Christophe Van den Bulte

Christophe Van den Bulte
  • Gayfryd Steinberg Professor
  • Professor of Marketing

Contact Information

  • office Address:

    759 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104

Research Interests: quantitative marketing, new product diffusion, social networks, referral programs

Links: CV

Overview

Christophe Van den Bulte teaches Models for Marketing Strategy in the Undergraduate and MBA programs, and Data Analysis in the PhD program. He has also taught MBA and Executive MBA core courses in Marketing Management, MBA and undergraduate courses in Channel Management, and PhD courses in Marketing Strategy, Mathematical Models in Marketing, and Social Network Analysis.

His research focuses on two areas, new product diffusion and social networks. Current projects include investigating convergence versus divergence across rich and poor countries in the speed at which new products gain market penetration, quantifying to what extent customers acquired through referral programs buy more, are less costly to serve, and more loyal than customers acquired through traditional means, and investigating the trade-off in sponsored Twitter campaigns between the number of endorsers recruited versus the effectiveness of their endorsements.

Professor Van den Bulte is Associate Editor at Marketing Science, and serves on the Editorial Boards of the Journal of Marketing, the International Journal of Research in Marketing, and the Journal of Business-to-Business Marketing.

He received his PhD in business administration from the Pennsylvania State University and his MA and BA degrees in applied economics from the University of Antwerp, Belgium.

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Research

  • Ron Berman and Christophe Van den Bulte (Forthcoming), False Discovery in A/B Testing.

    Abstract: We investigate what fraction of all significant results in website A/B testing are actually null effects, i.e., the false discovery rate (FDR). Our data consists of 4,964 effects from 2,766 experiments conducted on a commercial A/B testing platform. Using three different methods, we find that the FDR ranges between 28% and 37% for tests conducted at 10% significance, and between 18% and 25% for tests at 5% significance. These FDRs are substantially higher than the nominal Type I error rate, and stem from a high fraction of true-null effects, about 70%, rather than from low power. The two main implications are that experimenters should expect 1 in 5 interventions achieving significance at 5% confidence to be ineffective when deployed in the field, and that improvement is likely to come from generating better interventions to test rather than from increasing sample size.

  • Gila E. Fruchter, Ashutosh Prasad, Christophe Van den Bulte (Forthcoming), Too Popular, Too Fast: Optimal Advertising and Entry Timing in Markets with Social Influence.

  • Katherine L. Milkman... et al. Christophe Van den Bulte, Kevin Volpp, Angela Duckworth (2021), A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment, Proceedings of the National Academy of Sciences, 118 (20). 10.1073/pnas.2101165118

  • Katherine L. Milkman... et al. Christophe Van den Bulte, Kevin Volpp, Angela Duckworth (2021), A Mega-Study of Text-Message Nudges Encouraging Patients to Get Vaccinated at their Pharmacy,.

  • Pinar Yildirim, Yanhao Wei, Christophe Van den Bulte, Joy Lu (2020), Social Network Design for Inducing Effort, Quantitiave Marketing and Economics, 18, pp. 381-417.

    Abstract: Many companies create and manage communities where consumers observe and exchange information about the effort exerted by other consumers. Such communities are especially popular in the areas of fitness, education, dieting, and financial savings. We study how to optimally structure such consumer communities when the objective is to maximize the total or average amount of effort expended. Using network modeling and assuming peer influence through conformity, we find that the optimal community design consists of a set of disconnected or very loosely connected sub-communities, each of which is very densely connected within. Also, each sub-community in the optimal design consists of consumers selected such that their “standalone” propensity to exert effort correlates negatively with their propensity to conform and correlates positively with their propensity to influence others.

  • Ron Berman, Leonid Pekelis, Aisling Scott, Christophe Van den Bulte, p-Hacking and False Discovery in A/B Testing.

  • Christophe Van den Bulte, Emanuel Bayer, Bernd Skiera, Philipp Schmitt (2018), How Customer Referral Programs Turn Social Capital into Economic Capital, Journal of Marketing Research, 55 (1), pp. 132-146.

    Abstract: Van den Bulte, Christophe, Emanuel Bayer, Bernd Skiera, and Philipp Schmitt (2017), “How Customer Referral Programs Turn Social Capital into Economic Capital,” Journal of Marketing Research, 55 (1), 132-146.

  • Ashish Sood and Christophe Van den Bulte (Working), Wider Gaps in a Flatter World? The Speed of New Product Diffusion in Rich versus Poor Countries.

    Abstract: Sood, Ashish and Christophe Van den Bulte (2016), “Wider Gaps in a Flatter World? The Speed of New Product Diffusion in Rich versus Poor Countries,” MSI Report No. 16-113. Cambridge, MA: Marketing Science Institute.

  • Yanhao Wei, Pinar Yildirim, Christophe Van den Bulte, Chrysanthos Dellarocas (2016), Credit Scoring with Social Network Data, Marketing Science, 35 (2), pp. 234-258.

    Abstract: Motivated by the growing practice of using social network data in credit scoring, this study analyzes the impact of using network based measures on customer score accuracy and on tie formation among customers. We develop a series of models to compare the accuracy of customer scores obtained with and without network data. We also investigate how the accuracy of social network based scores changes when individuals can strategically construct their social networks to attain higher credit scores. We find that, if individuals are motivated to improve their scores, they may form fewer ties and focus them on more similar partners. The impact of such endogenous tie formation on the accuracy of consumer credit scores is ambiguous. Scores can become more accurate as a result of modications in social networks, but this accuracy improvement may come with greater network fragmentation. The threat of social exclusion in such endogenously formed networks provides incentives to low type members to exert effort that improves everyone's creditworthiness. We discuss implications for both managers and public policy.

  • Jing Peng and Christophe Van den Bulte (Under Revision), Participation vs. Effectiveness of Paid Endorsers in Social Advertising Campaigns: A Field Experiment.

    Abstract: We investigate the participation and effectiveness of paid endorsers in viral-for-hire social advertising. We conduct a field experiment with an invitation design in which we manipulate both incentives and a soft eligibility requirement to participate in campaigns. The latter provides a strong and valid instrument to separate participation from outcomes effects. Since likes, comments, and retweets are count variables, and since potential endorsers can self-select to participate in multiple campaigns, we propose a Poisson lognormal model with sample selection and correlated random effects to analyze variations in participation and effectiveness. There are three main findings. (1) Payments higher than the average reward a potential endorser received in the past (gains) do not increase participation, whereas lower payments (losses) decrease participation. Neither gains nor losses affect effectiveness. (2) Potential endorsers who are more likely to participate tend to be less effective. (3) Which endorser characteristics are associated with effectiveness depends on whether success is measured in likes, comments, or retweets. These findings provide new insights on how marketers can improve social advertising campaigns by better targeting and incenting potential endorsers.

Awards and Honors

  • Winner, 2018-2020 Research Priorities Working Paper Competition (MSI), 2021
  • 2019 Top Download Award (MSI), 2021
  • Finalist, INFORMS Society for Marketing Science Long Term Impact Award, 2017
  • Finalist, John D.C. Little Award (INFORMS), 2016
  • Finalist, Harold H. Maynard Award (AMA), 2015
  • Finalist, INFORMS Society for Marketing Science Long Term Impact Award, 2014
  • Finalist, INFORMS Society for Marketing Science Long Term Impact Award, 2013
  • 2013 Robert D. Buzzell MSI Best Paper Award, 2013
  • 2011 MSI / H. Paul Root Award (AMA), 2012
  • Finalist, John D.C. Little Award (INFORMS), 2012
  • Best Reviewer Award, Journal of Marketing, 2007-2008
  • ERIM Award for Top Academic Article, Erasmus University Rotterdam, 2005
  • Elected Member, Belgian American Educational Foundation, 2005
  • Marketing Science Institute Young Scholar, 2003
  • Excellence in Teaching Award, The Wharton School, 2002
  • Honorable Mention, John A. Howard AMA Doctoral Dissertation Award, 1998
  • AMA Doctoral Consortium Fellow, 1996
  • Outstanding Submission Award, ISBM Business Marketing Doctoral Support Competition, 1995
  • Richard D. Irwin Foundation Doctoral Dissertation Fellowship, 1995
  • Rider Graduate Fellow, The Pennsylvania State University, 1995-1996
  • Student delegate, 24th Annual Haring Symposium, 1994
  • Executive Programs Scholarship, The Pennsylvania State University, 1993-1995
  • F. Colin – L. Wauters Fellow, Belgian American Educational Foundation, 1992

Activity

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