Marketers have been using personalization in their direct marketing strategies for a long time and in many forms. From a “welcome back” message for returning users to more individualized product recommendations in email newsletters, it is a known fact that customers who experience a more personalized shopping experience are more likely to spend and keep returning, whether it’s B2B or B2C.
Rule-based segmentation is a more simplistic way of personalizing marketing efforts than using machine learning. It uses rules determined by “if-then” scenarios, such as if it detects a website user from a specific location, it shows nearby events to that person. Or it might divide users by demographics such as age and gender and push marketing campaigns geared towards those groups.
But using rule-based systems takes a lot of work to add in all those variables manually, and many incorrect assumptions are made about customers. Marketers may pick a variable they believe to have the most significant impact on decision-making when it might actually be far from reality. With all the data marketers have access to, more advanced techniques are needed to use it to its full potential.
Personalized marketing is now considered a standard service, with 72% of consumers claiming they only engage with personalized messaging, so it’s only a matter of time until it becomes the norm for B2B too.
Enter machine learning. You feed the algorithm your data which is then used to determine patterns. This type of marketing goes far beyond rule-based personalization, as the algorithm can define segments in a way that humans simply can’t or wouldn’t have time to.
Machine learning can analyze each traffic segment and provide the most relevant content to different audience groups. As a result, personalization can exist for every single visitor, and it can be done automatically.