Tom is an analytics partner in McKinsey’s Boston office. Over the past 25 years, he has served clients across a diverse set of sectors, from manufacturing and retail organizations to healthcare providers and entertainment companies. He now leads McKinsey’s Storytelling Analytics capability, where he serves clients in advanced analytics topics such as customer micro-segmentation, creative content optimization, behavioral prediction, customer acquisition and retention, and marketing campaign personalization.
Tom is the former CEO of a boutique consulting firm, Analytics Operations Engineering—acquired by McKinsey in October 2016—which specialized in the development and deployment of advanced analytics and artificial intelligence models to optimize processes across the enterprise.
Examples of Tom’s recent work include the following:
- Audience prediction modelling and portfolio optimization for a media distributor. Architected, developed, and implemented a suite of machine learning models used to predict content consumption behaviors among subscribers to a premium streaming media service and used audience predictions to optimize the content library given a fixed programming budget.
- Development and implementation of a “comp” model for a major motion picture studio. Created proprietary data pipeline and machine learning models to identify titles closest to a given project based on thematic elements, intended cast, crew, and other custom features.
- Marketing personalization strategy for a global, omnichannel retailer. Developed the analytical models needed to optimize offer structure, cadence, and overall marketing spending across a variety of campaigns and media vehicles.
- Demand planning and inventory optimization for a global luxury goods manufacturer and retailer. Used machine learning and AI to help identify early signals of success in new luxury product launches and predict existing products’ end-of-life sooner to reduce obsolescence risk.
- Schedule optimization for a major professional sports league. Built large-scale combinatorial optimization models to create the schedule of upcoming matches based on league requirements and the attractiveness of different match ups.
Tom holds a PhD, MS, and MBA from Massachusetts Institute of Technology, where he also taught courses in data, models, and decision-making, as well as behavioral economics. Prior to MIT, he attended Cambridge University as a Winston Churchill scholar and received a BS in industrial engineering from the University of Illinois at Urbana-Champaign.