How Data Scientists Are Helping Retailers Predict Purchases and Returns

Despite recent blows to the footwear industry, there’s ample reason to be hopeful with cutting-edge technology. That’s according to Suresh Acharya, chief scientist of JDA Software Group Inc.’s research and development team and supply chain innovation hub, JDA Labs.

“We’re living in amazing times where new innovations coming out of research in these fields is capturing people’s imaginations. And those innovations will be realities in the not-too-distant future,” said Acharya. “Today, we’re creating machine-learning algorithms to help retailers incorporate all these sources of data and solve new business challenges.”

He pointed to returns forecasting — and how data science can help — as one example. “Fifteen years ago, retailers probably saw around 7 to 10 percent of items returned. Today, pure-play e-tailers are seeing up to 60 or 70 percent of items coming back. That’s a logistical and forecasting challenge that can’t be ignored — and it’s one area where data science can begin to provide solutions,” said Acharya.

“If we analyze not only the attributes of items being returned but the how, when and where of the original sale, we can identify correlations between sales and returns. From there, it’s a matter of determining the probability of a certain item being returned in the future. Analytics can also identify the customer segments with the highest propensity to return,” Acharya explained. “In this case, retailers can use technology to move from relying on forecasting based only on what they can see to taking into account patterns they could never recognize on their own.”

The possibilities don’t end there. He explained there are several more ways retailers can take advantage of new technologies, using machine learning and artificial intelligence to improve their business processes.

“Many people use [the terms] ‘AI’ and ‘machine learning’ interchangeably, but they’re actually different,” Acharya noted. “Machine learning is part of a larger AI ecosystem that overlaps with other areas of data science. For example, predictive analytics can give us insights about future results, while prescriptive analytics can tell us the decisions we need to make today so that we’re more likely to see results we want.”

Acharya said that while retailers are already gathering plenty of data, all the information is meaningless until optimized and analyzed.

“All this data will be worth nothing in the future unless retailers are able to generate both predictive and prescriptive information from it, then interpret that information in ways that leaders can use to make decisions — that means powerful machine learning capabilities coupled with intuitive user experiences to make decision-making easier,” he said.

Acharya added that there are still other obstacles that AI can help solve. “We’re building new retail systems that can learn based on past interactions, then look ahead to predict issues that might impact our stores and our customers tomorrow, or in a few days, or next week,” he said. “Those new systems will help shed light on the problems retailers can’t easily solve today.”

And while brick-and-mortar retailers may be up against behemoths like Amazon, he said their best bet for now is to leverage loyalty information to track frequency of purchase, product choice and promotions that appeal to customers.

“With that information and the right software, you can create robust customer profiles that can drive product selection, ranging, pricing and other strategic planning,” said Acharya. “From there, AI can provide the ability to look at transaction and customer interaction information and create better customer segments, more localized product assortments, optimized pricing and better promotions.”

“Machine learning could break retailers free from relying only on historical data and the old ways of making decisions,” he said. “We know the future of retail will be data-driven, and with these emerging technologies, it’ll be more efficient and predictable than ever before. And the data divide between the haves and the have-nots will ultimately determine the retail landscape of the future.”

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