Artificial Intelligence in Marketing

The use of AI in marketing allows companies to offer customers a personalized consumer experience. Armed with products tailored to consumers’ needs, marketers can build relationships on the customer’s terms. More and more people, on both sides, are becoming aware of the opportunities offered by AI, which means that it will be used more frequently in sales.. Let’s find out how you could employ AI effectively for the benefit of your company.

Marketing is a crucial component of any company’s business management. It enables the company to grow and expand. Without a successful marketing strategy, it would be almost impossible to attract customers, which can lead to stagnation and even a decline in sales. It is, therefore, vital to keep up with the latest  marketing trends and stay ahead of your competitors.

The 21st century has brought incredible transformations into the marketing industry. The Internet has opened up an enormous variety of new opportunities, and we are now talking more about digital marketing instead of traditional marketing.. In fact, online marketing makes up almost half of the total marketing expenditures. One of the most sophisticated advancements in digital marketing is the use of artificial intelligence and machine learning.


How AI is used in marketing – potential use cases

Customer Service and Behavior Analysis

Description: Customers are essential for any business, including marketing. It is vital to provide them with the best possible service. Quick and quality help in difficult situations will build a strong relationship with the clients, which is crucial to retaining them and attracting new ones. Another important thing is to know your clients. Any statistic or insight extracted from customer data can be beneficial.

Influence on business: Artificial intelligence can help with both of these tasks. Chatbots created with natural language processing help customer service agents  answer simple questions and give recommendations. It allows workers to concentrate on other tasks that cannot be automated. On the other hand, such tools as churn analysis or customer insights analysis enable businesses to learn more about their customers  and adjust their processes to reduce churn and increase customer satisfaction.

Benefits: Reduced time, reduced costs, optimized strategy.

Complexity: 1 (low)

Data needed: Previous conversations with clients, customers’ feedback, classified trouble tickets, financial data, clients’ personal information, a history of clients’ purchases.

Examples: Chatbots, churn analysis, customer insights analysis, customer clusterization.

Media Buying

Description: Deciding where to place advertisements and messaging is a common problem in marketing. Choosing only several websites is usually not enough. It would be much more efficient to alter the advertising plan in real-time based on the latest information. 

Influence on business: Machine learning can bid on ad space relevant to customers. And the best part – it can be done in real-time. This allows the company to target the right channel at the right time without spending too much on advertising on the wrong websites.

Benefits: Flexibility, higher client acquisition, reduced costs.

Complexity: 2 (below average) 

Data needed: Consumer preferences, a history of visited websites, demographic information, purchase history.

Examples: Choice of  advertisements’ placement and messaging, uncovering new advertising channels, real-time bidding.

Automated Decision-Making

Description: The world is a dynamic and constantly changing place, and it is often tough to predict what will happen. But it is of high importance to plan the future, and, fortunately, AI can help.

Influence on business: Machine learning can simplify this process with the help of predictive analysis. It is a powerful tool that enables companies to plan the future with minimum risks. Predictive analytics can help marketing teams understand customers’ behaviors and their  future preferences to position campaigns more accurately.

Benefits: Reduced risks, optimized budget, reduced losses, optimized strategy.

Complexity: 3 (average)

Data needed: History of purchases and behaviors, clients’ personal information.

Examples: Predictive analytics, budget optimization, next best action prediction.


Description: Personalization is crucial in the modern world. According to Accenture’s study, 91% of consumers prefer brands that recognize, remember, and provide them with relevant offers and recommendations. Marketing messages should also be based on many different factors, including customer preferences, purchase history, visited websites, etc. This helps  create a complete user profile and get the most valuable insights.

Influence on business: Artificial intelligence allows marketers to go beyond traditional customer segmentation methods and learn about customer preferences on an individual level. This, in turn, enables marketers to find the best approach to each individual and create a personalized strategy with a higher probability of success.

Benefits: Optimized advertising, increased sales, increased customer satisfaction.

Complexity: 4 (above average)

Data needed: Customers’ personal information, a history of purchases and behaviors on the website, demographic data, previous interactions between consumers and brands.

Examples: Granular personalization, selection of the right message, dynamic pricing.

Content Generation

Description: Content marketing is probably the most popular marketing approach, which focuses on creating and distributing content for a targeted audience. Content marketing includes blogs, articles, videos, podcasts, etc. Interestingly, 70% of people would rather learn about a company through articles than traditional advertising. Therefore, generating high-quality content is of the utmost importance for any company.

Influence on business: Artificial intelligence has such subfields as natural language processing (NLP) and computer vision (CV) that can be effectively used to generate and curate content. NLP is already widely used, and it is expected to develop further. Generating ideas, topics, and even entire articles are only a few of many possible use cases of natural language processing in marketing. Of course, the accuracy of generated texts is not perfect yet, but it is continuously improving. Even now, you can find pretty impressive articles written entirely by AI. Similarly, computer vision can generate and enhance images and videos, creating visual content. Altogether, artificial intelligence has a vast amount of potential to improve the process of content creation.

Benefits: Larger audience, reduced human labor, higher speed of content creation. 

Complexity: 5 (high)

Data needed: Existing articles, blog posts, keywords, images, videos, previous advertisements.

Examples: Semantic core, articles, blog posts, ad content and image generation, content curation, and optimization.

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How to start?

The tangible impact of AI in marketing is already becoming evident. However, the real disruption is still ahead. The use of AI in marketing can fundamentally redefine the industry. For example, data scientists create tools to challenge traditional cost structures, opening prospects for new relationships with customers. Employing Artificial Intelligence in marketing will be commonplace in the future, so now is the right time to embrace these tools.

Here are three guiding principles for those companies, which venture to use AI in marketing in search of new insights:

  •  Data is the cornerstone. Machine learning marketing starts with determining the quantity, quality, and type of data. It will help to find some applicable and viable use cases.
    Nonetheless, little or no data to follow through with implementing
    AI in marketing isn’t a good reason to throw in the towel. Look for public data sources or business partners with the necessary data. In addition to this, you can design your strategy to start gathering valuable data. For implementing AI in digital marketing, you can employ a consumer-facing digital product to gather the necessary user data. 
  • Don’t try to rush the process. Starting with complex AI use cases in marketing to get the most value isn’t the best idea. A small data science project will take less time to complete and can be built on later. Artificial Intelligence in advertising and any other sphere is scalable. Over time, you can expand AI capabilities and get more value.
  • Failures are imminent. Using machine learning in digital marketing is a complex and challenging task. The technology can be socially stigmatized and require interdisciplinary collaboration, not to mention monetary and time investments. Marketers will have to embrace failures along the way to using AI efficiently as an integral part of innovation. Moreover, the “fail fast, fail early” strategy can help keep time and monetary investments under control.

Using AI in Marketing: summary

The marketing industry is going to change in the years to come. The opportunities of establishing contact with the customers increase and personalized consumer experiences are becoming a new trend. That’s why AI used in marketing will become a routine thing.

The use of data science tools will arm marketers with means of staying one step ahead of the competition. Machine learning models can help segment customers better, boost consumer engagement, and employ dynamic content strategies. These and other AI in marketing examples will soon make their way into a standard toolkit of each marketer.


What is AI marketing?

AI marketing relies upon data science and machine learning to automate routine tasks, understand customers better, and offer them products, tailored to their needs.

How is artificial intelligence used in marketing?

Artificial Intelligence is used to improve customer service and analysis, automate decision-making, generate content, etc. AI in advertising examples include tools, assisting marketers in choosing a place for advertisements and messaging.

How will artificial intelligence change the future of marketing?

The implementation of AI will improve customer segmentation, higher consumer engagement, and wider use of dynamic content strategies.

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