Advertisers and marketing professionals can use machine learning to optimize their marketing processes. In this field of work, you can expect to spend significant time, money, and other resources implementing marketing campaigns that don’t consistently provide clear feedback about what works and what doesn’t. Machine learning can help you understand how well your marketing campaigns are working, look for improvements, and reach your audience in new ways.
artificial intelligence market In 2023, it was valued at approximately Rs.3,480 billion [1] and Projected to reach Rs 21,136 crore by 2027 [2]. This data shows potential for growth. Additionally, the growing demand for artificial intelligence and machine learning in marketing has increased the demand for professionals who can work in these fields.
Learn how marketing professionals are leveraging machine learning in marketing, and explore some of the artificial intelligence careers that might interest you.
What is machine learning in marketing?
Machine learning is a type of artificial intelligence that can be used to collect and analyze data to make more insightful decisions about your marketing campaigns. What sets machine learning apart from other forms of artificial intelligence is that algorithms can learn and improve on data in real time. Machine learning can help you predict what actions your customers are likely to take and tailor campaigns to their specific interests and pain points.
What is machine learning used for in marketing?
Marketing professionals use machine learning to become more efficient with the vast amounts of data they have access to. Machine learning can be used in marketing in a variety of ways, including:
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Predict customer behavior: Machine learning, especially predictive analytics, can be used to segment customers into profiles based on demographic information. This allows you to predict how your customers will behave during your marketing campaign, giving you insight into how best to tailor your marketing messages to your target customers.
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Recommend new products to customers: You can use the information you already know about your customers to recommend other products and content that they might enjoy or want to buy. By personalizing these recommendations, you can show your customers what they want to buy and interact with, increasing engagement and making your marketing campaigns even more effective.
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Identify what content has the most impact on your customers. Machine learning can be used to examine how customers behave when interacting with content such as websites and social media posts. You can then use that insight to tailor your content to features that will maximize engagement.
Application of machine learning in marketing
Applications for machine learning range from customer analysis to making better predictions based on patterns that algorithms and models find in marketing data. You can use it to perform a variety of marketing tasks, including search engine and engagement optimization, and testing products to see which ones resonate best. You can use techniques such as:
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Customer analysis: Customer analytics helps you create different customer groups based on customer interests and behavior. You can then tailor your marketing campaign to each type of customer response you expect. This machine learning algorithm uses past customer behavior data to suggest how customers are likely to behave.
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Search engine optimization: Search engine optimization (SEO) helps websites maximize traffic from search engine users. Machine learning can be used to analyze data from search engine results and user behavior on your website to determine the best SEO strategy.
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Email marketing: Machine learning allows you to optimize your campaigns based on which actions drive engagement from your audience. For example, your browsing history can tell you what content you’ve shared and when you schedule emails to be sent.
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A/B testing: A/B testing is a method of optimizing products such as websites, emails, and advertisements by presenting two versions of a product to an audience (version A and version B) and comparing users’ reactions to and engagement with those products. A/B testing and machine learning can be combined to analyze large amounts of data more effectively.
Examples of machine learning in marketing
Many companies offer examples of machine learning in marketing. Find out how companies like Facebook (Meta), Amazon India, and Myntra are using machine learning to reach their audiences.
Facebook (meta)
Facebook collects data about your demographics, interests, location, and behavior. Using this information, companies can use machine learning to provide advertising space to businesses and tailor where they send their ads.
This level of personalization is only possible with the machine learning algorithms that manage the analysis of Facebook’s vast user data. This makes Facebook’s advertising packages more appealing to customers as they can be targeted to people who are already interested in the company’s products and services.
amazon india
Amazon India uses machine learning to provide better customer service, help customers find what they’re looking for, and make deliveries more accurate. For example, Amazon India uses machine learning to correct address errors and ensure packages reach their destination. It also uses machine learning to determine which products are more interesting to highlight, recommend additional products based on consumer preferences, and offer targeted deals such as holiday promotions.
Myntra
Online clothing retailer Myntra uses machine learning to design new clothing products based on the latest trends in fast fashion. This technology analyzes data from quick-selling trends and makes that information accessible to design teams creating the latest products. To further speed up the process, Myntra has started experimenting with all AI-designed generated products using AI-created design elements and real-world designs.
Who uses machine learning in marketing?
Potential marketing careers using machine learning include artificial intelligence product manager, business intelligence developer, and AI marketing specialist. Let’s take a look at each of these jobs and their average annual base salary, based on Glassdoor’s January 2025 data.
AI product manager
Average annual income in India: ₹34,00,012 [3]
Educational requirements: Typically requires a bachelor’s degree in a field such as computer science or business administration and technical skills in artificial intelligence.
AI Product Managers help design and develop products that include or use AI technology. You will collaborate with colleagues including engineers, data scientists, and senior executives to oversee development and collaborate with all stakeholders throughout development and operations. It also works with artificial intelligence to optimize project management.
business intelligence developer
Average annual income in India: ₹7,55,000 [4]
Educational requirements: Bachelor’s degree in computer science or related general field of study.
As a Business Intelligence Developer, you help companies manipulate data sets to extract data that senior leaders can use to make insightful business decisions. To do this, you can use artificial intelligence and machine learning to help you get insights from your data faster. Use software or create algorithms to collect, manage, and analyze data and generate reports to communicate the results to decision makers. In this role, you will collaborate with other data analysts, engineers, and senior stakeholders.
AI engineer
Average annual income in India: ₹11,00,000 [5]
Educational requirements: Typical Bachelor’s degree in Computer or Data Science
As an Artificial Intelligence Engineer, you will design and create artificial intelligence algorithms, infrastructure, and other solutions that use AI to solve problems. You may work for a company that designs solutions to specific problems, or you may be working on developing new consumer products using AI. In this role, you will collaborate with other data scientists and developers to ensure project success.
How to get started with machine learning
Starting a career in machine learning and marketing requires a solid foundation in mathematics and computer science. Additionally, you should be familiar with programming languages such as Python, Java, and C++, as well as subjects such as calculus. Then, you can enroll in an online learning program like Machine Learning Specialization from Stanford University or DeepLearning.AI from Coursera.
Alternatively, you can consider a degree course at a public or private university such as the Indian Institute of Technology. Finally, you can work on projects and participate in hackathons to gain experience, make connections, and build a portfolio that you can share with future clients.
Learn more on Coursera
Improving the ability to leverage data is one of the main benefits of machine learning in marketing. Learn the fundamentals of machine learning and build a solid foundation with Machine Learning for All, a beginner’s course from the University of London. You can also learn more about the technology with IBM’s Machine Learning Professional Certificate. This is a six-course series that will help you prepare for a career in machine learning. After completing this certificate, you will learn about machine learning algorithms, artificial intelligence, supervised learning, feature engineering, and more.
