Relevant bibliographies by topics / Classification of customers

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Classification of customers'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Classification of customers"

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Gholami, Atena, Reza Sheikh, Neda Mizani, and Shib Sankar Sana. "ABC analysis of the customers using axiomatic design and incomplete rough set." RAIRO - Operations Research 52, no. 4-5 (October 2018): 1219–32. http://dx.doi.org/10.1051/ro/2018022.

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Customer’s recognition, classification, and selecting the target market are the most important success factors of a marketing system. ABC classification of the customers based on axiomatic design exposes the behavior of the customer in a logical way in each class. Quite often, missing data is a common occurrence and can have a significant effect on the decision- making problems. In this context, this proposed article determines the customer’s behavioral rule by incomplete rough set theory. Based on the proposed axiomatic design, the managers of a firm can map the rules on designed structures. This study demonstrates to identify the customers, determine their characteristics, and facilitate the development of a marketing strategy.
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Moudani, Walid, Grace Zaarour, and Félix Mora-Camino. "Fuzzy Classification of Customer Insolvency in Mobile Telecommunication." International Journal of Decision Support System Technology 6, no. 3 (July 2014): 1–29. http://dx.doi.org/10.4018/ijdsst.2014070101.

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This paper proposes a predictive model to handle customer insolvency in advance for large mobile telecommunication companies for the purpose of minimizing their losses while preserving an overall satisfaction of the customers which may have important consequences on the quality and on the consume return of the operations. A new mathematical formulation taking into consideration a set of business rules and the satisfaction of the customers is proposed. However, the customer insolvency is defined to be a classification problem since our main purpose is to categorize the customer in one of the two classes: potentially insolvent or potentially solvent. Therefore, a model with precise business prediction using the knowledge discovery and Data Mining techniques on an enormous heterogeneous and noisy data is proposed. A fuzzy approach to evaluate and analyze the customer behavior leading to segment them into groups that provide better understanding of customers is developed. These groups with many other significant variables feed into a classification algorithm based on Rough fuzzy Sets technique to classify the customers. A real case study is considered here, followed by analysis and comparison of the results for the reason to select the best classification model that maximizes the accuracy for insolvent customers and minimizes the error rate in the misclassification of solvent customers.
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Xu, Yong, Jian Liu, Baomei Ma, and Shuai Zhang. "Service Mechanism and Pricing Based on Fairness Preference of Customers in Queuing System." Journal of Systems Science and Information 6, no. 6 (December 10, 2018): 481–94. http://dx.doi.org/10.21078/jssi-2018-481-14.

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AbstractService providers often adopt the mechanism of customer classification due to the heterogeneity of customer waiting cost. However, the classification service may cause unfairness feeling of regular customers, then affect the revenue and social welfare. This paper provides the first exact analysis about the situation that service providers offer two classes of non-preemptive priority service when customer fairness perception is explicitly modeled. We model customer fairness perception as a negative utility on regular customers that’s proportional to the waiting time difference between the two queues. By analyzing a stylized M/M/1 queue in monopoly service system, we can derive important results some of which reaffirm existed research results. First, from the perspective of revenue maximization, service providers should also adopt the mechanism of customer classification and set up the two kinds of customers where they can see each other. Next, considering customer utility maximization, service providers should cancel the mechanism of customer classification, and keep one queue (regular customers) only. Then, from the perspective of social welfare maximization, service providers should also adopt the mechanism of customer classification but set up the two kinds of customers where they cannot feel each other. Finally, this paper concludes the optimal pricing based on customer classification in the above three different perspectives. This research shows important reference value and practical significance for service providers who adopt the mechanism of classification service.
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Du, Laihong, Hua Chen, Yadong Fang, Xiaowei Liang, Yujie Zhang, Yidan Qiao, and Zhen Guo. "Research on the Method of Acquiring Customer Individual Demand Based on the Quantitative Kano Model." Computational Intelligence and Neuroscience 2022 (April 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/5052711.

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In order to realize accurate marketing by analyzing customer individual demand, a new quantitative Kano model method is put forward, and it is helpful to provide customized products for heterogeneous customer classification groups. By improving the traditional Kano model, the customer satisfaction and the importance degree of products are defined, and the quantitative Kano demand model is established. Customers are classified as the price preference group, the brand preference group, and the service priority group, and decision-making of product attribute quality improvement for customer classification is realized. Lastly, electric vehicles (EVs) are selected as a study case, and their various demands for different classifications of customers are discussed by questionnaire survey and calculation of satisfaction and the importance degree. Furthermore, different customer group demands are classified as attractive demands, expected demands, nondifferential demands, or essential demands, and the important product attribute acquisition process for various customers is discussed to improve enterprise market competitiveness.
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Shen, Anyuan. "Recommendations as personalized marketing: insights from customer experiences." Journal of Services Marketing 28, no. 5 (August 5, 2014): 414–27. http://dx.doi.org/10.1108/jsm-04-2013-0083.

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Purpose – The purpose of this paper is an exploratory study of customers’ “lived” experiences of commercial recommendation services to better understand customer expectations for personalization with recommendation agents. Recommendation agents programmed to “learn” customer preferences and make personalized recommendations of products and services are considered a useful tool for targeting customers individually. Some leading service firms have developed proprietary recommender systems in the hope that personalized recommendations could engage customers, increase satisfaction and sharpen their competitive edge. However, personalized recommendations do not always deliver customer satisfaction. More often, they lead to dissatisfaction, annoyance or irritation. Design/methodology/approach – The critical incident technique is used to analyze customer satisfactory or dissatisfactory incidents collected from online group discussion participants and bloggers to develop a classification scheme. Findings – A classification scheme with 15 categories is developed, each illustrated with satisfactory incidents and dissatisfactory incidents, defined in terms of an underlying customer expectation, typical instances of satisfaction and dissatisfaction and, when possible, conditions under which customers are likely to have such an expectation. Three pairs of themes emerged from the classification scheme. Six tentative research propositions were introduced. Research limitations/implications – Findings from this exploratory research should be regarded as preliminary. Besides, content validity of the categories and generalizability of the findings should be subject to future research. Practical implications – Research findings have implications for identifying priorities in developing algorithms and for managing personalization more strategically. Originality/value – This research explores response to personalization from a customer’s perspective.
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G. Inyang, Udoinyang, Okure O. Obot, Moses E. Ekpenyong, and Aliu M. Bolanle. "Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification." Modern Applied Science 11, no. 9 (August 30, 2017): 151. http://dx.doi.org/10.5539/mas.v11n9p151.

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Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.
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Palaniappan, Shamala, Aida Mustapha, Cik Feresa Mohd Foozy, and Rodziah Atan. "Customer Profiling using Classification Approach for Bank Telemarketing." JOIV : International Journal on Informatics Visualization 1, no. 4-2 (November 15, 2017): 214. http://dx.doi.org/10.30630/joiv.1.4-2.68.

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Telemarketing is a type of direct marketing where a salesperson contacts the customers to sell products or services over the phone. The database of prospective customers comes from direct marketing database. It is important for the company to predict the set of customers with highest probability to accept the sales or offer based on their personal characteristics or behavior during shopping. Recently, companies have started to resort to data mining approaches for customer profiling. This project focuses on helping banks to increase the accuracy of their customer profiling through classification as well as identifying a group of customers who have a high probability to subscribe to a long term deposit. In the experiments, three classification algorithms are used, which are Naïve Bayes, Random Forest, and Decision Tree. The experiments measured accuracy percentage, precision and recall rates and showed that classification is useful for predicting customer profiles and increasing telemarketing sales.
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Fu, Ze, Bo Zhang, Lingjun Ou, Kaiyang Sun, Xinyi Sun, and Ningyan Chen. "Research on Enterprise Financial Customer Classification Method and Preference Based on Intelligent Algorithm." Wireless Communications and Mobile Computing 2021 (November 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/6585486.

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Compared with the past questionnaire survey, this paper applies the intelligent algorithm developed rapidly in recent years to identify the tendency of customers to buy financial products in the market. In addition, for the single state customer classification indicators based on the previous demographic information and action information, it is proposed to combine the action of market activities with demographic information; that is, the static integrated customer classification index is further combined with the improved neural network model to study the classification and preference of enterprise financial customers. Firstly, the enterprise financial customer classification model based on neural network algorithm is studied. Aiming at the shortcomings of easy falling into the local optimal solution of neural network algorithm, slow convergence speed of algorithm, and difficult setting of network structure, combined with the characteristics of genetic algorithm, the concept of adaptive genetic neural network algorithm is proposed. Then, the design of adaptive genetic neural network model is studied. Secondly, combined with the customer data of a financial enterprise and the characteristics of enterprise finance, this paper analyzes the risk influencing factors of enterprise financial customers, analyzes the customer data, evaluates the enterprise financial customers through the adaptive genetic neural network model, and realizes the classification of enterprise financial customers. Through an example, it is proved that the enterprise financial customer classification and preference model based on the adaptive genetic neural network algorithm discussed in this paper has better customer classification accuracy and can provide better method support for enterprise financial customer management.
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Thammaboosadee, Sotarat, Benjathip Chinomi, and Ehab Mohamed. "A Two-Stage Customer Journey Analytical Model in Single House Business." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 2 (September 8, 2020): 202–12. http://dx.doi.org/10.37936/ecti-cit.2020142.240239.

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The single housing industry is currently experiencing a continuous expansion in demand for housing. Addressing the needs of different customer groups is the key to increasing the rate of sales conversion. The objective of this research is to propose a two-stage single house customer journey analytical model that consists of two stages. The first stage concerns the customer journey between registration and reservation process and the second one identifies the customer loyalty from reservation to transfer stage. The four classification data mining techniques have experimented. The experiments include the accuracy and F-Measure in comparison and also perform the statistical testing. The Artificial Neural Network was the most accurate model for both stages. This model analyzes the probability of the customer progressing through the stages to the conclusion of purchase by learning the customer’s characteristics and the factors involved in the customer's decision. The model displays the reservation and transfer result for customers who have achieved the respective reservation and transference steps according to their registration profile. Experiments showed that the proposed two-stage models could predict customer loyalty, thereby enhancing relationship management between customers and organizations. It also confers a competitive advantage within the industry.
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Noura, Abdaoui, Hadj Khalifa Ismahène, and Faiz Sami. "Process of Personalizing the Ubiquitous Advertisements." International Journal of Knowledge Society Research 8, no. 2 (April 2017): 13–35. http://dx.doi.org/10.4018/ijksr.2017040102.

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In this paper, we propose an approach combining behavioral and targeting techniques for a better reaction of the customer with a star product using a personalized ubiquitous advertisement. We use the clustering to study the customer's behavior and the association rules to estimate the probability of star product's purchases in the near future. In order to validate our approach, we develop a prototype to send a personalized advertisement to loyal customers and potential customers in ubiquitous environment. Each target receives the advertising according his classification and his degree of loyalty obtained by the behavioral analysis. Loyal customers are the first to receive the personalized advertising in the ubiquitous environment.
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Dissertations / Theses on the topic "Classification of customers"

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CARVALHO, NORMA ALICE DA SILVA. "HYBRID INTELLIGENT SYSTEM FOR CLASSIFICATION OF NON-RESIDENTIAL ELECTRICITY CUSTOMERS PAYMENT PROFILES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33393@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
O objetivo desta pesquisa é classificar o perfil de pagamento dos consumidores não-residenciais de energia elétrica, considerando conhecimento armazenado em base de dados de distribuidoras de energia elétrica. A motivação para desenvolvê-la surgiu da necessidade das distribuidoras por um modelo de suporte a formulação de estratégias capazes de reduzir o grau inadimplência. A metodologia proposta consiste em um sistema inteligente híbrido composto por módulos intercomunicativos que usam conhecimentos armazenados em base de dados para segmentar consumidores e, então, atingir o objetivo proposto. O sistema inicia-se com o módulo neural, que aloca as unidades consumidoras em grupos conforme similaridades (valor fatura, consumo, demanda medida/demanda contratada, intensidade energética e peso da conta no orçamento), em sequência, o módulo bayesiano, estabelece um escore entre 0 e 1 que permite predizer o perfil de pagamento das unidades considerando os grupos gerados e os atributos categóricos (atividade econômica, estrutura tarifária, mesorregião, natureza jurídica e porte empresarial) que caracterizam essas unidades. Os resultados revelaram que o sistema proposto estabelece razoável taxa de acerto na classificação do perfil de consumidores e, portanto, constitui uma importante ferramenta de suporte a formulação de estratégias para combate à inadimplência. Conclui-se que, o sistema híbrido proposto apresenta caráter generalista podendo ser adaptado e implementado em outros mercados.
The objective of this research is to classify the non-residential electricity customer payment profiles regarding the knowledge stored in electricity distribution utilities databases. The motivation for development of the work from the need of electricity distribution by a support model to formulate strategies for tackling non-payment and late payment. The proposed methodology consists of a hybrid intelligent system constituted by intercommunicating modules that use knowledge stored in database to customer segmentation and then achieve the proposed objective. The system begins with the neural module, which allocates the consuming units in groups according to similarities (bill amount, consumption, measured demand/contracted demand, energy intensity and share of the electricity bill in the customer s income), in sequence, the Bayesian module establishes a score between 0 and 1 that allows to predict what payment profile of the units considering the generated groups and categorical attributes (business activity, tariff type, business size, mesoregion and company s legal form) that characterize these units. The results showed that the proposed system provides a reasonable success rate when classifying customer profiles and thus constitutes an important tool in the formulation of strategies for tackling non-payment and late payment. In conclusion, the hybrid system proposed here is a generalist one and could usefully be adapted and implemented in other markets.
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Kirkin, S., and K. V. Melnyk. "Intelligent Data Processing in Creating Targeted Advertising." Thesis, National Technical University "Kharkiv Polytechnic Institute", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/44710.

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Andersson, Martin, and Marcus Mazouch. "Binary classification for predicting propensity to buy flight tickets. : A study on whether binary classification can be used to predict Scandinavian Airlines customers’ propensity to buy a flight ticket within the next seven days." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160855.

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A customers propensity to buy a certain product is a widely researched field and is applied in multiple industries. In this thesis it is showed that using binary classification on data from Scandinavian Airlines can predict their customers propensity to book a flight within the next coming seven days. A comparison between logistic regression and support vector machine is presented and logistic regression with reduced number of variables is chosen as the final model, due to it’s simplicity and accuracy. The explanatory variables contains exclusively booking history, whilst customer demographics and search history is showed to be insignificant.
En kunds benägenhet att göra ett visst köp är ett allmänt undersökt område som applicerats i flera olika branscher. I den här studien visas det att statistiska binära klassificeringsmodeller kan användas för att prediktera Scandinavian Airlines kunders benägenhet att köpa en resa de kommande sju dagarna. En jämförelse är presenterad mellan logistisk regression och stödvektormaskin och logistisk regression med reducerat antal parametrar väljs som den slutgiltiga modellen tack vare sin enkelhet och träffsäkerhet. De förklarande variablerna är uteslutande bokningshistorik medan kundens demografi och sökdata visas vara insignifikant.
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Mazouch, Marcus, and Martin Andersson. "Binary classification for predicting propensity to buy flight tickets : A study on whether binary classification can be used to predict Scandinavian Airlines customers' propensity to buy a flight ticket within the next seven days." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-162412.

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A customers propensity to buy a certain product is a widely researched field and is applied in multiple industries. In this thesis it is showed that using binary classification on data from Scandinavian Airlines can predict their customers propensity to book a flight within the next coming seven days. A comparison between logistic regression and support vector machine is presented and logistic regression with reduced number of variables is chosen as the final model, due to it's simplicity and accuracy. The explanatory variables contains exclusively booking history, whilst customer demographics and search history is showed to be insignificant.
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Eriksson, Alexander, and Jacob Långström. "Comparison of Machine Learning Techniques when Estimating Probability of Impairment : Estimating Probability of Impairment through Identification of Defaulting Customers one year Ahead of Time." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160114.

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Probability of Impairment, or Probability of Default, is the ratio of how many customers within a segment are expected to not fulfil their debt obligations and instead go into Default. This is a key metric within banking to estimate the level of credit risk, where the current standard is to estimate Probability of Impairment using Linear Regression. In this paper we show how this metric instead can be estimated through a classification approach with machine learning. By using models trained to find which specific customers will go into Default within the upcoming year, based on Neural Networks and Gradient Boosting, the Probability of Impairment is shown to be more accurately estimated than when using Linear Regression. Additionally, these models provide numerous real-life implementations internally within the banking sector. The new features of importance we found can be used to strengthen the models currently in use, and the ability to identify customers about to go into Default let banks take necessary actions ahead of time to cover otherwise unexpected risks.
Titeln på denna rapport är En jämförelse av maskininlärningstekniker för uppskattning av Probability of Impairment. Uppskattningen av Probability of Impairment sker genom identifikation av låntagare som inte kommer fullfölja sina återbetalningsskyldigheter inom ett år. Probability of Impairment, eller Probability of Default, är andelen kunder som uppskattas att inte fullfölja sina skyldigheter som låntagare och återbetalning därmed uteblir. Detta är ett nyckelmått inom banksektorn för att beräkna nivån av kreditrisk, vilken enligt nuvarande regleringsstandard uppskattas genom Linjär Regression. I denna uppsats visar vi hur detta mått istället kan uppskattas genom klassifikation med maskininlärning. Genom användandet av modeller anpassade för att hitta vilka specifika kunder som inte kommer fullfölja sina återbetalningsskyldigheter inom det kommande året, baserade på Neurala Nätverk och Gradient Boosting, visas att Probability of Impairment bättre uppskattas än genom Linjär Regression. Dessutom medför dessa modeller även ett stort antal interna användningsområden inom banksektorn. De nya variabler av intresse vi hittat kan användas för att stärka de modeller som idag används, samt förmågan att identifiera kunder som riskerar inte kunna fullfölja sina skyldigheter låter banker utföra nödvändiga åtgärder i god tid för att hantera annars oväntade risker.
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Axén, Maja, and Jennifer Karlberg. "Binary Classification for Predicting Customer Churn." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171892.

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Predicting when a customer is about to turn to a competitor can be difficult, yet extremely valuable from a business perspective. The moment a customer stops being considered a customer is known as churn, a widely researched topic in several industries when dealing with subscription-services. However, in industries with non-subscription services and products, defining churn can be a daunting task and the existing literature does not fully cover this field. Therefore, this thesis can be seen as a contribution to current research, specially when not having a set definition for churn. A definition for churn, adjusted to DIAKRIT’s business, is created. DIAKRIT is a company working in the real estate industry, which faces many challenges, such as a huge seasonality. The prediction was approached as a supervised problem, where three different Machine Learning methods were used: Logistic Regression, Random Forest and Support Vector Machine. The variables used in the predictions are predominantly activity data. With a relatively high accuracy and AUC-score, Random Forest was concluded to be the most reliable model. It is however clear that the model cannot separate between the classes perfectly. It was also visible that the Random Forest model produces a relatively high precision. Thereby, it can be settled that even though the model is not flawless the customers predicted to churn are very likely to churn.
Att prediktera när en kund är påväg att vända sig till en konkurrent kan vara svårt, dock kan det visa sig extremt värdefullt ur ett affärsperspektiv. När en kund slutar vara kund benäms det ofta som kundbortfall eller ”churn”. Detta är ett ämne som är brett forskat på i flertalet olika industrier, men då ofta i situationer med prenumenationstjänster. När man inte har en prenumerationstjänst försvåras uppgiften att definera churn och existerande studier brister i att analysera detta. Denna uppsats kan därför ses som ett bidrag till nuvarande litteratur, i synnerhet i fall där ingen tydlig definition för churn existerar. En definition för churn, anpassad efter DIAKRIT och deras affärsstruktur har skapats i det här projektet. DIAKRIT är verksamma i fastighetsbranschen, en industri som har flera utmaningar, bland annat en extrem säsongsvariaton. För att genomföra prediktionerna användes tre olika maskininlärningamodeller: Logistisk Regression, Random Forest och Support Vector Machine. De variabler som användes är mestadels aktivitetsdata. Med relativt hög noggranhet och AUC-värde anses Random Forest vara mest pålitlig. Modellen kan dock inte separera mellan de två klasserna perfekt. Random Forest modellen visade sig också genera en hög precision. Därför kan slutsatsen dras att även om modellen inte är felfri verkar det som att kunderna predikterade som churn mest sannolikt kommer churna.
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Vallaud, Thierry. "Estimating potential customer value using customer data : using a classification technique to determine customer value /." Abstract and full text available, 2009. http://149.152.10.1/record=b3077978~S16.

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Thesis (M.S.) -- Central Connecticut State University, 2009.
Thesis advisor: Daniel Larose. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 37-39). Also available via the World Wide Web.
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Koch-Falkenberg, Carolyn. "Kundenloyalität in Dienstleistungsbeziehungen: untersucht am Beispiel der Deutschen Bahn AG." Universitätsverlag der Technischen Universität Chemnitz, 2018. https://monarch.qucosa.de/id/qucosa%3A35451.

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Warum sind Reisekunden der DB AG spezielle Kunden, deren Loyalität vergleichsweise wenig belastbar und besonders leicht zu verletzen ist? Was unterminiert ihre Leidenschaft für das Bahnfahren? Warum ist selbst für den Quasimonopolisten DB AG die Förderung einer uneingeschränkten Loyalität seiner Reisekunden relevant? Was charakterisiert die Bindung des Loyalitätstypus alter Art? Und was kennzeichnet die Entwicklung und spezifische Funktionsweise der Bindung des Loyalitätstypus neuer Art? Mit diesen Fragen greift die Autorin das Schnittstellenthema ‚Kundenloyalität in Dienstleistungsbeziehungen‘ auf, das viele Disziplinen bewegt, jedoch bislang in erster Linie quantitativ und aus Marketingsicht beforscht wurde. Carolyn Koch-Falkenberg fragt danach, wann und warum sich Kunden emotional an ein Unternehmen binden und diesem loyal sind, in welcher Form ihre Loyalität zu Tage tritt, was diese konterkariert und welche Folgen die Art ihrer Loyalitätsform nach sich zieht. Im Mittelpunkt steht damit eine spezifische Form der Bindungsorientierung, welche die Autorin explorativ mittels qualitativer Methoden konsequent aus der Subjektperspektive der Kunden am Beispiel der Dienstleistungsbeziehung zwischen Reisekunden und dem Unternehmen DB AG sozialwissenschaftlich untersucht.
Why are travel customers of DB AG special customers with a comparatively less resilient loyalty which is particularly easy to be violated? What undermines their passion for going by train? Why is even the promotion of the unlimited loyalty of travel customers relevant to the quasimonopolist DB AG? What characterizes the attachment of the old fashioned loyalty type? And what characterizes the development and specific functioning of the binding of the ‚new‘ loyalty type? The author seizes the interdisciplinary topic of the interface theme 'customer loyalty in service relationships', which ocupy many science disciplines, but has so far been primarily researched quantitatively from a marketing perspective. Carolyn Koch-Falkenberg asks when and why clients are emotionally attached to their loyality for a company, how their loyalty is revealed, how it counteracts and illustrates the consequences of their natural loyalty form. The focus is on a specific form of attachment orientation. The author is using socially and scientifically qualitative methods in order to research consistently the subject perspective of the customer at the example of the service relationship between travel customers and the company DB AG.
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Colesky, Theo. "A Comparative Study on Customs Tariff Classification." Thesis, University of Pretoria, 2014. http://hdl.handle.net/2263/42838.

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The field of customs is commonly referred to as that of imports and exports. It is perceived as a maze of processes, procedures, and forms required to enable a customs administration to perform their wide range of responsibilities. One of the responsibilities of a customs administration is the collection of duties, which necessitates classification of the goods in question. This study sets out to determine the extent of customs control in relation to tariff classification in South Africa. The starting point is the establishment of the foundations of customs, both internationally and in South Africa. After origin and valuation, tariff classification is the third technical customs-related focus area. An analysis of the responsibilities of the customs administration in South Africa confirms the importance of revenue collection and, subsequently, tariff classification. As a result of South Africa’s membership of the World Customs Organization, specific obligations in relation to tariff classification are incurred. The implementation and application of the international provisions are considered and compared in South Africa, Australia, and Canada. Not only is South Africa’s existing legislation considered, but also two new Acts. It is found that despite similarities in the implementation of the Harmonized System Convention into the legislation of the three countries, South Africa’s existing legislation makes the most detailed provision for the Harmonized System and its aids. This is based on the finding that the legislation in Australia and Canada, as well as the two new Acts in South Africa, do not have the same comprehensive provisions. A critical review of the varying processes of classification in the three countries suggests that more suitable and effective processes could be implemented in South Africa. In addition, a synopsis of some of the principles developed in case law is provided and compared. In relation to facilitation, the access to relevant information and the adequacy thereof, as well as the availability of rulings, are considered. Differences in the approach to dispute resolution in the three countries are furthermore provided. Proposals are made to address the discrepancies in the implementation and application of the legislation, the process of classification, the principles developed in case law, the enhancement of related guides, the publication of tariff classification rulings, and the extent of facilitation and dispute resolution. Finally it is recommended that an independent and expert tribunal is established to adjudicate technical customs matters.
Thesis (LLD)--University of Pretoria, 2015.
Mercantile Law
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Pettersson, Anders. "High-Dimensional Classification Models with Applications to Email Targeting." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168203.

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Email communication is valuable for any modern company, since it offers an easy mean for spreading important information or advertising new products, features or offers and much more. To be able to identify which customers that would be interested in certain information would make it possible to significantly improve a company's email communication and as such avoiding that customers start ignoring messages and creating unnecessary badwill. This thesis focuses on trying to target customers by applying statistical learning methods to historical data provided by the music streaming company Spotify. An important aspect was the high-dimensionality of the data, creating certain demands on the applied methods. A binary classification model was created, where the target was whether a customer will open the email or not. Two approaches were used for trying to target the costumers, logistic regression, both with and without regularization, and random forest classifier, for their ability to handle the high-dimensionality of the data. Performance accuracy of the suggested models were then evaluated on both a training set and a test set using statistical validation methods, such as cross-validation, ROC curves and lift charts. The models were studied under both large-sample and high-dimensional scenarios. The high-dimensional scenario represents when the number of observations, N, is of the same order as the number of features, p and the large sample scenario represents when N ≫ p. Lasso-based variable selection was performed for both these scenarios, to study the informative value of the features. This study demonstrates that it is possible to greatly improve the opening rate of emails by targeting users, even in the high dimensional scenario. The results show that increasing the amount of training data over a thousand fold will only improve the performance marginally. Rather efficient customer targeting can be achieved by using a few highly informative variables selected by the Lasso regularization.
Företag kan använda e-mejl för att på ett enkelt sätt sprida viktig information, göra reklam för nya produkter eller erbjudanden och mycket mer, men för många e-mejl kan göra att kunder slutar intressera sig för innehållet, genererar badwill och omöjliggöra framtida kommunikation. Att kunna urskilja vilka kunder som är intresserade av det specifika innehållet skulle vara en möjlighet att signifikant förbättra ett företags användning av e-mejl som kommunikationskanal. Denna studie fokuserar på att urskilja kunder med hjälp av statistisk inlärning applicerad på historisk data tillhandahållen av musikstreaming-företaget Spotify. En binärklassificeringsmodell valdes, där responsvariabeln beskrev huruvida kunden öppnade e-mejlet eller inte. Två olika metoder användes för att försöka identifiera de kunder som troligtvis skulle öppna e-mejlen, logistisk regression, både med och utan regularisering, samt random forest klassificerare, tack vare deras förmåga att hantera högdimensionella data. Metoderna blev sedan utvärderade på både ett träningsset och ett testset, med hjälp av flera olika statistiska valideringsmetoder så som korsvalidering och ROC kurvor. Modellerna studerades under både scenarios med stora stickprov och högdimensionella data. Där scenarion med högdimensionella data representeras av att antalet observationer, N, är av liknande storlek som antalet förklarande variabler, p, och scenarion med stora stickprov representeras av att N ≫ p. Lasso-baserad variabelselektion utfördes för båda dessa scenarion för att studera informationsvärdet av förklaringsvariablerna. Denna studie visar att det är möjligt att signifikant förbättra öppningsfrekvensen av e-mejl genom att selektera kunder, även när man endast använder små mängder av data. Resultaten visar att en enorm ökning i antalet träningsobservationer endast kommer förbättra modellernas förmåga att urskilja kunder marginellt.
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Books on the topic "Classification of customers"

1

Werro, Nicolas. Fuzzy Classification of Online Customers. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6.

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United States. Patent and Trademark Office. Working for our customers: A Patent and Trademark Office review. Washington, D.C: U.S. Dept. of Commerce, U.S. Patent and Trademark Office, 1995.

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Authority, Financial Services. Customer classification. London: Financial Services Authority, 2000.

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Customs tariff and trade classification. Belmopan, Belize: Ministry of Finance, 2010.

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Excise, Canada Customs and. Customs commercial system - Classification in the harmonized system. [Ottawa, Ont.?: Customs Canada?, 1987.

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Canada. Customs tariff : schedule =: Tarif des douanes : annexe. Ottawa, Ont: Dept. of Finance = Ministère des finances, 1997.

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Excise, Canada Customs and. Customs commercial system. [Ottawa, Ont.?: Customs Canada?, 1987.

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Laurent, Donzé, ed. Fuzzy methods for customer relationship management and marketing: Applications and classifications. Hershey PA: Business Science Reference, 2012.

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Panova, Anna. Commodity science in customs. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1134545.

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The textbook outlines the theoretical foundations of commodity science, describes the consumer properties of goods, reveals the classification of goods, the concepts of metrology, standardization, certification, etc. Particular attention is paid to the quality of the goods, the definition of the country of origin of the goods, labeling, bar coding, etc. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for students studying in the specialty 38.05.02 "Customs".
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Belize. Customs tariff and trade classification: Laws of Belize, chapter 38. Belize: [Ministry of Finance], 1998.

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Book chapters on the topic "Classification of customers"

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Werro, Nicolas. "Relational Databases & Fuzzy Classification." In Fuzzy Classification of Online Customers, 27–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_3.

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Werro, Nicolas. "Customer Relationship Management." In Fuzzy Classification of Online Customers, 51–65. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_4.

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Werro, Nicolas. "Fuzzy Customer Classes." In Fuzzy Classification of Online Customers, 67–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_5.

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Werro, Nicolas. "Introduction." In Fuzzy Classification of Online Customers, 1–4. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_1.

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Werro, Nicolas. "Fuzzy Set Theory." In Fuzzy Classification of Online Customers, 7–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_2.

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Werro, Nicolas. "Fuzzy Classification Applied to Online Shops." In Fuzzy Classification of Online Customers, 89–105. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_6.

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Werro, Nicolas. "fCQL Toolkit." In Fuzzy Classification of Online Customers, 107–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_7.

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Werro, Nicolas. "Conclusion." In Fuzzy Classification of Online Customers, 127–30. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15970-6_8.

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Brijs, Tom, Gilbert Swinnen, Koen Vanhoof, and Geert Wets. "Comparing Complete and Partial Classification for Identifying Latently Dissatisfied Customers." In Machine Learning: ECML 2000, 88–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45164-1_10.

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Yang, Ping, Dan Wang, Xiao-Lin Du, and Meng Wang. "Evolutionary DBN for the Customers’ Sentiment Classification with Incremental Rules." In Advances in Data Mining. Applications and Theoretical Aspects, 119–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95786-9_9.

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Conference papers on the topic "Classification of customers"

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Bertsimas, Dimitris J., Adam J. Mersereau, and Nitin R. Patel. "Dynamic Classification of Online Customers." In Proceedings of the 2003 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2003. http://dx.doi.org/10.1137/1.9781611972733.10.

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Shahrokhi, Nazanin, Roxana Dehzad, and Soheila Sahami. "Targeting customers with data mining techniques: Classification." In 2011 International Conference on User Science and Engineering (i-USEr 2011). IEEE, 2011. http://dx.doi.org/10.1109/iuser.2011.6150567.

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Werro, Nicolas, Henrik Stormer, and Andreas Meier. "A Hierarchical Fuzzy Classification of Online Customers." In Proceedings. IEEE International Conference on e-Business Engineering. IEEE, 2006. http://dx.doi.org/10.1109/icebe.2006.4.

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Jyostna, J. V. S. L., B. Jameema, N. Anusha, and P. Ramva. "A Classification Model For Mall Customers Data." In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2021. http://dx.doi.org/10.1109/icirca51532.2021.9544515.

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Ben Ayed, Alaidine, and S. Selouani. "Market customers classification using Hidden Markov Models toolkit." In 2013 International Conference on Computer Applications Technology (ICCAT 2013). IEEE, 2013. http://dx.doi.org/10.1109/iccat.2013.6521974.

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Wamundson, Mikael, Johan Hoglund, Math H. J. Bollen, Anders Holm, and Eva Pending Wiberg. "Classification of industrial customers regarding sensitivity towards interruptions." In 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe). IEEE, 2010. http://dx.doi.org/10.1109/isgteurope.2010.5638993.

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Adeoye, A. O. M., and T. Sze´csi. "The Use of Hybrid System of Classification for the Retrieval and Modification of Mechanical Products." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50157.

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With people becoming more individualistic in their choices they make in personalizing the goods and services they use, as resulted in major development that has been recorded in the customisation world. This individualism has resulted in the increase in demand of customized products in many industries especially in the footwear, kitchen and computer industries. However, little has been done when it comes to mechanically oriented products and little flexibility has been given to the consumers in the co-creation of customized products. The Hybrid system of classification is one way to satisfy the customers’ need for the products that are mechanically oriented in nature thereby meeting their desire needs. This paper presents a framework in which an Hybrid system of classification is used to integrates Customers into the design process by defining, configuring, matching, or modifying personal product that is mechanically oriented in nature and grouping the products into classes and sub-classes using a wide range of product parameters, products configuration which make it possible to add and/or change functionalities of a core product, a coding system for mechanical designs which is applicable to each product in the hierarchy, the use of a database for the products information. And the retrieval system to retrieve a similar product code from the database if the initial customer configuration data does not yield a feasible product code through the application of Analytic Hierarchy Process and finally modifying the existing similar product to suit the customers desire.
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Bi, Bin, Lei Ji, and Qian Hu. "Comparative Study on Classification Techniques to Identify Potential Customers." In 2008 International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2008. http://dx.doi.org/10.1109/iscid.2008.153.

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Zheng, Rui-ying, Tieying Zou, Hong-fang Li, and Yinghuan Wu. "Customers' Classification Based on Attributes Reduction of Rough Set." In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462345.

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Ramos, Sergio, and Zita Vale. "Data Mining techniques to support the classification of MV electricity customers." In Energy Society General Meeting. IEEE, 2008. http://dx.doi.org/10.1109/pes.2008.4596669.

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