How Machine Learning Is Changing the Content Game Right Now #Semrushchat Recap

Maria Kalyadina

May 29, 201820 min read
Machine Learning Is Changing the Content Game

Nowadays, computer algorithms can be programmed to learn from the data and the inputs that it is fed. Machine learning is set to impact the content marketing industry in exciting ways, in fact, 84% of marketing organizations are now implementing or expanding to AI and machine learning. However, the key question remains — how exactly can machine learning affect the content game?

We set to find out the answer to that in our latest #Semrushchat with special guest, Geoff Colon who is the Communications Designer at Microsoft, and a compelling voice at the intersection of technology and marketing. His insights, along with our other chat participants, on machine learning and its impact on content cannot be missed. Here is what they had to say:

Q1. In your opinion, will machine learning be the death of content A/B testing? Why or why not?

The AI algorithms that use machine learning can process large amounts of data in milliseconds. In fact, it combines both the speed and the accuracy that goes into the regular, human-based, A/B testing process. Machine Learning has opened up the possibilities to test not just two variables, but multiple variables a lot quicker and simultaneously.

However, there are some roadblocks before it can completely replace A/B testing in mainstream marketing. 

Availability of Data for Machines to Learn

For machine learning to give accurate results, the initial data should be in a format that the machines are able to understand. Therefore, using structured schema markup is important.

So, companies that are late to adapt to this kind of format may not be able to churn out that data with enough accuracy to actually facilitate machine learning.

The Size of the Company

Using machine learning to carry out the tests can be adopted by a lot of companies, provided they have the infrastructure to support it. Bigger companies, which have access to data that is usable by machines, will have the ability to switch to machine learning first and in turn, can replace regular A/B testing.

Others, which do not have the necessary infrastructure to support scaling, may suffer, unless they have the right tools developed to execute it.

Knowing What Exactly There Is to Analyze

Another reason that machine learning is not expected to take over completely is that although it can speed up the actual process of testing, in the end, it is still a test. It learns from past data that it is fed and the results will vary vastly from real-world situations.

Therefore, unless the algorithm gets human help on what exactly it should be testing, it may not give accurate results (despite its efficiency).

Ultimately, machine learning still has a long way to go before it becomes completely foolproof and can only happen with data that is learned from the regular A/B testing that we do today.

On the other hand, it can help to smooth out a lot of challenges that we see in A/B testing. For example, it can shorten and narrow the length of testing and can probably give suggestions on how to approach a problem (i.e., it can tell you how to search a query, instead of just merely giving you a choice between two questions).

As long as users are doing the searches and the machine can understand the data, machine learning can be used to predict user behavior, but will not entirely replace A/B testing...yet.


Q2. What are the benefits of machine learning when it comes to your content strategy?

Machine learning can take over the repetitive day-to-day grunt work from marketers and give them the opportunity to focus more on strategy and ideation. This enables the entire workflow to be more sophisticated and more intelligent to bring in better results.

Here are a few ways machine learning can add value to your ongoing content marketing process and strategy:

Better Efficiency

Content creation is not easy; it involves a lot of thought, a lot of labor, and a lot of time to create content. On top of all of that, you also have to test to make sure that what you are doing actually resonates with your target audience — this is where machine learning can have a huge impact. It can make it a lot more efficient by taking over testing, finding out which creative works well, and, in turn, get you quicker results.

Quicker Feedback Loop

With AI algorithms, the learning is instantaneous and can automatically make adjustments to the content or campaign, based on what it has just learned to provide optimal results. For example, when machine learning finds out that “Creative A” works better than “Creative B,” it will instantly adjust and show your audience more of “Creative B,” without any manual intervention. This saves you the effort of having to go back to the drawing board to analyze the data and make corrections.

Easier Keyword Research

Keyword research and content ideation are the keys to a successful content strategy, and both can be simplified by using machine learning. Once the algorithm identifies a popular and relevant keyword for your business, it can conduct in-depth keyword research and provide content inspiration for your content pipeline.

More Personalization

Machine learning can understand what kind of content is appreciated by a specific target audience and then curate the content tailored to them. This kind of personalization can enhance your pay-per-click ROI and improve engagement.

Predicting Trends

Better yet, machine learning can be predictive and give marketers a first-hand look at which trends to expect. This will help marketers to create a content pipeline that resonates with the audience better.

Despite all of the obvious advantages, the algorithm is still controlled by a machine, and like A/B testing, it may need the occasional human help. More importantly, it could be manipulated, which will need to be re-checked with a human eye to see if the conclusions drawn are indeed accurate.


Q3. What are some concerns marketers may have about automating content marketing or using machine learning to analyze and gather content?

Depending solely on machine learning to curate and analyze content comes with a variety of advantages, as we have just seen. However, as with any technology, it also comes with its own set of disadvantages.

Here are some of the drawbacks:

Lacks Human Emotion

To resonate with a target audience, the content you are putting out has to connect to them on some kind of emotional level. This is still not possible with machine learning as its understanding and capacity for compassion is severely limited. When trying to connect with your audience on an emotional level, human-created content will most likely work better than machine learning generated content.

No Human Intelligence

Creativity, sarcasm, context, and semantics are all indications of human intelligence that is still missing with machine learning. The algorithm can easily struggle when trying to make a distinction between a sarcastic remark and an exclamatory statement and thus, can diminish the value of the content created. Similarly, the content created may not be creative enough to attract attention.

Limited to Learning From Data It is Exposed To

All machine learning algorithms are programmed to learn from the data it comes in contact with, which can cause two problems.

First, if the data it is exposed to is faulty or biased, then its learning can be affected. Second, there is no way it can broaden its scope or learning by exploring and discovering data on its own. This data dependency can cause severe knowledge gaps within its learning, which can only be overcome with the help of humans.


Machine learning algorithms are still guided by humans and need to be controlled by them. They still need approvals from people to implement and execute decisions that are made with the data that it generates (hopefully!). One wrong move and it is not the machine that takes the blame, but the marketer who gave the system the clearance to go ahead with its faulty reasoning.

Given such limitations, machine learning can be looked upon as a highly skilled aid to any marketer. It can wield technology and make the job simpler and give them the opportunities to make more informed decisions.


Q4. How might machine learning-based content marketing technologies fit into the marketing technology stack?

Provides Customized Content

The best fit for machine learning within content marketing could be to further customize content and make it highly specific for the audience — including the channel in which the content is served.

Simplifies A/B Testing

Currently, it is incredibly similar to everyday A/B testing, but a lot faster and more accurate as it runs numerous variables and large amounts of data. Therefore, it will enhance creative testing, but not completely replace it, as stated earlier.

For instance, machine learning can present website data or content, even if it is in a different UX format for each of your site visitors. Furthermore, it can learn from the conversion metrics it gets to optimize your website even more. Testing thousands of variables and then analyzing the results take an extremely long time, but, both of these problems can easily be solved with machine learning.

Enables Better Reporting

Automating research and analytics through machine learning can reduce marketers’ workloads and help them to focus more on their strategies. Its speed and accuracy in dealing with big data can make it a very helpful reporting tool.

Improves Customer Service

Machine learning, such as chatbots, can also drastically simplify customer service by providing your customers with answers to their most frequent support queries.

Takes Over Back-end Tasks

Other backend tasks such as keyword research, locating influencers, and identifying new content opportunities can easily be taken up by machine learning. It can be the perfect addition to the learning and implementation side of marketing.


Q5. Last, but not least, how exactly is machine learning changing the content game?

Machine learning can be applied to various aspects of content marketing to enhance it and increase its overall impact. Our chat participants mentioned a few interesting ways in which it is improving and changing the content marketing game as we know it:

Improved Data Analysis

Machine learning is definitely changing the way we test and analyze data. It allows us to make informed decisions and gives us an idea about future trends.

It also makes us smarter marketers by helping us understand the ROI of content marketing better and by providing quicker insights into the metrics that determine a successful content marketing strategy.

Better Content Analysis

Though machine learning is still not adept at content creation, it can efficiently analyze what worked and what didn’t in real-time with concrete results.

More importantly, it can identify the holes in your content strategy and highlight what content you should be creating more of. This will give you a significant boost in identifying new content opportunities as well.

Both of these give content marketers more of an opportunity to plan their strategies better.

Personalized Email Marketing

Another channel where machine learning has an impact is email marketing. It can help by making it more personalized, behavior-based, and extremely targeted.

Identifying New Target Audiences

With repeated testing of large numbers of variables, machine learning can identify a whole new group of leads that want to consume the content that is generated.

Understanding Context Search

With Google’s search algorithm becoming significantly sophisticated, search engine optimization is no longer restricted to a few “golden” keywords. Instead, it also includes voice search and context search, both of which are understood within machine learning.

Improving Customer Experience

Machine learning will improve user-experience, which in turn, will facilitate better engagement and more conversions.

It is evident that machine learning is already bringing some significant changes to the content marketing world. However, if you are using it or planning on using it to help with more than just improving the speed or accuracy of your strategy, then the learning aspect of the algorithm needs more of your attention.

It is crucial for you to do your research on how machine learning can improve your existing marketing strategy, instead of just jumping on the bandwagon because other people are. Otherwise, you may not succeed with this ground-breaking, new technology.


Do you agree with our chat participants? How do you think you can use machine learning to tweak your content strategy? Leave us a comment and let us know!

Join us Tomorrow 11 AM EST on Twitter for #Semrushchat

Each week our chat offers a lot of educational information, and we can't possibly fit all the tips and information into our recap. The best way to learn is to join us. Tomorrow Kate Morris and our chat participants will help us demystify SEO for ecommerce!

Author Photo
Maria KalyadinaMaria is the Social Media Marketing Manager at Semrush and the host of the weekly #Semrushchat. You can always connect with her on Facebook and follow her on Twitter! Join the #SemrushChat every Wednesday at 11 am ET/ 4 pm BST!
Subscribe to learn more about Content Creation
By clicking “Subscribe” you agree to Semrush Privacy Policy and consent to Semrush using your contact data for newsletter purposes