Name: Optimal Product Design by Sequential Experiments in High Dimensions (Online)
Start: 2018-07-19T12:00:00-05:00
End: 2018-07-19T13:00:00-05:00

The identification of optimal product and package designs is challenged when attributes and their levels interact. Firms recognize this by testing trial products and designs prior to launch where the effects of interactions are revealed. A difficulty in conducting analysis for product design is dealing with the high dimensionality of the design space and the selection of promising product configurations for testing. We propose an experimental criterion for efficiently testing product profiles with high demand potential in sequential experiments. The criterion is based on the expected improvement in market share of a design beyond the current best alternative. We also incorporate a stochastic search variable selection method to selectively estimate relevant interactions among the attributes. A validation experiment confirms that our proposed method leads to improved design concepts in a high-dimensional space compared to alternative methods.

Date: Jul 19, 2018
YouTube Link: https://www.youtube.com/watch?v=0L7Tjko6rjY
Time: 12:00 – 13:00 pm (Central Time)

About the speaker:

Mingyu (Max) Joo is a Assistant Professor of Marketing at the Ohio State University. His research interests include advertising effectiveness, pricing, and product design using quantitative modeling methods.

In 2015, Joo is the recipient of the John D.C. Little Award for the best marketing paper published in Marketing Science or Management Science, and the finalist for the Frank M. Bass Award for the best marketing paper derived from a Ph.D. thesis published in INFORMS journals. He has presented his research at several marketing and pricing conferences, including UTD-FORMS Conference, INFORMS Marketing Science Conference, INFORMS Revenue Management and Pricing Conference, and Annual Pricing Conference.

Hui Lin

Central Iowa R User Group

Technology

Open Source

New Technology

Statistical Computing

Big Data

Machine Learning

Data Analytics

Data Visualization

Predictive Analytics

Data Mining

R Project for Statistical Computing

Business Analytics

Data Science

Applied Statistics

Optimal Product Design by Sequential Experiments in High Dimensions (Online)

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Optimal Product Design by Sequential Experiments in High Dimensions (Online)

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