Forecasting city arrivals with Google Analytics

Gunter, Ulrich; Önder, Irem

doi:10.1016/j.annals.2016.10.007

Cited by 155 publications

(113 citation statements)

References 53 publications

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“…The most frequently used Bayesian time‐series forecasting model is the Bayesian vector autoregressive (BVAR) model. The BVAR has been used to forecast city arrivals from Google analytics data (Gunter and Önder ). A recent innovation saw the BVAR combined with a principal components analysis‐based approach to resulting in the Bayesian factor‐augmented vector autoregression (BFAVAR) (Gunter and Önder ).…”

Section: Spatiotemporal Estimation and Forecasting For Public Opinionmentioning

confidence: 99%

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Williams

Haworth

Blangiardo

et al. 2018

Geographical Analysis

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Public confidence in the police is crucial to effective policing. Improving understanding of public confidence at the local level will better enable the police to conduct proactive confidence interventions to meet the concerns of local communities. Conventional approaches do not consider that public confidence varies across geographic space as well as in time. Neighborhood level approaches to modeling public confidence in the police are hampered by the small number problem and the resulting instability in the estimates and uncertainty in the results. This research illustrates a spatiotemporal Bayesian approach for estimating and forecasting public confidence at the neighborhood level and we use it to examine trends in public confidence in the police in London, UK, for Q2 2006 to Q3 2013. Our approach overcomes the limitations of the small number problem and specifically, we investigate the effect of the spatiotemporal representation structure chosen on the estimates of public confidence produced. We then investigate the use of the model for forecasting by producing one-step ahead forecasts of the final third of the time series. The results are compared with the forecasts from traditional time-series forecasting methods like na€ ıve, exponential smoothing, ARIMA, STARIMA, and others. A model with spatially structured and unstructured random effects as well as a normally distributed spatiotemporal interaction term was the most parsimonious and produced the most realistic estimates. It also provided the best forecasts at the London-wide, Borough, and neighborhood level.

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Section: Spatiotemporal Estimation and Forecasting For Public Opinionmentioning

confidence: 99%

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Williams

Haworth

Blangiardo

et al. 2018

Geographical Analysis

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“…In the past, research using GA has been applied to the business performance level of enterprise management [5][6][7][8][9]. Budd [5] claimed that companies can develop marketing strategies that improve their business performance through the collection of website data by, for example, improving the ability of a company's marketing information to reach a wider market [6].…”

Section: Introductionmentioning

confidence: 99%

“…Budd [5] claimed that companies can develop marketing strategies that improve their business performance through the collection of website data by, for example, improving the ability of a company's marketing information to reach a wider market [6]. Gunter and Onder [7] studied the tourism website in Vienna and used GA analysis to explore website traffic data to predict travel demand. Plaza [8] claimed that the performance evaluation of travel websites is a key issue for the effectiveness of online marketing.…”

Section: Introductionmentioning

confidence: 99%

Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

Chen

Tsao

Lue

et al. 2020

Preprint

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Background: Under the lower fertility rate in Taiwan, colleges and universities face the harsh conditions of sourcing students. Therefore, each faculty must determine the strengths and weaknesses of its department, including the factors that students consider when choosing a major area.Methods: This study uses Google Analytics (GA) tools to perform a webpage analysis and conducts qualitative research to understand students’ needs. Among the webpage visitors, there were more women than men, with the highest frequency among those aged 18–24 years, followed by those aged 45–54 years.Results: The web information analysis provides information about the core content of the research department, the stay time, and the visitor characteristics.Conclusions: The website can enhance the introduction of the faculty-specific courses and highlight the differences in the characteristics of the curriculum design in competing departments to have clear market segmentation. The findings provide an information for the school’s enrollment strategies in each department.

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“…In the past, research using GA has been applied to analyze business performance to inform enterprise management [5][6][7][8][9]. Budd [5] claimed that companies can develop marketing strategies that improve their business performance through the collection of website data by, for example, improving the ability of a company's marketing strategy to reach a wider market [6].…”

Section: Introductionmentioning

confidence: 99%

“…Budd [5] claimed that companies can develop marketing strategies that improve their business performance through the collection of website data by, for example, improving the ability of a company's marketing strategy to reach a wider market [6]. Gunter and Onder [7] studied a tourism website in Vienna and applied GA analysis to explore website tra c data and predict travel demand. Plaza [8] claimed that the performance evaluation of travel websites is a key issue for the effectiveness of online marketing.…”

Section: Introductionmentioning

confidence: 99%

Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

Chen

Tsao

Lue

et al. 2020

Preprint

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Background: Due to decreasing fertility rates in Taiwan, colleges and universities face significant difficulties in maintaining student attendance. This study aims to investigate students in the higher education market and explore their online behavior patterns in relation to university departmental websites to provide response strategies with the aim of potentially increasing enrollment.Methods: We used Google Analytics (GA) tools to analyze two departmental websites from a medical university between February 1 and July 30, 2018. We also observed website patterns during relevant periods for three college admission routes (Stars Program, Personal Application, and Admission by Examination and Placement).Results: Visitors were mostly between 18 and 24 years of age during the three visiting date ranges for the two departments analyzed. The visitor groups were mainly freshmen at the university, and secondarily their parents. The majority of visitors visited “Homepage,” “Subject Credits,” “Course Planning,” “Teacher Lineup,” and “Certificate of Subjects.” The overall timing of daily page views varied depending on academic events.Conclusions: Results suggest that the department should consider enhancing the presentation of its featured courses on the web page or distinguish their course characteristics from those of competing departments in the curriculum to ensure clear market segmentation. It is recommended that each department consider the obtainable data online and identify suitable high schools to visit to attract potential students and improve student willingness to choose this university.

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Forecasting city arrivals with Google Analytics

Cited by 155 publications

References 53 publications

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

A Spatiotemporal Bayesian Hierarchical Approach to Investigating Patterns of Confidence in the Police at the Neighborhood Level

Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

Google analytics of a pilot study to characterize the visitor website statistics and implicate for enrollment strategies in Medical University

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