Nino Hardt received his Ph.D. in marketing from the Catholic University Eichstätt-Ingolstadt in Germany. He joined the faculty at the Ohio State University in October 2011.

His research is dedicated towards developing new Bayesian methods for use in marketing practice. His field of work includes choice models, direct utility models and rating-scale based survey research. He enjoys industry collaboration, and his research with practitioners has appeared in Marketing Science.

Areas of Expertise


  • Product Development
  • Survey Research and Rating Scales
  • Applied Bayesian Modeling in Marketing
  • Customer Satisfaction
  • Quantitative Marketing


  • Ph.D. Marketing, Catholic University Eichstätt-Ingolstadt, Germany, 2011



Hardt, N., Alex Varbanov and Greg M. Allenby (2016) Monetizing Ratings Data for Product Research, Marketing Science 35(5), pp. 713-726

Kim, Dong Soo, Bailey, Roger A., Hardt, Nino and Greg M. Allenby (2017) Benefit-Based Conjoint Analysis. Marketing Science 36(1), pp. 54-69

Allenby, Greg M., Nino Hardt and Peter E. Rossi (2018) Economic Foundations of Conjoint Analysis in Handbook of the Economics of Marketing, JP Dube and Peter Rossi, editors, Elsevier

Working Papers

Nino Hardt and Peter Kurz (2020) Volumetric Demand and Choice Set Size

Youngju Kim, Nino Hardt, Jaehwan Kim and Greg M. Allenby, Conjunctive Screening in Models of Multiple Discreteness (IJRM, revising for round 2)


  • BUSML 4202 - Marketing Research

    Course examines the role of marketing research in the formulation and solution of marketing problems. Emphasis is placed on problem formulation, research design, data collection methods (instruments, sampling, operations) and analysis techniques. Prereq: 3250 (650), and AcctMIS 2200 (211) , 2300 (212), and BusMGT 2320 (330), 2321 (331); and BusMHR 2291 or 2292 (BusADM 499.01). Not open to students with credit for 758.

  • BUSML 7219.02 - Customer Management, Pricing, and Analytics I

    Tools for the analysis of survey and marketplace data. Topics include deign of the surveys, scale development, analysis of the recency, frequency and monetary value of transactions, web-based 'click-stream' data and others. Prereq: MBA 6250, 6252, or 6253.

  • BUSMGT 7256 - Tools for Data Analysis

    This course is designed to introduce students to commonly used software programs in data science and improve students' problem solving skills and logical thought processes. Students will be exposed to R, SAS, and SPSS. Prereq: MBA 6271 or 6273.