About

Artem Timoshenko is an Associate Professor of Marketing at the Kellogg School of Management, Northwestern University. His research examines how emerging technologies are reshaping innovation, customer engagement, and marketing strategies. Current projects focus on how generative AI can facilitate new product development and on using data-driven personalization to accelerate marketing effectiveness. Professor Timoshenko collaborates closely with industry partners to identify high-impact challenges, develop novel methodological approaches, and design proof-of-concept studies that generate actionable insights. He holds a Ph.D. in Marketing from MIT Sloan School of Management, an M.A. in Economics from New Economic School, and a Diploma in Applied Mathematics and Computer Science from Moscow State University.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Business
• Marketing
• Data Mining
• Computer Security
• Political Science
• Management science
• Process management
• Database
• Economics
• Management
• Mathematics
• Engineering

Selected publications

• Profit-Aligned CATE Estimation: Reconciling Policy Learning and Inference

  ArXiv.org · 2025-12-15

  preprintOpen access1st authorCorresponding

  We propose a framework that aligns Conditional Average Treatment Effect (CATE) estimation with profit maximization. Our method recognizes that, for customers with extreme treatment effects, additional estimation accuracy is unlikely to change the recommended actions. In contrast, accuracy is critical near the decision boundary, where treatment effects are close to treatment costs. Our approach optimizes a novel objective function that concentrates learning capacity along this boundary. The proposed objective is Fisher consistent with respect to the original profit function and yields a consistent estimator for CATEs. Theoretically, our framework unifies standard plug-in optimization and direct policy optimization as limiting cases of the same optimization problem. We further show that entropy-regularized policy optimization is a special case of our framework. This result has a direct practical implication: firms can recover consistent CATE estimates from existing profit-maximization pipelines. We use synthetic data to demonstrate how the proposed framework allows firms to explicitly navigate the trade-off between global prediction accuracy and profit maximization.

  PublisherOA PDFDOI

• A Sample Size Calculation for Training and Certifying Targeting Policies

  Management Science · 2025-03-24 · 1 citations

  article

  We propose an approach for determining the sample size required when using an experiment to train and certify a targeting policy. Calculating the rate at which the performance of a targeting model improves with additional training data is a complex problem. We address this challenge by assuming that customers are grouped into segments that capture relevant information about their responsiveness to the firm’s marketing actions. We consider two problem formulations. The first formulation identifies the sample size required to train a targeting policy and certify that its expected performance exceeds a predefined threshold. The second formulation identifies the sample size required to train a targeting policy and certify that it outperforms a baseline in an out-of-sample statistical test. We establish theoretical properties of these problems, based on which we propose computationally efficient algorithms for optimal sample size calculations. We illustrate our algorithms and analysis using data from a luxury fashion retailer. This paper was accepted by David Simchi-Levi, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02947 .

  PublisherDOI

• Improving Targeting Policies Using Transfer Learning

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Can Large Language Models Extract Customer Needs as well as Professional Analysts?

  SSRN Electronic Journal · 2025-01-01 · 2 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs

  ArXiv.org · 2025-02-25

  preprintOpen access1st authorCorresponding

  Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC) applications have relied on professional analysts to manually interpret qualitative data and formulate "jobs to be done." This task is cognitively demanding, time-consuming, and difficult to scale. While current practice uses machine learning to screen content, the critical final step of precisely formulating CNs relies on expert human judgment. We conduct a series of studies with market research professionals to evaluate whether Large Language Models (LLMs) can automate CN abstraction. Across various product and service categories, we demonstrate that supervised fine-tuned (SFT) LLMs perform at least as well as professional analysts and substantially better than foundational LLMs. These results generalize to alternative foundational LLMs and require relatively "small" models. The abstracted CNs are well-formulated, sufficiently specific to guide innovation, and grounded in source content without hallucination. Our analysis suggests that SFT training enables LLMs to learn the underlying syntactic and semantic conventions of professional CN formulation rather than relying on memorized CNs. Automation of tedious tasks transforms the VOC approach by enabling the discovery of high-leverage insights at scale and by refocusing analysts on higher-value-added tasks.

  PublisherOA PDFDOI

• Guided Creativity: AI Intermediation for Enhancing Originality and Quality in Visual Design

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Analysis of Frequency-Controlled Asynchronous Motor Parameters in Electric Vehicle Electric Drive Control Systems

  2024-07-01

  article

  the article describes the purpose, scope, main parameters of the control system for an electric drive of an electric vehicle based on a frequency-controlled asynchronous motor, and also touches upon the problems facing developers. The advantages of using two master oscillators are shown. The need to study the quality of regulation of the control system is described.

  PublisherDOI

• POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of a Two-wheeled Robot in Highly Cluttered Environments

  arXiv (Cornell University) · 2023-07-16

  preprintOpen access

  This paper focuses on Passable Obstacles Aware (POA) planner - a novel navigation method for two-wheeled robots in a highly cluttered environment. The navigation algorithm detects and classifies objects to distinguish two types of obstacles - passable and unpassable. Our algorithm allows two-wheeled robots to find a path through passable obstacles. Such a solution helps the robot working in areas inaccessible to standard path planners and find optimal trajectories in scenarios with a high number of objects in the robot's vicinity. The POA planner can be embedded into other planning algorithms and enables them to build a path through obstacles. Our method decreases path length and the total travel time to the final destination up to 43% and 39%, respectively, comparing to standard path planners such as GVD, A*, and RRT*

  PublisherOA PDFDOI

• POA: Passable Obstacles Aware Path-Planning Algorithm for Navigation of a Two-Wheeled Robot in Highly Cluttered Environments

  2023-10-01 · 2 citations

  article

  This paper focuses on Passable Obstacles Aware (POA) planner - a novel navigation method for two-wheeled robots in a highly cluttered environment. The navigation algorithm detects and classifies objects to distinguish two types of obstacles - passable and unpassable. Our algorithm allows two-wheeled robots to find a path through passable obstacles. Such a solution helps the robot working in areas inaccessible to standard path planners and find optimal trajectories in scenarios with a high number of objects in the robot's vicinity. The POA planner can be embedded into other planning algorithms and enables them to build a path through obstacles. Our method decreases path length and the total travel time to the final destination up to 43 % and 39 %, respectively, comparing to standard path planners such as GVD, A*, and RRT*.

  PublisherDOI

• Product Aesthetic Design: A Machine Learning Augmentation

  Marketing Science · 2023 · 82 citations

  Senior authorCorresponding
  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  Aesthetics are critically important to market acceptance. In the automotive industry, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing aesthetics. A single automotive “theme clinic” can cost more than $100,000, and hundreds are conducted annually. We propose a model to augment the commonly used aesthetic design process by predicting aesthetic scores and automatically generating innovative and appealing product designs. The model combines a probabilistic variational autoencoder (VAE) with adversarial components from generative adversarial networks (GAN) and a supervised learning component. We train and evaluate the model with data from an automotive partner—images of 203 SUVs evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs—43.5% improvement relative to a uniform baseline and substantial improvement over conventional machine learning models and pretrained deep neural networks. New automotive designs are generated in a controllable manner for use by design teams. We empirically verify that automatically generated designs are (1) appealing to consumers and (2) resemble designs that were introduced to the market five years after our data were collected. We provide an additional proof-of-concept application using open-source images of dining room chairs. History: Puneet Manchanda served as the senior editor. Funding: A. Burnap received support from General Motors to partially fund a postdoctoral research position for the research conducted in this work. He certifies that none of the research or its results were censored or obfuscated in its publication. J. Hauser and A. Timoshenko certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Supplemental Material: The data files are available at https://doi.org/10.1287/mksc.2022.1429 .

  PublisherDOI

Frequent coauthors

• John R. Hauser

  Massachusetts Institute of Technology

  12 shared

• Spyros I. Zoumpoulis

  Decision Sciences (United States)

  9 shared

• Alex Burnap

  8 shared

• Duncan Simester

  6 shared

• Eric M. Schwartz

  3 shared

• Pavel Kireyev

  INSEAD

  3 shared

• Dokyun Lee

  3 shared

• Kanishka Misra

  University of California, San Diego

  3 shared

Awards & honors

• Chairs’ Core Course Teaching Award (Kellogg)
• MSI Young Scholar Award, ISMS
• Marketing Science Institute (MSI) Frank M. Bass Dissertation…
• John D.C. Little Best Paper Award, Finalist
• Robert D. Buzzell Best Paper Award, Finalist, ISMS