Content marketing is proving to be the fast and quantitative market testing in today's inbound marketing world. About 90 percent of today's marketers are implementing a content strategy to make sales, establishing thoughts and increasing brand engagement.
Journalism and technology platforms created an automation process for mining content, fused together as a type of digital marketing. With digital marketing, marketers are exposed to content technology and many new tasks.
With all the advantages content marketing with technology can provide, they can also be confusing. And especially with complex terms like semantic search, machine learning and natural language processing. However, the importance of these terms is growing fast for content marketers.
The reason for its fast growth is because people want fresh and purposeful content, all placed in a convenient and expected ecosystem. For example, people may expect content from a website. If they aren't getting the content they want, they're going to look somewhere else. This is potentially disregarding the website as a dependable source. Marketers should understand how a website's credibility can affect how the audience sees the business.
Content should be added and updated as frequent as possible. However, marketers don't always have the time, or resources. Marketers should master the technology behind today's digital marketing world, so they can get to work on digital contents without having an IT source in time.
Below is a list of some introduction for technologies, and how they can be implemented to advance content marketing strategy.
Artificial Intelligence
Artificial intelligence (AI) is the basic in all technology terms. AI is a broad term that describes intelligent software and computing, allowing a machine to "think" through programming specifications it's build. AI stands as the backbone to many fields, such as natural language processing and sentiment analysis. These technologies allow content marketers and their constituents to search for and discover relevant content, collect consumer data, explore predictive analytics, and so forth.
Machine Learning
Machine learning is a branch of AI where machines can learn from data and predict potential outcomes. For example, machine learning can utilizes predictive analytics to score leads, or to learn what content is the most relevant. This increases the potential of big data and allows marketers to focus on the information that match their service.
Natural Language Processing
The AI for intelligently understanding or generating "natural language", is to understand the language that humans write. This is known as natural language processing (NLP). NLP aims to understand the structure of human linguistics, not just the words themselves.
This is useful for content marketers looking to dive into the world of mobile marketing as part of their online marketing plan. NLP can also be used to help marketers understand customer inquiries and better educate their users, leading to improved buyer satisfaction and content congruence.
Machine Translation
This is automated content translation that translates specific words, sentence, paragraph, up to a whole page to other language programmed into its database. These machine translation tools can cost-effectively transfer content into new markets, international and domestic, reaching a larger audience and increasing potential for improved brand awareness.
Sentiment Analysis
Sentiment analysis is an automated NLP task used to determine the feeling (sentiment) of a piece of content. By understanding the sentiment of content posted by potential consumers on social media, marketers have the opportunity to adjust and present targeted information to those users.
Information Retrieval
In order for search results to be precise, information overload should be avoided. Information retrieval is the field of retrieving the correct information given by a query. This is the science that Google and other search engines use to return the relevant content to the inquirer.
Without understanding how Google works, marketers can have a more difficult time optimizing content. A solid understanding of Google’s PageRank and algorithms gives content marketers an exponentially better shot at reaching a wider audience.
Collaborative Filtering
Collaborative filtering is a technique used to recommend content based on the content consumption habits of similar users. It filters through large amounts of data to obtain the most useful information available to the user.
This is helpful for marketers to understand how to optimize their content for sites that use collaborative filtering. It also proves as a great example of how content marketing and technology can be used to increase product awareness and drive sales.
Document Clustering
The task behind document clustering includes the automatic grouping of related content.
Document clustering proves itself useful for marketers because it often powers content recommendation engines to suggest other similar content for the searcher to read, improving the reach of content and usefulness for the user significantly. This automation can also be used to suggest call to actions.
Semantic Search
Semantic web is a common data format for adding metadata to concepts and interpreting their relationship. This automation takes the intent of a query and polishes it to provide more customized search results.
Semantic search is a beneficial tool for marketers because it can be used to find extended content related to the initial search, giving that associated content an expanded reach. However, the amount of human annotation required for semantic search is causing user interest to decline, as it cannot comprehensively "learn" from existing preferences to make the search process more efficient.
The trends has highlighted the importance for increasing the potential content marketing of businesses. Including content in your marketing strategy is essential to establishing thought leadership and staying ahead of the competition. In order to do this effectively, marketers must understand the technology behind digital content.