Personalization has been with us for many years. Websites, using a user’s ID (whether logged in or identified only by a cookie), tailor content to meet their expectations and preferences. But how are the possibilities of personalization evolving in the age of ubiquitous LLMs and the capabilities they offer?
Understanding the Basics
Let’s start with the basics. To implement personalization, access to the user’s preferences is essential. We can obtain this information in two ways: by asking the user directly or by tracking their actions. Based on the collected data, we gradually build a user profile, getting to know them better over time.
This process is similar to getting to know a new person. Initially, we know very little about them, as if they were a blank sheet of paper. However, over time, through each interaction and piece of information, we fill in their profile, often without even realizing it.
AI and Hyper-Personalization
Can language models bring us to a new level of personalization, or even hyper-personalization based on artificial intelligence? Absolutely. The mechanism we use in our interactions with new and familiar people has been applied in one of our projects. A well-designed solution based on LLM models continuously updates the user’s profile during conversations. This process occurs automatically and operates independently of the number of simultaneous conversations. Imagine 500 concurrent conversations where each client or user is treated individually, in line with previously recognized preferences, habits, communication style, and other important details.
The Role of AI in Client Relations
Previously, the presence of a consultant or personal client advisor gave users the feeling that someone knew their problems and expectations, and that they were always being served by a familiar person. Today, this aspect of personalization can be achieved through AI mechanisms.
The Value of Personalization
Personalization significantly enhances the value of a product. Users are often willing to pay a premium for personalized treatment, as noted in estimations by Andreessen Horowitz.
Author: Mariusz Jażdżyk
For more on personalization, see our product: Personal Advisor
