Although initially developed for the fashion industry, the technology could be used in other sectors such as furniture and grocery stores. The tool does not leverage Mastercard data and uses third-party Large Language Models (LLMs) and image recognition algorithms. It also takes into account the shopper’s likes and dislikes based on session browsing history or past purchases to better estimate future buying intent. Despite the rapid progress in AI technology, experts suggest that a super-intelligent AI tool could take years or even decades to develop.
Key takeaways:
- Mastercard's Dynamic Yield has developed a generative artificial intelligence (GAI) tool called Shopping Muse, which uses first-party data from the retailer's website to provide personalized shopping recommendations.
- Unlike other conversational search tools, Shopping Muse recommendations are personalized to match an individual consumer’s unique profile and intent, and build on the context of conversations over time.
- The tool uses integrated advanced image recognition tools to recommend relevant products based on visual similarities, even if they lack the right technical tags.
- While Shopping Muse was developed for the fashion industry, it can be used in other industries such as furniture and grocery stores to help consumers make decisions based on their specific needs.