A methodology for the evaluation of high response time on E-commerce users and sales

Poggi, Nicolás; Carrera, David; Gavaldà, Ricard; Ayguadé, Eduard; Torres, Jordi

doi:10.1007/s10796-012-9387-4

Cited by 29 publications

(28 citation statements)

References 12 publications

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“…To remain competitive, online retailers need to adapt in an agile, non-structured way, resulting in large, unstructured websites and rapidly changing server resource demands [14]. Moreover, Conversion Rates (CR), the fraction of users that reach a certain goal, such as buying a product on the site, are decreasing: less than 2% of visits result in a purchase on most sites [14].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Conversion Rates (CR), the fraction of users that reach a certain goal, such as buying a product on the site, are decreasing: less than 2% of visits result in a purchase on most sites [14]. A low CR is influenced by factors including affiliation programs, changes in user habits such as comparing different sites at the same time [15], and meta-crawling.…”

Section: Introductionmentioning

confidence: 99%

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Business Process Mining from E-Commerce Web Logs

Poggi

Muthusamy²,

Carrera

et al. 2013

Lecture Notes in Computer Science

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Abstract. The dynamic nature of the Web and its increasing importance as an economic platform create the need of new methods and tools for business efficiency. Current Web analytic tools do not provide the necessary abstracted view of the underlying customer processes and critical paths of site visitor behavior. Such information can offer insights for businesses to react effectively and efficiently. We propose applying Business Process Management (BPM) methodologies to e-commerce Website logs, and present the challenges, results and potential benefits of such an approach.We use the Business Process Insight (BPI) platform, a collaborative process intelligence toolset that implements the discovery of looselycoupled processes, and includes novel process mining techniques suitable for the Web. Experiments are performed on custom click-stream logs from a large online travel and booking agency. We first compare Web clicks and BPM events, and then present a methodology to classify and transform URLs into events. We evaluate traditional and custom process mining algorithms to extract business models from real-life Web data. The resulting models present an abstracted view of the relation between pages, exit points, and critical paths taken by customers. Such models show important improvements and aid high-level decision making and optimization of e-commerce sites compared to current state-of-art Web analytics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Business Process Mining from E-Commerce Web Logs

Poggi

Muthusamy²,

Carrera

et al. 2013

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Following this work, the same authors introduce a methodology to determine how sales are affected when response times increase. This methodology can then be used in capacity planning studies 10 . For take capacity planning it is necessary to represent how users interact with web applications.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Performance evaluation studies need to include customer satisfaction as a main business metric 10 because dissatisfied customers represent lost sales and generate poor reputation, which finally result in economic losses. New indexes related to economic variables are required.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for Economic Evaluation of Cloud-Based Web Applications

Domènech

Peña-Ortiz

Gil

et al. 2016

Int. J. Info. Tech. Dec. Mak.

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World Scientific Publishing Domenech, J.; Peña Ortiz, R.; Gil, JA.; Pont Sanjuan, A. (2016) Cloud technology is an attractive infrastructure solution to optimize the scalability and performance of web applications. The workload of these applications typically fluctuates between peak and valley loads and sometimes in an unpredictable way. Cloud systems can easily deal with this fluctuation because they provide customers with an almost unlimited on-demand infrastructure capacity using a pay-per-use model, which enables Internet-based companies to pay for the actual consumption instead of peak capacity. In this paradigm, this paper links the business model of an Internet-based company to the performance evaluation of the infrastructure. To this end, the paper develops a new methodology for assessing the costs and benefits of implementing web-based applications in the cloud. Traditional performance models and indexes related to usage of the main system resources (such as processor, memory, storage, and bandwidth) have been reformulated to include new metrics (such as customer losses and service costs) that are useful for business managers. Additionally, the proposed methodology has been illustrated with a case study of a typical e-commerce scenario. Experimental results show that the proposed metrics enable Internet-based companies to estimate the cost of adopting a particular cloud configuration more accurately in terms of the infrastructure cost and the cost of losing customers due to performance degradation. Consequently, the methodology can be a useful tool to assess the feasibility of business plans.

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“…According to the results of earlier analyses for e-business workloads the share of robot requests has differed from several (3.2% in [17]) to a dozen or so (15% in [22], 16% in [23]) percent. In our data set 22.3% of all HTTP requests were identified as generated by bots (Fig.…”

Section: Bot Share In Overall Web Server Trafficmentioning

confidence: 99%

Analysis of Aggregated Bot and Human Traffic on E-Commerce Site

Suchacka¹

2014

Annals of Computer Science and Information Systems

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Abstract-A significant volume of Web traffic nowadays can be attributed to robots. Although some of them, e.g., searchengine crawlers, perform useful tasks on a website, others may be malicious and should be banned. Consequently, there is a growing need to identify bots and to characterize their behavior. This paper investigates the share of bot-generated traffic on an e-commerce site and studies differences in bots' and humans' session-based traffic by analyzing data recorded in Web server log files. Results show that both kinds of sessions reveal different characteristics, including the session duration, the number of pages visited in session, the number of requests, the volume of data transferred, the mean time per page, the number of images per page, and the percentage of pages with unassigned referrers.

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A methodology for the evaluation of high response time on E-commerce users and sales

Cited by 29 publications

References 12 publications

Business Process Mining from E-Commerce Web Logs

Business Process Mining from E-Commerce Web Logs

A Methodology for Economic Evaluation of Cloud-Based Web Applications

Analysis of Aggregated Bot and Human Traffic on E-Commerce Site

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