Profiling Subscribers According to Their Internet Usage Characteristics and Behaviors (original) (raw)

Characterizing ADSL customer behaviours by network traffic data-mining

Annals of Telecommunications Annales Des Telecommunications, 2007

The very rapid adoption of new applications by some segments of the ADSL customers may have a strong impact on the quality of service delivered to all customers. This makes the segmentation of ADSL customers according to their network usage a critical step both for a better understanding of the market and for the prediction and dimensioning of the network. Relying on a "bandwidth only" perspective to characterize network customer behaviour does not allow the discovery of usage patterns in terms of applications. In this paper, we shall describe how data mining techniques applied to network measurement data can help to extract qualitative and quantitative knowledge.

Characterizing broadband user behavior

Proceedings of the 2004 ACM workshop on Next-generation residential broadband challenges - NRBC '04, 2004

This paper presents a characterization of broadband user behavior from an Internet Service Provider standpoint. Users are broken into two major categories: residential and Small-Office/Home-Office (SOHO). For each user category, the characterization is performed along four criteria: (i) session arrival process, (ii) session duration, (iii) number of bytes transferred within a session and (iv) user request patterns.

Classifying web search queries to identify high revenue generating customers

Journal of the American Society for Information Science and Technology, 2012

Traffic from search engines is important for most online businesses, with the majority of visitors to many websites being referred by search engines. Therefore, an understanding of this search engine traffic is critical to the success of these websites. Understanding search engine traffic means understanding the underlying intent of the query terms and the corresponding user behaviors of searchers submitting keywords. In this research, using 712,643 query keywords from a popular Spanish music website relying on contextual advertising as its business model, we use a k-means clustering algorithm to categorize the referral keywords with similar characteristics of onsite customer behavior, including attributes such as clickthrough rate and revenue. We identified 6 clusters of consumer keywords. Clusters range from a large number of users who are low impact to a small number of high impact users. We demonstrate how online businesses can leverage this segmentation clustering approach to provide a more tailored consumer experience. Implications are that businesses can effectively segment customers to develop better business models to increase advertising conversion rates.

Traffic analysis and characterization of Internet user behavior

International Congress on Ultra Modern Telecommunications and Control Systems, 2010

Internet usage has changed, and the demands on the broadband access networks have increased, both regarding bandwidth and QoS. Characterizing the traffic, as seen by a broadband access network, can help understanding both the demands of today and the demands of tomorrow. In this paper we analyze traffic measurements from a Swedish municipal broadband access network and derive corresponding user behavior models. The paper focuses on Internet usage in terms of traffic patterns, volumes and applications. Also, user activity characteristics, as session lengths and traffic rate distributions, are analyzed and modeled. Notably, the resulting models for user session lengths turn out different than traditionally assumed.

Estimating Internet users' demand characteristics

2001

We investigate two parametric approaches and one non-parametric approach to estimating Internet users' value-of-time, an important characteristic of demand for Internet services. The advantages of these approaches are made clear and their limitations discussed. The models are tested with data generated from our similation model of the Internet economy. Given the characteristics of the data, we investigate parametric count-data models first. While reasonably good results are obtained on all medium-and large-sized jobs, the method fails on small-sized ones. Second, we develop a nonparametric quasi-Bayesian update algorithm for retrieving the underlying distribution function of Internet users' value-of-time purely from observations on their choices. Compared with the parametric count-data models, good results are obtained in every case.

An Intelligent Model for Internet Advertising Selection Based on User-Profile

International Journal of Engineering and Technology

With the growth of using the internet advertising, display error rate has been subsequently increased. As an instance of display error rate, it can be referred to advertisement inappropriate to user demand of modifying wrong advertising display. The most important problem related to marketing and advertising is to absolutely consider advertising true or false. To cope with such a problem, personalized advertising is made with respect to users' profile and behavior in order that accurate internet advertising is selected, and each user receives her/his favorite internet advertising. In this study, we presented a new profile with the internet advertising in an online bookstore to students and gathered their responses. Then, we used decision tree in data mining applications and modeled two separated datasets in two states of with a profile and without a profile. The results obtained for both datasets revealed that users profile can highly influence proper classification of the internet advertising.

Enabling customer relationship management in ISP services through mining usage patterns

Expert Systems with Applications, 2006

The monopoly of state ownership of telecommunication industry in Taiwan was lifted in 1997. In choosing an ISP, pricing was and still is a main differentiating factor in the mind of customers; however, service quality has emerged as a major concern among users lately. Management of ISP has discovered that service quality is important not only for attracting new customers, but, more importantly, for retaining existing customers who may otherwise be lured away by lower fees. Hence, it is essential to develop a CRM system, which could help keeping existing customers and exploring further business opportunities at the same time. In this study, we, based on the IP traffic data, developed a CRM systematic approach for a major ISP company in Taiwan to enhance customers' longer-term loyalty. This approach employs CRISP-DM methodology, and applies Attribute-Oriented Induction as the mining technique to discover network usage behaviors of customers, which help management identify usage pattern and also pinpoint the time when usage is excessively heavy. The former allows management to make effective personal calls for services or maintenance, and the latter presents opportunities for management to offer personalized cares and advanced products. Pixel-oriented visualization is applied to improve the understanding of mining results. q

Cluster Analysis of Internet Users Based on Hourly Traffic Utilization

Ieice Transactions, 2007

Internet access traffic follows hourly patterns that depend on various factors, such as the periods users stay in the access point (e.g. at home or in the office) or their preferences for applications. The clustering of Internet users may provide important information for traffic engineering and tariffing. For example, it can be used to set up service differentiation according to hourly behavior, resource optimization based on multi-hour routing and definition of tariffs that promote Internet access in low busy hours.

Consumption Behavior Analysis of Over The Top Services: Incremental Learning or Traditional Methods?

IEEE Access, 2019

Network monitoring and analysis of consumption behavior are important aspects for network operators. The information obtained about consumption trends allows to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over The Top applications are known by their large consumption of network resources. Service degradation is a common mechanism that applies limits to the amount of information that can be transferred and it is usually applied in a generalized way, affecting the performance of applications consumed by users while leaving aside their behavior and preferences. With this in mind, a proposal of personalizing service degradation policies applied to users has been considered through data mining and traditional machine learning. However, such approach is incapable of considering the swift changes a user can present in their consumption behavior over time. In order to observe which approach is capable of a continuous model adaptation while maintaining their usefulness over time, this paper introduces a performance comparison of traditional and incremental machine learning algorithms applied to information about users' Over The Top consumption behavior. Two datasets are implemented for the tests: the first one is built through a real network experiment holding 1,581 instances, and the second one holds 150,000 instances generated in a synthetic way. After analyzing the obtained results, the best algorithm from the traditional approach was a Support Vector Machine while the best classifier from the incremental approach was an ensemble method composed by Oza Bagging and the K-Nearest Neighbor algorithm.

Broadband User Behavior Characterization

Handbook of Research on Global Diffusion of Broadband Data Transmission

This chapter presents a broadband user behavior characterization from an Internet service provider standpoint. Understanding these user behavior patterns is important to the development of more efficient applications for broadband users. Our characterization divides the users into two categories: residential and small-office/home-office (SOHO). It employs four characterization criteria: session arrival process, session duration, number of bytes transferred within a session, and user request patterns. Our results show that both residential and SOHO session interarrival times are exponentially distributed, and point out that a typical SOHO user session is longer and transfers a larger volume of data. Our analysis also uncovers two main groups of session request patterns within each user category: (i) sessions that comprise traditional Internet services, such as WWW services, e-mail, and instant messenger, and (ii) sessions that comprise peer-to-peer file sharing applications, basicall...