Yohei Nishimura

I am a PhD candidate in Quantitative Marketing at Wisconsin School of Business, University of Wisconsin-Madison.

My research develops and applies quantitative methods at the intersection of structural econometrics, deep learning, and Bayesian statistics to address substantive problems in marketing. I build structural models that integrate unstructured data—images and text—processed through deep-learning pipelines (computer vision and NLP) into economically interpretable frameworks, and employ Bayesian methods for estimation, prediction, and optimization.

Prior to my PhD, I accumulated 15+ years of industry experience in product management, software engineering, and business development, which informs my research with practical perspectives on digital platforms and marketing operations.

  • Position: Business PhD Candidate
  • Location: Madison, WI, USA
  • Citizenship: Japan
  • Email: ynishimura　@　wisc.edu

Journal Publications

Leveraging Generative AI for Visual Content in Digital Advertising

Marketing Science (forthcoming)

Abstract
  • Develop a novel creative design process combining generative AI with deep Bayesian prediction models to identify high-performance, brand-compatible ad content.
  • Demonstrate superior performance of AI-generated visuals compared to human designs in a field application, providing a framework for effectively integrating generative AI in digital advertising.

AI-Human Hybrids for Marketing Research: Leveraging LLMs as Collaborators

Journal of Marketing (Vol 89, 2025)

Abstract
  • We argue that a human-LLM hybrid approach improves marketing research by enhancing data generation, analysis, and insight quality.
  • The paper demonstrates that combining human and LLM strengths surpasses either alone, with successful applications in both qualitative and quantitative studies.
  • The study replicates a 2019 project with a Fortune 500 company, showcasing LLMs as valuable collaborators in research.

Working Papers

Adaptive Conjoint Analysis Combining with Synthetic Data and Reinforcement Learning Approach

Abstract
  • This project addresses the limitations of Hierarchical Bayes (HB) models in data-scarce environments, such as “cold-start” problems for new technologies or B2B contexts. We propose a methodology to leverage LLMs and deep generative models to construct high-quality, informative priors.

The Value of Fit in Micro-Influencer Marketing: A Two-Sided Matching Model with Counterfactual Image Generation

Abstract
  • This research develops a three-stage structural econometric model, enriched by high-dimensional image embeddings, to analyze the two-sided matching process and content production dynamics in microinfluencer marketing. Empirical estimations reveal that inherent match quality and strategic content adjustment act as strategic substitutes, highlighting systematic inefficiencies and costly over-adjustment under decentralized matching. A counterfactual Deferred Acceptance algorithm, visually validated by a custom latent diffusion model, eliminates this over-adjustment to yield a 26% increase in gross engagement and a 9.4% expansion in matches.

Education

Doctor of Philosophy in Business, specialization in Quantitative Marketing

2023 – Present

University of Wisconsin-Madison, Madison, WI, USA

Marketing Research with advanced deep learning technologies for natural language processing and computer vision.

Master of Science, Computer Sciences

2021 – 2023

University of Wisconsin-Madison, Madison, WI, USA

Focus: Machine Learning, Deep Learning for visual recognition, Operating System, Optimization, Learning based Computer Vision, Reinforcement Learning, Learning based Image Synthesis.

Bachelor of Engineering, Computer Science

2019 – 2021

Teikyo University, Tokyo, Japan

Focus: Operating System, Database System, Data structure and Algorithm, Computer Architecture, Information Security, Image processing, Computer Graphics, Information Theory, Graph Theory.

Bachelor of Art, Economics

2003 – 2007

The University of Tokyo, Tokyo, Japan

Focus: Markov Chain Monte Carlo Method, Bayesian statistics, Game theory, Empirical Macroeconomics.

Thesis: Estimation of volatility in Japanese equity market index by using Markov Chain Monte Carlo method.

Selected Professional Experience

Software Engineer (Full Stack), Machine Learning Engineer

Oct 2021 – Feb 2022

Aoyama Art, Inc., Tokyo, Japan (Remote, Part-time)

  • Launched and maintained the media part of titel.jp.
  • Constructed new databases and an infra layer API to connect the database to the front end by a clean architecture and RESTful API.
  • Installed an article recommendation system. Built the recommended article component as a template, and also created new views and handlers. Using a naive Bayesian classifier, the same classified articles are displayed as recommended articles.

Senior Product Manager / Project Leader

Jul 2020 – Jun 2021

Mercari Inc., Tokyo, Japan

  • Responsible for introduction of business users into Mercari service: "Mercari Shops".
  • Developed 10 sets of public APIs with five backend engineers. Drove product vision, go-to-market strategy, and design discussions.
  • Launched the new service with engineers, designers, other PdMs, lawyers, and financial specialists.

Chief Operating Officer / Software Engineer / Product Manager

Dec 2017 – Jun 2020

Michael Inc., Tokyo, Japan

  • Responsible for products' management on CARTUNE (Apps / Web) and CARTUNE Parts Market.
  • Launched a new media service named "CARTUNE MAGAZINE" using Python and Google's services with two engineers and developed the UI/UX design by myself.
  • Monthly active users grew from 20,000 to 2,700,000 in two years.
  • Michael Inc. was acquired by Mercari Inc. in Oct 2018.

Project Leader / Product Manager

Apr 2016 – Nov 2017

Donuts Ltd., Tokyo, Japan

  • MixChannel renewal from a short movie app to a live streaming app as a project leader with 30+ people.
  • Developed the marketing strategy, and released TV commercial.
  • Planned annual P/Ls and product strategies.

Skills

Versatile methodological toolkit spanning structural econometrics, deep learning, and Bayesian statistics for quantitative marketing research.

Structural Modeling & Econometrics

  • Discrete choice models
  • Counterfactual simulation & policy evaluation
  • Selection bias correction

Deep Learning (Vision & Text)

  • Computer vision (image embeddings, visual feature extraction)
  • Natural language processing (text embeddings, LLMs)
  • Generative AI (image generation, synthetic data)
  • Variational autoencoders

Bayesian Statistics

  • MCMC methods & Hierarchical Bayes estimation
  • Bayesian optimization / Active learning

Additional Methods

  • Reinforcement learning / Multi-Armed Bandits
  • Causal inference / Quasi-experimental methods
  • Conjoint analysis

Programming & Tools

  • Python (PyTorch, NumPy, Pandas)
  • Julia
  • R
  • SQL / BigQuery
  • GCP / Cloud infrastructure