Practical examples of LLM-based hyper-personalization activities
In this article, I focus on hyper-personalizationa topic I recently became familiar with during a Statist webinar on consumer trends (find the attached white paper) here). This exposure sparked my curiosity about how companies could leverage Large Language Models (LLM) to improve their hyper-personalization strategies. Here I will share the results of my exploration, presenting some practical examples of such activities, specifically using ChatGPT. Although hyper-personalization is not a new idea for e-commerce, the advent of LLMs could potentially further increase its importance.
Hyper-personalization is a form of marketing communication aimed at individuals. It uses artificial intelligence (AI), machine learning (ML) and real-time behavioral data to create tailored consumer experiences. This is a significant improvement over standard personalization practices, which might include using a customer’s first name in emails or suggesting products based on past purchases.
Critical aspects of hyper-personalization
Use of Big Data
Hyper-personalization leverages numerous data sources, such as online interactions, purchase histories and social media activities. This data is crucial to understanding consumer habits and preferences.
Relevant, real-time communication
By analyzing consumer behavior, businesses can offer personalized communication, both relevant and timely. This may mean offering products or services that meet the customer’s immediate needs or interests.
AI and ML
AI and ML are at the heart of hyper-personalization, enabling the analysis of large data sets to identify patterns and predict future consumer behavior. This predictive capability allows businesses to anticipate and respond to customer needs and preferences, often before they are explicitly expressed.
