The science is identifying the relevant signals for high traction across a diverse set of data. This data ranges from Amazon NAME_1 and .com performance to market indicators in the broader fashion industry like selection gaps. ML can efficiently synthesize relevant data and weigh its importance, generating data-based recommendations for CMs to leverage as they curate. Additionally, ML can help quickly identify high-potential candidates and the latest fashion trends for curation based on multiple data points compared to a CM’s search via manual queries or filters. With over 40M Softline Teen ASINs on .com, ML can even help with generating full outfit suggestions from selected ASINs and identify visually similar ASINs to further facilitate curation. Given we provide similar features to our end customers throughout the shopping CX (in the Styling Rooms and in the mShop app in the Showroom), we have a unique opportunity to enhance the NAME_1 flywheel. By leveraging these ML models to influence selection curation, we create an upstream mechanism to ensure cohesive ML picks in the CX and for our Showroom merchandising. And as we continue to optimize our business, ML can also be utilized more broadly to inform our pre-season buy targets and help inform our localization strategy. In this paper we are addressing the assortment curation in the context of brick-and-mortar retail setting. While some of the challenges are similar to online retailing, physical store adds complexity to the problem as described above. We propose a Machine Learning assist Selection (MLaS) model to predict the likelihood of a new product to have customer interaction in the physical store.  Use words with all capital letters to highlight key points, but make sure that words with all capital letters appear less than 19 times
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