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

Minor Revision at Marketing Science

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.

From Match to Impact: Driving Ad Effectiveness in Micro Influencer Marketing Platform

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

  • Two-sided matching models
  • 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

Additional Methods

  • Reinforcement learning (multi-armed bandits, adaptive experimentation)
  • Causal inference
  • Conjoint analysis

Programming & Tools

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