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How is Machine Learning Used Today in Marketing?

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How is Machine Learning Used Today in Marketing?

Published: 2023/09/21

7 min read

Machine learning is one of the most rapidly growing areas of AI. Its versatile applications span various walks of life and industries, including, increasingly, marketing and advertising. Even today, advanced machine learning algorithms are a crucial tool in the implementation of many companies’ marketing strategies. How is machine learning currently being used in marketing? What are the benefits of using algorithms and how do they affect the effectiveness of marketing specialists? Read on to find out.

What does machine learning in marketing involve?

Machine learning (ML) is a field of artificial intelligence that deals with the development of advanced algorithms and statistical models that enable computers to learn from data. Properly trained models, when fed with new data, can make decisions, recommend solutions, predict specific phenomena or meet other business objectives.

Unlike traditional programming, where the programmer precisely defines specific steps and scenarios for the computer to perform, in machine learning the computer is trained based on a pre-prepared set of data. Machine learning algorithms thoroughly analyze them, look for all sorts of patterns, relationships and dependencies, and then use this information to make decisions or predictions on new data, which very often flows into the model in real-time. Notably, machine learning models are constantly improving their performance over time, thus automating repetitive processes even more effectively and assisting people in making more or less complex decisions.

Given that many marketing activities today are carried out online, marketing and advertising relies on data more than ever before. The implementation of even the simplest marketing strategies requires marketing professionals to constantly monitor a multitude of parameters and metrics about customers, including their demographics, behaviors, preferences, purchase history and social media interactions. These involve massive volumes of data, with enormous potential. Finding it, however, is not easy – as it requires not only considerable marketing experience, intuition or knowledge of the industry and customers, but increasingly also technical competence to effectively analyze the data generated by marketing systems and tools. This is where machine learning comes to the rescue, as it is able to analyze enormous data sets in real time and perform specific tasks or automate complex business processes.

Read also: What is Meta learning?

It is already becoming increasingly common for marketing professionals to work “hand in hand” with artificial intelligence, which takes care of many of their daily tasks, and on top of that, supports decision-making and identification of events that need to be addressed urgently. It is in no way realistic for a human to do the work that advanced algorithms do today. The things that a skilled analyst would need hours to do, algorithms do in fractions of seconds. As a result, marketing activities today are being taken to entirely new levels. The right marketing messages reach the right users at the right time. Without AI, achieving this level of marketing personalization would certainly not be possible. Therefore, one can venture to say that machine learning positively affects the quality of marketing activities, to the benefit of everyone. Customers are provided with advertising content that has a good chance of gaining their attention and prompting them to perform a specific action, while  a business can significantly improve its success rates and make even more effective use of marketing budgets.

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Machine learning in digital marketing – use cases

Machine learning in marketing can be applied in a variety of areas and in many different ways. Think about the implementation of intelligent solutions in the context of processes that rely on available and reliable data. This is because it is the data that drives machine learning algorithms and enables  them to discover previously unseen relationships or regularities. This is one of the reasons why the marketing industry eagerly harnesses ML models to segment customers, create personalized recommendations, conduct complex predictive analysis, sentiment analysis and optimize ongoing campaigns. They also enable marketers to better understand customer behavior, reach specific audiences and provide personalized experiences to audiences, which directly translates into increased engagement and, consequently, the effectiveness of implemented measures.

Using machine learning techniques, marketers can automate tedious and repetitive tasks to free up resources and use the potential of employees for those tasks that require creativity and out-of-the-box thinking, something that artificial intelligence cannot handle. At least as of today. In addition, machine learning provides concrete conclusions and insights, the result of continuous analysis of large data sets, which makes it possible to make more accurate decisions, quickly address the identified problems, increase customer engagement, improve customer satisfaction and, as a result, drive a company’s further growth.

Benefits of machine learning in marketing

What are the specific advantages of using machine learning in marketing? Some of the key benefits include:

  • Personalization – machine learning enables the dynamic creation of personalized offers and marketing messages, which increases customer engagement and return on investment (ROI). By analyzing data on customer preferences and behavior, the algorithm can tailor offers, ads and other marketing assets so that they are maximally tailored to individual needs and interests. For this reason, among others, we can often feel that certain ads follow us or respond dynamically to our actions – i.e. when browsing an article on Warsaw attractions, the algorithm can suggest ads for travel-related products and services (such as travel insurance).
  • Precise targeting – machine learning algorithms can thoroughly analyze users’ demographics, behavior or purchase history to accurately determine the target audience for a specific ad. This strategy makes it possible to target the right marketing messages to the right people at the right time. Airlines, for example, are well aware of this. Thanks to advanced models they can predict when a particular customer will be planning their next trip, and knowing what destinations they have chosen in the past, they can offer offers that are likely to appeal to them.
  • Improving the effectiveness of advertising campaigns – a poorly set up advertising campaign can burn through the advertising budget very quickly and fail to deliver the expected results. Machine learning can help optimize it by analyzing data on the effectiveness of individual advertising channels, the advertising content used or the target groups. With such guidance, marketing specialists can make the necessary adjustments and allocate the advertising budget to achieve better results. The model can both identify problems and solve them on its own, while optimizing individual parameters.
  • Automation: ML can automate many tedious and repetitive marketing processes – like the aforementioned data analysis, but also can generate reports, give product recommendations or personalize offers. This brings measurable savings and essentially eliminates the risk of human error. Broadly understood automation also translates into improved satisfaction among employees. This is because they can focus on high-level tasks that require them to be creative.
  • More effective forecasting and planning – one of the most important tasks of marketing departments is to analyze market trends, demand and many other factors that need to be taken into account at the stage of developing a marketing campaign. It is also necessary to monitor market realities on an ongoing basis to proactively adjust marketing activities. In this area, machine learning is also widely used and, importantly, is distinguished by its extremely high efficiency.
  • Ongoing sentiment analysis – ML models can also be harnessed to work on analyzing sentiment toward a brand, its products or services offered. Here we are talking about monitoring and scanning posts from social media, review sites and many other sources. For what purpose? To be able to react even faster to negative reviews, counter crises and build a positive brand image.

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6 Examples of Machine learning in marketing

It’s worth examining specific examples of processes, tasks and problems that can be dealt with by using machine learning capabilities:

#1 Customer segmentation

ML models can analyze the customer data available to a  company and help identify similarities and patterns, which help in creating groups with similar preferences, expectations and behaviors. Such segmentation opens up the possibility of better personalizing offers, advertising creations, outreach channels or communication methods for each group, thus generating higher conversions.

#2 Product recommendations

Up-selling or cross-selling are sales techniques commonly used by online stores. Thanks to machine learning algorithms, the implementation of such strategies can be much more effective. This is because the model can analyze customer behavior data – such as past purchase history, preferences or ratings – in fractions of a second – to then deliver maximally personalized product recommendations.

#3 Predictive analysis

Machine learning models can be trained on historical data and then used to predict future performance or changes for which a company should prepare accordingly – these include losing customers, conversion rate changes and other metrics that have a major impact on the shape and scope of the current marketing strategy. With precise forecasts at their disposal, marketing professionals may take appropriate countermeasures or focus on planning changes to the current strategy.

#4 Ad targeting and optimization

Optimizing advertising campaigns is one of the tasks that, as a rule, should be automated, as it is a never-ending process that requires constant attention and a high level of commitment. By implementing artificial intelligence in this area, it is possible to optimize digital advertising campaigns, automatically adjust rate-setting strategies, as well as dynamically match ads and ad creations to defined audience segments.

#5 Content generation

Machine learning can also help generate content – this could be personalized emails, product descriptions or social media posts. Here it is worth thinking about Chat GPT-4 or LangChain, which, by bridging the gap between large language models and corporate databases, opens up many new possibilities.

#6 Fraud detection

Machine learning algorithms can identify patterns and any anomalies in user behavior to identify fraudulent activities – click fraud, impression fraud or fake accounts i.e. conversion fraud. This helps maintain the integrity of ongoing marketing campaigns and effectively manage advertising budgets.

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About the authorSoftware Mind

Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world. 

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