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What are the successful usages of AI in Fashion Merchandising?

Written by Maavrus

October 20, 2022

Customer

Hi. Welcome to Expert Talks at Insights Now. Every year, around 100 to 120BN units of fashion apparel are produced globally, out of which goes around 30% go unsold. Another one-third gets marked down before it is sold in stores. So upwards of around 50% of the global fashion apparel production does not sell at the price at which it was intended to in the first place. So obviously there’s a lot of financial wastage. But more importantly, there are also serious implications from the perspective of the environment and sustainability.

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AI is today enabling fashion merchandisers and planners to plan allocate and stock the right product quantities at the right location and sell it at the right price so that they can maximize their self-loss, reduce waste, and increase their exit margins.

Traditionally, businesses look at the sales performance of a particular product in a particular location and overlaid it with factors like seasonality, day of the week, price and promo, elasticity, and marketing events and occasions to plan for the quantities that need to be allocated to the location In the future.

However, with AI, one can bring in real-time inputs like new competition, competition performance, competition articles, weather and climate forecast, and also changing consumer segment mix in that particular store or location to ensure that there is a lot more science behind the quantity that is allocated to that particular location.

The second area where AI is helping businesses is in what I call substitution product planning. So not only can retailers analyze the performance of a product in that location, but they can also analyze the performance at a product attribute level. For example, the colors, pattern, size, sleeve length, and silhouettes that are actually selling in that location create a substitution product should the primary product go out of stock or have broken sizes.

The third area is in terms of understanding and imputing customer Omni-shopping behavior into the planning process. So, for example, today customers would like to buy online and pick up in stores, but they may want to return to stores. So AI can help quantify and predict such pickup in stores, such returns to stores, etc. And ensure that those inputs are also factored in while planning for the quality allocation to a particular store.

It is proven now that retailers who have used AI efficiently in their merchandising processes are able to improve their exit margins by around two to 4% and improve their sell-through by around 6 to 10%. I hope you found this video interesting. You can follow us at maavrus.com. Thank you.

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