Jan7

Computational Lexical Analyses and the Modern Era of Wordsmithing

Writing Blog ImageIn a world fast becoming more interested in, impressed by, and capable of producing brilliant digital imagery, I’m the unfortunate one who gets to sit here and try to remind everyone that words still matter. Excited yet? Give me four minutes of your time, this is a brief post.

We work in what is first and foremost an advertising agency. There may be individual groups whose work is not entirely captured by that description (mine is one of them), but it’s the most condensed way of summarizing Ogilvy CommonHealth. Visually and creatively, the work that comes from many of our groups is stunning. It’s often best-in-class, and I’m not here to deter anyone from thinking so.

But this brings me back to what this post is about. Words, or language. Awe-inspiring as the visual work may be, our clients still often struggle with questions like, “What do we actually call this drug? How do we define and describe its therapeutic effect? How do we communicate that to clinicians? To patients?” Basic as these questions may seem, they are fundamental to the immediate and sustained success of the product. A drug needs a consistent, precise, ownable and differentiating lexicon in addition to a strong marketing campaign.

Easier said than done. Language is organic, a living, breathing document that evolves over time. Let’s look at the word good as an example. Once universally and unambiguously having meant desirable or of high quality, a recent article titled “The Art of the Amateur Online Review” in the New York Times describes why that’s no longer the case (the article is a good, quick read for anyone in advertising). Analyses of users’ product reviews show that good is starting to mean ambivalent. Reviewers say things like “it’s good, but….” In other words, good no longer means desirable, but simply good enough.

The same issues present themselves in a medical and scientific context. Clients wonder if they should say their drug is targeted or selective or honing. Perhaps others have created a drug with a new mechanism of action and they want to describe it in not just a differentiating way but also in a meaningful and exciting one. In medical language, the same words can have unique meanings across different categories.

Tools are available to help guide these decisions. In a computational lexical analysis, we can generate a database of language relevant to whatever subject area it is that we’re interested in. That can help us to know how the words in the category are used, and to see what opportunities there may be to create new language. It’s grounded in data, but this is a strategic exercise that seeks to provide guidance around what language is most appropriate for a given molecule/condition/category. Have a client with problems like this? Send them our way, we may be able to help.

 

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