Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

Thorleuchter, Dirk; Poel, Dirk Van den; Prinzie, Anita

doi:10.1016/j.eswa.2011.08.115

Cited by 48 publications

(47 citation statements)

References 36 publications

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“…Thorleuchter, Van den Poel and Prinzie [22] used Web TM. They analyzed the customers of a large German business-to-business mail-order company.…”

Section: Related Researchmentioning

confidence: 99%

Mining Customer Feedback Documents

Stoica¹,

Özyirmidokuz²

2015

IJKE

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Abstract-Managing customer feedback data has become a necessity for firms in order for them to gain competitive advantage in the sector. Analyzing customer complaints' data to find useful information that's hidden is an important step in understanding customers. This important, hidden knowledge must be extracted automatically to allow firms to gain a better understanding of the general market and of their own and their competitors' customers. A firm can learn the needs of customers and show how its products and services satisfy these needs by analyzing these documents.The aim of this research is to summarize and extract data from unstructured customer feedback documents which are about ignoring subscriptions to a telecommunication firm in Turkey. The data are transformed to a collection of documents by generating a document for each record. Text processing techniques are applied. Cosine similarity analysis is used to classify documents into relevant categories. Clusters are determined.

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“…Thorleuchter, Van den Poel and Prinzie [22] used Web TM. They analyzed the customers of a large German business-to-business mail-order company.…”

Section: Related Researchmentioning

confidence: 99%

Mining Customer Feedback Documents

Stoica¹,

Özyirmidokuz²

2015

IJKE

View full text Add to dashboard Cite

show abstract

“…The vector components consist of weighted term frequencies [13]. All vectors are used to create a term-by-war log matrix A. NMF is used to find two nonnegative matrices U and V where the product of U and V provides a good approximation to A and where the rank is reduced from r to k. The selection of k is critical [7]. If k is too large then too many column vectors of U exists that represent many irrelevant or unimportant latent semantic textual information.…”

Section: Methodsmentioning

confidence: 99%

“…This makes the results of NMF more comprehensible for a human expert than results of other matrix factorization techniques for text mining e.g. Singular Value Decomposition (SVD) [7]. Based on a term-bywar log matrix of weighted term frequencies, NMF factorizes this m x n matrix A = [a ij ] into two non-negative matrices: U = [u ij ] and V = [v ij ] with m the number of war logs, n the number of different terms, and r = rank(A) ≤ min(m,n).…”

Section: Introductionmentioning

confidence: 99%

Using NMF for Analyzing War Logs

Thorleuchter

Poel

2012

Communications in Computer and Information Science

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Abstract. We investigate a semi-automated identification of technical problems occurred by armed forces weapon systems during mission of war. The proposed methodology is based on a semantic analysis of textual information in reports from soldiers (war logs). Latent semantic indexing (LSI) with non-negative matrix factorization (NMF) as technique from multivariate analysis and linear algebra is used to extract hidden semantic textual patterns from the reports. NMF factorizes the term-by-war log matrix -that consists of weighted term frequencies -into two non-negative matrices. This enables natural parts-based representation of the report information and it leads to an easy evaluation by human experts because human brain also uses parts-based representation. For an improved research and technology planning, the identified technical problems are a valuable source of information. A case study extracts technical problems from military logs of the Afghanistan war. Results are compared to a manual analysis written by journalists of 'Der Spiegel'. Keywords. Non-negative matrix factorization, NMF, Text Mining IntroductionWar logs written by soldiers during mission of war are a valuable source of information. They indicate e.g. technical problems occurred by armed forces weapon systems in use. Considering some of these problems in current research and technology (R&T) projects may be necessary for an increase reliability of future weapon systems. Thus, extracting this feedback from war logs is an important task in R&T planning. We provide a methodology for a semi-automated identification of technical problems in soldiers' war logs. A manual identification of these problems e.g. by human experts is not possible because of the large amount of the logs. Although war logs describe the events of the war, technical problems are just a part of the content e.g. an event is described in detail and besides the malfunction of a weapon system during that event is also mentioned. A frequently occurred malfunction of a specific system in different events can be discovered by identifying the underlying (hidden) semantic textual patterns from the collection of war logs because different soldiers formulize malfunctions by using different words. This excludes the use of text classification

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“…com. Thorleuchter, et al [4] conducted an investigation into predicting profitable new customers based on textual information from customer's websites within a B2B scope. In another study, Thorleuchter, et al [5] conducted the identification of success factors of e-commerce and their impact on the success of trade transactions between companies.…”

Section: Introductionmentioning

confidence: 99%

Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining

2018

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Abstract. Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in ecommerce products by identifying those products that prompt visitor interest.

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Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

Cited by 48 publications

References 36 publications

Mining Customer Feedback Documents

Mining Customer Feedback Documents

Using NMF for Analyzing War Logs

Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining

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