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Article
Publication date: 5 June 2017

Patrick Mair, Horst Treiblmaier and Paul Benjamin Lowry

The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users’ online behavior. This information enables…

Abstract

Purpose

The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users’ online behavior. This information enables real-time personalization of web content.

Design/methodology/approach

This paper models transitions between pages based upon the dwell time of the initial state and then analyzes data from a web shop, illustrating how pages that are linked “compete” against each other. Relative risks for web page transitions are estimated based on the dwell time within a clickstream and survival analysis is used to predict clickstreams.

Findings

Using survival analysis and user dwell times allows for a detailed examination of transition behavior over time for different subgroups of internet users. Differences between buyers and non-buyers are shown.

Research limitations/implications

As opposed to other academic fields, survival analysis has only infrequently been used in internet-related research. This paper illustrates how a novel application of this method yields interesting insights into internet users’ online behavior.

Practical implications

A key goal of any online retailer is to increase their customer conversation rates. Using survival analysis, this paper shows how dwell-time information, which can be easily extracted from any server log file, can be used to predict user behavior in real time. Companies can apply this information to design websites that dynamically adjust to assumed user behavior.

Originality/value

The method shows novel clickstream analysis not previously demonstrated. Importantly, this can support the move from web analytics and “big data” from hype to reality.

Details

Internet Research, vol. 27 no. 3
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 23 February 2010

R. Dale Wilson

This paper is designed to illustrate how clickstream data, collected from a B2B web site and then analyzed using web analytics software, can be used to evaluate and…

Abstract

Purpose

This paper is designed to illustrate how clickstream data, collected from a B2B web site and then analyzed using web analytics software, can be used to evaluate and improve B2B web site performance. A number of issues in the application of clickstream data and web analytics software are to be identified and discussed.

Design/methodology/approach

A case study approach is used to present some of the technical issues in the field of web analytics and to demonstrate their value in B2B web site management. Three field experiments, focusing on incorporating ways to discourage shopping‐cart abandonment and the use of two different free‐shipping promotions, were used as the basic research method for collecting the data. Web traffic conversion funnels are used to conduct the analysis and present the findings.

Findings

The analysis of clickstream data using web analytics procedures serves as a useful tool in the enhancement of a B2B web site by investigating how visitors move through the web site conversion process and complete their purchase. Improved sales result from each of the three field experiments.

Research limitations/implications

Researchers may use the paper as evidence that web analytics methods can be applied successfully in a B2B application for a technology‐oriented company.

Practical implications

The paper illustrates the use of clickstream data to measure the progression of web site visitors through the conversion process toward purchase.

Originality/value

Insight is provided into the usefulness of web analytics as a framework for performance measurement that is used to drive success for B2B web sites.

Details

Journal of Business & Industrial Marketing, vol. 25 no. 3
Type: Research Article
ISSN: 0885-8624

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Article
Publication date: 29 April 2021

Hei-Fong Ho

This study is to propose a more effective and efficient analytic methodology based on within-site clickstream associated with path visualization to explore the channel…

Abstract

Purpose

This study is to propose a more effective and efficient analytic methodology based on within-site clickstream associated with path visualization to explore the channel dependence of consumers' latent shopping intent and the related behaviors, with which in turn to gain insight concerning the interactivity between webpages.

Design/methodology/approach

The primary intention of the research is to design and develop a more effective and efficient approach for exploring the consumers' latent shopping intent and the related behaviors from the clickstream data. The proposed methodology is to use text-mining package, consisting of the combination of hierarchical recurrent neural networks and Hopfield-like neural network equipped with Laplacian-based graph visualization to visualize the consumers' browsing patterns. Based on the observed interactivity between webpages, consumers' latent shopping intent and the related behaviors can be understood.

Findings

The key finding is to evidence that consumers' latent shopping intent and related behaviors within website depend on channels the consumers click through. The accessing consumers through channels of paid search and display advertising are identified and categorized as goal-directed and exploratory modes, respectively. The results also indicate that the effect of the content of webpage on the consumer's purchase intent varies with channels. This implies that website optimization and attribution of online advertising should also be channel-dependent.

Practical implications

This is important for the managerial and theoretical implications: First, to uncover the channel dependence of consumer's latent shopping intent and browsing behaviors would be helpful to the attribution of the online advertising for the sales promotion. Second, in the past, webmasters did not understand users' preferences and make decisions of reorganization purely on the user's browsing path (sequential page view) without appraising psychological perspective, that is, user's latent shopping intent.

Originality/value

This study is the first to explore the channel dependences of consumer's latent shopping intent and the related browsing behaviors through within-site clickstream associated with path visualization. The findings are helpful to the attribution of the online advertising for the sales promotion and useful for webmasters to optimize the effectiveness and usability of their websites and in turn promote the purchase decision.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 28 February 2020

Mingjun Zhan, Hongming Gao, Hongwei Liu, Yidan Peng, Dan Lu and Hui Zhu

The objective of this paper is to propose a consumer-behavior-based intelligence (CBBI) model to identify market structure so as to monitor product competition…

Abstract

Purpose

The objective of this paper is to propose a consumer-behavior-based intelligence (CBBI) model to identify market structure so as to monitor product competition. Competitive intelligence extracted from Chinese e-business clickstream data is exploited to examine the relevance of consumers' heterogeneous behavioral feedback, namely, click, tag-into-favorite, time-of-browsing, add-into-cart, and remove-from-cart, to visualize the competitive product market structure and to predict product-level sales.

Design/methodology/approach

Our proposed CBBI model consists of visualization and prediction, which explore e-business clickstream data. We conduct the visualization and segmentation of market structure in the form of a perceptual map by employing K-means clustering algorithm and multidimensional scaling technique. Concurrently, we developed an updated Bayesian linear regression (BLR) to predict product-level sales by considering consumers' heterogeneous feedback. Our updated BLR specifically integrated the estimated knowledge of the previous periods to verify whether product sales are period-dependent due to the consumer memory effect in e-commerce, improving the conventional BLR of diffuse prior distribution setup in terms of mean absolute error (MAE) and root mean squared error (RMSE).

Findings

Considering the performance of consumers' heterogeneous actions, the present research visualized three different segments of the competitive market structure in a perceptual map, and its horizontal axis is shown as a signal of the ascending trend of product sales. The previous five-day period was ascertained to be the best size of a time window for the consumer memory effect on product sales prediction. This hypothesis is supported by the concept that product sales are period-dependent. The results of the proposed updated BLR indicate that consumer tag-into-favorite, add-into-cart, and remove-from-cart feedback have positive impacts on product-level sales while click and time-of-browsing have the opposite effect.

Originality/value

While the identified competitive product market structure elaborates consumer heterogeneous feedback toward alternative product choices, this paper contributes by extending those homogeneous consumer preferences-related marketing studies. The perceptual map's configuration in respect to period-dependent product sales facilitates the effective inclusion of consumer behavior application in product sales prediction research in e-commerce. This paper helps sellers and retailers better comprehend the impacts of heterogeneous feedback and the consumer memory effect on the degree of competition in the form of product sales. The research results also offer a managerial implication about shaping the competitive edge by conducting different product management strategies in e-commerce platforms.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 33 no. 1
Type: Research Article
ISSN: 1355-5855

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Article
Publication date: 30 October 2019

Tingting Jiang, Qian Guo, Shunchang Chen and Jiaqi Yang

The headlines of online news are created carefully to influence audience news selection today. The purpose of this paper is to investigate the relationships between news…

Abstract

Purpose

The headlines of online news are created carefully to influence audience news selection today. The purpose of this paper is to investigate the relationships between news headline presentation and users’ clicking behavior.

Design/methodology/approach

Two types of unobtrusive data were collected and analyzed jointly for this purpose. A two-month server log file containing 39,990,200 clickstream records was obtained from an institutional news site. A clickstream data analysis was conducted at the footprint and movement levels, which extracted 98,016 clicks received by 7,120 headlines ever displayed on the homepage. Meanwhile, the presentation of these headlines was characterized from seven dimensions, i.e. position, format, text length, use of numbers, use of punctuation marks, recency and popularity, based on the layout and content crawled from the homepage.

Findings

This study identified a series of presentation characteristics that prompted users to click on the headlines, including placing them in the central T-shaped zones, using images, increasing text length properly for greater clarity, using visually distinctive punctuation marks, and providing recency and popularity indicators.

Originality/value

The findings have valuable implications for news providers in attracting clicks to their headlines. Also, the successful application of nonreactive methods has significant implications for future user studies in both information science and journalism.

Details

Aslib Journal of Information Management, vol. 72 no. 1
Type: Research Article
ISSN: 2050-3806

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Article
Publication date: 12 November 2020

Yeongbae Choe and Daniel R. Fesenmaier

The purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics.

Abstract

Purpose

The purpose of this paper is to describe the core of an advanced destination management system, which uses a series of data matching techniques and business analytics.

Design/methodology/approach

This study first proposes the conceptual framework for an advanced destination management system and then illustrates the core components of the proposed system using real-world data from Northern Indiana. In this study, search interests, devices used and other forms of website use derived from online clickstream data were merged with visitor demographic and tripographic information obtained from an online survey to develop an analytic model used to describe the core market structure.

Findings

Key demographic factors (e.g. gender, age and income), search interests, referred websites, the number of total sessions, temporal aspects and spatial aspects of visitor travel provide essential information defining the structure and dynamics of the visitor marketing in Northern Indiana.

Originality/value

The process and data used in this study provide a “proof of concept” for developing highly personalized marketing systems, which can substantially improve the competitiveness of a destination management organization.

Details

Industrial Management & Data Systems, vol. 121 no. 6
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 4 April 2019

Amit Bhatnagar, Atish P. Sinha and Arun Sen

Online search effort is routinely measured by the duration of visit at the website as obtained from clicksream data or surveys. Measuring search effort by the time spent…

Abstract

Purpose

Online search effort is routinely measured by the duration of visit at the website as obtained from clicksream data or surveys. Measuring search effort by the time spent at a website assumes that all consumers who search for the same duration obtain the same amount of information. This would be acceptable if all consumers possessed the same navigational ability. In practice, different consumers have different levels of ability to navigate a website. The purpose of this study is to find whether an individual’s navigational ability has an influence on visit duration and purchase likelihood.

Design/methodology/approach

The authors use visit duration data from a real website which makes it possible to partition the visit duration into the times spent on relevant and irrelevant pages. The data were collected through an experimental study. The authors develop an empirical model, comprising hazard and choice models, to assess the relationship between navigational ability and elements of website usage.

Findings

A consumer with poor navigational ability spends more time searching on the Web and has lower purchase probability compared to a consumer with superior ability.

Research limitations/implications

The study is limited to one data.

Practical implications

This research has managerial implications for website design, such as link-structure, appearance, size and the number of graphics.

Originality/value

This is the first study to research navigational ability’s influence on online consumer behavior.

Details

European Journal of Marketing, vol. 53 no. 5
Type: Research Article
ISSN: 0309-0566

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Article
Publication date: 6 August 2019

Karzan Wakil, Fatemeh Alyari, Mahdi Ghasvari, Zahra Lesani and Lila Rajabion

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the…

Abstract

Purpose

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the electronic commerce. To evaluate the validity of the model, the structural equation modeling technique is employed.

Design/methodology/approach

A method has been suggested to evaluate the impact of customer history, product classification and prices on the success of the recommender systems in electronic commerce. After that, the authors investigated the relationship between these factors. To achieve this goal, the structural equation modeling technique was used for statistical conclusion validity. The results of gathered data from employees of a company in Iran is indicated the impact of the customer history on the success of recommender systems in e-commerce which is related with the user profile, expert opinion, neighbors, loyalty and clickstream. These factors positively influence the success of recommender systems in ecommerce.

Findings

The obtained results demonstrated the efficiency and effectiveness of the proposed model in term of the success of the recommender systems in the electronic commerce.

Originality/value

In this paper, the effective factors of success of recommender systems in electronic commerce are pointed out and the approach to increase the efficiency of this system is applied into a practical example.

Details

Kybernetes, vol. 49 no. 5
Type: Research Article
ISSN: 0368-492X

Keywords

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Abstract

Details

Review of Marketing Research
Type: Book
ISBN: 978-0-85724-727-8

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Article
Publication date: 15 June 2021

Soyeon Park and Kihun Cho

This study aims to investigate and compare mobile and desktop user search behaviours of the 1300K site, a Korean shopping search engine, by using transaction log analysis.

Abstract

Purpose

This study aims to investigate and compare mobile and desktop user search behaviours of the 1300K site, a Korean shopping search engine, by using transaction log analysis.

Design/methodology/approach

Transaction logs of 1300K site were collected over a three months’ period, from 1 January to 31 March 2018. The data set of this study consists of 1,149,690 desktop queries, 2,346,938 mobile queries, 2,481,747 desktop browsing activities and 2,550,309 mobile browsing activities. This study quantitatively analyses transaction log of 1300K site.

Findings

The results of this study show that mobile usage is higher than desktop usage: there are more mobile sessions than desktop sessions and the number of mobile queries is more than double of desktop queries. Overall, mobile query search behaviours are more simple, targeted and focused than desktop query search behaviours. Also, mobile browsing behaviours are more simple and passive than desktop browsing behaviours. However, mobile click behaviours are more active than desktop click behaviours.

Originality/value

To the best of the authors’ knowledge, this study appears to be the first of its type in Korea that compared search behaviours of a large number of users on desktop computers and mobile phones. To identify various characteristics of user search behaviours, this study analyses users’ directory browsing behaviour and click behaviour as well as query search behaviour. The results of this study can be implemented to address the effective improvement and development of search services and interfaces for different devices.

Details

The Electronic Library , vol. 39 no. 2
Type: Research Article
ISSN: 0264-0473

Keywords

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