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The aim of this paper is to develop a new user‐friendly in‐house tracking methodology for academics to analyse the effectiveness of visits (return visit behaviour and length of sessions) depending on their traffic source: direct visits, referring site entries and search engine visits. In other words, how deep do visitors navigate into the web site? Which is their internal performance depending on their traffic source?

This paper addresses these questions by time series analysis of Google Analytics data. Some statistical matters with regard to the use of Google Analytics data in combination with time series methodology are fine‐tuned.

Return visits are the main engine for nurturing session length, but which type of traffic source nurtures these return visits? In order to answer this question, an important distinction must be made between “total return visits” and “marginal return visits”. Site entries stay longer to the extent their “marginal return effectiveness” is higher. For our particular web site direct visits are the most effective ones, followed by search engine visits and only thirdly link‐entries.

This methodology is critical for an effective web site traffic source monitoring and benchmarking that may lead to better web site strategies.

The importance of this paper is not the particular web site but the new methodology tested to arrive at these results, an experiment that could be repeated with different web sites.

This paper seeks to investigate how to use a web analytics tool to conduct deep analysis of users' web behaviors. This study aims to focus on examining whether the types of traffic sources and temporal fluctuation influence the web visitors' performance on the web portal of a K‐12 resource inventory.

One year's data were collected via the Advanced Segmentation function of Google Analytics. To compare visitors' behavior from different types of traffic recourses with the intervention of temporal effect, clickstream data of three visitor segments were collected.

Traffic sources and temporal effect have been found to influence web site visitors' performance interactively. Search engines seemed good at bringing a significantly large amount of traffic to the eThemes site, but most visitors are likely “information encounters”. However, visitors from direct traffic (bookmark/typed URLs) seemed to visit the eThemes site purposefully – stay for a long time on the site and view more web pages. Additionally, loyal users of the site seemed to employ the eThemes site as an everyday life information source.

This study introduces a strategic approach to study and analyzes web site visitors' behavior longitudinally. The findings of this study contribute to loyal user behavior identification. Empirical evidence has been found to support the correlational relationship between traffic sources, the temporal factor, and Key Performance Identifiers of a site.

Online advertising such as Google AdWords gives small and medium-sized enterprises access to new markets at reduced costs. The purpose of this paper is to analyse the visibility and performance of a website and to test the effectiveness of online marketing using the data provided by Google Analytics.

The authors use a class of econometric time series models with unobservable components, Structural Time Series Models (STSM). The authors allow for time-varying trends to take into account the non-stationary behaviour displayed by time series. The authors illustrate the model using daily data from a local tourist website. Three specific questions are addressed: do paid keywords campaigns increase the volume and quality of search traffic? Do paid keywords affect the volume and quality of the unpaid traffic? How do paid and unpaid keywords perform?

The results for the case study show that: first, online campaigns affect traffic volume positively but their effectiveness on traffic quality is uncertain; second, paid keywords do not affect the volume and quality of unpaid traffic; third, the increase in traffic volume is not always due to the paid keywords and the lowest quality visits come from paid traffic.

This analysis may help webmasters to design successful online advertising strategies.

This study contributes to the development of user-friendly methodologies to monitor website performance. The analysis shows that STSM is a suitable methodology to test the effectiveness of online campaigns and to assess the changes over time in the performance of a website.

This paper aims to evaluate the early effects of the pandemic of coronavirus disease 2019 (COVID-19) and accompanying stay-at-home orders on restaurant demand in US counties.

The following two sets of daily restaurant demand data were collected for each US county: foot traffic data and card transaction data. A two-way fixed-effects panel data model was used to estimate daily restaurant demand from February 1 to April 30, 2020.

Results show that a 1% increase in daily new COVID-19 cases led to a 0.0556% decrease in daily restaurant demand, while stay-at-home orders were collectively associated with a 3.25% drop in demand. The extent of these declines varied across counties; ethnicity, political ideology, eat-in habits and restaurant diversity were found to moderate the effects of the COVID-19 pandemic and stay-at-home orders.

These results characterize the regional restaurant industry’s resilience to COVID-19 and identify particularly vulnerable areas that may require pubic policies and managerial strategies for intervention.

This study represents a pioneering attempt to investigate the economic impact of COVID-19 on restaurant businesses.

As faculty professional development increasingly occurs online and through social media, it becomes challenging to assess the quality of learning and effectiveness of programs and resources, yet it is important to evaluate such initiatives. The purpose of this paper is to explore how one faculty development center experimented with using analytics to answer questions about the use and effectiveness of its web and social media resources.

The case study was based on direct observation of the center’s practice and review of selected data generated by the analytic tools.

Unfortunately, while some analytics are available from a variety of sources, they are often distributed across tools and services. The center developed an analytics strategy to use data from Google Analytics and social media reporting tools to assess the use of online and social professional development resources. Initial results show that the center’s online and social professional development resources are widely used, both within and outside the university. However, more work is necessary to improve the strength and scope of the available analytics.

As a result of the analysis, the center has streamlined online resources, targeted social media use, and has begun developing methods to allow faculty to report online resource use as professional development for academic personnel purposes.

Many faculty development centers have not explored methods of evaluating online and social media resources. This paper outlines a strategic evaluation plan to measure the usage of online resources as well as engagement and interaction through social media.

Common promotional activities employed by shopping mall marketers were ranked by a sample of customers on their likelihood of encouraging increases in the two key performance indicators used by shopping malls – sales and visits. Results suggest clear distinctions between sales drivers and visit drivers and show possible combinations that would be effective in generating optimum customer behaviour. Some traditional promotions (fashion shows and product displays) are shown to be poor performers in generating either response, whilst school/community displays appear to be encouraging non‐customer visits. Whilst mall‐wide sales are the preferred promotion, a combination of general entertainment and price‐based promotions are found to be a strong alternative way to encourage visits and spending. Actual sales, visits, and promotional types for a three‐month period were analysed to assess the degree to which customers’ behaviour matched stated behaviour likelihood, with supportive results.

This study aims to analyze the impact of newly created brand awareness on customer’s buying behavior in online environment.

The authors analyzed more than 280,000 online customer journeys from four e-commerce stores based in Slovakia. Within the results of the interaction analysis of individual customer journeys, the authors determined three criteria based on the level of theoretical brand awareness. The purpose was to determine their occurrence in real-world data.

It was found that each of the specified criteria accounts for the significant share of the company’s revenues. Based on these criteria and the level of their occurrence, the authors introduced the term direct traffic effect.

Because of the available Web analytics tools, the data might be imprecise because of data collection issues. There is also ambiguity in the interpretation of the customer journey.

The company can build awareness among prospective customers by offering them a positive customer experience during the first interactions online. Data proved that customer will not only repeatedly visit the website from the direct traffic source but also his customer journey will end with the purchase of the company’s products.

This paper fulfills the need for further research on the impact of multi-channel marketing on brand awareness and consumer behavior, respectively.

The unexpected outbreak of COVID-19 has expedited the trend toward online education. To facilitate undisruptive learning, EdTech companies are continuously working on providing solutions to restore teaching and learning practices. This has caused a significant behavioral shift of the investors in the EdTech market. This study aims to analyze the effects of Web Market Traffic on the increased number of investors funding an EdTech Company in the market.

By drawing on the multi-method web analytics approach, this study analyses the nexus between Web Market Traffic and Investor's Behavior in the US and India, proving the hypothesized relationship in the proposed Model using a data sample of 300 EdTech Players.

There is a significant difference between the investor's behavior in India and the US. This study shows that the investors in the US are more inclined towards investing in EdTech companies in comparison to India. The Results demonstrate that monthly visits of consumers and the number of acquisitions by players positively affect the investor's behavior, while bounce rates take a toll on the number of investors.

This Study suggests that EdTech investors in the US and India should harness Web Traffic to capture the EdTech market. Further, this study offers practical implications that EdTech players can use to attract potential investors and increase brand visibility by improving web market traffic parameters.

This paper's original contribution is to empirically shed light on the effects of web market traffic on the investor's behavior. The study emphasizes the quintessentiality of managing the bounce rates and monthly visits for an EdTech market to attract more investors and capital inflow that enhance brand visibility. The study found that the investors behave distinctly in the developed and emerging markets in the US and India.

The purpose of this study is to unpack the antecedents and consequences of clickbait prevalence in online media at two different levels, namely, (1) Headline-level: what characteristics of clickbait headlines attract user clicks and (2) Publisher-level: what happens to publishers who create clickbait on a prolonged basis.

To test the proposed conjectures, the authors collected longitudinal data in collaboration with a leading company that operates more than 500 WeChat official accounts in China. This study proposed a text mining framework to extract and quantify clickbait rhetorical features (i.e. hyperbole, insinuation, puzzle, and visual rhetoric). Econometric analysis was employed for empirical validation.

The findings revealed that (1) hyperbole, insinuation, and visual rhetoric entice users to click the baited headlines, (2) there is an inverted U-shaped relationship between the number of clickbait headlines posted by a publisher and its visit traffic, and (3) this non-linear relationship is moderated by the publisher's age.

This research contributes to current literature on clickbait detection and clickbait consequences. Future studies can design more sophisticated methods for extracting rhetorical characteristics and implement in different languages.

The findings could aid online media publishers to design attractive headlines and develop clickbait strategies to avoid user churn, and help managers enact appropriate regulations and policies to control clickbait prevalence.

The authors propose a novel text mining framework to quantify rhetoric embedded in clickbait. This study empirically investigates antecedents and consequences of clickbait prevalence through an exploratory study of WeChat in China.

The purpose of this paper is to provide an introduction to the various web metrics tools that are available, and to indicate how these might be used in libraries.

The paper describes ways in which web metrics can be used to inform strategic decision making in libraries.

A framework of possible web metrics is provided that can be adapted for use as appropriate in libraries.

The paper offers assistance to any web site manager in planning new developments, given limited resources.