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Consumer brands and retailers often struggle to fully understand ever-changing customer needs. That is why you mostly find XL sizes in your favorite fashion store and no M sizes. That is why you have to spend hours looking for the style you saw on Instagram and still not find it. That is why the cost of dead inventory to fashion retailers in the US alone is an estimated to be a whopping USD 50 billion. And that is part of the reason why the US generated 16 million tons of textile waste in 2014.
This is not because of any lack of intention or effort in the industry; rather, it is extremely difficult to understand consumers at scale. Characterizing consumers with broad brush definitions of age, gender and income is not effective given diverse and ever-changing consumer preferences, and retailers now need to look at much finer market segments—even down to single individuals. Increasingly, consumers are driving trends rather than merchants defining them, and this goes hand in hand with much more experimentation and disruption in the market.
Thing - Environment - Designers - Buyers - Merchandisers
To create and sell the "next big thing" in such a dynamic environment, designers, buyers and merchandisers must use their own creativity but also consider, with unprecedented granularity, how consumer preferences are changing and how different design, merchandising and marketing choices will perform. This is where AI and automation come in.
For example, consider a fashion retail buyer. She is responsible for the financial success of the merchandise she selects in any given season, but it's impossible for her to predict the performance of any design 12 months before the target season, or to identify the best promotional interventions to apply in-season. This is because she has very little visibility into how consumer preferences are changing across her stores over time, and how competing products...
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