Artificial Intelligence in the Publishing World

Machine learning, which is at the heart of the AI revolution, has expanded beyond robots, self-driving vehicles, and even Netflix suggestions. Despite their youth and technological prowess, these sectors are drastically altering the landscape of digital publication. According to an Accenture analysis, the information and communications sector, and hence the publishing business, would benefit the most from artificial intelligence in the future.

When  people hear about artificial intelligence in publishing, they immediately think of machines writing books. Of course, attempts have been made in the past, but the outcomes have been less than ideal.  Here, we have taken a comprehensive look at artificial intelligence's future in the publishing industry.  

What’s the role of artificial intelligence in the publishing industry?

 AI-driven recommendation engine: 

AI-driven recommendation engine:  Its focus has shifted from mass media to customised media. Previously, print media catered to a wide range of readers, while television networks broadcast programmes to appeal to a diverse audience. Today's smart digital devices have fully replaced this scenario, giving the spectator complete control over the choosing process. On Netflix or Prime Video, people may view any material they choose. 

This is where AI may assist the publishing sector; its involvement is critical in recognising and comprehending the target audience's content preferences. Instead of supplying generic material, firms must give a tailored experience to engage a technologically aware audience or reader. Any personalised experience requires a cutting-edge recommendation engine that considers individual preferences based on media consumption history.

Leveraging AI to predict the future

 For any media or publishing firm, predicting future trends is typically the most difficult task. It has also grown considerably more complicated as viewers expect more tailored content. AI, driven by data and analytics, may be a game changer at each level of the value chain in the following ways:

Content development

Predictive analysis of viral media consumption and sentiment analysis of owned media consumption can give insight into future content trends.

Content aggregation 

Automated AI-driven media metadata tagging can aid in the co-relationship of disparate media and the identification of appropriate material.

Content distribution

AI may be used to propose the appropriate material - in the right format - to the right audience at the right time to increase engagement.

Content consumption 

Future trends can be recovered or refined using content consumption and sentiment analysis. Knowing what people want to see in the future and correlating their preferences on a large scale may help media and publishing firms invest in developing the correct content for their target audiences, and AI can help.

Short summaries and reviews

Individuals' attention spans have shrunk even more to 8 seconds. This is all we need to capture the user's interest and keep him on the page for longer. A short review or summary, particularly in the case of journals, chapters, research papers, academics, or stories, might assist the user decide and remain longer. From lengthier chunks of writing, coherent and accurate bits of text might be generated using Natural Language Processing (NLP).

Translations of content

AI has aided in the development of professional translation automation technologies, resulting in increased accessibility to material in several languages. AI also reduces the danger of human mistake while also shortening the time it takes for translated information to reach the market.

Marketing via e-mail

Marketing tactics, such as e-mail marketing, benefit from AI. It might be useful for customising emails. The average open rate for static emails in the media and publishing business is roughly 19.24 percent, according to Zeta (a customer lifecycle management marketing company). For customised emails, the same number rises to 63.22% (the click rates are 13.16 % versus 26.29% accordingly). 

Evolok AI and Machine Learning

User Engagement Scoring

We can measure users and their level of engagement through machine learning algorithms. From their visit history, spend, frequency, content affinity, all of these are used by algorithms to calculate a unique score in real time for each user and those scoring close to those conversion points, will be challenged or encouraged to convert. For content owners, the paywall becomes automated and dynamic - removing any manual intervention and allowing a conversion strategy to be truly dynamic.

User to Content Recommendations

At Evolok we have redefined content recommendations to offer the most advanced, fully automated 'user to content recommendations' instead of content to content suggestions. Evolok collects data about the content, such as keywords, categories. and match users with the right content based on their likes and interests. This gives you increased engagement, click throughs, less bounce rates and sticky users resulting in increased engagement, brand loyalty and overall conversion growth.

These are just a handful of the many ways artificial intelligence may aid journalists in their profession. Unfortunately, because of the subject's complexity and fear of new technology, magazine and newspaper proprietors have preferred to watch rather than act more bravely. We don't think AI will ever be able to take the role of journalists.  Simply because human creativity is a vital component of producing high-quality material. AI can assist media and publishing organisations optimise the monetization of their media and content by solving unique and complicated functional challenges. To know more about how Evolok AI can help your business grow contact us today.