Ron Berman is an Associate Professor of Marketing at Wharton. He focuses his research on digital marketing and marketing analytics. Recently Ron has been investigating how firms assess and optimize marketing effectiveness through experiments, how curation algorithms may create filter-bubbles on social media, and how descriptive analytics affects online firm performance. His research has been published in top marketing journals such as Marketing Science and the Journal of Marketing Research and he is a member of the editorial boards of Marketing Science, the Journal of Marketing Research, and Quantitative Marketing and Economics. Ron disseminates his research by teaching Digital Marketing courses in undergrad, MBA and Executive Education programs, and is often invited by market leading firms including Google, Facebook, and Wayfair to share and discuss his research.
Ron’s experience includes early-stage venture capital investing at Viola Ventures (formerly Carmel Ventures) and developing software for the Israeli Defense Forces (IDF). Ron is an active advisor and investor, involved with startups such as Desti (travel planning, acquired by Nokia), Zimperium (cyber security, acquired by Liberty Strategic Capital), Abakus (advertising attribution, acquired by SAP), Peerspace (P2P venue marketplace), Netlify (serverless website deployment), Stackbit (content management), cauzal.ai (conversion optimization) and Honeycomb Insurance (commercial real-estate insurance).
Ron holds a PhD and MSc in Business Administration from the University of California, Berkeley, an MBA and MSc in Computer Science from Tel-Aviv University, and a BSc in Computer Science, Physics and Mathematics from the Hebrew University in Jerusalem.
More information is available at Ron’s personal page: www.ron-berman.com
Links to my research and publications can be found on my personal website www.ron-berman.com.
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.
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.
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.
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.
Daniela Schmitt and Ron Berman, Suspenseful and Surprising Content.
Abstract: Not much research has been done about the impact of content types on subscriber demand and long-term revenue. Answering this question empirically is hard because editors make an endogenous decision about the content they solicit and the allocation to the paid and free sections. One type of content, however, shows promise in answering this question - suspenseful and surprising content. Suspenseful events are events for which there is high variance in the uncertainty of their outcome. For example, a close match between two soccer teams. Surprising events are those where the realized outcome is very different than the expectation prior to the event. An example would be an upset victory by an underdog soccer team. Other important contexts where suspense and surprise are common include political debates, voting and elections. Because these events have inherent uncertainty, their realized outcome allows us to use randomness for empirical identification purposes when estimating demand and consumer preferences. Our research project focuses on empirically determining how content editors should treat surprising and suspenseful events - how much coverage should they allocate to them, and whether the content should be paid or free.
Abstract: ‘‘More is better’’ has been a belief held by many retailers when they manage product assortments. We challenge this conventional wisdom by demonstrating that a retailer may face more price sensitive demand for existing products when expanding its assortments. To measure the effects of assortment expansion on price sensitivity, we exploit the state of Washington’s privatization of liquor sales in 2012 that generated exogenous variation in retailers’ assortments over time. We find that customers are on average more price sensitive when purchasing from other drink categories after a store started to carry liquor but its impact is heterogeneous. To understand the differential changes in the price sensitivity across product categories depending on whether they are complements or substitutes to the new one, we build a demand model that simultaneously estimates the degree of complementarity between product categories and the changes in price sensitivity upon assortment expansion. We find that the increase in the price sensitivity happens in product categories that are complements to the new one, and that these changes cannot be rationalized by alternative explanations, e.g., correlated preferences across product categories and changes in error variance. Based on the demand estimates, we conduct counterfactual simulations and show that the observed prices are consistent with retailers’ (biased) belief that the price sensitivity does not vary with assortment, which results in significant profit loss.
MKTG9900001 ( Syllabus )
The effect of the Internet and related technologies on business and social institutions is more profound than that of any prior invention, including the printing press and the internal combustion engine. Furthermore, marketing is critical to the success of firms that will shape the consumption-led economies that are fueled by these technologies. MKTG 2270 provides a research-based and framework-driven approach to succeeding in this environment, through a rigorous approach to understanding digital marketing and electronic commerce. The course is organized into two sections and utilizes relevant theory, empirical analysis, and practical examples, to develop the key learning points. Guest speakers will participate as well, as appropriate.
MKTG 2700 explores the digital marketing environment from both a consumer and business perspective. The course provides an overview of various online business models and delves into digital advertising and social media marketing techniques and technologies. A mixture of case studies, guest speakers and assignments, including one that uses real advertising data, translates theory into practice. It is recommended that students enrolling in the course be comfortable using Excel and are knowledgeable in applying regression analysis techniques. Students who would prefer a less technical course may wish to take MKTG 2270, Digital Marketing and Electronic Commerce, a half cu course offered by the department.
The effect of the Internet and related technologies on business and social institutions is more profound than that of any prior invention, including the printing press and the internal combustion engine. Furthermore, marketing plays a key role in shaping the modern consumption-led economies fueled by these technologies. MKTG 7270 provides a research-based and framework-driven approach to understanding digital marketing and electronic commerce. The course is organized into two sections and utilizes relevant theory, empirical analysis, and practical examples, to develop the key learning points. Guest speakers will participate as well, as appropriate.
MKTG 7700 explores the digital marketing environment from both a consumer and business perspective. The course provides an overview of various online business models and delves into digital advertising and social media marketing techniques and technologies. A mixture of case studies, guest speakers and assignments, including one that uses real advertising data, translates theory into practice. It is recommended that students enrolling in the course be comfortable using Excel and are knowledgeable in applying regression analysis techniques. Students who would prefer a less technical course may wish to take MKTG 7270, Digital Marketing and Electronic Commerce, a half cu course offered by the department.
A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.
This is a continuation of MKTG 954. This doctoral seminar reviews analytical models relevant to improving various aspects of marketing decisions such as new product launch, product line design, pricing strategy, advertising decisions, sales force organization and compensation, distribution channel design and promotion decisions. The primary focus will be on analytical models. The seminar will introduce the students to various types of analytical models used in research in marketing, including game theory models for competitive analysis, agency theory models for improving organization design and incentives within organizations, and optimization methods to improve decision making and resource allocation. The course will enable students to become familiar with applications of these techniques in the marketing literature and prepare the students to apply these and other analytical approaches to research problems that are of interest to the students.
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.
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.
Requires written permission of instructor and the department graduate adviser.
Free shipping is great for shoppers, but it’s becoming an increasingly significant cost for online sellers. Wharton’s Barbara Kahn and Ron Berman discuss the free-shipping conundrum faced by retailers large and small.…Read MoreKnowledge at Wharton - 12/10/2019