Personalized marketing has become a fundamental strategy in today’s business environment, enhancing customer engagement and driving growth across industries. By customizing marketing efforts to individual preferences and behaviors, companies build stronger connections, boost conversion rates, and improve customer satisfaction. Traditional personalization has mainly focused on recommendation systems and targeted ads, which have proven effective. However, expanding personalization to include personalized offer generation offers an opportunity to further improve marketing outcomes. Recent research shows that well-executed personalization strategies can increase revenue by up to 40 percent, highlighting the importance of developing advanced, precise models for generating tailored marketing offers. In response to these advancements, a novel framework called SLM4Offer has been introduced, leveraging generative artificial intelligence (AI) to create personalized offers. Built on Google’s pre-trained encoder-decoder language model T5-Small (60M parameters), SLM4Offer is fine-tuned for personalized offer generation using a sophisticated contrastive learning technique, differentiating it from conventional supervised methods. Its core innovation is the use of the InfoNCE (Information Noise-Contrastive Estimation) loss function during training, which aligns customer persona embeddings—abstract representations of customer traits and preferences—with relevant offers in a shared latent space. This alignment enables the model to better identify the most suitable offers for specific customer profiles, enhancing targeting precision. Contrastive learning dynamically reshapes the latent space throughout training, enabling the model to develop a nuanced understanding of the relationships between diverse customer segments and offers.

This adaptability improves the model’s generalizability and overall performance. To evaluate SLM4Offer, the model was fine-tuned and tested on a synthetically generated dataset designed to mimic real-world customer behavior and offer acceptance patterns. Experimental results showed a 17 percent increase in offer acceptance rates compared to a baseline model trained via traditional supervised fine-tuning. These findings demonstrate that integrating contrastive objectives into the fine-tuning of generative AI models holds great promise for advancing personalized marketing. By employing techniques like those in SLM4Offer, businesses can deliver more relevant and appealing offers, leading to higher engagement and conversion rates. As personalized marketing evolves, incorporating generative AI models with contrastive learning represents a major advancement, offering not only more effective campaigns but also deeper insights into customer preferences and decision-making. Future work will likely extend these models to diverse industries and customer demographics, refining their capabilities and broadening their impact. In summary, SLM4Offer exemplifies how generative AI combined with contrastive learning can transform personalized marketing. By moving beyond traditional approaches and embracing sophisticated data-driven methods, businesses can unlock new growth opportunities and strengthen customer relationships, driving sustained success in an increasingly competitive market.