Clickstream data are defined as the electronic record of Internet usage collected by Web servers or third-party services. The authors discuss the nature of clickstream data, noting key strengths and ...limitations of these data for research in marketing. The paper reviews major developments from the analysis of these data, covering advances in understanding (1) browsing and site usage behavior on the Internet, (2) the Internet's role and efficacy as a new medium for advertising and persuasion, and (3) shopping behavior on the Internet (i.e., electronic commerce). The authors outline opportunities for new research and highlight several emerging areas likely to grow in future importance. Inherent limitations of clickstream data for understanding and predicting the behavior of Internet users or researching marketing phenomena are also discussed.
Using clickstream panel data from an automobile ad campaign conducted on a mobile platform, we investigate the relevance of mobile advertising, the interrelationships between ad content, information ...search behavior, and advertising response. Temporally, we compare mobile users' search behavior and advertising response before and during a focal campaign event of an automobile show. Spatially, we examine their search behavior and advertising response in relation to their proximity to the show's location. Estimation results from individual-user random effects binary Logit and Poisson count models show that users' responses to mobile advertising are related to the depth and breadth of search and the ad content. While informative and persuasive ad content exhibits differential non-linear effects on the depth and breadth of search, they have similar effects on advertising response. Interestingly, spatial and temporal proximity of mobile ad campaigns may not lead to increased relevance of mobile campaigns; it depends on the type of ad content and the type of measure used to assess relevance.
•Informative and persuasive content exhibits different non-linear effects on the depth and breadth of information search.•Informative and persuasive content has a similar U-shaped influence on advertising response.•Increased depth and breadth of search are associated with higher propensities to respond to mobile advertisements.•Spatial and temporal proximity of mobile ad campaigns may not lead to increased relevance of mobile campaigns.•The relevance of mobile campaigns depends on the type of ad content and the type of measure used to assess it.
Online learning with the characteristics of flexibility and autonomy has become a widespread and popular mode of higher education in which students need to engage in self-regulated learning (SRL) to ...achieve success. The purpose of this study is to utilize clickstream data to reveal the time management of SRL. This study adopts learning analytics to investigate the differences in time management (time investment and time use patterns) in a large-scale authentic online learning environment based on 8019 students' clickstream data of over one term recorded by the starC system log. This study quantitatively reveals the SRL process in a higher education online learning environment, which presents the detailed differences in time management among students with different academic performance categories. These research results will have inspirations in the design of SRL interventions for optimizing students' learning processes and overall achievement.
•We assess the applicability of graph metrics to predict purchase probabilities.•Real-world clickstream data of two online retailers is used.•Graphs are derived out of sessions of website ...visitors.•Distance- and centrality-based graph metrics are useful for prediction.•Closeness vitality, radius, number of circles and self-loops are most important.
The prediction of online user behavior (next clicks, repeat visits, purchases, etc.) is a well-studied subject in research. Prediction models typically rely on clickstream data that is captured during the visit of a website and embodies user agent-, path-, time- and basket-related information. The aim of this paper is to propose an alternative approach to extract auxiliary information from the website navigation graph of individual users and to test the predictive power of this information. Using two real-world large datasets of online retailers, we develop an approach to construct within-session graphs from clickstream data and demonstrate the relevance of corresponding graph metrics to predict purchases.
Mining Clickstream Patterns Using IDLists Huynh, Huy M.; Nguyen, Loan T. T.; Vo, Bay ...
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC),
2019-Oct.
Conference Proceeding
To date, there remains a lack of works that focus on the problem of mining clickstream patterns. Although it is an alternative to use the general algorithms for sequential pattern mining to mine ...clickstreams, their performance may suffer and the resources needed are more than necessary. In this paper, we present a novel data structure, called index-IDList, that is suitable for clickstream pattern mining. Based on this data structure, we present a vertical format algorithm named CUI (Clickstream pattern mining Using Index-IDList). The experiments are carried out on four real-life clickstream databases and the results show that our proposed method is effective and efficient in terms of runtimes and memory consumption.
Sequential pattern mining in general and one particular form, clickstream pattern mining, are data mining topics that have recently attracted attention due to their potential applications of ...discovering useful patterns. However, in order to provide them as real-world service applications, one issue that needs to be addressed is that traditional algorithms often view databases as static, although in practice databases often grow over time and invalidate parts of the previous results after updates, forcing the algorithms to rerun from scratch on the updated databases to obtain updated frequent patterns. This can be inefficient as a service application due to the cost in terms of resources, and the returning of results to users can take longer when the databases get bigger. The response time can be shortened if the algorithms update the results based on incremental changes in databases. Thus, we propose PF-CUP (pre-frequent clickstream mining using pseudo-IDList), an approach towards incremental clickstream pattern mining as a service. The algorithm is based on the pre-large concept to maintain and update results and a data structure called a pre-frequent hash table to maintain the information about patterns. The experiments completed on different databases show that the proposed algorithm is efficient in incremental clickstream pattern mining.
Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users' online activities. Therefore, firms have been either developing or utilizing ...social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users' incidence and time spent. Drawing from the PageRank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend; holiday; time of day; and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users' incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers' path navigational clickstream data from the parent social commerce platform.
Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required ...for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added into each node element in both the SPPC-tree and C-Lists to quickly prune nonclosed clickstream. Experimental results conducted on several datasets show that the proposed method is better than the previous techniques and improves the runtime and memory usage in most cases, especially when using low minimum support thresholds on the huge databases.
You, the Web, and Your Device Vassio, Luca; Drago, Idilio; Mellia, Marco ...
ACM transactions on the web,
11/2018, Volume:
12, Issue:
4
Journal Article
Peer reviewed
Open access
Understanding how people interact with the web is key for a variety of applications, e.g., from the design of effective web pages to the definition of successful online marketing campaigns. Browsing ...behavior has been traditionally represented and studied by means of
clickstreams
, i.e., graphs whose vertices are web pages, and edges are the paths followed by users. Obtaining large and representative data to extract clickstreams is, however, challenging.
The evolution of the web questions whether browsing behavior is changing and, by consequence, whether properties of clickstreams are changing. This article presents a longitudinal study of clickstreams from 2013 to 2016. We evaluate an anonymized dataset of HTTP traces captured in a large ISP, where thousands of households are connected. We first propose a methodology to identify actual URLs requested by users from the massive set of requests automatically fired by browsers when rendering web pages. Then, we characterize web usage patterns and clickstreams, taking into account both the temporal evolution and the impact of the device used to explore the web. Our analyses precisely quantify various aspects of clickstreams and uncover interesting patterns, such as the typical short paths followed by people while navigating the web, the fast increasing trend in browsing from mobile devices, and the different roles of search engines and social networks in promoting content.
Finally, we contribute a dataset of anonymized clickstreams to the community to foster new studies.<sup;>1</sup;>
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