One of the most exciting aspects of machine learning for business is its potential to redefine content management. Admittedly, this is a rather broad discipline encompassing external customer-facing processes and internally oriented ones in order to enhance operations as well as the customer experience. But the ultimate quest of content management has always been to identify ways to extract the most value from content.
To aid in this, we have identified specific areas in which machine learning applications can infuse intelligence into content and optimize value.
Digital Content Auto-Tagging
Businesses invest millions in efforts aimed at creating valuable content. However, few are able to tap fully into the potential of the content they work so hard to produce. One way to harness that potential is by sorting your digital content and making it easier to search for it.
Machine learning tools hold the potential both to enrich the content you create and make it easier to find through auto-tagging. Traditional methods often involve searching manually through existing content to select and apply the most relevant, accurate and effective tags. Not only is this a cumbersome process, but when an employee who handles the task leaves, their knowledge and experience get lost.
The automated approach involves having a machine learning model comb through countless pages of content for purposes of learning context. Once it completes the learning process, it can auto-tag with high accuracy. In turn, this will allow you to offer more relevant content that can enhance customer experience.
Faceted Image Searching
For businesses that work with a high volume of rich media, the importance of image auto-tagging cannot be overstated. Manually sifting through countless images to identify the most suitable one for your content is both time-consuming and cumbersome.
But the use of machine learning algorithms with image-processing capabilities can automate this process. Essentially, this approach involves image analysis and the identification of specific features such as color, gender, estimated age, emotional state and objects.
It can come in particularly handy when running new campaigns, as this often requires lots of content variations to ensure efficiency. Filtering images using machine learning is faster and more likely to get you precisely what you need.
SEO Automation and Keyword Extraction
Keywords form a crucial component in content success as they offer a concise representation of what an article is about. They also contribute significantly to Search Engine Optimization (SEO) and article retrieval from a given information system. Appropriate use of keywords also enables the categorization of content into relevant disciplines or subjects.
Conventional keyword extraction processes typically involve manually assigning keywords to articles based on the author’s judgment and article content. On one hand, this could take considerable effort and time and on the other, it might not offer optimal accuracy.
With machine learning, you can turn these tedious processes over to computers with the promise of higher efficiency and effectiveness. By taking care of the mundane and repetitive analysis tasks, machine learning allows content managers to focus on value-adding tasks like content writing and monetization.
Getting accurate results from keyword extraction and SEO optimization will inevitably guide you in creating more satisfactory content for users. This could eventually mean better visibility and indexation as well as a higher click-through rate.
Errors greatly limit the impact of posts, portray a negative brand image and can even hamper content tagging. Though popular platforms for content writing such as MS Word have spell-checking and autocorrect features, not all business communication takes place there. There are numerous other applications in the business world which lack self-correcting capacity.
Business communication with other businesses or clients takes place on social media via email, support services or web chat. And with a higher amount of communication comes a higher likelihood of having spelling and grammatical errors. At the same time, there are some mistakes that regular spell checkers will not catch and humans cannot proof-check all communication.
Errors create a negative impression of sloppiness or even put in question the credibility of a business. For these reasons, using advanced machine learning-based spell-checking models is paramount to success.
Such models can go beyond the ordinary levels of traditional approaches as they can identify both spelling mistakes and proper spellings in wrong contexts. Integrating such checks into business communication will help mitigate the above challenges and make content more effective.
Automated Translation for Bulk Files
Machine learning is proving to be extremely handy at translating between human languages. Before the advent of machine learning translation, the computer-aided translation produced output that was largely incomprehensible.
Neural machines defer in the sense that they base their learning process on a large input of data in the languages of translation. From this data, they learn the rules and patterns of the languages and ostensibly train themselves. To start with, they recognize individual words and then join these up according to the grammar rules of the target language. It may, thus, create a number of possible translations and refine these to pick out the most likely one.
Having content accessible in a variety of languages has obvious advantages, especially when targeting an international market. A practical application for this would be in communicating with website visitors. An automated translation model makes the process fast and painless, allowing you to reap the benefits of customer satisfaction. It also means that your business can enjoy a greater global reach.
Consumer Behavior Analysis
Your content management strategy would be far from complete if it does not include the analysis of consumer behavior. Machine learning algorithms facilitate the analysis of user data such as location, demographics and timing. They allow you to get a clear analysis of the people reading your content, their reasons for doing so and the exact time they visit your website.
It becomes easier to target your content better through the use of behavior and engagement trends to understand relevance and context. It can also boost site performance by eliminating content that simply crowds your feed.
Make Machine Learning the Backbone of Your Content Management Strategy
For content to have success, it does not only need to be relevant and resonate with the target audience but also trigger a reaction. The use of machine learning tools uncovers the insights that make it possible to streamline your strategy to have the desired effect. Ultimately, the aim of your content management system is to drive more traffic to your website.
Algorithmic analysis powered by machine learning offers limitless potential in the creation of an effective strategy. With proper implementation, it can help in solving innumerable problems in the traditional content management system. This will help enhance efficiency and productivity, positively impacting customer satisfaction and fostering long-term growth. It will also save on costs by automating repetitive processes that staff members currently handle, freeing them up for value-adding roles. Notably too, it will allow you to figure out what works and what does not based on hard data tracking.
Currently, the use of machine learning in content management is more comparable to the use of a power steering for your vehicle rather than autonomous driving. While you still take the driver’s seat, it gives you access to a variety of tools to enhance processes.