Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Effectiveness of Complaint Management in CRM System

Customer Relation Management (CMR) is a tool that has shown prospect even beyond customers’ retention to customers’ attraction. A focus on an aspect of CRM is important to retain customers and also attract customers. The aim of this study was to propose a Complaint Management System that could add more value to CRM in businesses. The research employed the use of secondary data and telephone interview. The study showed that there is a need to re-direct attention/focus in the area of complaint management, in order to facilitate and enhance intelligence in business. Keywords: Complaint Management, Customer Relation Management, Customers’ retentio

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

APPLICATION OF PREDICTIVE ANALYTICS IN CUSTOMER RELATIONSHIP MANAGEMENT: A LITERATURE REVIEW AND CLASSIFICATION

This study is aimed to provide a comprehensive literature review and a classification scheme for application of predictive analytics and tools in customer relationship management (CRM). The application of predictive analytics in CRM is an emerging trend. PA methods help to analyze and understand customer behaviors and acquire and retain customers and also maximize customer value. Thus it facilitates CRM decisions making and supports development of CRM strategies in a customer-centric economy. This paper is aimed to present a comprehensive review of literature related to application of predictive analytics in CRM published in both academic and practitioner journals between 2003 and 2013

ICT for external use in Croatian four- and five-star hotels

The Purpose – The objective of the paper is to examine the implementation of advanced technology and promotional supports in hotel companies and to compare their performance in four- and five-star hotels. More specifically, ICT for external use are analysed, i.e. customer relationship management (CRM), communication with customers, promotional supports, and online order receptions. Design/Methodology/Approach – The empirical research took place in 38 upscale (26 four and 12 five-star) hotels located in Croatia, in the regions of Dalmatia, Istria, and Kvarner, and in the city of Zagreb. The data were collected through a structured questionnaire administered mainly during personal interviews with hotel and marketing managers. The SPSS software was used for data analysis. Findings – Findings reveal a relatively high implementation of technology solutions, reaching promotional supports the highest scores. Four-star hotels perform significantly better than fivestar hotels in most of the ICT items. Originality – The study provides insights into the external role of ICT for upscale hotels from the standpoint of customer relationship management (CRM), communication with customers, promotional supports, and online order receptions as tools for enhancing hotel promotion. These can be used as a valuable source of information between academics and can be additionally useful to marketing managers in hotels that intend to find as more as innovative ways for hotel promotion

Vpliv organizacijskih dejavnikov na odnos do uporabe analitičnih orodij informacijske rešitve CRM

Background and Purpose: Information solutions for analytical customer relationship management CRM (aCRM IS) that include the use of analytical tools are becoming increasingly important, due organizations’ need for knowledge of their customers and the ability to manage big data. The objective of the research is, therefore, to determine how the organizations’ orientations (process, innovation, and technology) as critical organizational factors affect the attitude towards the use of the analytical tools of aCRM IS. Design/Methodology/Approach: To measure the orientation of the organization (process, innovation, and technology), we redesigned the existing scale, which was validated using exploratory factor analysis. In the next phase, we created a model by which we examined the impact of the organization’s stance in relation to the use of the analytical tools of aCRM IS, where we used multiple regression analysis. The model was verified on a sample of Slovenian Organizations (n=105), which use the analytical tools of aCRM IS for analyzing the data they have on their customers and business partners. Results: In the study we found that all critical factors of the organization, specifically process, technology, and innovation orientation, have a positive impact on the attitudes towards using the analytical tools of aCRM IS. Innovation orientation is particularly important and has the strongest influence on the attitude towards using the analytical tools of aCRM IS. We found that innovation orientation on new ideas, methods, and approaches has the strongest effect, followed by the impact of innovation orientation on acceptance of novelty. Conclusion: The more innovation-, technology-, and process-oriented organizations are, the more positive their attitude towards using the analytical tools of aCRM IS. The study is particularly important for organizations that are introducing an aCRM IS into their business system.Ozadje in namen: Informacijska rešitev analitičnega upravljanja s strankami (aCRM IR) zajema med drugim uporabo analitičnih orodij, ki zaradi potrebe organizacij po znanju strank in upravljanju masivnih podatkov, pridobivajo na vse večji veljavi. Zato je bil cilj raziskave ugotoviti, kako naravnanost organizacije (procesna, inovativna in tehnološka) kot kritični organizacijski dejavnik vpliva na odnos do uporabe analitičnih orodij aCRM IR. Zasnova/metodologija/pristop: Za merjenje naravnanosti organizacije (procesna, inovativna in tehnološka) smo preoblikovali obstoječe merilne lestvice, ki smo jih validirali s pomočjo eksploratorne faktorske analize. V naslednji fazi smo oblikovali model, s katerim smo preverjali vpliv naravnanosti organizacije na odnos do uporabe a analitičnih orodij aCRM IR, pri čemer smo uporabili multiplo regresijsko analizo. Model smo preverjali na vzorcu slovenskih organizacij (n=105), ki za analiziranje podatkov o strankah in poslovnih partnerjih uporabljajo analitična orodja. Rezultati: V študiji smo ugotovili, da imajo vsi trije vidiki naravnanosti organizacije, procesna, tehnološka in inovativna naravnanost, pozitivni vpliv na odnos do uporabe analitičnih orodij aCRM IR v organizaciji. Še zlasti je pomembna inovativna naravnanost organizacije, ki ima najmočnejši vpliv na odnos do uporabe analitičnih orodij aCRM IR. Inovativno naravnost je dvodimenzionalni konstrukt. Ugotovili smo, da ima inovativna naravnanost na nove ideje, metode in pristope največji vpliv na odnos do uporabe analitičnih orodij aCRM IR, sledi pa inovativna naravnanost na sprejem novosti v organizaciji. Zaključek: Bolj kot so organizacije inovativno, tehnološko in procesno naravnane, bolj pozitivni odnos imajo v povprečju do uporabe analitičnih orodij aCRM IR. Študija je še posebej pomembna za organizacije, ki vpeljujejo aCRM IR v poslovni sistem

Customer Portfolio Analysis Using the SOM

In order to compete for profitable customers, companies are looking to add value using Customer Relationship Management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analyzing the profitability of customers. This study was made for a case organization, who wanted to identify their profitable and unprofitable customers, in order to gain knowledge on how to develop their marketing strategies. Data about the customers were gathered from the case organization’s own database. The Self-Organizing Map (SOM) was used to divide the customers into segments, which were then analyzed in light of product sales information