Christophe Van den Bulte

• Katherine L. Milkman, Sean F. Ellis, Dena Gromet, Alex S. Luscher, Rayyan S. Mobarak, Madeline K. Paxson, Ramon A. Silvera Zumaran, Robert Kuan, Ron Berman, Neil A. Lewis Jr. John A. List, Mitesh S. Patel, Christophe Van den Bulte, Kevin G. Volpp, Maryann V. Beauvais, Jonathan K. Bellows, Cheryl A. Marandola, Angela Duckworth (2024), Megastudy Shows That Reminders Boost Vaccination But Adding Free Rides Does Not, Nature. https://doi.org/10.1038/s41586-024-07591-x Abstract

  Encouraging routine COVID-19 vaccinations is likely to be a crucial policy challenge for decades to come. To avert hundreds of thousands of unnecessary hospitalizations and deaths, adoption will need to be higher than it was in the autumn of 2022 or 2023, when less than one-fifth of Americans received booster vaccines. One approach to encouraging vaccination is to eliminate the friction of transportation hurdles. Previous research has shown that friction can hinder follow-through and that individuals who live farther from COVID-19 vaccination sites are less likely to get vaccinated. However, the value of providing free round-trip transportation to vaccination sites is unknown. Here we show that offering people free round-trip Lyft rides to pharmacies has no benefit over and above sending them behaviourally informed text messages reminding them to get vaccinated. We determined this by running a megastudy with millions of CVS Pharmacy patients in the United States testing the effects of (1) free round-trip Lyft rides to CVS Pharmacies for vaccination appointments and (2) seven different sets of behaviourally informed vaccine reminder messages. Our results suggest that offering previously vaccinated individuals free rides to vaccination sites is not a good investment in the United States, contrary to the high expectations of both expert and lay forecasters. Instead, people in the United States should be sent behaviourally informed COVID-19 vaccination reminders, which increased the 30-day COVID-19 booster uptake by 21% (1.05 percentage points) and spilled over to increase 30-day influenza vaccinations by 8% (0.34 percentage points) in our megastudy. More rigorous testing of interventions to promote vaccination is needed to ensure that evidence-based solutions are deployed widely and that ineffective but intuitively appealing tools are discontinued.

• Jing Peng and Christophe Van den Bulte (2024), Participation vs. Effectiveness in Sponsored Tweet Campaigns: A Quality-Quantity Conundrum, Management Science (in press). Abstract

  We investigate the participation and effectiveness of paid endorsers in sponsored tweet campaigns. We manipulate the financial pay rate offered to endorsers on the Chinese paid endorsement platform weituitui.com, where payouts are contingent on participation rather than engagement outcomes. Hence, our design can distinguish between variation in participation and variation in outcomes, even if people self-select to endorse only specific tweets. The main finding is that endorsers exhibited adverse selection: Several observed and unobserved endorser characteristics associated with a higher propensity to participate had a negative association with being an effective endorser given participation. This adverse selection results in a conundrum when trying to recruit a sizable number of high-quality endorsers. Only 9% to 17% of the endorsers were above the median in both the propensity to participate and the propensity to be effective, compared to a benchmark of 25% in the absence of any association. A simulation analysis of various targeting approaches that leverages our data of actual endorsements and outcomes shows that targeting candidate endorsers by scoring and ranking them using models taking into account adverse selection on observables improves campaign outcomes by 12% to 40% compared to models ignoring adverse selection.

• Kathleen T. Li and Christophe Van den Bulte (2023), Augmented Difference-in-Differences, Marketing Science, 42 (4), pp. 746-767. Related
  Links

  Files

  Li & VdB 2023
• Ron Berman and Christophe Van den Bulte (2022), False Discovery in A/B Testing, Management Science, 68 (9), pp. 6762-6782. 10.1287/mnsc.2021.4207 Abstract

  We investigate what fraction of all significant results in website A/B testing is actually null effects (i.e., the false discovery rate (FDR)). Our data consist 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 (two sided). These high FDRs stem mostly from the high fraction of true null effects, about 70%, rather than from low power. Using our estimates, we also assess the potential of various A/B test designs to reduce the FDR. The two main implications are that decision makers should expect one in five interventions achieving significance at 5% confidence to be ineffective when deployed in the field and that analysts should consider using two-stage designs with multiple variations rather than basic A/B tests.

  Related
  Links
• Stefan Wuyts and Christophe Van den Bulte, “Control and Coordination in B2B Networks”. In Handbook of Business-to-Business Marketing, 2nd Ed,, edited by Gay L. Lilien, J. Andrew Petersen, and Stefan Wuyts, (Cheltenham, UK: Edward Elgar, 2022)
• Gila E. Fruchter, Ashutosh Prasad, Christophe Van den Bulte (2022), Too Popular, Too Fast: Optimal Advertising and Entry Timing in Markets with Peer Influence, Management Science, 68 (6), pp. 4725-4741. 10.1287/mnsc.2021.4105 Abstract

  We study optimal advertising and entry timing decisions for a new product being sold in two-segment markets in which followers are positively influenced by elites, whereas elites are either unaffected or repulsed by product popularity among followers. Key decisions in such markets are not only how much to advertise in each segment over time but also when to enter the follower segment. We develop a continuous-time optimal control model to examine these issues. Analysis yields two sets of two-point boundary value problems where one set has an unknown boundary value condition that satisfies an algebraic equation. A fast solution methodology is proposed. Two main insights emerge. First, the optimal advertising strategy can be U-shaped, that is, decreasing at first to free-ride peer influence but increasing later on to thwart the repulsion influence of overpopularity causing disadoption. Second, in markets where cross-segment repulsion triggers disadoption, advertising is only moderately effective, and entry costs are high, managing both advertising and entry timing can result in significantly higher profits than managing only one of these levers. In markets without disadoption, with high advertising effectiveness or with low entry costs, in contrast, delaying entry may add little value if one already manages advertising optimally. This implies that purveyors of prestige or cool products need not deny followers access to their products in order to protect their profits, and can use advertising to speed up the democratization of consumption profitably.

• Mitesh Patel, Katherine L. Milkman, Linnea Gandhi, Heather N. Graci, Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Jake Rothschild, Modupe Akinola, John Beshears, Jonathan E. Bogard, Alison Buttenheim, Christopher F. Chabris, Gretchen B. Chapman, James J. Choi, Hengchen Dai, Craig R. Fox, Amir Goren, Matthew D. Hilchey, Jillian Hmurovic, Leslie K. John, Dean Karlan, Melanie Kim, David Laibson, Cait Lamberton, Brigitte C. Madrian, M. Meyer, Maria Modanu, Jimin Nam, Todd Rogers, Renante Rondina, Silvia Saccardo, Maheen Shermohammed, Dilip Soman, Jehan Sparks, Caleb Warren, Megan Weber, Ron Berman, Chalanda N. Evans, Seung Hyeong Lee, Christopher K. Snider, Eli Tsukayama, Christophe Van den Bulte, Kevin Volpp, Angela Duckworth (2022), A Randomized Trial of Behavioral Nudges Delivered through Text Messages to Increase Influenza Vaccination Among Patients with an Upcoming Primary Care Visit, American Journal of Health Promotion, 37 (3), pp. 324-332. Abstract

  Purpose: To evaluate if nudges delivered by text message prior to an upcoming primary care visit can increase influenza
  vaccination rates.
  Design: Randomized, controlled trial.
  Setting: Two health systems in the Northeastern US between September 2020 and March 2021.
  Subjects: 74,811 adults.
  Interventions: Patients in the 19 intervention arms received 1-2 text messages in the 3 days preceding their appointment that
  varied in their format, interactivity, and content.
  Measures: Influenza vaccination.
  Analysis: Intention-to-treat.
  Results: Participants had a mean (SD) age of 50.7 (16.2) years; 55.8% (41,771) were female, 70.6% (52,826) were
  White, and 19.0% (14,222) were Black. Among the interventions, 5 of 19 (26.3%) had a significantly greater vaccination
  rate than control. On average, the 19 interventions increased vaccination relative to control by 1.8 percentage points
  or 6.1% (P = .005). The top performing text message described the vaccine to the patient as “reserved for you” and led
  to a 3.1 percentage point increase (95% CI, 1.3 to 4.9; P < .001) in vaccination relative to control. Three of the top five
  performing messages described the vaccine as “reserved for you.” None of the interventions performed worse than
  control.
  Conclusions: Text messages encouraging vaccination and delivered prior to an upcoming appointment significantly increased
  influenza vaccination rates and could be a scalable approach to increase vaccination more broadly.

  Related
  Links
• Katherine L. Milkman, Linnea Gandhi, Mitesh Patel, Heather N. Graci, Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Jake Rothschild, Jonathan E. Bogard, Ilana Brody, Christopher F. Chabris, Edward Chang, Gretchen B. Chapman, Jennifer E. Dannals, Noah J. Goldstein, Amir Goren, Hal E. Hershfield, Alexander Hirsch, Jillian Hmurovic, Samantha Horn, Dean Karlan, Ariella Kristal, Cait Lamberton, M. Meyer, Allison H. Oakes, Maurice Schweitzer, Maheen Shermohammed, Joachim H. Talloen, Caleb Warren, Ashley Whillans, Kuldeep N. Yadav, Julian J. Zlatev, Ron Berman, Chalanda N. Evans, Rahul Ladhania, Jens Ludwig, Nina Mazar, Sendhil Mullainathan, Christopher K. Snider, Jann Spiess, Eli Tsukayama, Lyle Ungar, Christophe Van den Bulte, Kevin Volpp, Angela Duckworth (2022), A 680,000-Person Megastudy of Nudges to Encourage Vaccination in Pharmacies, Proceedings of the National Academy of Sciences, 119 (6). 10.1073/pnas.211512611 Abstract

  Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested increased pharmacy vaccination rates by an average of 2.0 percentage points, or 6.8%, over a 3-mo follow-up period. The most effective messages reminded patients that a flu shot was waiting for them and delivered reminders on multiple days. The top performing intervention included two texts delivered 3 d apart and communicated to patients that a vaccine was “waiting for you.” Neither experts nor lay people anticipated that this would be the best-performing treatment, underscoring the value of simultaneously testing many different nudges in a highly powered megastudy.

• Katherine L. Milkman, Mitesh Patel, Linnea Gandhi, Heather N. Graci, Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Modupe Akinola, John Beshears, Jonathan E. Bogard, Alison Buttenheim, Christopher F. Chabris, Gretchen B. Chapman, James J. Choi, Hengchen Dai, Craig R. Fox, Amir Goren, Matthew D. Hilchey, Jillian Hmurovic, Leslie K. John, Dean Karlan, Melanie Kim, David Laibson, Cait Lamberton, Brigitte C. Madrian, M. Meyer, Maria Modanu, Jimin Nam, Todd Rogers, Renante Rondina, Silvia Saccardo, Maheen Shermohammed, Dilip Soman, Jehan Sparks, Caleb Warren, Megan Weber, Ron Berman, Chalanda N. Evans, Christopher K. Snider, Eli Tsukayama, 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 Abstract

  Many Americans fail to get life-saving vaccines each year, and the availability of a vaccine for COVID-19 makes the challenge of encouraging vaccination more urgent than ever. We present a large field experiment (N = 47,306) testing 19 nudges delivered to patients via text message and designed to boost adoption of the influenza vaccine. Our findings suggest that text messages sent prior to a primary care visit can boost vaccination rates by an average of 5%. Overall, interventions performed better when they were 1) framed as reminders to get flu shots that were already reserved for the patient and 2) congruent with the sort of communications patients expected to receive from their healthcare provider (i.e., not surprising, casual, or interactive). The best-performing intervention in our study reminded patients twice to get their flu shot at their upcoming doctor’s appointment and indicated it was reserved for them. This successful script could be used as a template for campaigns to encourage the adoption of life-saving vaccines, including against COVID-19.

• Pinar Yildirim, Yanhao Wei, Christophe Van den Bulte, Joy Lu (2020), Social Network Design for Inducing Effort, Quantitative 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.

• All Research from Christophe Van den Bulte »

Current Courses

• MKTG2710 - Models For Marketing Strategy

  The course develops students’ skills in using analytics to make better marketing decisions. Compared to other courses in marketing analytics, the focus is less on ‘what is happening?’ or ‘what will happen?’ and more on ‘what should we do?’ I.e., the course moves beyond descriptive and predictive analytics into prescriptive analytics. It covers a variety of topics, models and tools: (1) Marketing mix modeling & optimization, (2) Choice modeling, choice-based conjoint analysis & market simulators, (3) Modeling churn & maximizing customer lifetime value, and (4) Quantifying causal effects in marketing. The course requires familiarity with Excel and linear regression from the very first day, but is otherwise self-contained. Lectures are organized around a mini-case or illustrate the model/technique at hand through one or more real-life applications.

  MKTG2710401 ( Syllabus )

• MKTG7710 - Models For Marketing Strategy

  The course develops students’ skills in using analytics to make better marketing decisions. Compared to other courses in marketing analytics, the focus is less on ‘what is happening?’ or ‘what will happen?’ and more on ‘what should we do?’ I.e., the course moves beyond descriptive and predictive analytics into prescriptive analytics. It covers a variety of topics, models and tools: (1) Marketing mix modeling & optimization, (2) Choice modeling, choice-based conjoint analysis & market simulators, (3) Modeling churn & maximizing customer lifetime value, and (4) Quantifying causal effects in marketing. The course requires familiarity with Excel and linear regression from the very first day, but is otherwise self-contained. Lectures are organized around a mini-case or illustrate the model/technique at hand through one or more real-life applications.

  MKTG7710401 ( Syllabus )

Past Courses

• MKTG2710 - Models For Mktg Strategy

  The course develops students’ skills in using analytics to make better marketing decisions. Compared to other courses in marketing analytics, the focus is less on ‘what is happening?’ or ‘what will happen?’ and more on ‘what should we do?’ I.e., the course moves beyond descriptive and predictive analytics into prescriptive analytics. It covers a variety of topics, models and tools: (1) Marketing mix modeling & optimization, (2) Choice modeling, choice-based conjoint analysis & market simulators, (3) Modeling churn & maximizing customer lifetime value, and (4) Quantifying causal effects in marketing. The course requires familiarity with Excel and linear regression from the very first day, but is otherwise self-contained. Lectures are organized around a mini-case or illustrate the model/technique at hand through one or more real-life applications.

• MKTG6120 - Dynamic Mktg Strategy

  Building upon Marketing 611, the goal of this course is to develop skills in formulating and implementing marketing strategies for brands and businesses. The course will focus on issues such as the selection of which businesses and segments to compete in, how to allocate resources across businesses, segments, and elements of the marketing mix, as well as other significant strategic issues facing today's managers in a dynamic competitive environment. A central theme of the course is that the answer to these strategic problems varies over time depending on the stage of the product life cycle at which marketing decisions are being made. As such, the PLC serves as the central organizing vehicle of the course. We will explore such issues as how to design optimal strategies for the launch of new products and services that arise during the introductory phase, how to maximize the acceleration of revenue during the growth phase, how to sustain and extend profitability during the mature phase, and how to manage a business during the inevitable decline phase.

• MKTG7710 - Models For Mktg Strategy

  The course develops students’ skills in using analytics to make better marketing decisions. Compared to other courses in marketing analytics, the focus is less on ‘what is happening?’ or ‘what will happen?’ and more on ‘what should we do?’ I.e., the course moves beyond descriptive and predictive analytics into prescriptive analytics. It covers a variety of topics, models and tools: (1) Marketing mix modeling & optimization, (2) Choice modeling, choice-based conjoint analysis & market simulators, (3) Modeling churn & maximizing customer lifetime value, and (4) Quantifying causal effects in marketing. The course requires familiarity with Excel and linear regression from the very first day, but is otherwise self-contained. Lectures are organized around a mini-case or illustrate the model/technique at hand through one or more real-life applications.

• MKTG9400 - Meas Data Analys Mktg A

  MKTG 9400 and MKTG 9410 provide an understanding and working knowledge of statistical data analysis for assessing how one variable is predicted (and possibly caused) by other variables. The courses focus on "funny Y's and messy X's" and extend the students' tool kit beyond classic linear regression and ANOVA in two directions. (1) Analyzing binary data, ordered response data, choice data, count data, truncated or censored data, and duration data; (2) Identifying and tackling causal identification challenges when analyzing non-experimental data. All assignments can be completed using R, SAS, or Stata.

• MKTG9410 - Meas Data Analys Mktg B

  MKTG 9400 and MKTG 9410 provide an understanding and working knowledge of statistical data analysis for assessing how one variable is predicted (and possibly caused) by other variables. The courses focus on "funny Y's and messy X's" and extend the students' tool kit beyond classic linear regression and ANOVA in two directions. (1) Analyzing binary data, ordered response data, choice data, count data, truncated or censored data, and duration data; (2) Identifying and tackling causal identification challenges when analyzing non-experimental data. All assignments can be completed using R, SAS, or Stata.

• MKTG9720 - Adv Topics Mktg Part B

  Taught collectively by the faculty members from the Marketing Department, this course investigates advanced topics in marketing. It is organized in a way that allows students to 1) gain depth in important areas of research identified by faculty; 2) gain exposure to various faculty in marketing and their research values and styles; and 3) develop and advance their own research interests.

• MKTG9740 - Research Sem Mktg Part B

  This course is taught collectively by the faculty members from the Marketing Department. It is designed to expose Doctoral students to the cutting-edge research in marketing models in order to help them to define and advance their research interests. This course will offer: in-depth discussions on some important topics in marketing by experts in respective areas; tools, and methodologies required for conducting research in those areas; broad exposure to our faculty members and their proven research styles.

• MKTG9950 - Dissertation

  Dissertation

• Excellence in Teaching Award, The Wharton School, 2023
• 2019 Top Download Award (MSI), 2021
• Winner, 2018-2020 Research Priorities Working Paper Competition (MSI), 2021
• Finalist, INFORMS Society for Marketing Science Long Term Impact Award, 2018
• 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, 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
• Richard D. Irwin Foundation Doctoral Dissertation Fellowship, 1995
• Outstanding Submission Award, ISBM Business Marketing Doctoral Support Competition, 1995
• Rider Graduate Fellow, The Pennsylvania State University, 1995
• Student delegate, 24th Annual Haring Symposium, 1994
• Executive Programs Scholarship, The Pennsylvania State University, 1993
• F. Colin – L. Wauters Fellow, Belgian American Educational Foundation, 1992

• MSI - Leader Interview: Exploring the "Network" in Social Networks, Marketing Science Institute - 01/01/2012
• MSI - Leader Interview: Exploring the "Network" in Social Networks, Marketing Science Institute - 01/01/2012

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