How Machine Learning is Transforming Content MarketingContent marketing isn’t new, but how we think and talk about it is constantly evolving. Ultimately, though, the ongoing concern is how to keep your content fresh and valuable for your audiences.
The term “Machine Learning” shows its impact on almost every industry like self-driving cars, paid ads, spam detecting, etc. In fact, content marketing is one of the most exciting potential use cases for machine learning. Artificial Intelligence (AI) and Machine Learning technology are fascinating and beneficial in every facet of content marketing. Similarly, through advanced machine learning technology, the process of making strategies for content marketing has become easier.
What is Machine Learning?
Machine learning is a subset of Artificial Intelligence (AI) where computer algorithms are programmed to learn themselves from the new and large sets of data (Big data) to perform certain actions accordingly. These self-driving systems learn and change their actions based on historical data and patterns.
The more information they gather, the more precise they move toward becoming. Through the Big data, machine learning can create insights and help to build a personalized strategy. Therefore content marketers will have better chance to create an effective content which is easy to find on the SERPs.
Here are the Ways that Machine Learning can Transform the Content Marketing
1. Improved Productivity
Machine learning algorithms can be used effectively based on the big data. Further, the repetitive and time-consuming tasks like keyword analysis - Collecting highest search volume keywords with lowest keyword difficulty can be done quickly by intelligent automation. Therefore, you will be able to focus more on content writing. When you give the underlying information, which requires involvement and knowing the accepted procedures, the remaining part can be done by the intelligent automation much faster.
For example Content Calendar, this tool helps you to organize the content into a perfect and tidy schedule. Here we can gather your content ideas, keywords, headlines, objectives, distributing channels, and date of publish. Utilize this to design your content ahead of time and continue everything organized.
You can optimize this procedure for the better result by using the right data powered CMS. Content marketing tools help to deal with optimizing and manage the creation, curation, distribution, sharing, monitoring, and sourcing of content. These tools also help businesses to plan, track and manage activities. Therefore, businesses can optimize their marketing strategies over various channels.
2. Targeted and Personalized Content
In traditional content marketing strategy, creating a customized content for demographics to trace out customer specific intent cannot help.
Today, personalization is the latest trend in digital marketing. Since technology can gather real and precise data, the system can able to understand about the users. Based on user interaction with content, machine learning algorithms can foresee what kind of subjects or topics they are interested in. Therefore, you can understand and give proper solutions according to the user existing topics.
Based on the user’s behavior, the machines will categorize the audience into groups and predict how these groups respond to specific events. It encourages you to make personalized content for every user travel stage and focus on the individual. It’s an important feature for one-to-one marketing.
Here are some tools for Targeted and Personalized Content
- Gravity (content personalization for publishers)
- Apsalar (mobile behavioral targeting)
- Vero (targeted emails based on user behavior)
- OneSpot (personalized content ads)
- PersuasionAPI (in private beta as of this writing)
3. Finding the Right Content
To write in-depth and valuable content, finding the intent of audience is the crucial element. AI gathers the important data like trending topics and search queries on search engines and predicts the future.
Sometimes, content writers go through many articles to get the effective topic ideas what to write and how they could write the content better than their competitors. It may take a lot of time. In this case, machines can help you to find the high-quality content and which is getting highest social engagement in real-time. This social share insight indicates what kind of topic is useful for an audience. Through TFIDF, computers can comprehend the topic of each article also. This process can be blended with Latent Dirichlet to enable content writers to make better content over the top positioning ones.
4. Analyzing content
The achievement of digital marketing depends on the results and realizing what works. It is an essential factor to enhance the marketing strategy. The use of analyzing data entirely depends on predicting the future based on historical behavior. Machines are trained to predict the what changes could strengthen your execution based on real-time data.
Sometimes you might miss an important keyword in your content optimizations. Or on the other hand, another writer's article may be effective on social, and your article could be an exceptional fit as a backlink. With this kind of recommendations, computers can help to boost your executions.
5. Reduced Cost
Content marketing strategy is always based on analyzed guesswork. Anyhow, creating an article that nobody interested to read is a waste of time.
A budget plan can be spent on a more brilliant path because of improved work processes. Machine learning can deliver instruction to create a data-driven strategy and make good content. Creating a valuable content which has the higher opportunity to get high-quality leads will succeed in the long-term.
As computers can do repetitive tasks, you can also spend your assets more shrewdly. You need not use many tools to find best keywords or backlinks. Some good tools are available in the market like Intellyo. They can perform the creation procedure within one single platform.
6. Content automation
Machine learning algorithms can understand the English language and provide the guidelines to improve content. They can perceive the passive voice and can exclude clichés. They can accomplish more advanced tasks also.
Normal Language Generation (NLG) process has the ability to translate the data into human language. To do as such, algorithms make machines to comprehend the connection between data and text. It appears like a simple solution. Though tools are designed to create content by just one click, they have their limitations. In this automated generation content, the emotional touch is missing. However, they can save time but can't wipe out human activities.
Automation is playing an important role in both small and medium-sized businesses to stay competitive in this age of cutthroat competition. It has the capability to present specific content to targeted audiences and has been a distinct advantage for boosting ROI and reducing costs. By implementing resources into a system that automatically oversees things like promoting, user management, planning, and more, small businesses can ensure better productivity, and increased revenue, and can save an incredible amount of time.
7. AI for video content
It is the latest advanced feature in Artificial Intelligence(AI). Through this feature, the machines can recognize the content in the images and videos. Here, so many things are going on in a long duration video. Is it possible to brief the content in the single footage? Some companies already started experimenting with AI, and some are quite exciting.
IBM Watson made its first-ever cognitive film based trailer through AI in the year 2016. The machines analyzed the film and created the trailer automatically. It recognized estimation changes and cut the film. The outcome is exciting.
IBM Watson also generated last year US Open match highlights for the first time. IBM used machine learning algorithms to teach their supercomputer, Watson, how to choose impactful moments from the match. For instance, in the below image, a player Juan Martín del Potro raising his hands in triumph after winning a match represents the sort of moments Watson was prepared to search for.
Image source: NYTimes
Below is a simple flowchart that clarifies how IBM Watson was able to generate highlights from full match video footage using AI
Image Source: IBM Research
There are some automated cameras available in the market to produce this kind of video content. GoPro provides more accessible options and developed a smart application, called quick stories, which can deal with your GoPro 5 film and rapidly create a shareable video. Although it's focusing on a genuinely particular sort of user, the application has a lot of promise for things to come. Another alternative is SoloShot, which also offers automated editing tools.
Machines are incredible for gathering information and helping writers to make more useful content for the targeted audience. I am sure that using machine learning to create valuable content will be the standard. However, to maintain insight perspective, some human interaction is required.