One of the biggest marketing challenges that any organization faces is creating enough high-quality content to drive positive business outcomes. What makes things even harder is that your content needs to be unified. It should have the same tone of voice, regardless of who wrote it. It also needs to engage your target audience and accurately represent your brand. Otherwise, you risk confusing or even alienating your prospects and customers.
If you’re a small organization with just a couple of writers, creating good content that’s consistently on brand is fairly easy. Having a basic style guide or simply comparing notes with the other writer may be all that it takes. But if you work at a larger organization with lots of writers—whether five or five hundred—it’s a very different proposition. Even more so if those writers are spread across different departments, work in different offices, or don’t all speak English as their native language.
In situations like these, traditional style guides tend to fall short because they’re difficult to enforce. And although you can hire an army of editors to oversee the quality and consistency of your content, it can be cost-prohibitive. The good news is that artificial intelligence (AI) offers a better solution.
Acrolinx is one such AI tool designed for this purpose. Acrolinx’s linguistic analytics engine uses AI to read and analyze your content, then offers step-by-step guidance to make it better.
Sure, it will catch and help you fix any grammar and spelling mistakes. But if you’re thinking it’s just a glorified spell checker, you’ve got another thing coming. Fundamentally, it helps you write content that’s clear, compelling, and aligned to your company’s specific brand standards. That means that no matter who’s writing your content, it’s accurate, consistent, on brand, and really well written.
Let’s take a look under the hood at the AI that Acrolinx uses to analyze your content and make the right recommendations to fix it.
Using Natural Language Processing to “Read” Your Content
When most people think of AI, they tend to think of things like pattern recognition. This is the kind of technology that lets Netflix recommend movies to you and Facebook to recognize people in your photos. What isn’t as widely understood is natural language processing (NLP) — the ability for machines to interpret and analyze natural language. (Just to be clear, by natural language we mean English, French, German, etc., as opposed to machine languages such as C++ or JavaScript).
While reading and analyzing natural language is something humans do all the time, getting a machine to do so is no small feat. That’s because language is dynamic and complex. To understand it, machines first need to break it down and then add information to it to facilitate interpretation. In very simple terms, our engine does this following a four-step process:
1. Filtering the text from the noise.
The first step is to filter the content. We want Acrolinx focused on the text you care about and not any of the other noises associated with it. These noises might include things like metadata, comments, tracked changes, or formatting information, which, while important, aren’t what we want to be analyzing.
It’s worth pointing out, however, that some of these noises can be very useful for context. Knowing that a particular line of text is a headline or a bullet point, for example, is important because it has implications for how you would expect that text to be written. As such, we want to ensure that the platform tailors its suggestions for the context of the content.
2. Breaking the text into units.
Once the engine filters out the noise and knows what it needs to analyze, it looks to see how individual letters are grouped into words, and how words are grouped into sentences and paragraphs. And while that’s pretty straightforward in English, in other languages such as Chinese or Japanese, it’s not a trivial task.
3. Applying linguistic data.
Next, the engine tags the different words in each sentence with linguistic data pertaining to its morphology. It looks to see if the words are nouns, verbs, adjectives, or any other part of speech. This, too, is easier said than done, since many words can work in multiple capacities (“run,” for example, can be both a verb and a noun). To figure this out, the engine looks at how words are structured together to try to deduce their function in a given sentence. It then validates its guesses using a dictionary.
4. Applying other knowledge.
The final step is to apply our knowledge about what constitutes good writing. And while correct grammar and spelling are part of that, so too are things like your company’s unique terminology, style, and tone of voice. Armed with this information, the engine can identify sentences that are too wordy or formal, use the wrong words, or that don’t sound like you. It’s able to pinpoint anything that deviates from your company’s preferred style.
Of course, simply being able to read your content and identify any problems isn’t particularly helpful. You want a system that acts like your personal assistant, providing suggestions on how to improve it, inside your content authoring tool of choice.
Using Statistics-Based Machine Learning to Correct Your Content
Any time you give advice, you want to be absolutely confident you’re giving the best suggestions possible. But when it comes to getting a machine to tell you how to create higher-quality, more engaging content, it’s no easy task. That’s particularly true when you’re looking at issues like style and tone of voice, where it’s less about the mechanics of your writing and more about the impression it makes. Take clarity as an example.
To help Acrolinx learn what good writing is, we analyzed tens of thousands of pieces of content. We used Acrolinx to analyze them and assess which were clear and easy to read—and which weren’t. We then identified 40 different individual factors that contribute to clarity using a statistics-based approach to machine learning.
While it’s currently a largely manual process, we’re using deep, reinforced learning to automate it. In the months and years ahead, we’ll be able to aggregate all of the data Acrolinx has collected and put it to use for all of our customers. This will lead to better suggestions for how to improve your writing, and also yield important insights.
For example, you’ll be able to use Acrolinx to develop a unique tone of voice that helps you stand out from your competitors. Conversely, if you want to sound just like Apple or Google, you’ll be able to use Acrolinx to replicate their style in all of your content.
Is AI the Future of Content?
While content promises to become ever more important as a tool for attracting, engaging, and retaining customers, how we go about creating it is changing. AI has huge potential to help companies create high-quality, consistent content at scale. Doing so can bring an array of benefits, from fostering greater brand trust, driving conversions, and increasing purchasing intent, to making your content creation efforts more efficient and cost-effective.
To learn how content quality directly correlates to key business metrics, such as conversions, trust, and purchase, download a new, helpful guide we’ve created below:
Christopher P. Willis
Christopher P. Willis is Acrolinx’s Chief Marketing Officer. He brings over 20 years of experience growing companies in the technology sector. Before joining Acrolinx, Willis held leadership roles in marketing, creative, technical, and business development at companies including Perfecto, Pyxis Mobile, KPMG-CT, ModelGolf, and Cambridge Technology Group.