EDUCATION

Columbia University, Graduate School of Business, NY
Ph.D., Marketing, 2018 (Expected)
M.Phil., Marketing, 2016

Tel-Aviv University, Coller School of Business Administration, Israel
M.Sc. in Management of Technology and Information Technologies, 2010
Master’s thesis: Factors Influencing Perceived Benefit and User Satisfaction in Knowledge Management Systems

Technion Institute of Technology, Faculty of Computer Science, Israel
B.Sc. in Information Systems Engineering (Cum laude), 2005


RESEARCH INTERESTS 

  • Automation (of salesforce and other knowledge intensive organizational roles)
  • Bridging behavioral decision theory and marketing science
  • Digital marketing
  • Machine learning
  • Field experiments

PUBLICATIONS
Karlinsky-Shichor, Yael and Moshe Zviran. “Factors Influencing Perceived Benefit and User Satisfaction in Knowledge Management Systems,” Information Systems Management, 2016, 33(1), 55-73.

WORKING PAPERS
“Automating the B2B Salesperson Pricing Decisions: Can Machines Replace Humans and When?” with Oded Netzer (based on 1st essay of dissertation)

  • Winner of the 2016 Institute for the Study of Business Markets Doctoral Support Award Competition
  • Awarded the 2015 Deming Center Doctoral Fellowship

RESEARCH-IN-PROGRESS 
“Leveraging Machine Learning to Create the Human-Machine Pricing Hybrid,” with Oded Netzer (2nd dissertation essay)
“The Value of Community Engagement: A B2B Field Experiment”


HONORS AND AWARDS

  • 2017 INFORMS Doctoral Consortium Fellow, University of South California, Los Angeles, California
  • 2017 University of Houston Doctoral Symposium, Department Representative
  • Winner, 2016 Institute for the Study of Business Markets Doctoral Support Award Competition ($5000)
  • 2015-2016 Paul and Sandra Montrone Doctoral Fellow ($16,000)
  • 2015 Deming Center Doctoral Fellow ($10,000)
  • Graduate Student Fellowship, Columbia Business School, 2012-present

CONFERENCE PRESENTATION

  • “Automating the B2B Salesperson Pricing Decisions: Can Machines Replace Humans and When?” with Oded Netzer, University of Southern California, Los Angeles, California, June 2017
  • “Salesperson vs. Model of the Salesperson: A B2B Pricing Application,” with Oded Netzer, Marketing in Israel Conference, Bar-Ilan University, Israel, December 2016
  • “Factors Influencing Perceived Benefit and User Satisfaction in Knowledge Management Systems,” with Moshe Zviran, ILAIS, University of Haifa, Israel, July 2012

INVITED TALKS 
New York University, New York, Trope Lab, Fall 2017 (planned)

OTHER ACADEMIC WORK
Referee, The Mediterranean Conference on Information Systems (MCIS), 2011.


TEACHING EXPERIENCE

Columbia Business School, Columbia University

Teaching Assistant

  • Mathematical Models in Marketing (PhD class, Spring 2014)
  • Retailing: The Design & Marketing of Luxury Products (MBA class, Fall 2014)
  • Game Theory & Business (MBA class, Summer 2013)

Coller (formerly Recanati) School of Business Administration, Tel Aviv University

Instructor

  • Managing Information in Organizations (MBA Core class, Fall 2011)

Teaching Assistant

  • Managing Information in Organizations (Fall 2010, Spring 2011)
  • Knowledge Management (Fall 2008)
  • Managing the Information Resource (Fall 2008)

DOCTORAL COURSEWORK
Marketing:

Advanced Empirical Methods - Oded Netzer
Applied Multivariate Statistics - Kamel Jedidi
Analytical Models - Miklos Sarvary
Mathematical Models in Marketing - Rajeev Kohli
Bayesian Modeling and Computation - Asim Ansari
Marketing, Models and Decisions - Donald Lehmann
Consumer Behavior: Information Processing, Memory and Attitudes - Dan Bartels
Consumer Behavior: Judgment and Decision Making - Eric Johnson

Economics:
Econometrics I - Jushan Bai
Econometrics II - Christoph Rothe
Economic Theory I&II - Paolo Siconolfi
Economic Theory III&IV - Paolo Siconolfi
Industrial Organization - Kate Ho
Math Methods for Economists - Jaromir Nosal

DRO/Statistics:
Foundations of Graphical Modeling - David Blei
Causal Inference - Jose Zubizarreta
Empirical methods in MS/OM - Marcelo Olivares


WORK EXPERIENCE

  • Project Manager Officer (2008 – 2010), SAP Labs Israel
  • Software engineer (2005 –2008), SAP Labs Israel
  • Software Developer Intern (2004 – 2005), Elbit Systems Ltd.

REFERENCES
Oded Netzer
(Dissertation Advisor)
Professor of Business Graduate School of Business Columbia University
3022 Broadway, 520 Uris Hall New York, NY 10027
Tel: +1 212-854-7647
Email: onetzer@gsb.columbia.edu

Miklos Sarvary (Doctoral Committee Member)
Carson Family Professor of Business
Graduate School of Business
Columbia University
3022 Broadway, 513 Uris Hall New York, NY 10027
Tel: +1 212-851-0165
Email: miklos.sarvary@gsb.columbia.edu

Ran Kivetz (Doctoral Committee Member)
Philip H. Geier Jr., Professor of Marketing
Graduate School of Business
Columbia University
3022 Broadway, 503 Uris Hall New York, NY 10027
Tel: +1 212-854-4555
Email: rk566@gsb.columbia.edu

Moshe Zviran (Co-author, Master Thesis Advisor)
Professor of Information Technology
Dean, Coller School of Management
Tel Aviv University
338 Recanati - Business Administration, Tel Aviv, Israel
Tel: +972 3-640-8720
Email: zviran@tau.ac.il


SELECTED ABSTRACTS 

“Automating the B2B Salesperson Pricing Decisions: Can Machines Replace Humans and When?” with Oded Netzer (Job market paper, 1st dissertation essay)
In a world going towards automation, we ask whether salespeople making pricing decisions in a high human interaction environment such as business to business (B2B) retail, could be automated, and under what conditions it would be most beneficial. Using sales transactions data from a B2B aluminum retailer, we create an automated version of each salesperson, that learns the salesperson's pricing policy based on her past pricing decisions. We find that the model of the salesperson performs at least as good as the salesperson when pricing instead of the salesperson, in spite the loss of non-codeable information that is available to the salesperson but not to the model. We show that by adopting a hybrid pricing approach, that follows the model's pricing most of time, but differs to the salesperson's pricing when the model is missing important information, expected profits could be increased by 3%. Conducting a large-scale field experiment, we provide salespeople in the B2B retailer with their own model's price recommendations, and allow them to adjust their original pricing accordingly. We find that providing the model's price increases gross profit and profit margins. Finally, using results from the empirical analysis and the field experiment, we discuss when it is best to direct a new quote to automatic pricing, and when to an expert salesperson.

“Leveraging Machine Learning to Create the Human-Machine Pricing Hybrid,” with Oded Netzer (2nd dissertation essay)
Building on previous findings that demonstrate that a hybrid approach is superior to either the salesperson or its automated version in making pricing decisions in business to business (B2B) settings (Karlinsky and Netzer 2017), we leverage machine learning (ML) to create an optimal hybrid. First, we use machine learning to create an automated version of the salesperson that best captures her (possibly non-linear) pricing process. Second, we add another layer to the hybrid structure by identifying which salespeople are best replaced with an automated process and under what conditions (e.g. level of experience). Third, using methods such as classifier trees and deep learning, we allow for flexible classification of cases to either model-based pricing or human-based pricing. Finally, we overlay the insights to design an optimal hybrid structure.

“The Value of Community Engagement: A B2B Field Experiment”
While marketing budgets in companies are increasingly allocated to expand the brand’s community and deepen customers’ engagement with the brand, it is not clear what is the monetary value of such engagement. In collaboration with a firm that provides a data analytics tool to business clients I design a field experiment that will address that question. As is common with software providers, the company maintains a community website that allows its customers to consume content (e.g. webinars), create content (e.g. ask a question regarding the product) and interact with company employees and other clients. Via a randomized field experiment, I test whether encouragement to engage with the company’s community website results in higher subsequent usage of the company’s product and higher retention rates, and quantify the monetary value of the effect.

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