How Gap is leveraging data and AI for retail success

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With data becoming the new oil, organizations across industries are racing to leverage their (often locked-in) information management systems and create value across all functions with viable AI applications. The need for skilled data scientists and analytics professionals has increased demand, but even with the right talent, bringing data-driven products to life can be a daunting task. According to two Gartner reports, only 53% of AI and ML projects make it to production and 85% of parties typically fail to achieve the intended results.

Despite the challenges, including data quality issues, Gap, one of the largest clothing and accessories retailers in the world, has successfully implemented data and AI to solve key business problems.

“We’ve really accelerated our data journey. One of the first priorities I got when I came into this role is how we can actually accelerate our data journey and make AI part of the DNA for how we do our work within Gap,” Heather Mickman, the interim CIO of the company, said during a panel at Venturebeat’s Data Summit.

The Gap Data Journey

Initially, Gap used traditional business intelligence (BI) capabilities and operational reports to make day-to-day decisions. Then, Mickman said, the company started investing in data science and analytics teams, working with IT teams to experiment with data and come up with potential business use cases.

“We went through slow cycles of creating custom datasets to enable different hypotheses for different use cases. And then we moved on to our next evolution which we have today: the Gap data platform,” she said. Shifting from manual data curation and experimentation to self-service (ready-to-use datasets compiled into a catalog), enabling teams to run AI/ML on-demand and truly embed them into data decisions.

In addition, the CIO emphasized that the company has also moved out of most of its data centers and moved to the cloud to leverage cloud-native capabilities while concentrating on data and data science projects.

Cleaning data at source remains a top priority

As GAP’s data platform begins to open up across the organization, the company is closely monitoring the information flowing through the pipeline to remain clean and accurate.

“There are a lot of nuances in the way you make that happen, but that has to be intentional for each of the data sets,” Mickman said. “I don’t want our teams cleaning up midstream data. It is something that has to be done at the source. Otherwise you get different data throughout the organization, which causes a lot of confusion.”

She also emphasized the importance of creating governance roles such as data owners, data stewards and who, within teams, have ultimate responsibility for data sets. Within Gap, these roles are set up within product management teams because they have more of that business context than technology teams.

Applications to solve business problems

With this data platform and governance strategy, Gap has built a number of powerful downstream applications to solve key business challenges and drive efficiencies. The biggest of all is an AI engine that predicts demand in terms of what products will be sold, where they will be sold, and when they will sell.

“It uses predictive analytics and processes a variety of data sets, including sales data and product attributes, to determine buying, positioning, and pricing for inventory across all markets. [the] Gap brands,” said Mickman, noting that the engine has significant potential to improve product availability and product margins.

In addition, the company has also developed AI-powered solutions to optimize inventory movement between Gap fulfillment centers and stores and help brands with size profiling.

“The inventory optimization model goes into quite a granular level… to understand the dynamics of sales and margins and what the potential is to make sure we position (inventory) in a very smart way to meet our demand. customers,” said Mickman.

Meanwhile, the sizing solution leverages ML to enable various Gap-owned brands to produce automated and more accurate sizing profiles that improve their product coverage and customer satisfaction.

“We use several attributes that you might imagine, such as sales and inventory data, to determine the sales size for a specific item in a store,” she said.

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