AI & Machine Learning used in marketing automation

Some refer to AI simply as machine intelligence because in a conceptual sense it combines human intelligence with machines in order to enable them to perform tasks based on problem-solving rules and algorithms. Artificial intelligence is a broad concept that can range from rudimentary algorithms to future-oriented technologies such as deep learning.

Many marketing platform solution providers claim to use AI. However, the reality can differ greatly in what this actually means to them - from basic operations, search capabilities or chatbots up to something more complex, sophisticated and powerful.


Machine learning is a subset of AI and an implementation technique that enables the computer to improve its ability to make decisions using learning algorithms. This technique requires huge amounts of data, in which the algorithm can continuously learn more about the information it processes.

Nowadays, ML is a fundamental tool for those who are interested in creating a personalized offer. It enables the processing of huge amounts of data, from information captured by development and data analysis teams to sales, customer service, and customer feedback. So rather than using a set of instructions, the software is trained to process the information and to interpret it. This is great for marketing applications in which the machine can evaluate customer interactions in real-time, and allows marketers to gain better insights from individuals to improve results.

These unique capabilities allow us to get from macro- and micro-segmentation to actual personalization. ML-enabled solutions can help you deliver the right offer at the right time for every customer.

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Personalization is often challenging, as proposal creation has long been a manual process that may not be able to reach customers in large numbers. ML helps automate the process, so that even the largest companies can create proposals that meet individual needs. Integration helps to identify the relationships between disparate data, and as a vast amount of data is collected during each interaction, the algorithm becomes smarter, allowing alternatives to be quickly tested, analyzed and fine-tuned to provide the most efficient customer journey for all interested parties.

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