Despite the off-putting headlines and the ongoing shuttering of physical store locations, fashion apparel retailers might have reason to be hopeful. New technologies continue to serve as a beacon of hope — and survival — for merchants and suppliers.

As chief scientist of JDA Software Group Inc.’s research and development team and supply chain innovation hub, Suresh Acharya described current conditions as “amazing times where new innovations” are coming out of research and are “capturing people’s imaginations.” Machine learning, artificial intelligence and predictive analytics is transforming retail. Here, Acharya shares his thoughts on the technologies that are shaping and disrupting the industry.

WWD: There is a lot of discussion in the market about how artificial intelligence and machine learning is transforming business processes. As these technologies become more available, how can retailers use it?

Suresh Acharya: Many people use AI and machine learning interchangeably, but they’re actually different. 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.

Today, we’re creating machine-learning algorithms to help retailers incorporate all of these sources of data and solve new business challenges. For example, returns forecasting: If you think back to 15 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.

WWD: What obstacles might retailers have to overcome as they incorporate these new data analytics approaches?

S.A.: These new systems are only as good as the data they interpret. Retailers are already collecting a great deal of data every hour, on everything from online and in-store customer interactions, purchases and returns, to product attributes and inventory information and so on. Many are exploring new sources of data that can add additional dimensions, such as in-store sensors, connected internet-of-things devices and data flows from social networks.

But all of this data will be worth nothing in the future unless retailers are able to generate predictive and prescriptive information from it, [and] 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.

WWD: Stores are already collecting data in a variety of ways. How will those dimensions of data that you referred to impact retail planning and strategy?

S.A.: Many sources of data from outside of the retail store can make retailers’ predictions easier. For instance, if a retailer could know that warm weather will come to one area earlier than usual, they could change out seasonal apparel offerings in those stores sooner and be ready when customers want to shop for spring and summer. [Or], besides daily and weekly sales information, what if you could gauge the social sentiment around a new product and use that insight to guide your sales forecast?

Retailers are exploring ways to correlate outside information from social media, news, events and weather to make better planning and strategic decisions. But this is only possible when your internal data house is in order. For example, if you lack detailed product attribute information, you may not be able to understand the decisions your customers might be making — for example, why someone who ordinarily buys “Product A” might decide to substitute “Product B” instead.

WWD: What are some other challenges AI might help solve?

S.A.: We’re not that far away from self-learning retail supply chains that can analyze correlations in data in those areas as well. For example, did a retailer have a stock-out because an inbound order was late? If so, was it late due to a traffic issue? Did weather cause that traffic issue? Or was it some other factor? If the retailer can bring all that information together and learn from it, can they predict and avoid future stock-outs?

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. Those new systems will help shed light on the problems retailers can’t easily solve today.

WWD: Brick-and-mortar retailers understand pure-play e-commerce competitors are already taking advantage of this type of information. Will it be easy to catch up?

S.A.: It’s true that online mega-marketplaces like Amazon collect tremendous information about products and customers — what people are buying, what purchases get abandoned, what items are browsed and how often. And this level of data analysis still isn’t common across the brick-and-mortar landscape.

The easiest solution today is to use loyalty information to track how often customers buy, the products they choose and the promotions that attract them. With that information and the right software, you can create robust customer profiles that can drive product selection, ranging, pricing and other strategic planning. 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.

WWD: Based on all of this, what is the one piece of advice you would give retail executives?

S.A.: The biggest takeaway is that this is a pivotal moment for retail. Machine learning could break retailers free from relying only on historical data and the old ways of making decisions. 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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