Evaluating Restaurants’ Profitability of a Daily Deal Promotion

Although group buying daily deal sites are widely popular among consumers, it is unclear if deal promotions are profitable for merchants, especially for restaurants. The goal of this study is: (1) to investigate if restaurants make profit from a group buying deal, break even or make significant investment and (2) to find out what factors affect deal profitability. A model for calculating the short-term profitability of restaurant\u27s deal promotions is developed, and ten variables are identified and tested using linear regression analysis to find the once affecting deal profitability measured by return on investment. The research was conducted on the case of Grouper.mk, the leading deal platform in Macedonia. Findings show that deal promotions are profitable and effective tool for restaurants. Deal promotions that provide takeout are less profitable for restaurants, while those that offer additional discount on extra purchases are more profitable for restaurants. Employees effort to upsell have positive impact on deal profitability. However, profitability varies across restaurants category, from least profitable for fast food restaurants to most profitable for fine dining restaurants. Based on the findings of this research, recommendations for maximizing the deal profitability are provided

Deal or No Deal? Consumer Expectations and Competition in Daily Deals

Daily deals have emerged as an integral part of the marketing mix for retail merchants and have enjoyed wide acceptance by consumers. However, there is considerable ambiguity about the effects of deals on brand evaluation, and resulting electronic word-of-mouth (eWOM). In this paper, we propose that the effects of deals on eWOM are contingent on merchant heterogeneity and whether consumers perceive merchants\u27 marketing efforts as desperate. We empirically model the effects of daily deals on eWOM for restaurants in Washington DC over 13 months. Results show that price segment, age, and competitive deal intensity strongly moderate the effect of deals on resulting eWOM. We also show that deals have significant spillover effects on neighboring merchants who do not offer deals. We confirm these effects using three controlled lab experiments, where similar results are obtained without the possibility of deal redemption

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies