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Terui Lab
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The lab builds models that draw decision-making insights from large-scale business data.
AboutAbout the lab
Turning big data into business decisions.
The Terui Lab studies modeling that derives optimal business decisions from diverse, large-scale information.
Using Bayesian statistics, machine learning, text analysis, and more, it analyzes data on consumer behavior.
In its introduction, the lab invites people to take on new data analysis that makes use of business data, a large part of the big data electronically accumulated in an IoT society.
Example themes include statistical modeling of large-scale data, network analysis of social media, and structural modeling of consumer behavior.
Key terms
- Heterogeneity
- The fact that preferences and reactions differ from one consumer to another. In heterogeneity modeling, these differences are built into the model
- User-generated content
- Content that users post themselves, such as word-of-mouth posts and reviews. Also called UGC
- Synthetic data
- Data created by a model so as to reproduce the characteristics of actual purchase data
ResearchResearch themes
Centered on Bayesian statistical modeling, the Terui Lab studies modeling that derives optimal business decisions from diverse, large-scale information. In projects carried out with researchers from other universities, it integrates marketing science and machine learning to advance the statistical modeling of consumer behavior.
Decision-Making Models Using Large-Scale Data
The lab studies models for marketing decisions by fusing diverse, large-scale information and integrating the theories and methods of marketing science and machine learning while incorporating the modeling of consumer heterogeneity. It carries out this work in an organized way with researchers from other universities and collaborates with practitioners and overseas researchers.
Models of Synthetic Data and Co-evolution
In its projects, the lab combined large-scale actual purchase data derived from scan panels with deep learning to develop a generative model of synthetic data suited to tabular data. It also built a model that captures "co-evolution," in which user-generated content and social networks influence and change each other, and conducted empirical analysis in a two-sided market.
Exploring Consumers Through Text and Field Experiments
In its projects, the lab applied natural language processing to free-text movie reviews (including spoiler information) to explore the structure of reviewers' motivations. It also conducted a field experiment on an online shopping site in which multiple chatbots were shown at random, and analyzed how the design of customer service affects consumer behavior.
Quantitative Analysis of Listening and Purchasing Behavior
In its projects, the lab fitted a hierarchical Bayesian two-sided censored Tobit model to radio listening time data and estimated the mechanisms that give rise to listening behavior. It also conducted quantitative analysis of sensory experiences in purchasing situations and of the effect that changes to the Foods with Function Claims system had on purchases of health foods.
MembersFaculty
- ProfessorNobuhiko Terui
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