How Machine Learning Helps to Automate Marketing

Machine learning plays a significant role in the growth and development of many industries. In marketing, it represents several technologies and tools able to recognize and analyze certain data feed patterns through an array of marketing channels. Today, marketers greatly depend on machine learning as it provides precise and easy-to-follow information that helps to keep up with modern trends and develop more efficient methods of influence.

The number of businesses that have implemented machine learning tools has skyrocketed in the past several years. Largely, this popularity has been stimulated by the outstanding results of such global market players like Amazon and Alibaba that have been actively implementing machine learning tools to optimize product recommendations and boost their sales, at the same time minimizing the efforts of marketing experts.

There is a direct link between machine learning analysis and improved interaction with clients. By analyzing past customer activity, their purchasing patterns, and demographic information, ML tools help to create more engaging content and predict customer behaviors with perfect accuracy.

In general, over 80% of large and small businesses worldwide admit the undeniable edge machine learning grants in personalizing the customer experience and increasing the efficiency of reaching clients.

Although machine learning techniques date back to the 1950s (when the first spelling check tools were introduced), their ubiquitous implementation for marketing purposes has grown quite recently. Largely because it saves marketers a lot of effort and boosts the efficiency of marketing campaigns.

Let’s see in what way machine learning helps to automate marketing:



The large value of machine learning in the marketing industry is especially visible in how it facilitates more targeted and personalized interaction with the potential client. Its efficiency in collecting and analyzing immense data sets gives marketers an undeniable advantage when it comes to reaching and influencing the customers.

Let’s take Facebook for one. Sometimes it gets creepy how well the social media giant knows you. A student blog writer confided in her post: “I hardly said to my friend that I wanted to purchase a dissertation online when Facebook offered me a service I can use.” This social media collects vast amounts of information about every user, and in doing so it allows marketers to match their campaigns with proper customers that may be potentially interested in what they offer, and turn into actual buyers. Due to this ML-assisted technique, marketers can build efficient predictive targeting strategies that bring results and help them achieve specific advertising objectives.

As now it became possible to sift massive data stocks in a matter of moment, marketers can perform segmentation and personalize content with top accuracy to hit their target with higher efficiency and minimum effort.



Customer churn is a serious problem for any business as it hits the company’s revenue and wastes the time spent on high-cost advertising campaigns. Bringing customer churn to a minimum is always one of the top priorities for businesses that want to keep their competitive power both in the local and global market.

With the introduction of ML-assisted tools and techniques, it has become much easier and removed a lot of guesswork that was the primary tool to fight churn some twenty years ago. The tools analyze customer behavior patterns and spot triggers that lead to clients leaving the brand. Based on this information, marketers can add more customer engagement, custom-change the pricing policy, or adjust their marketing strategies to stop or prevent the churn.



You must have heard about the Pareto Principle, or the 80/20 rule. It says that 20% of effort brings 80% of results and vice versa. In sales, it would mean that one-fifth of customers generate most of the business’s revenue. This brings us to the next point:

It is important to know customer lifetime value, or how much profit the company can get from the entire interaction with each client.

That’s where machine learning comes in handy. The tools gather and process the information about the amount and frequency of a customer’s purchases and general purchasing patterns, which allows marketers to build a model that helps to identify and predict CLV with high precision. With this knowledge in mind, companies can distinguish customers who bring actual profits from those who don’t and allocate their efforts to influence the former ones letting go of others.

Over 80% of the businesses who chose to calculate and be in control of customer lifetime value acknowledged that they had increased their sales.



Digital marketing has also reached a new level due to machine learning. Thus, it allows marketers to automate calls to action, emails, as well as their frequency and targeting to achieve sure results.

For instance, marketers can implement machine learning to optimize sending frequency and define the most suitable days for sending a new or a follow-up message to reach their potential customers. Or use autoresponders to gather different information that can help improve their future campaigns and get valuable insights to influence the audience.

Furthermore, ML-tools make it possible to apply segmentation of email lists to personalize delivery criteria for every customer or every particular group of customers. They also help to create more engaging and impactful content by suggesting the most pertinent keywords, content curation, or format.

Machine learning brings a lot of benefits to the marketing field. It allows to improve methods of reaching the customers, optimize various marketing techniques, and make them personalized.

ML-assisted tools have removed tedious tasks and guesswork from marketing. Now, when data collection and analysis are automated, professionals can focus on finding creative solutions to improve customer engagement and their overall experience.

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Written by Floyd Colon


January 9, 2021

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