Leading retailers are finding machine learning innovations tailor-made for fast fashion, providing data-driven insights to make sure they always have the top-selling clothing colors and styles on hand, maximizing revenue while delighting ever more demanding shoppers.
When retailers can connect fashion trends to actual sales and inventory levels using predictive analytics on what’s out of favor and what’s coming next, even the most dedicated fashionistas won’t know what hit them. This is the concept Klaus Schimmer, director of Business Development for SAP Leonardo Machine Learning, shared with me at the SAPPHIRE NOW + ASUG Annual Conference, held last week in Orlando, Florida.
Insights Faster than Social
Picture a life-size screen, powered by SAP Leonardo, fully interactive with shoppers on the street or in the store, as well as store managers.
“It’s very important to see what 16-year old girls, who typically love to shop on a weekly basis, are wearing so you can understand trends before they peak, and as they wane,” said Schimmer. “Using machine learning that scans shoppers who opt in as they walk by, retailers can in seconds immediately conduct intelligent advertising, recommending other available items personalized to someone’s tastes by color, style, gender, age, or even emotion based on their facial expression.”
At the same time, store managers can discover what’s hot, as analytics collect and analyze real-time information from social feeds, including posts and pictures from fashion bloggers, Instagram and Facebook. Combined with data from all the retailer’s stores, these analytics can help decision-makers stay one step ahead of lightning-fast fashion trends. The impact is tremendous on design, production planning, inventory control and dynamic pricing to ensure the right items end up in the right locations to reach the right consumers.
“There may be differences between what’s selling in London vs. Paris, but with real-time data, retailers can quickly see that cool color is already finished in Paris, but still trending high in London,” said Schimmer. “Instead of reducing the price of clothing in that color in the Paris stores, they can ship it quickly to London to take advantage of customer demands there. They can also calibrate markdowns depending on sales volumes in Paris. Maybe it’s more profitable to only reduce prices by 10 percent before demand reaches a certain tipping point.”
Conversational Yet Strategic Business Partner
Digital assistants are solving one of the biggest problems every business has in accessing critical data, and quickly turning it into insights. Forget static, historical dashboards. Using SAP CoPilot, Schimmer put SAP Leonardo Machine Learning through its paces, asking a series of questions in conversational language to instantly compare predicted demand with inventory, followed by a suggestion to order more inventory which was automatically routed to procurement. Easy-to-read, colorful graphical displays popped up on the large screen with each spoken answer. Here’s what it sounded like:
Schimmer: “Hello SAP CoPilot. Show me the trending colors in London today.”
SAP CoPilot: “Here are the trending colors for London based on social media traffic impacting fashion.”
Schimmer: “Show me the impact on my business.”
SAP CoPilot: “Here’s the projected demand vs. your supply. It seems you can’t keep up with the demand for clay-colored items.”
Schimmer: “Show me details on clay colors.”
SAP CoPilot: “Here’s the breakdown for the clay items. Shirts and tops seem to have the biggest mismatch. Do you want me to reorder the respective items to keep up with demand?”
Schimmer: “Make a recommendation.”
SAP CoPilot: “Okay, here are the recommendations. Do you want me to go ahead?”
Schimmer: “Yes please.”
SAP CoPilot: “Sure. The proposal has been sent to John in Procurement.”
Schimmer: “Thank you CoPilot.”
SAP CoPilot: “You’re welcome. Have a nice day.”
Machine learning has the power to drive not just one part of a business process, but the entire organization for intelligent decisions leading to faster innovation. Shoppers can expect a whole new level of personalized shopping.
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