Artem Timoshenko


My research focuses on performance-based marketing and new product development. I build on the methods from statistics, computer science, and operations research to propose solutions to important marketing problems. My projects aim for managerial relevance and academic rigor.

Product Aesthetic Design: A Machine Learning Augmentation
Marketing Science (2023) with Alex Burnap and John R. Hauser

Product Choice with Large Assortments: A Scalable Deep-Learning Model
Management Science (2022) with Sebastian Gabel

Efficiently Evaluating Targeting Policies: Improving on Champion vs. Challenger Experiments
Management Science (2020) with Duncan Simester and Spyros I. Zoumpoulis

Targeting Prospective Customers: Robustness of Machine Learning Methods to Typical Data Challenges
Management Science (2020) with Duncan Simester and Spyros I. Zoumpoulis

Identifying Customer Needs from User-Generated Content
Marketing Science (2019) with John R. Hauser

Is Deep Learning a Game Changer for Marketing Analytics?
MIT Sloan Management Review (2019) with Glen Urban, Paramveer Dhillon, and John R. Hauser

Working papers

A Sample Size Calculation for Training and Certifying Targeting Policies
with Duncan Simester and Spyros I. Zoumpoulis

Retail Media Platforms: Learning Marketing Effectiveness Across Brands
with Sebastian Gabel and Duncan Simester

Transferring Information Between Marketing Campaigns to Improve Targeting Policies
with Marat Ibragimov and Duncan Simester