Will Machine Learning Change the Landscape of Content Marketing

AI systems are becoming more and more prevalent in business and as content marketers, there’s a question popping up in our heads lately; “will machine learning change the landscape of content marketing and the overall digital marketing landscape?

They’re taking on many jobs traditionally handled by humans, and they’re doing it much faster than their human counterparts.

However, this automation is also a double-edged sword for the content marketing world.

On one hand, it’s going to make the industry much faster and safer, allowing businesses to save money and time.

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On the other hand, machine learning has the potential to displace many content marketers as it continues to grow.

This branch of AI is almost becoming par for the course in digital marketing, but what exactly is it? Should we digital marketers start to freak out?

Let’s dive in!

What is Machine Learning?

Machine Learning is a form of artificial intelligence (AI), which basically means that a computer can “learn” on its own.

It’s an adaptive technology that allows computers to analyze data, recognize patterns and learn human behavior.

Machine learning is the process of teaching a computer how to learn, with minimal code and input from a human.

It includes the use of algorithms that can automatically detect patterns from relevant data points and make predictions based on those patterns, without being explicitly programmed.

Simply put, machine learning is a branch of artificial intelligence that mimics human behavior and thought processes to perform tasks that are normally handled by people.

What is Artificial Intelligence?

An artificial intelligence system is a network of computers, software, and machines that act in a way that simulates human intelligence.

Artificial intelligence (AI) machines have the ability to learn and perform tasks based on experience.

Artificial intelligence is starting to become a big deal and is already a part of many sensible industries like the medical field.

Examples of AI Systems in Digital Marketing:

  • AI writing assistants
  • AI-powered chatbots
  • NLP (Natural Processing Language) Tools
  • Keyword Research Tools
  • Analytics Tools

Will Machine Learning Change the Landscape of Content Marketing?

How does machine learning impact content marketing? Let’s find out!

Artificial intelligence is already being used in content marketing to boost efficiency and productivity. It’s already being used to analyze, create and share content.

As I mentioned earlier, machine learning has gained a lot of traction within digital marketing.

And while it’s not necessarily replacing content marketing altogether, it has the potential to help automate certain tasks that are often allocated to a content marketer today.

So, how does it all work? Essentially, machine learning relies on algorithms to take information and draw conclusions based off of it.

The data is then fed through an algorithm that finds patterns in the information and makes predictions about future trends.

It’s important to note though that machine learning algorithms are not perfect, and they’re not always right.

They can make mistakes, especially in new situations, which is why businesses still need humans to understand how they work and how they forecast future trends.

How Can Machine Learning Improve Content Marketing?

1. Enhances Content Creation

Machine learning and content marketing are two peas in a pod.

Content marketing itself relies on providing value, which also happens to be machine learning’s main goal.

Machine learning can be used to create better content for marketing campaigns.

Understanding what your readers want and need allows you to provide more value, which is essentially the entire point of content marketing in the first place.

Machine learning can make it easier to identify the trends of your audience and what they want out of your content.

For example, Surfer SEO is the best AI-powered tool that comes to mind when we talk about the ability of machine learning or ai technology that helps in creating content.

Using natural language processing and machine learning, it’s able to generate content that seems more natural and human to your website visitors.

Surfer helps content marketers create and optimize their content for search engines.

In fact, it crawls the top 50 results in the SERPs, analyzes them, and comes back with tons of suggestions on what will make the best piece of content that your target audience will find useful and that Google will love and rank.

Read Surfer SEO | Does it Really Boost Your Rankings?

2. Content Automation

Progressive businesses often talk about the 4th phase of the web, which is basically the concept that humans and machines will work in symbiosis.

We’re already living in an automated world, and content marketing is no different.

Machine learning is already automating processes that marketers use today, like social media scheduling and email marketing.

Essentially, it would make the content creation process less time-consuming, freeing up marketers to focus on higher-value projects that require human attention.

3. Improved Data Analysis

The ability to analyze data effectively is essential for businesses looking to grow their brands.

Consumer data analysis has always been a big part of digital marketing.

Knowing what people are saying about your brand, listening to their feedback, and improving your product is crucial to growing a brand.

Machine learning can improve the accuracy of data analysis and uncover patterns that might otherwise go unnoticed.

The goal here is to get as much information as possible and use it to effectively understand your audience.

ML can collect, store, process, and analyze huge amounts of data (big data) in a much more effective way than humans.

For example, Google Analytics has been using machine learning to analyze data for years now.

And, as you may have guessed, it’s an essential part of their business strategy.

4. Predictive Analysis

Another major aspect of machine learning and content marketing is predictive analysis.

Essentially, this involves anticipating what might happen and preparing your offerings in advance to meet demand.

Predictive analysis can help a content marketer create more value and give their readers what they want before they even know it.

For example, predictive analysis can be used to anticipate what a brand will do on a particular social channel or different social media platforms.

It can also be used to predict the best time to tweet and how many times it should be retweeted.

Or, it can be used to predict what your audience wants to see from you in the future.

It’s all about giving people what they want before they even know it. It’s a great way to increase engagement and keep readers coming back for more.

Predictive analysis can also be applied to other aspects of content marketing.

By identifying major trends and consumer habits, marketers can create more value for themselves, their audience, and their company.

5. Better Bidding

Another aspect of machine learning and content marketing is bidding. Machine learning can help you improve your bidding strategy on search engines.

Bidding is based on a business’ willingness to spend money to get its message in front of its target audience.

This can tremendously improve the results of your digital marketing efforts, which will ultimately lead to more revenue.

More and more companies are starting to realize that increasing the top of the funnel (content marketing) can lead to an increase in the bottom of the funnel (sales).

ML can help you make smarter decisions regarding where to spend your money and how much money to spend.

While there are always going to be certain aspects of bidding (like PPC advertising) that rely on human intelligence, machine learning is able to uncover patterns that humans don’t notice.

By understanding your audience and what they want from your content, machine learning will be able to better calculate the highest ROI.

It is often used in conjunction with other aspects of technology to create the best bid for each piece of content.

6. Improved Customer Experience

Although machine learning doesn’t directly impact customers, it has the ability to improve their experience.

The goal here is to make it easier and faster for businesses to deliver a high-quality product or service.

When it comes to user experience, a lot of things play into that.

Machine learning can help you create content and provide services that are more tailored to your audience, which will make them feel like you’re here to listen to their needs and provide an authentic experience.

The way your business can use AI machine learning systems to improve customer experience is by providing more personalized and targeted content and services.

7. Improved User Interaction

User interaction is a huge part of SEO and digital marketing, so it’s no surprise that machine learning can help improve it.

The goal here is to make it easier for the average user to interact with your brand through your website and other digital channels.

Machine learning can be used to improve the way UX works.

For example, it can segment your targeted audience and customize how you present different information, which will ultimately make your content more engaging.

Machine learning systems can learn how to present the same content in different ways to different people based on customer behavior, actions taken, and the amount of time they spend on the site etc…

The more content your audience interacts with, the more interactions the ML system will have to learn and improve itself.

Wrap Up

So, will machine learning change the landscape of content marketing and the overall digital marketing landscape? I’d have to say, yes.

It is slowly making its way into different areas of content marketing and who knows it might be possible to even develop content marketing strategies with a robot!!!

It will take a few more years to really sink in but we cannot unsee the role artificial intelligence is playing in our today’s society.

While artificial intelligence and machine learning algorithms are still in the early stages, it is undoubtedly here to stay.