Google Cloud Launches AI and ML Solutions, “Retailers Are in Dire Need of Agile Operating Models”

Power Retail By Power Retail | 20 Jan 2021

Google Cloud is launching a new discovery tool to help bolster online retail as it continues to expand in popularity worldwide.

The Google Cloud Product Discovery Solutions for Retail is to deliver increased personalised customer experiences and help retailers adapt to the ever-increasing popularity of e-commerce.

The solution is intended to increase a retailer’s capability and deliver a ‘highly personalised consumer experience’ for the first phase of the online shopping journey.

Carrie Tharp, the VP of Retail and Consumer at Google Cloud, said that retailers were in ‘dire need’ of an agile operating model, such as AI or ML.

“As the shift to online continues, smarter and more personalised shopping experiences will be even more critical for retailers to rise above their competition,” said Tharp.

“Retailers are in dire need of agile operating models powered by cloud infrastructure and technologies like artificial intelligence and machine learning (AI/ML) to meet today’s industry demands. We’re proud to partner with retailers around the world, and bring forward our Product Discovery offerings to help them succeed.”

There are several solutions that Google Cloud is launching for retailers, including ‘Recommendations AI’, ‘Vision API Product Search’ and ‘Google Cloud Search for Retail’.

The former solution provides a ‘highly personalised’ solution for retailers to scale and use on many channels. The solution is ‘able to piece together the history of a customer’s shopping journey and serve them with customised product recommendations’, the company explained.

The tool uses Google Cloud’s ML architectures and allows retailers to adapt to consumer behaviour changes in real-time.  The Visual API Product Search is another tool that has been a component for some of the most innovative retailers online. This tool allows online shoppers to search for products using an image – they then receive a ranked list of visually and semantically similar items.

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