What Is Machine Learning in Marketing?

Key takeaways

In the face of consumers' ever-evolving needs and wants, marketers using emerging technologies like machine learning can better keep pace and optimize their campaigns. Here are some important things to know: 

• Only six in ten marketers feel they can fully personalize their approach across various marketing channels, a gap that presents a challenge for marketing teams trying to engage their target audiences [1].

• Key components of marketing strategy, such as market research and analysis, target audience identification, content development, performance measurement, and persuasion, provide prime opportunities for marketing professionals to leverage machine learning capabilities. 

• You can bridge your marketing skills gap by learning to implement machine learning techniques, empowering you to personalize and optimize campaigns, among other functions.

Explore ways you might implement machine learning in marketing, along with its potential impact and examples of machine learning in marketing, to envision how you can use technology to optimize your next marketing campaign. Then, consider enrolling in the Meta Marketing Analytics Professional Certificate, where you’ll gain proficiency in visualizing data using Tableau and how to use Meta Ads Manager to create campaigns, evaluate results, and run experiments to optimize your campaigns. 

What is machine learning for marketing?

Machine learning, a subset of artificial intelligence (AI), uses algorithms to analyze data, identify patterns and trends, and make predictions based on these insights, ultimately improving decision-making. In marketing, machine learning allows marketers to automate and accelerate processes related to data analysis and decision-making. This results in more personalized customer experiences, highly targeted advertising, improved customer segmentation, optimized pricing and recommendation strategies, and enhanced customer support.

For example, you might use machine learning to conduct comprehensive analyses of customer behavior and predict actions they may take in the future. By segmenting your audience based on interaction patterns identified through algorithms, you can tailor your email campaigns accordingly to enhance customer engagement and increase conversion rates. 

Additionally, you can improve your e-commerce site with real-time, personalized product recommendations that drive cross-selling and improve your customers’ experience. As you continue to leverage machine learning tools, such as dynamic pricing and chatbots, you can uncover even more valuable insights, perpetuating the cycle of marketing performance optimization. 

How is machine learning used in marketing?

Businesses across various sectors use machine learning to enhance efficiency, effectiveness, and customer experience, ultimately driving their evolution. With over one-third of marketers facing challenges such as maximizing performance across diverse marketing platforms, segmenting and targeting audiences, and scaling programs, content, and personalization, exploring how you can use machine learning and artificial intelligence (AI) to optimize your marketing strategy may be high on your to-do list [2]. 

Consider the following ways machine learning algorithms can help you improve your marketing plan:

• Customer segmentation: This process categorizes customers into groups based on similar characteristics, allowing marketers to tailor their campaigns more effectively. Machine learning software can automate this process, helping you uncover hidden patterns and providing a more accurate and efficient segmentation approach. It helps ensure that each customer receives marketing messages customized for them. 

• Customer sentiment/journey analysis: Machine learning algorithms offer fast and comprehensive insights into how customers experience your brand. These algorithms help you prioritize the marketing channels, touchpoints, and advertising services that your customers engage with most, guiding you on where to focus your marketing efforts. Additionally, machine learning can assist in managing customer churn, enabling you to take a proactive approach to keeping customers before they make the decision to leave.

• Recommendations: Analyzing data on customers’ past purchases alongside your company’s current inventory, machine learning can help pinpoint the products and services customers are likely to purchase. Providing tailored product and content suggestions can also help customers navigate large online catalogs more easily, making it easier for them to find what they are looking for. This form of personalization can help increase your customer retention rate and sales.

• Content generation: Machine learning can optimize your content to ensure you deliver the most relevant ideas to your target audience and search engines. It can also help analyze competitors’ content and identify trends to help you pinpoint popular topics and ideal keyword placement, thereby increasing content viewership.

Machine learning in marketing examples

Due to the widespread popularity of using AI in marketing, including machine learning, to optimize strategies, numerous companies use these technologies to achieve measurable positive results. Consider the following examples from various industries that benefited from machine learning in marketing:

Retail: Drugstore chain Walgreens used machine learning to deliver personalized allergy medication ads based on weather conditions, time, and user location. This ensured that ads reached clients during allergy season when they need medication the most. This targeted approach generated 160 ad variations, resulting in a 276 percent increase in click-through rates and a 64 percent reduction in cost per click [3].

Finance: Vanguard, an asset management firm, employed machine learning to analyze customer sentiment regarding their messaging and adjusted content based on customers’ emotional responses. By optimizing tone and language according to customer feedback, Vanguard increased conversion rates by 15 percent [4]. 

Health care: Pharmaceutical company GlaxoSmithKline (GSK) applied machine learning and text analytics to analyze online conversations and identify parents' concerns about vaccine safety and comfort. GSK then developed a targeted communications strategy to address these fears and shift customer sentiment toward vaccination. 

E-commerce: Stitch Fix, a personal styling service, uses machine learning algorithms to analyze customer data, including style preferences, return habits, and customer feedback. This analysis enables the company to identify clothing trends based on location and offer outfits that best meet customer needs.

What impact has machine learning made on the marketing industry?

The impact of machine learning in marketing includes decreased costs, enhanced customer experience, risk mitigation, and accelerated revenue growth. Fifty-seven percent of marketers responding to a recent survey estimate their return on investment (ROI) from general AI investments at over 20 percent, while an additional 31 percent estimate ROI between 10 and 20 percent [5], indicating the significant impact of machine learning and other forms of AI on marketing.  

How to use ML in marketing

As a marketer, using machine learning (ML) in marketing typically requires you to implement software programs and technologies, including management systems for content, customer relationships, marketing automation, and the customer journey; personalization or recommendation engines; chatbots; sentiment analysis tools; and advertising or customer data platforms. These tools can help you quickly analyze and extract insights from large data sets or automate repetitive tasks. 

Fortunately for marketing professionals, many cloud service providers offer pre-trained machine learning models that, with slight modifications, can meet your organization’s unique needs. These services simplify the process of using machine learning for marketing and give organizations an affordable option to leverage its benefits.

Finally, because machine learning relies heavily on substantial amounts of high-quality data, securing new data sources can ensure you maximize the potential benefits of the tools you employ.

Who uses machine learning in marketing?

ML is transforming the marketing job market by creating a demand for new roles focused on personalization, automation, and predictive analytics. The following positions blend traditional marketing principles with emerging AI technologies, enabling you to help your organization connect with its audience and drive results.

• Business intelligence developer

• AI marketing strategist

• Marketing data scientist

• Predictive analytics strategist

• SEO analyst

• Chatbot developer

• Customer journey analyst

• Market research analyst

Continue learning about machine learning in marketing on Coursera

Explore the possibilities of your career in marketing analytics with our Digital Marketing Career Paths Guide. Or, check out these other free resources:

• Hear from an expert: Marketing Career Path: How I Became a Brand Marketing Manager at Coursera 

• Take the quiz: Digital Marketing Career Quiz: Is It Right for You? Find Your Role

• Watch on YouTube: 3 Ways AI is Changing Marketing

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