Internet users actively search for trending products on various social media services like Instagram and YouTube which serve as popular hubs for discovering and exploring fashionable and popular items. It is imperative for e-commerce giants to have the capability to accurately identify, predict and subsequently showcase these trending products to the customers. E-commerce stores can effectively cater to the evolving demands of the customer base and enhance the overall shopping experience by offering recent and most sought-after products in a timely manner. In this work we propose a framework for predicting and surfacing trending products in e-commerce stores, the first of its kind to the best of our knowledge. We begin by defining what constitutes a trending product using sound statistical tests. We then introduce a machine learning-based early trend prediction system called TrendSpotter to help users identify upcoming product trends. TrendSpotter is a unique adaptation of the state-of-the-art InceptionTime model[6] that predicts the future popularity of a product based on its current customer engagement, such as clicks, purchases, and other relevant product attributes. The effectiveness of our approach is demonstrated through A/B tests, where we first showcase the effectiveness of our statistical test based labeling strategy, resulting in an incremental sales lift of 59 bps1 across two experiments on home page and search page. Subsequently, we conduct a comparison between our machine learning model and the statistical labeling baseline and observe an additional sales gain of 14 bps, reflecting the importance of early identification of trending products.
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