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Marketing & Behavioural Science Division

Research seminars

Date: Friday, September 23, 2022
Speaker: Joseph (Joey) Reiff, Anderson School of Management
Topic: When Impact Appeals Backfire: Evidence from a Multinational Field Experiment and the Lab
Time: 10:00AM - 11:30AM
Location: HA 966
Abstract: To motivate customers to share feedback, firms often try to persuade them that their opinions will have an impact on the organization (e.g., “Have your say in our company’s direction”, “Shape our customer experience”). We examine whether such “impact appeals” are effective in increasing compliance with customer feedback requests. In a field experiment across seven countries, 430,666 customers of a Fortune 500 technology company received an email with a customer feedback survey invitation where the subject lines were exogenously manipulated. Contrary to our initial prediction and expert forecasts, we found that impact appeals on average decreased feedback provision (compared to a straightforward control message).  Importantly, impact appeals reduced feedback provision to a greater extent in countries with lower trust in business (e.g., Japan) than in countries with higher trust in business (e.g., China). We theorize and offer pre-registered lab evidence (N = 3,005) that impact appeals are more likely to reduce compliance among customers with lower trust in business because these customers perceive impact appeals as more inauthentic. Altogether, this research advances the field’s understanding of when and why highlighting impact can fail to motivate customers and even backfire.

Date: Tuesday, September 27, 2022
Speaker: Mohsen Foroughifar, University of Toronto
Topic: The Challenges of Deploying an Algorithmic Pricing Tool: Evidence from Airbnb
Time: 4:00PM - 5:30PM
Location: HA 968
Abstract: We study the deployment of an algorithmic pricing tool, Smart Pricing (SP), on Airbnb’s platform. SP is a machine learning algorithm that uses past data to predict demand and employs proxies that are correlated with the host’s marginal cost to set prices for listings. The success of such deployments depends on how good the performance of the algorithm is and how sellers use the tool for their business decisions. Our analyses suggest that adopting SP is associated with higher benefits for hosts who rarely change their prices compared to those who flexibly adjust their prices before adoption. However, hosts who rarely change their prices are surprisingly less likely to adopt SP. To understand how the platform can overcome this challenge, we propose and estimate a dynamic structural model in which hosts make adoption decisions based on their expectations of the algorithm’s behavior. Our estimation results identify a gap between the actual performance of the SP algorithm and the host’s prior belief about it. Specifically, hosts with a pessimistic prior belief about SP think they will need to manually correct algorithmic prices if they adopt SP, and this belief is disproportionately stronger for hosts with higher adjustment costs, making the SP adoption less attractive to them. Our counterfactual simulations indicate that the introduction of SP has had a small positive impact on the average host profit and the total platform revenue. But this boost can be significantly raised if Airbnb helps hosts to correct their beliefs about the SP algorithm. This highlights the need for proper communication of how the algorithm works and its benefits in order to successfully deploy a machine learning tool. The counterfactual analyses also demonstrate that, since the platform does not fully capture the host’s private marginal cost in training the algorithm, using the estimated costs from the structural estimation to re-train the algorithm can significantly increase the profitability of SP for both hosts and the platform. It suggests that combining the results of structural models and machine learning tools can help platforms design better algorithms.

Date: Thursday, September 29, 2022
Speaker: Tony Ke, The Chinese University of Hong Kong (CUHK) Business School
Topic: Competitive Algorithmic Targeting and Model Selection
Time: 12:30PM - 2:00PM
Location: HA 966
Abstract: We consider competition between firms that design and use algorithms to target consumers. Firms first choose the design of a supervised learning algorithm in terms of the complexity of the model or the number of variables to accommodate. Each firm then appoints a data analyst to estimate demand for multiple consumer segments by running the chosen design of the algorithm. Based on the estimates, each firm devises a targeting policy to maximize estimated profit. The firms face the general trade-off between bias and variance in model selection. We show that competition may induce firms to choose algorithms with more bias leading to simpler (less flexible) algorithmic choice. This implies that complex (more flexible) algorithms such as deep learning that show greater variance in the estimates are more valuable to firms with greater monopoly power.

Date: Friday, October 7, 2022
Speaker: Jiarui Liu, The University of Chicago
Topic: Who Should be Subsidized for Electric Vehicles? Demand Estimation and Policy Design under Network Effects
Time: 10:00AM - 11:30AM
Location: Zoom
Abstract: I quantify the heterogeneous network effects, direct effects from social influences and indirect effects from charging stations, in consumer demand for electric vehicles and design a targeted pricing policy in light of both effects. To examine the equilibrium effects of counterfactual policies under social influences, I model consumers’ decisions jointly as equilibrium outcomes. Multiple equilibria might arise, posing challenges to estimation and identification. I show that if the average social influence effect is within  my  derived  bounds,  then  unique  equilibrium  is  guaranteed  even  under  counterfactual  policies. I test whether the data patterns suggest unique equilibrium; if so, estimation requires searching for parameters within the derived bounds.  Another  challenge  comes  from  the  endogenous  charging  stations and prices. I construct instruments for the endogenous covariates and prove identification of each effect. Using zip code level vehicles and charging stations data in Texas over six years, I find positive heterogeneous social influence effects; moreover, ignoring social influence effects over-estimates price elasticities by 20%. The socio-economically disadvantaged group is less affected by social influences and more sensitive to prices. I design a targeted pricing policy which charges this group $9k less than the others. Under the recommended policy, firms’ annual sales increase by  13k  and  annual  profits increase by $99m; distributional equity also improves. This  shows  that  firms’  private  incentives  of profits and efficiency are not always at odds with  policy-makers’  public  incentives  of  distributional equity.

Date: Tuesday, October 11, 2022
Speaker: Nofar Duani, New York University, Stern School of Business
Topic: Brought to You Live: Watching Live Streams Creates Connection and Enhances Enjoyment​​​​​​​
Time: 4:00PM - 5:30PM
Location: HA 968
Abstract: Peer-to-peer live streaming is a rapidly growing phenomenon that has exploded in popularity on a variety of social media platforms. The current research investigates the unique social appeal of viewing live (versus pre-recorded) content. Across seven experiments—5 in the main manuscript and 2 in the Online Supplement—we find that watching videos live (vs. pre-recorded) leads consumers to feel higher levels of social connection, which in turn increases their enjoyment of the viewing experience. This "mere liveness" effect persists even when experimentally controlling for differences in video content, viewing environment, indeterminacy, and the presence of other simultaneous viewers. The social connection induced by watching content live also has important downstream consequences, increasing consumers’ willingness to continue watching similar videos and to attend similar events in the offline world. Critically, the appeal of live content depends on the desirability of the social connections it offers; consumer preference for live broadcasts is strongest when it offers the opportunity to connect with in-group (vs. out-group) members. These findings have clear substantive implications: marketers, platform developers, and media personalities can enhance consumer connection and enjoyment by going live.

Date: Tuesday, October 18, 2022
Speaker: Nils Wernerfelt, Massachusetts Institute of Technology (MIT)
Topic: Estimating the Value of Off Data to Advertisers on Meta​​​​​​​​​​​​​​
Time: 4:00PM - 5:30PM
Location: HA 967
Abstract: We study the extent to which advertisers benefit from data that are shared across applications. These types of data are viewed as highly valuable for digital advertisers today. Meanwhile, product changes and privacy regulation threaten the ability of advertisers to use such data. We focus on one of the most common ways advertisers use offsite data and run a large-scale study with hundreds of thousands of advertisers on Meta. Within campaigns, we experimentally estimate both the effectiveness of advertising under business as usual, which uses offsite data, as well as how that would change under a loss of offsite data. Using recently developed deconvolution techniques, we flexibly estimate the underlying distribution of treatment effects across our sample. We find a median cost per incremental customer using business as usual targeting techniques of $43.88 that under the median loss in effectiveness would rise to $60.19, a 37% increase. Similarly, analyzing purchasing behavior six months after our experiment, ads delivered with offsite data generate substantially more long-term customers per dollar, with a comparable delta in costs. Further, there is evidence that small scale advertisers and those in CPG, Retail, and E-commerce are especially affected. Taken together, our results suggest a substantial benefit of offsite data across a wide range of advertisers, an important input into policy in this space.

Date: Monday, October 24, 2022
Speaker: Ling-Ling Zhou, Duke University Fuqua School of Business
Topic: Essays on How Consumers Respond to Positive Brand-to-brand Interactions​​​​​​​
Time: 12:30PM - 2:00PM
Location: HA 968
Abstract: With the current digital age and the rise of social media, consumers are privy to a wide variety of content from brands and media figures (e.g., celebrities, content creators, athletes, influencers). I focus on investigating the effects of observing positive interactions between brands or human brands on consumer perceptions. In the first essay, I showcase that praising one’s competitor—via “brand-to-brand praise”— often heightens preference for the praiser more so than other common forms of communication, such as self-promotion or benevolent information. This is because brand-to-brand praise increases perceptions of brand warmth, which leads to enhanced brand evaluations and choice. In my second essay, I demonstrate how consumers enjoy viewing positive interactions between media figures and that viewing these interactions increase interest in and attitudes towards the focal media figure. This effect is driven by the humanization of the media figure, such that these positive interactions allow the media figure to seem more human and relatable to the consumer.

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