Across industries, companies are faced with the challenge of how to sift through the massive amount of readily available user-generated content (UGC) to uncover valuable customer insights. A new study published in Marketing Science describes how to use machine learning to expedite the discovery of customer needs from user-generated content. The details of this approach are published in "Identifying Customer Needs from User-Generated Content" by Artem Timoshenko and John Hauser of the MIT Sloan School. This study is the culmination of the authors' work over the past several years. Applied Marketing Science (AMS) was instrumental in the development, testing, and refinement of the aforementioned algorithm, and has used the process various times to help clients identify consumer insights.