The use of AI has helped the apparel company make better decisions in areas such as pricing and shipping, Chief Strategy and AI Officer Katia Walsh, Ph.D., says.
One key to the effort is a massive data repository that the company has built on Alphabet Inc.'s Google Cloud, according to Katia Walsh. It contains inventory and sales information from Levi Strauss & Co.'s stores as well as some stores operated by other retailers.
The repository includes information that Levi's shoppers share with the company. It also houses a range of external data, derived from public and private sources, that track consumer buying patterns and behaviors, weather and climate forecasts, economic trends and more.
This cache, Ms. Walsh said, is vital to implementing Levi's enterprise-wide AI capability. The application of machine learning and automation to the data helped the company enhance personalization of consumer marketing, make informed pricing decisions, predict demand and optimize fulfillment, all of which have helped the business, she said.
The company employs machine learning, a subset of AI, that uses statistics and probability to automatically recognize patterns in data and make predictions.
The company wouldn't be able to spot such trends without machine learning, given the volume of products that the company sells online and in some 50,000 retail locations in more than 110 countries, according to Ms. Walsh.
Levi Strauss & Co. also uses the data repository and machine learning to support pricing and shipping decisions. This helps better manage store inventory and control shipping costs—but also often results in shoppers receiving their merchandise sooner.
The use of AI has given the company a new ability to precisely target customers, according to Ms. Walsh. "That has helped increase revenues," she said.
Source: McCormick, J. (2021, December 17). Levi's AI Chief Says Algorithms Have Helped Boost Revenue. CIO Journal, The Wall Street Journal.
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