Predicting Season Ticket Holder Retention Using Rich Behavioral Data

2020 ◽  
pp. 1-14 ◽  
Author(s):  
Adam Karg ◽  
Ali Tamaddoni ◽  
Heath McDonald ◽  
Michael Ewing
Keyword(s):  
Data Mining ◽  
Survey Data ◽  
Team Performance ◽  
Research Agenda ◽  
Predictive Accuracy ◽  
Behavioral Data ◽  
Important Research ◽  
Season Ticket ◽  
As Relationship ◽  
Game Attendance

Season ticket holders are a vital source of revenue for professional teams, but retention remains a perennial issue. Prior research has focused on broad variables, such as relationship tenure, game attendance frequency, and renewal intention, and has generally been limited to survey data with its attenuate problems. To advance this important research agenda, the present study analyzes team-supplied behavioral data to investigate and predict retention as a loyalty outcome for a single professional team over a 3-year period. Specifically, the authors embrace a broad range of loyalty measures and team performance to predict retention and employ novel data mining techniques to improve predictive accuracy.

scholarly journals Demand Supply Oriented Taxi Suggestion System for Vehicular Social Networks with Fuel Charging Mechanism

2019 ◽  
pp. 38-44
Author(s):  
Selvi C ◽  
Keerthana D
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Large Scale ◽  
Recommendation System ◽  
Important Research ◽  
Taxi Drivers ◽  
Charging Station ◽  
Gps Trajectories ◽  
Charging Mechanism ◽  
Supply Level

Data mining depends on large-scale taxi traces is an important research concepts. A vital direction for analyzing taxi GPS dataset is to suggest cruising areas for taxi drivers. The project first investigates the real-time demand-supply level for taxis, and then makes an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. This project constructs a recommendation system by jointly considering the profits of both drivers and passengers. At last, the qualified candidates are suggested to drivers based on analysis. The project also provides a real-time charging station recommendation system for EV taxis via large-scale GPS data mining. By combining each EV taxi’s historical recharging actions and real-time GPS trajectories, the present operational state of each taxi is predicted. Based on this information, for an EV taxi requesting a recommendation, recommend a charging station that leads to the minimal total time before its recharging starts.

Comments on ‘Key issues for attention from ecological economists’ by Paul Ehrlich

2008 ◽  
Vol 13 (1) ◽  
pp. 25-28
Author(s):  
STEPHEN POLASKY
Keyword(s):  
Climate Change ◽  
Research Agenda ◽  
Environmental Issues ◽  
Toxic Substances ◽  
Important Research ◽  
Close Attention ◽  
Big Ideas ◽  
Economics Profession ◽  
Key Issues ◽  
Paul Ehrlich

Paul Ehrlich has a clear message for the economics profession: humanity faces a number of serious environmental problems and economists have a central role to play if we are to successfully address them. The article lays out an important research agenda for economists, which includes climate change, loss of biodiversity, release of toxic substances, epidemiological concerns, population, and over-consumption. Even if one disagrees with some of the particulars, and my guess is that many economists who read the article will, the big ideas contained in this article and the main messages are worthy of close attention. Rather than being on the periphery of the economics profession, those of us in economics who focus on environmental issues, whether called ecological economists, environmental economists or resource economists, should be at the heart of an economics profession focused on the most important and relevant issues facing society.

Abusive Supervision in Work Organizations: Review, Synthesis, and Research Agenda

2007 ◽  
Vol 33 (3) ◽  
pp. 261-289 ◽  
Author(s):  
Bennett J. Tepper
Keyword(s):  
Research Agenda ◽  
Empirical Work ◽  
Abusive Supervision ◽  
Future Research ◽  
Important Research ◽  
Work Organizations ◽  
Review Of The Literature ◽  
Direct Reports ◽  
Antecedents And Consequences ◽  
Research Domain

A growing literature explores abusive supervision, nonphysical forms of hostility perpetrated by managers against their direct reports. However, researchers have used different terminology to explore phenomena that overlap with abusive supervision, and extant research does not devolve from a unifying theoretical framework. These problems have the potential to undermine the development of knowledge in this important research domain. The author therefore provides a review of the literature that summarizes what is known about the antecedents and consequences of abusive supervision, provides the basis for an emergent model that integrates extant empirical work, and suggests directions for future research.

Data Mining with Incomplete Data

2011 ◽  
pp. 293-296 ◽  
Author(s):  
Hai Wang ◽  
Shouhong Wang
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Survey Data ◽  
Incomplete Data ◽  
Knowledge Engineering ◽  
Data Set ◽  
Survey Question ◽  
Data Source ◽  
The Common ◽  
Extent Of Damage

Survey is one of the common data acquisition methods for data mining (Brin, Rastogi & Shim, 2003). In data mining one can rarely find a survey data set that contains complete entries of each observation for all of the variables. Commonly, surveys and questionnaires are often only partially completed by respondents. The possible reasons for incomplete data could be numerous, including negligence, deliberate avoidance for privacy, ambiguity of the survey question, and aversion. The extent of damage of missing data is unknown when it is virtually impossible to return the survey or questionnaires to the data source for completion, but is one of the most important parts of knowledge for data mining to discover. In fact, missing data is an important debatable issue in the knowledge engineering field (Tseng, Wang, & Lee, 2003).

scholarly journals The LSST Data Mining Research Agenda

2008 ◽  
Author(s):  
K. Borne ◽  
J. Becla ◽  
I. Davidson ◽  
A. Szalay ◽  
J. A. Tyson ◽  
...  
Keyword(s):  
Data Mining ◽  
Research Agenda
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