Compliments to Accomplishments: The Effect of Compliments by Digital Platforms on Consumer Behavior

When shopping online, consumers sometimes hesitate, for example, because they are uncertain about product quality, or they do not know whether the price is reasonable. In the offline shopping context, sellers can encourage purchases by complimenting consumers. This study aims to explore how digital platforms can adopt the compliment tactic to catalyze consumers’ purchase decisions. We hypothesize that online compliments, like offline compliments, can effectively reduce consumers’ uncertainties in online shopping and thus encourage purchases. We plan to run a lab experiment to test the hypothesis. This study enhances previous research on offline compliments and contributes to e-commerce research by providing causal evidence of how digital platforms can use compliments to influence consumer behavior

Airline price discrimination: a practice of yield management or customer profiling?

Airline ticket prices frequently change, which is usually caused by yield management as price discrimination practice. Recently, buyers of online airline tickets tend to complain about price discrimination based on customer profiling, e.g. by means of cookie data. As cookie data and other directly or indirectly obtained customer information is easily available via the Internet, airlines may use this information to offer personalized ticket prices. In a month-long experiment, in which prices of airline tickets were tracked, we found that cookies were not used to determine prices. However, customer information from other direct sources seems to be important in dynamic pricing. Besides, it was discovered that most price changes occurred in the morning; these were usually minor price changes and were mostly seen at full-service carrier

Empirical Studies Of Revenue Management Practices: Understand Your Competition And Customers

This dissertation empirically examines factors that challenge revenue management practices in travel industry --- air-travel and lodging. In particular, it focuses on strategic interactions among firms and strategic interactions between firms and customers. While most traditional revenue management focuses on single firm problems, better understanding competition and customers recently become two emerging themes in both theories and practices of revenue management. Meanwhile, with 20 years\u27 successful implementation of sophisticated revenue management systems in both airline and hotel industries, they have accumulated rich data to better understand threats and opportunities currently facing both industries. Furthermore, with the proliferation of online distribution channels, extensive information has been made available to both customers and competitors. How to utilize such opportunity to understand customers and competition remains a question to both industry professionals and academic researchers. This dissertation contains three parts. The first part studies implications of strategic alliances in the airline industry. Airlines in the same alliance are competitors and partners at the same time. After alliances are formed, airlines\u27 networks are expected to be consolidated and capacity redundancies would be eliminated, as intensity of competition decreases among alliance partners. However, we find that alliance partners seek to overlap more in their networks. We also find evidence that average prices increase by about $11 per one-way segment coupon in markets where two partners are both present. After ruling out other plausible competing mechanisms, we conclude that these findings are most likely driven by multimarket competition. The second part of the dissertation studies travelers\u27 strategic decision to delay purchases in anticipation of price decreases when purchasing air-tickets. By estimating a structural model on booking and posted fare data, we find that 4.9% to 44.9% of the population are strategic, and that incorporating such strategic customer behavior will increase revenues by 3% to 5% in certain city-pair markets. The third part of the dissertation bridges the two themes by applying a consumer-centric lens to better understand competition in hotel industry. Using online search and clickstream data, we propose a methodology to identify key competitors based on which hotels customers have compared. This approach also provides a network view of localized competition structure. We also find that there is approximately 50% mismatch between competition sets perceived by customers and hoteliers. Independent hotels and distant hotels are usually left out of competition sets

Toward Automating and Systematizing the Use of Domain Knowledge in Feature Selection

University of Minnesota Ph.D. dissertation. August 2015. Major: Computer Science. Advisor: Maria Gini. 1 computer file (PDF); xi, 185 pages.Constructing prediction models for real-world domains often involves practical complexities that must be addressed to achieve good prediction results. Often, there are too many sources of data (features). Limiting the set of features in the prediction model is essential for good performance, but prediction accuracy may be degraded by the inadvertent removal of relevant features. The problem is even more acute in situations where the number of training instances is limited, as limited sample size and domain complexity are often attributes of real-world problems. This thesis explores the practical challenges of building regression models in large multivariate time-series domains with known relationships between variables. Further, we explore the conventional wisdom related to preparing datasets for model calibration in machine learning, and discuss best practices for learning time-varying concepts from data. The core contribution of this work is a novel wrapper-based feature selection framework called Developer-Guided Feature Selection (DGFS). It systematically incorporates domain knowledge for domains characterized by a large number of observable features. The observable features may be related to each other by logical, temporal, or spatial relationships, some of which are known to the model developer a priori. The approach relies on limited domain-specific knowledge but can replace or improve upon more elaborate domain specific models and on fully automated feature selection for many applications. As a wrapper-based approach, DGFS can augment existing multivariate techniques used in high-dimensional domains to produce improved modeling results particularly in situations where the volume of training data is limited. We demonstrate the viability of our method in several complex domains (natural and synthetic) that have significant temporal aspects and many observable features