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As the retail industry gears up for the big NRF 2024 conference starting on Jan. 13, it’s clear that generative AI will be a major focus, and Google aims to play a significant role.
Today, Google Cloud announced several new AI technologies and services designed to help retailers offer more personalized and efficient shopping experiences. These new services enhance Google’s existing generative AI technologies, such as Vertex AI, PaLM 2, and Gemini large language models (LLMs), with a special focus on the retail industry.
A key highlight is Google Cloud’s new conversational commerce solution, which allows retailers to easily set up AI-powered chatbots on their websites and apps. Additionally, Google has updated its Vertex AI Search for retailers to improve search quality and recommendations. They also introduced AI-driven solutions to modernize customer service and manage product catalogs. The updated customer service solutions integrate with existing customer relationship management (CRM) systems to deploy chatbots that can offer personalized recommendations and schedule appointments.
Google is no stranger to the retail world and has already seen success in integrating AI into retail settings.
Retailers and consumer brands are already using several Google Cloud generative AI technologies,” said Amy Eschliman, Managing Director of Cloud Retail Industry Solutions at Google. For example, Gymshark is using Google Vertex AI to power virtual assistants that guide consumers through product selection and enhance the Gymshark training app to seamlessly record activities via text and provide insights for fitness goals.
The future for generative AI in retail looks promising. As part of its NRF 2024 efforts, Google is releasing new research on the demand for generative AI in retail. The research, based on a survey of 274 retail industry executives, indicates a high interest and demand, with 95% of decision-makers expecting generative AI to impact customer experience. Furthermore, 72% of respondents are ready to implement generative AI technology in 2024.
In retail operations, 59% of those surveyed want to use generative AI to enhance customer service automation. Marketing support and product description generation are also significant use cases, cited by 49% of respondents.
Google’s new conversational commerce solution helps retailers create virtual agents that can have natural conversations with shoppers, providing personalized product recommendations. This solution combines Google’s PaLM (Text and Chat) and natural language models (Speech-to-Text and Text-to-Speech). Google Cloud’s search and recommendation machine learning models can also be used by virtual assistants.
“This solution brings together multiple Google Cloud products within a reference architecture,” Eschliman explained. “Besides standalone chatbots, we are offering a conversational experience pre-embedded within Vertex AI Search for retail.”
Another important addition is the catalog and content enrichment solution, which helps retailers improve product descriptions. For instance, a sporting goods store’s merchandising team can use this technology to add complete and accurate descriptions to their entire product catalog. It can reduce errors like incorrect product information and automatically translate product descriptions into different languages. This technological help allows teams to easily onboard new products or update descriptions and images quickly to capitalize on new trends. For example, a sudden surge in popularity of Kansas City Chiefs merchandise can be swiftly addressed with enriched product data that boosts SEO performance on their site.
Vertex AI Search for retail is another key feature. Google is enhancing this product to integrate generative AI attributes, improving the shopping experience on digital platforms with Google-quality search. This search tool offers advanced features like better user intent understanding and self-learning ranking models that benefit retailers.
“What we’re announcing at NRF is an upgrade to Vertex AI Search for retail,” Eschliman said. “We’ve developed a large language model (LLM) that can be custom-trained for each retailer to enhance product rankings, improving product relevance and increasing retailer revenue.”