Slowly but surely, content marketers are finding greater value in artificial intelligence, and natural language generation in particular. In this post, we look at these five specific benefits of NLG:
- Consistent, high-quality content
- Increased content generation
- Topical coverage that would otherwise be unprofitable
- Conserving human energy for high-value activities
- Personalization at scale
NLG isn’t at the point where machines can converse in human language, but we’re getting there. In the meantime, content marketing can experience these benefits.
Consistent High-Quality Content
Algorithmic models drive natural language generation. As such, the more training they receive, the better the output across the board.
MarketMuse First Draft is one of a handful of real-world applications. It’s designed to generate an initial content draft that meets the KPIs as defined in the content brief that drives text generation. In the content brief, there are several metrics, including content score. This proprietary metric assesses the topical richness of a document.
One measure of content quality is how comprehensively it covers a subject. Articles with a high content score are judged to cover its topic extensively.
Before publication, you’ll probably check the article using a grammar checker. If you’re using Grammarly, AI is giving you a helping hand. Technically, it’s not NLG, but natural language processing, machine learning, and deep learning of which you’re taking advantage
Increased Content Generation
Natural language can substantially increase an organization’s content output. Simply put, NLG can create content faster than a human writer.
In the case of the Associated Press, they use NLG technology to take raw quarterly earnings data and turn it into publishable content. In their case, they’ve experienced a 15 times increase over manual story creation.
Topical Coverage That Would Otherwise Be Unprofitable
With natural language generation, it’s like having your cake and eating it too. The ability to generate quality content in such a large volume brings down the cost per unit.
As a result, organizations can cover topics that otherwise would be too costly. Take, for example, Yahoo! Sports. They use NLG to produce over 70 million reports and match recaps. Doing this manually would be cost-prohibitive. Natural language generation not only makes it possible, it also makes it profitable.
Conserving Human Energy for High-value Activities
Where do humans fit into the content equation? If NLG is taking over all these content generation tasks, can humans even play a role in content creation?
Unequivocally, the answer is yes. However, the nature of human participation is changing as we carve out these new roles.
Natural language generation takes care of repetitive and mundane content creation tasks. No one really wants to write a few hundred earning reports or product descriptions. Once you’ve done a few, the rest are all the same.
But NLG technology creates room for human ingenuity. Take, for example, articles created using MarketMuse First Draft.
The low-level task of the initial text generation is taken care of by natural language generation applications. Then a human takes that article, edits it adding their own personal insight, and turns a rough draft into a publication-ready narrative.
Personalization at Scale
Another vital area where organizations can benefit from NLG is personalized digital experiences. A large global eCommerce company with over a billion members uses Quill to create personalized customer experience in the form of performance reports. These reports generate over a million dollars in revenue, save $50,000 in time, and reduce churn by 20%.
What Are The Benefits of Natural Language Generation?
The benefits that organizations experience in using NLG are many and continues to grow. The ability to rapidly produce content at a sufficient level of quality not only saves money but allows coverage of topics that otherwise would be ignored. Done properly, more NLG content means more opportunity to connect with a human audience.
Written by Stephen Jeske