Artificial intelligence technology is gradually penetrating all stages of interaction with the client. Content marketing is no exception. A smart algorithm allows marketers to do the job better and more efficiently. It helps to:

Find the right keywords to identify popular topics with great organic potential. Such AI-powered tools as MarketMuse, BrightEdge, and Concurred help marketers with this. The HubSpot app, for example, identifies topic clusters and suggests interesting topics by showing parameters such as competitiveness and relevance.

Track user behavior and create relevant personalized content. It’s more than just mentioning a customer’s name in an email. Granify, Personyze, CaliberMind, and other programs are designed to provide users with information, services, and offers that are of interest to the audience. With them, you can achieve high-precision customer segmentation. For example, you can focus not on women in their 30s who like to play sports, but group them according to their interests (those who prefer Pilates to marathon running, etc). Therefore, specific content is more likely to hit the mark than generic content.

Predict the outcome of a content marketing campaign. Adobe’s Marketo AI app analyzes a site and previous interests and suggests what type of content (video, news, or research) needs to be added to the blog.

Create relevant content. AI content marketing helps an author to create and edit texts or select recommendations for users. For example, online stores with thousands of products must create a catalog description for each of them. Firms use natural language generation technology to automatically create many unique texts.

The 2021 State of Marketing AI Report published by Drift and the Marketing AI Institute proves that AI is essential for the success of marketing campaigns, but a lack of training is holding back its spread. Still, tech leaders are giving rosy predictions. Jason Mars, CEO of the ZeroShotBot smart assistant, notes: “Marketing is a data-centric industry and lends itself well to AI. AI is going to become the core of much of the marketing art form in the future.”

Machine learning is an AI model. It is an algorithm that “teaches” machines to “think” and “act” like humans. By learning from large datasets, computers solve problems on their own and predict results. Gartner estimates that by 2025, 75% of B2B organizations will drive sales using machine learning and AI.

For example, Netflix uses many ML applications, and one of the most important is a content-based recommendation system. The algorithm takes into account the statistics of watched movies, preferred genres, movie duration, and other parameters. Such analysis helps the streaming service to create an accurate list of recommendations that a user is more likely to find interesting. The ML algorithm also automatically generates thumbnails for viewers (movie advertisements) so that they encourage clients to watch particular films.

Machine learning even “monitors” the quality of video streaming, even though the service has more than 200 million subscribers. By analyzing user behavior, the algorithm predicts how many viewers will watch a particular movie at the same time. Based on this information, employees cache regional servers so that there are no interruptions in video streaming. The algorithm also monitors the quality of the content, focusing on the specified parameters. It makes sure that the subtitles are correct and that the audio does not lag behind the video.

Natural Language Processing is a technology that teaches a computer to recognize the natural human language. NLP seeks to learn not only individual words but also to understand how their meaning changes in context. Thanks to NLP, people “communicate” with chatbots, smart speakers, or Apple’s virtual assistant. Thanks to this content marketing technology, handwriting recognition, message sorting, speech-to-text conversion, and spell checking have become possible.

NLP-based Hootsuite or Buffer tools track brand mentions on social media so that companies can respond to negative reviews promptly. Apps like Wonderboard help online retailers to do text analysis of their product reviews. This is how organizations find out what customers like or dislike about products and how customers use them.

For example, Q-go employees have moved away from the standard keyword search for products. The developers have implemented a new way to find answers to frequently asked questions. NLP technology compares similar options “How do I change the email address on my account?” and “How do I update my account?” and produces the same result. This makes it easier for users to find information, and they are not disappointed with the service.

Document clustering means an automatic search for related content. This function is important for content marketers because a recommendation mechanism for articles, audio, and video content is formed based on clusters. This technology increases the coverage of content since users receive more interesting and relevant information.

Self-learning algorithms like BrightInfo crawl a website and analyze the semantics of pages and content. Also, intelligent tools record the transition of users between the pages, the time spent on the site, and the content viewed. The algorithm matches the parameters and provides each visitor with personalized content in real time. Due to this mechanism, the conversion of sites rises to 320%, according to the creators.

Lastly, according to the W3C definition, semantic webs are common data formats that make it easy to combine data from different sources. Marketers use the Semantic Web as an auxiliary tool to find rich content related to the user’s initial query. For example, when a reader enters the phrase “Thanksgiving Day”, they find information not only about the date of the holiday, but also the origins of the event, related news, and events.

Semantic search analysis helps to determine the user’s intent (what they are looking for) and the contextual meaning of the query. Related content covers more areas. The search engine not only models topics but also knows how they are related. The Semantic Web is becoming a new channel to market, a new way to advertise a brand.

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In 2022, it is important to use the possibilities of smart technologies (AI, ML, NLP, and others) in content marketing. Innovation opens up new ways to promote products, which brings in greater ROI than older methods. But you can’t mindlessly innovate for the sake of innovation: this business requires a strategic approach.

Overall, it is important to consider what technologies will make your content marketing strategy reasonable. You should plan how to implement AI or ML in business and what tools will help you to realize this idea. To embrace innovation correctly, marketers need to understand digital content technologies. Innovation and professional knowledge working together will make the content and content management strategy more effective.