When it comes to personalization, there are two main approaches that marketers can use: rule-based personalization and machine-learning personalization.
Rule-based personalization involves creating and manipulating business rules to deliver experiences to predefined groups of people based on segments. Marketers can set up rules to display experiences to these groups, which can be broad (such as all individuals from a specific region) or narrow (such as individuals from a target industry who have not yet downloaded a piece of content and have spent more than a minute viewing a particular category of content on your site). By setting up rules, marketers can deliver personalized experiences that are more relevant to their audience.
However, the more targeted, relevant, and granular you want to get with your experiences, the more rules you need to create and maintain. This can become a time-consuming process, especially as your audience and business goals evolve.
On the other hand, machine-learning personalization uses algorithms and predictive analytics to determine and display the most relevant content, offers, recommendations, and complete experiences in real-time and at a highly scalable level. Machine learning processes vast quantities of data, detecting patterns in a manner — and at a speed — that humans can’t.
With machine learning, marketers can simply set up a single algorithm or an algorithmic “recipe” to automatically provide a unique, relevant experience for each individual. Certain machine-learning algorithms get smarter the more data they’re fed and can even automatically detect and respond to changes in individuals’ buying patterns and behaviors.
This approach to personalization can be extremely powerful and efficient, as it doesn’t require manual intervention for each experience or segment. Instead, marketers can focus on creating the right rules and feeding the algorithm with the right data.
Moreover, as the technology becomes more accessible and its benefits are more widely known, the use of AI across marketing is skyrocketing. According to the most recent “State of Marketing” report, 68% of marketers say they have a fully defined AI strategy — up from 60% in 2021 and 57% in 2020.
For personalization, the use of AI, in particular, machine learning, is also becoming more mainstream. Companies can deliver real-time, 1-to-1 experiences at scale, creating individualized experiences within specific segments by combining machine learning and rules.
However, it’s important to note that all AI is not created equal. Content generators are not the same powerful approach to personalization as humans. Artificial intelligence in segmentation and personalization is a great thing, but many marketers are opting for a more transparent, human-guided approach to machine learning. With this approach, they have insight into and can fine-tune the algorithms that power their campaigns.
Both rule-based and machine-learning approaches have their place in a successful personalization strategy, and many marketers and digital professionals use a combination of the two. The approach you choose will depend on your business goals, the size of your audience, the level of personalization you want to achieve, and the resources you have available.
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