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Discounter uses machine learning to stay 'on Target' with shopper demand


Target’s omnichannel journey may have started five years ago, but newly-emerging digital touchpoints continue to change the game.

By leveraging machine learning to tap into customer demand, the retailer is defining which touchpoints are not only valuable, but influencing its shoppers' paths-to-purchase. The initiative was discussed during a session ("Determining New Omni-KPIs To Hit Goals And Key Drivers") at the recently held eTail West 2017, in Palm Springs, California.

Retailers continue to break down the barriers separating channels and lines of business as a means of delivering a seamless shopping experience. However, as the industry moves even closer to the holy grail of frictionless, unified commerce, they need to take the next step: transforming their organization, business processes and technology to align with the demands of their customers.

For Target, this means delivering convenience, customer satisfaction and better engagement regardless of the touchpoint consumers use when shopping with the chain.

“We need to tailor assortments to individual shoppers’ needs, and make sure merchandise is available both digitally and in-store,” Meghna Sinha, senior director for enterprise data analytics and business intelligence, Target, said at the session. “This requires us to understand our customers’ demand. To do so, we needed a single view of the shopper.”

This can be a complex task when shoppers engage with the brand through five touchpoints: the Web, a mobile-optimized site, Target's native app, the Cartwheel app, and its network of approximately 1,800 stores.

Aligning digital and in-store shopping data in a central location was the first step in getting a single view of the shopper. However, “looking at [their] total store and enterprise sales wasn’t enough. We needed to dig deeper if we wanted to understand how they visit us and make decisions,” she said.

Enter the value of machine learning — sophisticated artificial intelligence (AI) that is not only robust enough to siphon through increasing levels of unstructured digital information, but continues to learn from previous computations, improving the level of data analysis.

Target began its machine learning journey about a year ago, applying the analysis method to its supply chain operations. Running daily and weekly tests, machine learning is already helping the chain to optimize operations based on insights derived through tests. “We are still at the beginning of this journey,” Sinha said. “The only way to prepare for the future is to understand customer demand.”

Knowing this demand is harder to harness at store-level, Target will use machine learning to measure the influence of its touchpoints — including those used in-store — on customer purchase decisions. “It allows us to test the customer journey as one experience vs. multiple broken experiences,” Sinha said.

Target is in good company when it comes to leveraging AI and machine learning, as 50% of retailers reported they already extensively use automation powered by AI to deliver IT tasks. Meanwhile, 47% use the technology for customer interactions, according to “People First in Digital Retail: Accenture Technology Vision for 2016.”

While it was too soon to share results, Sinha believes the chain will remain committed to machine learning. “Knowing this is not a slam dunk, we will continue to test and learn,” she added. “It’s an ongoing, multi-year journey that will be fine-tuned over time.”
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