SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning

TL;DR

SLM4Offer is a novel AI model that uses contrastive learning to generate personalized marketing offers, significantly improving offer acceptance rates by better aligning customer personas with relevant offers.

Contribution

The paper introduces SLM4Offer, a contrastive learning-based fine-tuning method for personalized offer generation using a pre-trained language model, enhancing model generalizability and effectiveness.

Findings

17% improvement in offer acceptance rate

Effective alignment of customer personas and offers

Contrastive learning enhances personalization quality

Abstract

Personalized marketing has emerged as a pivotal strategy for enhancing customer engagement and driving business growth. Academic and industry efforts have predominantly focused on recommendation systems and personalized advertisements. Nonetheless, this facet of personalization holds significant potential for increasing conversion rates and improving customer satisfaction. Prior studies suggest that well-executed personalization strategies can boost revenue by up to 40 percent, underscoring the strategic importance of developing intelligent, data-driven approaches for offer generation. This work introduces SLM4Offer, a generative AI model for personalized offer generation, developed by fine-tuning a pre-trained encoder-decoder language model, specifically Google's Text-to-Text Transfer Transformer (T5-Small 60M) using a contrastive learning approach. SLM4Offer employs InfoNCE (Information Noise-Contrastive Estimation) loss to align customer personas with relevant offers in a shared embedding space. A key innovation in SLM4Offer lies in the adaptive learning behaviour introduced by contrastive loss, which reshapes the latent space during training and enhances the model's generalizability. The model is fine-tuned and evaluated on a synthetic dataset designed to simulate customer behaviour and offer acceptance patterns. Experimental results demonstrate a 17 percent improvement in offer acceptance rate over a supervised fine-tuning baseline, highlighting the effectiveness of contrastive objectives in advancing personalized marketing.