Essays on Business Analytics
| dc.contributor.advisor | Ryzhov, Ilya O | en_US |
| dc.contributor.author | Gu, Liyi | en_US |
| dc.contributor.department | Business and Management: Decision & Information Technologies | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2019-10-01T05:36:23Z | |
| dc.date.available | 2019-10-01T05:36:23Z | |
| dc.date.issued | 2019 | en_US |
| dc.description.abstract | With the rapidly increasing availability of business-related big data in recent years as well as the advancements in statistical and machine learning techniques, business analytics (BA) is becoming an essential practice to explain past events, predict future trends and optimize decision making. Using BA, the two essays in this dissertation aim to address some important questions in two emerging topics: humanitarian fleet management and social behaviors in online gaming industry. In the first essay, we analyse how vehicle management is carried out in a humanitarian setting. In humanitarian fleet management, the performance of purchase, assignment, and sales decisions is determined by dynamic interactions between the fleet composition, the time-varying and uncertain demands on the fleet, and the depreciation of the vehicles as they are exploited. We propose to evaluate purchase, assignment, and sales policies in a realistic simulation environment that directly models heterogeneous vehicle attributes and tracks their evolution over time. Using data from a large international humanitarian organization (LIHO), the simulator can identify the rationale behind seemingly ad-hoc decisions by field managers at LIHO. For instance, by selling vehicles later than LIHO recommends, managers are actually reducing their costs; similarly, managers decline to switch vehicles between mission types because the benefits to the operational cost turn out to be marginal at best. In the second essay, we conduct an empirical study of the relationship between social interaction and user engagement, retention, and purchase behavior, based on a high-resolution player-level dataset from a major international video game company for one of its premier titles. We engineer a set of features that characterize social behavior within the game, and link these behaviors to several measures of user engagement using statistical and econometric models. Our results show that user engagement is highly correlated with certain social dynamics; meanwhile, social interaction does not always translate to better retention rates or more purchases. In some cases, high dependence on a small set of friends is positively correlated with churn, indicating a tradeoff between engagement in one title and adoption of others. Early adopters are generally more responsive to the social experience than late adopters. | en_US |
| dc.identifier | https://doi.org/10.13016/phu8-c77p | |
| dc.identifier.uri | http://hdl.handle.net/1903/25114 | |
| dc.language.iso | en | en_US |
| dc.subject.pqcontrolled | Management | en_US |
| dc.title | Essays on Business Analytics | en_US |
| dc.type | Dissertation | en_US |
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