, it’s the ability of a machine to learn on its own, without being explicitly programmed.
But what does it really mean? If we were telling a story involving machine learning, like we often do for one of our tech clients, how would we make sure the audience really “got” it?
We might try contextualizing it. For example, rather than relying only on strict definitions of “deep learning” and “artificial neural networks,” we can pivot to an example like facial recognition.
In that context, machine learning is about recognizing specific curves and lines that, when merged together in a specific way, create a larger image. After scanning hundreds and thousands of images, the machine records specific patterns and learns to match and remember them when they come up again. We can see this kind of machine learning in real life when Facebook suggests we tag someone in a photo we upload.
And we can easily understand what machine learning is when it’s defined it in a context like that, which is easy to relate to.
Use cases: Understanding the role of a network technician
Network technicians are responsible for installing, maintaining and troubleshooting Local Area Networks (LAN), Wide Area Networks (WAN) and data communications equipment. While that accurately describes part of the workforce of one of our clients, it doesn’t give a reporter much of a story to latch on to.
So try this: When a wildfire knocks out cell service, it’s the job of network technicians to either fix the network or set up a temporary emergency network to aid in rescue and recovery efforts.
Or this: With hoards of people congregating on the coasts of Lake Washington for the annual Seafair, who makes sure everyone’s calls, texts and Snaps of the Blue Angels get sent to their friends and family? Network technicians do, by setting up extra network infrastructure to handle the increased volume of cellular traffic.
Now it’s clear not only what a network technician does but also why it matters.
I’m not an expert in cell traffic or machine learning. But the longer I’ve worked with clients who are, the more I can talk the talk.
That’s good for my personal growth, but as a communicator I have to keep in mind: My audience may be brand new to this information.
That’s why I’m sure to add qualitative color, context and use cases to my storytelling. It’s the right way to talk nerdy to someone who’s new to a subject.
Photo credit: Ashes Sitoula