Target Leverages GenAI to Boost Accessory Sales by 11%

AI-Powered Recommendation Deep Dive

This article highlights a compelling application of generative AI in retail, specifically Target's use of a large language model (LLM) to improve accessory recommendations. The 11% increase in add-to-cart interactions and the rise in display-to-conversion rates are significant indicators of the model's effectiveness. The integration of a human-in-the-loop process, allowing merchants to curate recommendations, is a smart move, ensuring a balance between automated scalability and maintaining business relevance. The article, however, lacks some technical depth. While it mentions the use of LLMs and attribute weighting, it doesn't provide granular details about the model's architecture, training data, or the specific LLM used. More information on the evaluation metrics beyond the reported percentages would also be beneficial.

Key Points

• Target implemented a generative AI-based accessory recommendation system (GRAM) to improve product pairings.
• GRAM uses large language models to analyze product attributes and generate relevant accessory recommendations.
• The system led to an 11% increase in add-to-cart interactions and a 12% rise in display-to-conversion rates.
• Human input is incorporated to curate recommendations and align with merchandising goals.

---

📖 Source: Target Improves Add to Cart Interactions by 11 Percent with Generative AI Recommendations