What Is Machine Learning in Marketing?

Advertisers and marketing professionals can use machine learning to optimise their marketing processes. In this line of work, you can expect to spend ample time, money, and other resources to run marketing campaigns that don’t consistently deliver clear feedback on what did and didn’t work. Machine learning can help you understand how well your marketing campaigns are working, look for areas of improvement, and reach out to your audience in new ways. 

The artificial intelligence market was valued at roughly ₹348 billion in 2023 [1] and projected to reach ₹2113.60 billion by 2027 [2]. This data demonstrates the growth potential. Additionally, the increased demand for artificial intelligence and machine learning in marketing drives an increased demand for professionals who can work in these areas.

Discover how marketing professionals use machine learning in marketing and explore some careers working with artificial intelligence that may pique your interest.

What is machine learning in marketing?

Machine learning is a form of artificial intelligence that you can use to collect and analyse data to make more insightful decisions about your marketing campaigns. What separates machine learning from other forms of artificial intelligence is that it allows the algorithm to learn in real-time and improve against the data. Machine learning can help you predict what actions your customers will likely take and help you cater campaigns to their specific interests or pain points. 

What is machine learning in marketing used for?

Marketing professionals use machine learning to increase the efficiency with which they can work with the enormous amounts of data they are privy to. You can use machine learning for marketing in many different ways, including:

• Predicting customer behaviour: You can use machine learning, specifically predictive analytics, to segment your customers into profiles based on their demographic information. Then, you can predict how they will behave during your marketing campaign, offering insight into the best way to tailor your marketing message to your target customer. 

• Recommending new products to customers: Using what you already know about your customers, you can recommend other products or content they are likely to enjoy or want to purchase. By personalising these recommendations, you can show customers something they want to buy or interact with, which can increase engagement and help you get more power from your marketing campaign. 

• Identifying which content impacts customers most: You can use machine learning to examine how customers behave when interacting with your content, such as your website or social media posts. Then, using that insight, you can tailor your content to the features that get maximum engagement. 

Applications of machine learning in marketing

Machine learning has uses ranging from customer analytics to making pertinent forecasts based on patterns the algorithm or model finds in marketing data. You can use it to perform various marketing tasks, including optimising for search engines and engagement and testing products to see which resonates the most. You could use techniques like: 

• Customer analytics: Customer analytics can help you create different groups of customers based on their interests and behaviour. You can then tailor marketing campaigns to each type of customer's likely response. This machine learning algorithm uses data from past customer behaviour to suggest how customers will likely behave. 

• Search engine optimisation: Search engine optimisation, or SEO, helps your website maximise its traffic from search engine users. You can use machine learning to analyse data from search engine results and user behaviour on your website to determine the best SEO strategy.

• Email marketing: You can use machine learning to optimise your campaigns according to which behaviours encourage engagement from your audience. For example, browsing history can inform the content you share or the schedule you establish for sending emails. 

• Social media marketing: You can use machine learning to analyse how your audience responds to your social media campaigns. Doing so can help you post more of what people like to see and want to engage with and less of the content that people aren’t interested in. 

• A/B testing: A/B testing is a method for optimising products like a website, an email, or an ad where you present your viewers with two versions of a product (version A and version B) and compare how they react to and engage with those products. You can pair A/B testing with machine learning to analyse a larger amount of data more effectively. 

Examples of machine learning in marketing 

Many companies offer examples of machine learning in marketing. Let’s examine how companies like Facebook (Meta), Amazon India, and Myntra use machine learning to reach their audience. 

Facebook (Meta)

Facebook collects data about users’ demographics, interests, locations, and behaviour. Using this information, the company can use machine learning to offer advertising space to companies, allowing it to adjust who it sends its advertising to. 

This level of personalisation is only possible with machine learning algorithms that manage the analysis of Facebook’s vast amount of user data. This makes Facebook's advertising packages more attractive to customers because it can target its audience to people who are already more interested in its products or services. 

Amazon India

Amazon India uses machine learning to offer better customer service, help customers find what they are looking for, and make shipping more accurate. For example, Amazon India uses machine learning to correct address errors to help packages arrive at their destination. It also uses machine learning to determine which listings are more interesting to highlight and additional products to recommend according to consumer preferences, along with offering targeted deals, such as holiday promotions. 

Myntra

Online clothing retailer Myntra uses machine learning to design new clothing based on the latest trends in fast fashion. This technology analyses data from trends that sell quickly, making that information accessible to design teams creating the newest products. To make the process even faster, Myntra started experimenting with all generative AI-designed products with AI-created design elements and actual designs. 

Who uses machine learning in marketing? 

Potential marketing careers that use machine learning include artificial intelligence product manager, business intelligence developer, and AI marketing specialist. Let’s examine each of these job fields and their average annual base salaries according to Glassdoor’s January 2025 data.

AI product manager

Average annual salary in India: ₹34,00,012 [3]

Education requirements: Bachelor’s degree in areas such as computer science or business administration and technical skills in artificial intelligence typically required 

As an AI product manager, you will help design and develop products that include or use AI technology. You will collaborate with colleagues, such as engineers, data scientists, and senior leadership, to oversee development and liaise with all parties through development and production. You will also work with artificial intelligence to optimise project management. 

Business intelligence developer

Average annual salary in India: ₹7,55,000 [4] 

Education requirements: Bachelor’s degree in computer science or a related area of study common 

As a business intelligence developer, you will help companies work with data sets to extract data that senior leaders can use to make insightful business decisions. To do so, you may use artificial intelligence and machine learning, which can help you gain faster insights from data. You will use software or create algorithms to collect, manage, and analyse data, then generate a report to communicate your findings to decision-makers. In this role, you will work with other data analysts, engineers, and senior stakeholders. 

AI engineer

Average annual salary in India: ₹11,00,000 [5]

Education requirements: Bachelor’s degree in computer or data science typical 

As an artificial intelligence engineer, you will design and create artificial intelligence algorithms, infrastructure, and other solutions to solve problems using AI. You may work for a company designing solutions for its specific issues, or you may work to develop new consumer products that use AI. You will work with other data scientists and developers in this role to ensure project success. 

How to get started with machine learning

To begin a machine learning and marketing career, you must have a solid foundation in maths and computer science. Additionally, you must become familiar with programming languages like Python, Java, and C++, as well as subjects like calculus. Next, you can enroll in an online learning programme, such as the Machine Learning Specialisation offered by Stanford University and DeepLearning.AI on Coursera. 

Alternatively, you could consider a degree course at a public or private university such as one of the Indian Institutes of Technology. Last, you can gain experience working on projects or participating in hackathons to make connections and build a portfolio you could share with future clients. 

Learn more with Coursera

Improving your ability to leverage data is one of the primary benefits of machine learning in marketing. Learn the basics and gain a solid foundation in machine learning with the University of London’s beginner-friendly course, Machine Learning for All. You can also explore technology further in IBM’s Machine Learning Professional Certificate, a six-course series that can help you prepare for a career in machine learning. While completing this certificate, you will learn about machine learning algorithms, artificial intelligence, supervised learning, feature engineering, and more.