Content is insights! Expressed in myriad forms and styles, it is information that makes readers feel, think and change their lives. Latest advances in AI such as generative AI, Natural Language Processing (NLP) and Natural Language Generation (NLG) have enabled companies to automate routing content generation/automation tasks. We explained all related terms and technology:
What is content automation?
Content automation is a set of technologies that automate manual processes in content marketing. Content automation is more than automated content generation. Its purpose is to automate every stage of the content lifecycle from content generation to keeping the content up-to-date.
What is content marketing?
Content marketing is one of the most effective strategies for inbound and digital marketing. Content raises awareness of your brand, builds trust with your audience and turns them into customers who may become product evangelists. Content marketing is related to both your company’s website and blog as well as social media platforms such as Twitter, Facebook, and Pinterest which have audiences that are exploring new content daily.
What is the content experience?
The content marketing process is creating and distributing the content and improving the content experience. Improving content experience can be more important than improving content quality.
For example; I like swimming. If you take me swimming on a snowy day, probably it would not be the best swimming experience I have ever had (I know Nordic people are swimming in ice-cold waters, I am not one of them).
Therefore, the channel, time, frequency or rather content distribution are also as important as the content. Many software tools can help marketers reach the right customer via the right channel with the right message at the right time such as Nudge (helps identify which accounts to contact) and Crystal Knows (helps personalize your message taking into account the recipient’s personality).
Here we focus on creating the content but feel free to examine our AI in marketing section to explore other parts of the content experience.
What are AI use cases in content marketing?
AI can assist organizations in content ideation by facilitating content creation, curation and distribution, and analysis of the content’s performance.
1. Identifying topics to write about
The success of a piece of content relies on the interest in the topic and how existing content satisfies that interest. For example, both of these values can be estimated using free tools like Google Search Console for the web. It is a relatively easy and automatable task to come up with adjacent topics to write about, analyze their potential and pick the topics with the most potential.
Coming up with an adjacent topic may seem difficult, however, even relatively simple word embedding models trained on Google search console data can identify similar words based on how frequently they appear together. Word embedding is one of Natural Language Processing (NLP) techniques
2. Generating content
With the help of generative AI, NLP and natural language generation (NLG) technologies, organizations can fasten the process of content creation. Content creation bots can create content such as text, image, and sound.
Proofreading content that is written by a human is another application area of AI in content marketing. For example, Grammarly is a sophisticated artificial intelligence product built to analyze sentences written in English and corrects user when it identifies grammar mistakes.
4. Personalizing content
AI algorithms can be used to track individuals’ behavioral patterns and their preferences regarding content consumption. These insights can be used to personalize content. For example, Überflip aggregates the contents and uses artificial intelligence and intent data to predict, recommend, and automate personalized content experiences.
5. Real-Time SEO Recommendations
Some tools claim to increase an article’s chances of getting ranked higher on search engines by using AI. We are quite skeptical about the current products on the market that tend to use simplistic approaches such as keyword counting but this is an area where a sophisticated solution could make an impact.
6. Updating content
Content that remains unchanged quickly loses value. Auto-updating can take a significant burden from content producers. Quark, for example, automatically updates every document, content, and component. It also automatically adjusts the format for different devices to ensure that each piece of content is delivered to right customer in a right way to improve the customer experience.
How can content generation be automated?
Content creation is still mostly manual. Getting started from scratch, and creating innovative, effective and original content is a hard job. Many aspects should be considered during content creation such as SEO (Search Engine Optimization), style, uniqueness and content objectives.
Different types of content such as visual, audio, and text can be automatically created by AI algorithms. Machine learning and deep learning techniques search, analyze and learn from articles that are related to a given keyword to create unique and optimized content. However, most machine generated content will be perceived as derivative, and may not fit the style of your organization or serve your content goals. Therefore, humans in most cases either need to quality check or build upon the content auto generated by machines.
The subbranch of AI that deals with text generation is called Natural Language Generation (NLG). We list
- several companies offering NLG services below.
- large language models that can automate content generation
Since we are also in the business of preparing text research, you could ask if we leverage these companies’ services. While these solutions can not prepare B2B-ready, concise, data-driven, well-researched articles, they can help build hypotheses and prepare starting points for FAQs. This situation was different just a year ago when these tools couldn’t even serve as a starting point, deleting their text and restarting on our own was faster.
Another use case is creating reports from data. This is a straightforward task and companies like Quill and Wordsmith offer promising tools for that.
Automated Insight’s Wordsmith turns documents into text. When you connect Wordsmith to Excel, Word or Tableau document, it produces press-ready concise verbal summaries of the data from the underlying data.
Wordsmith is already creating content for large customers:
- 4K company earnings reports/quarter for the Associated Press
- 50K personalized narratives/week for GreatCall
- 100K workout recaps/week for bodybuilding.com
Wordsmith operates as a SaaS platform where you can get 500 outputs/month for $24k/year. With the increased volume, subscription prices fall, enabling large platforms to produce content for just cents.
Narrative Science’s Quill is a similar content generation service. Quill is sold as a managed service with prices ~$10K/month for most use cases.
Foundation models such as Dall-E 3 are quite good at creating images.
Practical image generation started in 2017 with Google succeeded in creating meaningful images from sentences.
Deep Dream Generator combines two given images to transfer the style of one image onto another. Such filters are now common place in social media applications. Here are some examples created by Deep Dream Generator:
You can see the original images at Deep Dream Generator. (You need to sign up first)
Reading content is a relatively easy task and computers have been reading for years. Google’s duplex system even introduced human-like pauses and filler sounds and words in its speech.
Some artists also claim to be using AI algorithms to create songs however these are more novelties rather than mainstream entertainment. In the song below, the music was claimed to be composed with artificial intelligence while the lyrics were written by Taryn.
Why rely on content automation?
Automating content has advantages for content management compared to traditional content marketing. It can
- Increase efficiency of content creation and management
- Provide workflow automation and reduce manual mistakes
- Improve customer experience
- Provide regulatory compliance
What are the best practices of content automation?
- Caution is advised while investing in this space: Even though some vendors are making big claims about the capabilities of their tools, we have not yet come across a content automation tool compelling enough for us to use. It is still not possible for a bot to write popular, engaging content without any human guidance.
- Even the top content automation tools of today can be augmented by human effort. Even with state-of-the-art technology, full or significant automation of content is not possible. Companies should focus on finding tools that augment their teams. These could be proofreading tools or auto content creators that create articles that are later improved by humans. Proofreading tools are already quite helpful since they provide automated plagiarism checks, engagement level gauging, and tonality assessments.
What are the challenges of content automation?
Because the field is so now, what is right or wrong is not clear. But content automation should be performed with some clear principles:
Transparency: Readers need to know the author. Sports Illustrated published machine-generated articles with author bios that included synthetic humans which was followed by significant backlash and executive departures.1
Quality: Machines can be great writers but they require human supervision in writing.
Happy to help if you have questions related to NLG or content automation:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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