市场营销学系学术讲座

时间：2023年6月29日（周四）12:30-14:00

地点：思源楼624 腾讯会议: 298 390 958 密码: 230629

主讲人：Ning Zhong, Assistant Professor of Marketing, Pennsylvania State University

主持人：肖莉副教授复旦大学管理学院市场营销学系

主题：Using Text Analytics Models to Examine Consumer Mobility Patterns

摘要:

Mobile location data has emerged as a rich data source for marketers. Collected by commercial vendors, such data consist of geographic coordinates that can be enriched with information about the point of interest (POI) located at the coordinates, enabling the use of text analytic methods to examine consumer mobility patterns. In this research, we develop a hierarchical, time-varying topic model to examine how the share of visits across business categories shifted over time. In Study 1, we use location data collected from mobile devices in the state of Georgia between January 2020 and August 2020, a time period that includes the state-mandated shelter-in-place order at the beginning of the COVID-19 pandemic, we investigate whether and how quickly consumers returned to “business as usual” in their patterns of frequenting different businesses. Our analysis reveals that temporal shifts in visitation behavior are related to the severity of the pandemic and demographic factors. We also show that some business categories are slower to return to their pre-COVID levels of consumer visits, indicating an uneven economic recovery across industries. In Study 2, we use location data of mobile devices in the states of Colorado, Texas, and Washington between October 2021 and June 2022 to examine how rising gasoline prices and inflation influence consumers’ travel distances and their patronage to various business categories. Our research suggests how marketers and policymakers can draw insights from large-scale historical location data by employing text analytics methods.

简介:

Ning Zhong is an assistant professor of marketing at Smeal College of Business, Pennsylvania State University. He holds a PhD degree from Emory University and is an AMA-Sheth Doctoral Consortium Fellow (2017) and a Goizeuta Fellow (2018). His research interests lie primarily in social media analytics, text analytics, location analytics and customer relationship management using machine learning techniques and Bayesian statistics. He has published papers in top journals of both marketing and operations.