Marketing professionals face increasing complexity due to the explosion of digital and data touchpoints, as well as unprecedented consumers’ expectations in terms of interaction, content, and offer personalization. Such a degree of complexity is driving the adoption of a large variety of smart algorithms and models that marketers require to turn the vast array of historical data into actionable insights.

Machine learning (ML) technologies, embedded or not into marketing software, are already powering every single functional area of marketing and each step of the consumer journey. The interest of marketers for machine learning is at least threefold. The vast majority of the current use cases can be classified based on the technology’s ability to 1) predict individual behaviors, 2) anticipate consumption trends, and 3) hyper-personalize messages.

Predicting individual behavior means providing the best value proposition at the right stage of the consumer journey. Marketers come to realize that traditional marketing tools are unable to keep pace with the velocity, variety, and volume of data. Machines can help managers in reducing today’s level of complexity in cross-channel customer engagement and make more accurate individual behavior predictions. As an example, the German e-commerce merchant Otto uses an AI model that predicts what will be sold within 30 days with 90% accuracy. This system allows Otto to automatically purchase more than 2 million items per year from third-party brands while consumers receive faster deliveries and reduce returns.

Anticipating consumption trends affects the offering of the best products and services at the right price. Managers exploit aggregated consumer data with the intent of creating products that anticipate future trends. ML enhances the business objective of anticipating consumer need, as required to differentiate services and re-architect business models from the ground up. Consumers have the opportunity to receive unexpected and delightful experiences that they did not have time to desire yet. For instance, Netflix develops original TV shows analyzing creative elements of successful movies at a granular level through the lenses of AI. This practice doubled the success rate of original shows versus traditional ones (from 40% to 80%).

Message hyper-personalization is the delivery of relevant content at the right time and channel. AI marketing enables the collection and analysis of data, generation of insights, and definition of actions that more effectively reach the individual. Designing hyper-personalized experiences that drive relevancy has become a key priority for most organizations. L’Oréal Paris personalizes videos using insights on interests and affinities of the audience, as provided by Google’s AI-powered platforms. Recently, L’Oréal created twelve versions of a YouTube video to appeal to each specific segment. An increase of 109% in brand interest and 30% in purchase intent showed the user’s value delivered through personalized content.

As Dr. A. K. Pradeep, the author of “AI for Marketing and Product Innovation” (Wiley, 2019), suggested, “Prediction, anticipation, and hyper-personalization are an integral part of what marketing is going to be. Marketing managers will truly use the tools of AI and machine learning to understand the drivers of the non-conscious human mind which is responsible for 95% of consumer behavior.”

Although the hype surrounding the application of ML in marketing describes immense possibilities for marketers, such a technology can be both overwhelming and dangerous, especially if the organization is new to data-driven processes. Managers need to be aware of algorithm discrimination phenomenon such as misclassification and echo chamber and their harmful effects on consumers.

Designing a successful ML strategy requires managers to systematically anticipate what the next bias in your model might be while evaluating marketing needs in terms of automation, optimization, and augmentation in relation to the searched benefits of prediction, anticipation, and personalization.

Further readings:

“The Rise of Machine Learning in Marketing: Goal, process, and benefit of AI-driven marketing” ( A research report endorsed by SwissCognitive ( which captures the insights and experiences of more than 30 international experts.

Further information about the author: Alex Mari