The Beginner’s Guide to Using Natural Language Generation to Scale Content Marketing
You may have heard about the potential of artificial intelligence, machine learning, and related technologies.
Indeed they can improve marketing performance, productivity and personalization.
But getting started with these technologies isn't always easy.
This post describes how to apply AI technologies to your marketing efforts.
In it, we'll give you a 30,000-foot view of an AI technology called natural language generation (NLG) that is used by companies like the Associated Press to scale content creation and general natural language using machines.
Then, we'll teach you how to get started with that technology today.
What Is AI and Why Should Marketers Care?
Artificial intelligence is the science of making machines smart.
Traditional marketing technology is built on algorithms that are told what to do by humans. With AI, the machine creates its own algorithms, determines new paths, and unlocks increased potential to advance marketing and business goals.
But "artificial intelligence" isn't just one thing.
It's a group of technologies, like machine learning, image recognition, deep learning, neural networks, natural language processing (NLP), and natural language generation (NLG).
Image Credit: Medium, John Koetsier
Each of these technologies can play a role in uncovering insights from massive datasets and achieving marketing and business goals using those insights.
And they're used widely already today by companies like Amazon, Google, and Apple to power product recommendations, voice commands on your phone, online search, and much, much more.
What Is Natural Language Generation (NLG) and Why Should You Use It?
NLG is a type of AI that generates natural language from structured data. With the right data in the right format, an NLG system can automatically turn numbers in a spreadsheet into data-driven narratives or even use associations between words to create fully or partially machine-written text.
NLG is related to, but distinct from, natural language processing, which converts text into numbers, so a machine can "understand" what is being said.
As put by NLG provider Narrative Science , "Natural language processing reads while NLG writes."
What's more, NLG technology is progressing rapidly to transform how we live and work.
A system like Gmail's Smart Compose now offers suggestions on what you should type next in an email. Then, it learns each time you pick one of its suggestions. It's using advanced NLG and NLP to read your emails while you write them, then predict what you are trying to say next—in real-time.
The Associated Press uses NLG to automatically create machine-written corporate earnings reports. In the past, a human reporter reviewed earnings data when it was released, then wrote a report on it. Today, the AP uses NLG to ingest that data, then produce a narrative in seconds, freeing up reporters to pursue higher-value tasks within the organization. All the AP earnings reports follow the same exact format. So, once NLG is set up, their system can produce thousands of stories at scale.
Even weirder, in 2019 OpenAI, a non-profit AI research company, announced they built an AI model that essentially writes coherent paragraphs of text at scale . The model is called GPT-2 and it learned how to write this well by analyzing eight million web pages. In 2020, OpenAI updated the model and released GPT-3.
In early experiments, GPT-3 has been used to produce everything from coherent blog posts to press releases to technical manuals, often with a high degree of accuracy. To do that, GPT-3 uses 175 billion parameters in its language model, compared to GPT-2's 1.5 billion.
It's still early days for GPT-3, and the validity of the model hasn't been fully explored. But one thing should give marketers pause:
The speed of improvement in OpenAI's language models.
The first GPT model came out in 2018. GPT-2 was released with greatly expanded capabilities in 2019. Just a year later, GPT-3 uses 100x as much data as its predecessor and is beginning to display incredible content creation capabilities, including turning text into code and evaluating investment memos.
This type of machine-powered language generation has serious implications for content marketers.
With properly structured data, marketers can use NLG to automate some narratives that are based on data. That might include analytics reports, data-driven sections of blog posts, product descriptions with standardized formats and lots of specifications, or even partial or entire articles.
With the right data and a properly configured NLG template, you can leave some types of content creation to the machines while humans create other types of content and double down on promotion.
How Do You Get Started with NLG?
So how do marketers actually get started using natural language generation? There are a ton of exciting use cases for natural language technology that might now possible thanks to recent developments like GPT-3. We're just beginning to explore what might be done with fully machine-generated articles. This work can be highly experimental and quite technical—and may not be appropriate for businesses just getting started with a natural language generation tool.
But, you can get started in simpler, yet powerful, ways with this technology—without hiring a data scientists or revamping your entire operation. Here are some initial steps that any marketer can take, starting today.
1. Determine if you have a use case for basic NLG.
Look first to the stories you're already manually telling with numbers. Think about "stories" as any narratives that make sense of data. This might include external-facing or internal-facing reports, summaries, fact sheets, etc.
Do you produce these types of stories regularly? Are any of these narratives in a consistent, repeatable formats (i.e. you're reporting on or telling a story about the same types of numbers each week or month)? These might be candidates for natural language generation.
For instance, PR 20/20 , the marketing agency behind the Marketing Artificial Intelligence Institute, has used NLG to cut down analysis and production time of Google Analytics reports by 80%. This isn't an advanced NLG use case that leverages something as robust as GPT-3, but it is a valuable one.
It just goes to show that low-hanging fruit may create value faster for your organization, while teaching you the basics of natural language generation.
2. Look at how your data is structured.
Even with a use case, natural language generation needs structured data to work.
Are your datasets organized in ordered columns and rows? The current NLG solution we use requires a CSV upload, so data needs to be clean and relatively consistent to get value out of this technology.
Or, you may need to invest time into cleaning up your data before uploading it to a system that uses natural language generation.
3. Be realistic about your ROI.
NLG solutions, even basic ones, typically require substantial time to set up. You also need to pay for a solution, and possibly related NLG services. You'll want to take a realistic look at the technology, what it can do for you, and how much you can scale using it.
Start by analyzing how long reports, articles or narratives currently take, then see how much time NLG can potentially shave off.
Finally, apply those time savings to all staff whom NLG would affect. An hour saved per week per employee may make financial sense for your organization.
Read more about how to use NLG to automatically generate content at scale here.
About Mike Kaput
Mike Kaput is the Director of Marketing AI Institute and a senior consultant at PR 20/20. He writes and speaks about how marketers can understand, adopt, and pilot artificial intelligence to increase revenue and reduce costs. Full bio.