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Relevant bibliographies by topics / Customer segmentation

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Academic literature on the topic 'Customer segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 June 2025

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Journal articles on the topic "Customer segmentation"

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Hong Dien, Le, Nguyen Phuc Son, Pham Hoang Uyen, and Le Van Hinh. "On a segmentation of Coopextra customers in Thu Duc district." Science & Technology Development Journal - Economics - Law and Management 3, no. 1 (2019): 28–36. http://dx.doi.org/10.32508/stdjelm.v3i1.537.

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Customer segmentation is the process of grouping customers based on similar characteristics such as behavior, shopping habits…so that businesses can do marketing to each customer group effectively and appropriately. Customer segmentation helps businesses determine different strategies and different marketing approaches to different groups. Customer segmentation helps marketers better understand customers as well as provide goals, strategies and marketing methods for different target groups. This paper aims to examine the customer segmentation using clustering method in statistics and unsupervised machine learning. The algorithms used are K-means and Elbow which are famous algorithms that have been successfully applied in many areas such as marketing, biology, library, insurance, finance... The purpose of clustering is to find meaningful market segments. However, the adoption and adjustment of parameters in the algorithms so as to find significant customer segmentations remain a challenge at present. In this paper, we used data of customers of Thu Duc CoopExtra and found significant customer segmentations which can be useful for more effective marketing and customer care by the supermarket.

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Khodijatunnuriyah, Siti, and Hasih Pratiwi. "Klasifikasi Jenis Pencabutan Layanan oleh Pelanggan Indihome Menggunakan Metode Chi-Square Automatic Interaction Detection." Indonesian Journal of Applied Statistics 2, no. 2 (2019): 80. http://dx.doi.org/10.13057/ijas.v2i2.34526.

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<p>Market segmentation is a classic topic in marketing which is never loss its attractiveness. In addition to market segmentation, customer satisfaction is important in the field of marketing. Customer satisfaction is a person's feelings after using goods or services produced by a company. High customer satisfaction shows a company's success in producing goods or services. Statistics provides many tools for segmentation research. One of statistical tool for segmentation research which takes the dependency method as an approach is Chi-Squared Automatic Interaction Detection (CHAID) analysis. CHAID analysis would provide decision tree like diagram which provide information about degree of association from dependent variable to the independent variables and the information about segments characteristic. In this case, the CHAID analysis is used to determine the type of service revocation segmentation by Indihome customers. Based on CHAID analysis, 25 segmentations were obtained, which consisted of revocation of the downgrade category of 45314 customers and the number of revocation of the Churn Out category by 11137 customers.</p><strong>Keywords : </strong>market segmentation, customer satisfaction<strong>, </strong>CHAID, Indihome

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SABUNCU, Ä°brahim, Edanur TÃœRKAN, and Hilal POLAT. "CUSTOMER SEGMENTATION AND PROFILING WITH RFM ANALYSIS." TURKISH JOURNAL OF MARKETING 5, no. 1 (2020): 22–36. http://dx.doi.org/10.30685/tujom.v5i1.84.

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This paper is a case study on segmentation and profiling of customers according to their lifetime value by using the RFM (Recency, Frequency and Monetary Value) model which is an analytical method for behavioral customer segmentation. Real customer data that is gathered from a fuel station in Istanbul, Turkey is used for the case study. The data contain 1015 customersâ€™ arrival frequency, last arrival date and total spend amount in the first half of 2016, and 10 descriptor variables of customers. First, demographic characteristics of fuel station customers were analyzed by descriptive statistics. Then customers' RFM score was calculated through SPSS program, and customers were divided into 5 segments according to their RFM scores by cluster analysis. Finally, the customer profile of segments has been created by using Correspond analysis and Discriminant analysis. Although fuel station managers think that the most valuable customer for their company are automobile drivers, result of the analysis suggests that the most valuable customers are Truck drivers. At the end of the paper, recommendations are made based on customer profiles of two most valuables segments that are named VIP and GOLD.

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Niranjan, K., Y. Vasanth, and K. Sathwik. "User Segmentation of Ecommerce." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 1183–88. http://dx.doi.org/10.22214/ijraset.2023.48782.

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Abstract: The emergence of many competitors and entrepreneurs created a lot of excitement as companies competed to find new buyers and retain old ones. As a result of its predecessor, , the need for excellent customer service became relevant regardless of the size of the company. Additionally, each company's ability to understand the needs of each of its customers provides better customer support in its targeted delivery of his customer service and the development of customized customer service plans. This understanding is possible through 's structured customer service. Each segment has customers with the same market characteristics. Big data ideas and machine learning have made automated customer segmentation approaches more widely accepted than traditional market analysis, which often fails on large customer bases. In this paper, the k-means clustering algorithm is used for this purpose. The Sklearn library was developed for the k-means algorithm (described in the appendix) , and the program is trained on his two-factor dataset of 100 samples obtained from a retail store. Characteristics of average purchasers and monthly average customers

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Wicaksono, Yanuar. "SEGMENTASI PELANGGAN BISNIS DENGAN MULTI KRITERIA MENGGUNAKAN K-MEANS." Indonesian Journal of Business Intelligence (IJUBI) 1, no. 2 (2019): 45. http://dx.doi.org/10.21927/ijubi.v1i2.872.

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Customer knowledge is an important asset, in gathering, and managing from sharing customer knowledge into valuable capital for the company. This causes the company to continue to innovate in producing products and serving according to customer needs. To find out the needs of each customer, the company needs to make customer segmentation. Customer segmentation is defined as the division into different groups with similar characteristics to develop marketing strategies that are tailored to customer characteristics. The easiest, simplest, well-known and commonly used model of customer characteristics is the model of the recency, frequency, monetary (RFM) criteria. The RFM model still has weaknesses in low customer segmentation capacity and does not provide information on the continuity of customer transactions in understanding customer loyalty. The research method used is the Knowledge Discovery in Database (KDD) method. The data is transformed into another format that suits the needs of analysis and then the customer is segmented using clustering data mining techniques with the K-Means algorithm. From the experiments, the RFM model guesses loyal customers when reviews, frequency and monetary are high. In reality, the recency only provides information on the customer making the last transaction and the high number of transaction frequencies can be done without the customer's stability in making transactions each period. Implementing multi-criteria in customer segmentation can be better than just RFM criteria. So it will not be wrong to treat customers according to the groups that have been formed.

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Hendrawan, Made Chandra, and I. Putu Gede Hendra Suputra. "Customer Segmentation Using RFM Model." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 2 (2020): 153. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p07.

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At the time of the ASEAN Economic Community (MEA), Indonesia was selected by several companies from other countries to sell its products, including overseas paint companies. Therefore, the increasingly fierce market competition business is unlikely to focus solely on products sold, but it is also important to pay attention to the process of managing customer relationships with retailers. Segmentation is an early process that knows which customers can be sustained. In segmentation, customers who have certain similarities will be grouped into one. Customer segmentation is a model built in grouping customers according to certain standards to be used as a variable grouping. Customers will be the same group if they have certain similarities, while different groups or segments are customers who have different characteristics.

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Güçdemir, Hülya, and Hasan Selim. "Integrating multi-criteria decision making and clustering for business customer segmentation." Industrial Management & Data Systems 115, no. 6 (2015): 1022–40. http://dx.doi.org/10.1108/imds-01-2015-0027.

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Purpose – The purpose of this paper is to develop a systematic approach for business customer segmentation. Design/methodology/approach – This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. Findings – Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. Research limitations/implications – The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment. Practical implications – The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers. Social implications – The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies. Originality/value – This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

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Yao, Lei Yue, and Jian Ying Xiong. "Customers Segmentation Using RFM and Two-Step Clustering." Advanced Materials Research 268-270 (July 2011): 631–35. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.631.

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Analyze RFM (recency, frequency, and monetary) paradigm with customer, and use two-step clustering to segment the customers. It is divided into five levels includes core customer, potential customer, new customer, worthless customer and lost customer. And then through the AHP to determine weights of RFM three dimensions of each cluster for further quantitative analysis of the cluster. Sort the lifetime value of customers according to scores of each type customer

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Gunandi, Ahmed, Heba Awang, Eman Alhawad, and Lotfy Shabaan. "Customer Value and Data Mining in Segmentation Analysis." International Journal of Information Technology and Computer Science Applications 1, no. 1 (2023): 20–34. http://dx.doi.org/10.58776/ijitcsa.v1i1.16.

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Customer Value is the accessed value that a customer has to an organization. In Business, the customer is always right. This statement gives us the impression that all customers are viewed as equal in terms of potential value. Each customer is treated differently according to how much profit they can bring to a company. We use various Data Mining techniques to determine who are these customers and how we can acquire more customers like them who can bring more profit. A loyal customer will be treated differently than a customer that rarely do business with the organization. These customers are usually given bonus gifts and special offers as a form of thanks for their loyalty thus further strengthening that bond. Companies need a way to determine which of their hundreds of thousands of customers are deserving of this attention. Customer Value Segments are used in this specific situation.   

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Chavhan, Sushilkumar, R. C. Dharmik, Sachin Jain, and Ketan Kamble. "RFM analysis for customer segmentation using machine learning: a survey of a decade of research." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (2022): 166–73. http://dx.doi.org/10.17993/3ctic.2022.112.166-173.

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Customer segmentation is a method of categorizing corporate clients into groups based on shared characteristics. In this study, we looked at the different customer segmentation methods and execute RFM analysis by using various clustering algorithms. Based on RFM values (Recent, Frequency, and Cost) of customers, the successful classification of company customers is divided into groups with comparable behaviors. Customer retention is thought to be more significant than acquiring new clients are analyzed on two different databases. Results show the significance of each method. Comparison is helps for selection of better customer segmentation.

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Dissertations / Theses on the topic "Customer segmentation"

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Armani, Luca. "Machine Learning: Customer Segmentation." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24925/.

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Con lo scopo di risparmiare capitale e incrementare i profitti tramite attività di marketing sempre più mirate, conoscere le preferenze di un cliente e supportarlo nell’acquisto, sta passando dall’essere una scelta all’essere una necessità. A tal proposito, le aziende si stanno muovendo verso un approccio sempre più automatizzato per riuscire a classificare la clientela, cos`ı da ottimizzare sempre più l’esperienza d’acquisto. Tramite il Machine Learning è possibile effettuare svariati tipi di analisi che consentano di raggiungere questo scopo. L’obiettivo di questo progetto è, in prima fase, di dare una panoramica al lettore su quali siano le tecniche e gli strumenti che mette a disposizione il ML. In un secondo momento verrà descritto il problema della Customer Segmentation e quali tecniche e benefici porta con sé questo tema. Per finire, verranno descritte le varie fasi su cui si fonda il seguente progetto di ML rivolto alla classificazione della clientela, basandosi sul totale di spesa effettuata in termini monetari e la quantità di articoli acquistati.

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Bergström, Sebastian. "Customer segmentation of retail chain customers using cluster analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252559.

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In this thesis, cluster analysis was applied to data comprising of customer spending habits at a retail chain in order to perform customer segmentation. The method used was a two-step cluster procedure in which the first step consisted of feature engineering, a square root transformation of the data in order to handle big spenders in the data set and finally principal component analysis in order to reduce the dimensionality of the data set. This was done to reduce the effects of high dimensionality. The second step consisted of applying clustering algorithms to the transformed data. The methods used were K-means clustering, Gaussian mixture models in the MCLUST family, t-distributed mixture models in the tEIGEN family and non-negative matrix factorization (NMF). For the NMF clustering a slightly different data pre-processing step was taken, specifically no PCA was performed. Clustering partitions were compared on the basis of the Silhouette index, Davies-Bouldin index and subject matter knowledge, which revealed that K-means clustering with K = 3 produces the most reasonable clusters. This algorithm was able to separate the customer into different segments depending on how many purchases they made overall and in these clusters some minor differences in spending habits are also evident. In other words there is some support for the claim that the customer segments have some variation in their spending habits.<br>I denna uppsats har klusteranalys tillämpats på data bestående av kunders konsumtionsvanor hos en detaljhandelskedja för att utföra kundsegmentering. Metoden som använts bestod av en två-stegs klusterprocedur där det första steget bestod av att skapa variabler, tillämpa en kvadratrotstransformation av datan för att hantera kunder som spenderar långt mer än genomsnittet och slutligen principalkomponentanalys för att reducera datans dimension. Detta gjordes för att mildra effekterna av att använda en högdimensionell datamängd. Det andra steget bestod av att tillämpa klusteralgoritmer på den transformerade datan. Metoderna som användes var K-means klustring, gaussiska blandningsmodeller i MCLUST-familjen, t-fördelade blandningsmodeller från tEIGEN-familjen och icke-negativ matrisfaktorisering (NMF). För klustring med NMF användes förbehandling av datan, mer specifikt genomfördes ingen PCA. Klusterpartitioner jämfördes baserat på silhuettvärden, Davies-Bouldin-indexet och ämneskunskap, som avslöjade att K-means klustring med K=3 producerar de rimligaste resultaten. Denna algoritm lyckades separera kunderna i olika segment beroende på hur många köp de gjort överlag och i dessa segment finns vissa skillnader i konsumtionsvanor. Med andra ord finns visst stöd för påståendet att kundsegmenten har en del variation i sina konsumtionsvanor.

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Johansson, Axel, and Jonas Wikström. "Customer segmentation using machine learning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443868.

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In this thesis, the process of developing an application for segmenting customers with the use of machine learning is described. The project was carried out at a company which provides a booking platform for beauty and health services. Data about customers were analyzed and processed in order to train two classification models able to segment customers into three different customer groups. The performance of the two models, a Logistic Regression model and a Support Vector Classifier, were evaluated with different numbers of features and compared to classifications made by human experts working at the company. The results shows that the logistic regression model achieved an accuracy of 71% when classifying users into the three groups, which was more accurate than the experts manual classification. A web API where the model is provided has been developed and presented to the company. The results of the study showed that machine learning is a useful technique for performing customer segmentation based on behavioral data. Even in the case where the classes are not naturally divisible, the application provides valuable insights on user behaviour that can help the company become more data-driven.

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Koziczynski, Jakob, and Márcia Hammarström. "SEGMENTATION AND CUSTOMER RELATIONSHIPS AT SAPA." Thesis, Mälardalens högskola, Industriell ekonomi och organisation, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28372.

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Worawattananon, Prakit. "Customer service driven supply chain segmentation." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45247.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.<br>Includes bibliographical references (leaves 69-70).<br>The objective of this thesis is to develop a supply chain segmentation model for Company X, which is in the chemical and construction materials industry. The company sells products in an expanding Southeast Asia market. At the same time, it innovates and launches new products to these markets. A major issue for the company to consider is services offered to its customers. The company has to address customer needs, analyze them, and design the products and services that will fulfill those selective demands. This thesis leverages this concern for the company by developing a model to segment the company's supply chain based upon customer services. Company Y, a subsidiary company of Company X, is selected to be a case study for the model developed in this thesis. Quantitatively, the thesis examines collected data such as customer including portions of revenue and margin from each customer; and a customer's profile potential from the size of the firm. Qualitatively, the data and information collected from interviewing relevant people, such as sales and marketing personnel, is used to characterize the company's future customer prospects. Furthermore, some selected current practices in the industry will be reviewed and benchmarked for formulating the model.<br>by Prakit Worawattananon.<br>M.Eng.in Logistics

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Vuckic, Asmir, and Renato Cosic. "Design Of A General Customer Segmentation Process." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Industriell organisation och produktion, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-26371.

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Syfte - Att undersöka hur en kundsegmenteringsprocess kan utformas samt vilka variabler man bör iaktta för att kunna erbjuda en lämplig kundservicenivå. För att uppnå detta syfte skall följande frågeställningar besvaras: 1. Vilka variabler bör ingå i en kundsegmentering? 2. Hur kan en kundsegmenteringsprocess utformas? Metod - En generell kundsegmenteringsprocess utformades. Processen har utvecklats genom kvalitativ forskning baserad på litteraturstudier samt intervjuer i en fallstudie. Under litteraturstudien granskades teorier i ämnet för att besvara frågeställningarna. Detta jämfördes senare med empirin som samlats under fallstudien. Resultat - Den utformade processen innehåller sju dimensioner med tillhörande variabler. Under studien har variablerna utvärderats för att ta reda på hur de påverkar situationen. Endast de variabler som hade ett stort inflytande på situationen togs med i processen. Studien visade att det finns olika strategier för att utföra en kundsegmentering. Vid utformning av en kundsegmenteringsprocess är det viktigt att veta vilka variabler som passar organisationens bransch samt hur de påverkar resultatet. Omfång och Avgränsningar - Rapporten är begränsad till att utforma ett förslag på en kundsegmenteringsprocess. Processen kommer därför inte att tillämpas på fallföretaget under fallstudien. Processen kan fortfarande generaliseras och användas av företag med liknade egenskaper. Ytterligare forskning skulle kunna sträva efter att inkludera andra variabler som passar in på fler branscher. Implikationer - Den utformade processen hjälper till vid beslutssituationer avseende kundsegmentering. Genom att balansera de variabler som föreslagits möjliggör dem en grund för olika kundserviceerbjudanden. Dessa variabler beaktar den eftersträvade generaliseringen. Bidrag och Rekommendationer - Kundsegmenteringsprocessen som presenteras i denna rapport är, såvitt författarna vet, den första i sitt slag med sin layout. Variablerna kan även användas i andra segmenteringsprocesser vilket visar en hög grad av generalisering. Vad som är unikt med den designade processen i denna rapport är att den innehåller en mix av två väl beprövade teorier inom kundsegmentering nämligen, Kotler’s (2009) Bottom-Up-Approach och Weinstein’s (2004) B2B Market Segmentation.<br>Purpose – To examine how the process of customer segmentation can be designed, and which variables to consider to offer an appropriate customer service. To achieve this purpose the following questions will be answered: 1. Which variables should be included in customer segmentation? 2. How can a customer segmentation process be designed? Method – A general process was designed. The process has been developed through qualitative research based on literature review and interviews conducted in a case study. During the literature review the authors sought for theories on the subject in order to answer the research questions. This was later compared to the empirical evidence collected from the case study. Findings – The designed process contains seven dimensions with related variables. During the study the variables were evaluated concerning their impact on the situation. Only variables that had a high influence on the situation were implemented in the process. The study showed that that there are various approaches towards performing customer segmentation. When designing a customer segmentation process, it is of high importance to know which variables suit the organizations line of business and how they affect the outcome. Research limitations – The thesis is restricted into designing a customer segmentation process, the process will therefore not be applied on the case company during the case study. The process can still be generalized and usable for companies with similar distribution setup. Further research could strive to include other variables. Implications – The designed process assists in the decision-making situation regarding customer segmentation. By balancing the variables it enables a basis for customer service offering. These variables take the requested generalization in consideration. Originality/value – The customer segmentation process presented in this thesis is, as far as the authors know, the first in its kind with its layout. The variables could be used in other segmentation processes as well which show a high grade of generalization. What is unique with the designed process in this thesis is that it contains a mixture of two well proven customer segmentation theories namely, Kotler’s (2009) Bottom-Up-Approach and Weinstein’s (2004) B2B Market Segmentation.

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Hiziroglu, Abdulkadir. "A soft computing approach to customer segmentation." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503072.

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Improper selection of segmentation variables and tools may have an effect on segmentation results and can cause a negative financial impact (Tsai & Chiu, 2004). With regards to the selection of segmentation variables, although general segmentation variables such as demographics are frequently utilised based on the assumption that customers with similar demographics and lifestyles tend to exhibit similar purchasing behaviours (Tsai & Chiu, 2004), it is believed the behavioural variables of customers are more suitable to use as segmentation bases (Hsieh, 2004). As far as segmentation techniques are concerned, two conclusions can be made. First, the cluster-based segmentation methods, particularly hierarchical and non-hierarchical methods, have been widely used in the related literature. But, the hierarchical methods are criticised for nonrecovery while the non-hierarchical ones are not able to determine the initial number of clusters (Lien, 2005). Hence, the integration of hierarchical and partitional methods (as a two-stage approach) is suggested to make the clustering results powerful in large databases (Kuo, Ho & Hu, 2002b). Second, none of those traditional approaches has the ability to establish non-strict customer segments that are significantly crucial for today's competitive consumer markets. One crucial area that can meet this requirement is known as soft computing. Although there have been studies related to the usage of soft computing techniques for segmentation problems, they are not based on the effective two-stage methodology. The aim of this study is to propose a soft computing model for customer segmentation using purchasing behaviours of customers in a data mining framework. The segmentation process in this study includes segmentation (clustering and profiling) of existing consumers and classification-prediction of segments for existing and new customers. Both a combination and an integration of soft computing techniques were used in the proposed model. Clustering was performed via a proposed neuro-fuzzy two stage-clustering approach and classification-prediction was employed using a supervised artificial neural network method. Segmenting customers was done according to the purchasing behaviours of customers based on RFM (Recency, Frequency, Monetary) values, which can be considered as an important variable set in identifying customer value. The model was also compared with other two-stage methods (Le., Ward's method followed by k-means and self-organising maps followed by k-means) based on select segmentability criteria. The proposed model was employed in a secondary data set from a UK retail company. The data set included more than 300,000 unique customer records and a random sample of approximately 1 % of it was used for conducting analyses .. The findings indicated that the proposed model provided better insights and managerial implications in comparison with the traditional two-stage methods with respect to the select segmentability criteria. --' The main contribution of this study is threefold. Firstly it has the potential benefits and implications of having fuzzy segments, which enables us to have flexible segments through the availability of membership degrees of each customer to the corresponding customer segments. Secondly the development of a new two-stage clustering model could be considered to be superior to its peers in terms of computational ability. And finally, through the classification phase of the model it was possible to extract knowledge regarding segment stability, which was utilised to calculate customer retention or chum rate over time for corresponding segments.

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Basu, Probal, and Eun Kyun Kim. "Customer segmentation in the medical devices industry." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40110.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2007.<br>Includes bibliographical references (p. 73-76).<br>This thesis addresses Company X's concerns about its product shipment options. The company ships over 70% of its products to its customers using the primary service provider that ensures that the product is at the customer site by 10:30 AM next day. As per the understanding with its customers, the company, absorbs the cost of premium shipping and does not pass it on to most of its customers. The company believes that this priority service is a source of competitive advantage that helps it get customer loyalty and thereby increases sales. However it is not a normal industry practice to provide this service free to the customers. Keeping in mind this enormous cost burden, Company X wants to minimize this cost. Medical device sales are non-seasonal and do not show promotional effects. We analyzed data for the months of June and October, 2006 as a part of our research. The objective of our data analysis was to validate the proposed approaches we reviewed as a basis for proposing ways to segment customers for improving service while reducing cost. We proposed three types of segmentation: by region, by order method and by division. Segmentation by region looks at dividing the customers by into 4 regions based on their location.<br>(cont.) Segmentation by ordering method splits the customers in terms of whether they order using phone, fax or EDI while segmentation by division breaks up the customer base in terms of the various divisions the company has. Our study revealed that the company can expect to save over 3 million dollars annually by not offering this service free of charge to its customers. If customers are not convinced that the lower level of service meets their needs, they may pay for use of premium shipping. We demonstrate that the lower level of service will likely be just as effective and hence the company can guarantee that the product would reach the customer on time. Given the criticality of the parts that the company ships, it is advised to take its customers into confidence before making major policy changes.<br>by Probal Basu [and] Eun Kyun Kim.<br>M.Eng.in Logistics

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Jan, Nooreen Amir <1995&gt. "Robustness of Clustering-Based Customer Segmentation Models." Master's Degree Thesis, Università Ca' Foscari Venezia, 2022. http://hdl.handle.net/10579/21251.

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With the advancement of technologies and logistic capabilities, e-commerce companies have also drastically improved customer behavior analysis from classical store purchases to an elective and comparative purchase strategy. In addition, to improve their internal processes for customer satisfaction, companies exploit intelligent and well-designed ways to manage their customers by analyzing their behavior and revealing purchasing patterns. To this end, companies adopt customer segmentation algorithms to partition their customers into homogeneous segments for better planning and forecasting marketing campaigns. Customer segmentation is usually performed through clustering algorithms, a class of unsupervised machine learning methods that discover regularities in the data without needing prior information. For this reason, clustering algorithms play a central role in company decisions making, carrying a lot of responsibilities. This thesis aims to determine the robustness of these algorithms against adversarial/malicious attacks that alter the clustering results by injecting specially crafted samples into the dataset. A successful attack might have profound implications in critical company decisions. All the experiments have been carried out on a real dataset of customers and products provided to me during my internship at WWG Srl. After data preparation, we segmented customers using DBSCAN and K-means. And finally, we tested the robustness of these algorithms by poisoning the dataset using a Bridge-based strategy.

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Aziz, Andrew. "Customer Segmentation basedon Behavioural Data in E-marketplace." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-330461.

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In the past years, research in the fields of big data analysis, machine learning anddata mining techniques is getting more frequent. This thesis describes a customersegmentation approach in a second hand vintage clothing E-marketplace Plick.These customer groups are based on user interactions with items in themarketplace such as views and "likes". A major goal of this thesis was to constructa personal feed for each user where the items are derived from the user groups.The customer segmentation method discussed in this paper is based on theclustering algorithm K-means using cosine similarity as the similarity measure. Theinput matrix used by the K-means algorithm is a User-Brand ratings matrix whereeach brand is given a rating by each user. A visualization tool was also constructedin order to get a better picture of the data and the resulting clusters. In order tovisualize the highly dimensional User-Brand matrix, Principal Component Analysis isused as a dimensionality reduction algorithm.

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Books on the topic "Customer segmentation"

1

Tsiptsis, Konstantinos. Data mining techniques in CRM: Inside customer segmentation. Wiley, 2010.

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Customer segmentation and clustering using SAS Enterprise Miner. 2nd ed. SAS, 2011.

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Tsiptsis, Konstantinos. Data mining techniques in CRM: Inside customer segmentation. Wiley, 2010.

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Tsiptsis, Konstantinos. Data mining techniques in CRM: Inside customer segmentation. Wiley, 2009.

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Divide and conquer: Target your customers through market segmentation. Wiley, 1998.

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Conning & Company., ed. Strategic customer segmentation: Women business owners-doing it their way : 1997. Conning & Co., 1997.

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Sheila, Shekar, ed. Wonder woman: Marketing secrets for the trillion-dollar customer. Palgrave Macmillan, 2008.

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Weinstein, Art. Market segmentation: Using demographics, psychographics andother niche marketing techniques to predict and model customer behavior. Probus Publishing Company, 1994.

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Sargeant, Adrian. Market segmentation in the Indonesian banking sector: The relationship between demographics and desired customer benefits. Henley Management College, 1999.

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Market segmentation: Using demographics, psychographics, and other niche marketing techniques to predict and model customer behavior. Probus Pub. Co., 1994.

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Book chapters on the topic "Customer segmentation"

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Dannenberg, Holger, and Dirk Zupancic. "Customer segmentation." In Excellence in Sales. Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-8782-2_7.

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Barga, Roger, Valentine Fontama, and Wee Hyong Tok. "Customer Segmentation Models." In Predictive Analytics with Microsoft Azure Machine Learning. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1200-4_10.

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Barga, Roger, Valentine Fontama, and Wee Hyong Tok. "Customer Segmentation Models." In Predictive Analytics with Microsoft Azure Machine Learning. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0445-0_7.

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Kerkhove, Louis-Philippe. "Better Customer Segmentation." In Data-driven Retailing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12962-9_8.

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Das, Parichay, and Vijendra Singh. "Knowing Your Customers Using Customer Segmentation." In Computational Methods and Data Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3015-7_32.

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Dash, Manoj Kumar, Manash Kumar Sahu, Jishnu Bhattacharyya, and Shivam Sakshi. "Customer Segmentation: SMPI Model." In Customer-Centricity in Organized Retailing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-3593-0_4.

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Walters, David, and David White. "Customer Analysis and Market Segmentation." In Retail Marketing Management. Macmillan Education UK, 1987. http://dx.doi.org/10.1007/978-1-349-10666-0_4.

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Meidan, Arthur. "Customer Behaviour and Market Segmentation." In Marketing Financial Services. Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-24475-1_2.

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Sun, Zhongqun, and Xi Sun. "Customer Segmentation Based on Dual Perspectives of Customer Value." In Lecture Notes in Electrical Engineering. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4850-0_18.

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Vatreš, Amela, and Zerina Mašetić. "Exploring Customers’ Behavior – Analysing Customer Data, Customer Segmentation and Predicting Customers’ Behavior on Black Friday." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90055-7_2.

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Conference papers on the topic "Customer segmentation"

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Park, Seyoung, and Harrison M. Kim. "Data-Driven Customer Segmentation Based On Online Review Analysis and Customer Network Construction." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-70036.

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Abstract Recently, many studies on product design have utilized online data for customer analysis. However, most of them treat online customers as a group of people with the same preferences while customer segmentation is a key strategy in conventional market analysis. To supplement this gap, this paper proposes a new methodology for online customer segmentation. First, customer attributes are extracted from online customer reviews. Then, a customer network is constructed based on the extracted attributes. Finally, the network is partitioned by modularity clustering and the resulting clusters are analyzed by topic frequency. The methodology is implemented to a smartphone review data. The result shows that online customers have different preferences as offline customers do, and they can be divided into separate groups with different tendencies for product features. This can help product designers to draw segment-based design implications from online data.

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Luo, Ling, Bin Li, Irena Koprinska, Shlomo Berkovsky, and Fang Chen. "Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/336.

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Customer behavior modeling is important for businesses in order to understand, attract and retain customers. It is critical that the models are able to track the dynamics of customer behavior over time. We propose FC-CSM, a Customer Segmentation Model based on a Fragmentation-Coagulation process, which can track the evolution of customer segmentation, including the splitting and merging of customer groups. We conduct a case study using transaction data from a major Australian supermarket chain, where we: 1) show that our model achieves high fitness of purchase rate, outperforming models using mixture of Poisson processes; 2) compare the impact of promotions on customers for different products; and 3) track how customer groups evolve over time and how individual customers shift across groups. Our model provides valuable information to stakeholders about the different types of customers, how they change purchase behavior, and which customers are more receptive to promotion campaigns.

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Zhang, Xiaobin, Gao Feng, and Huang Hui. "Customer-Churn Research Based on Customer Segmentation." In 2009 International Conference on Electronic Commerce and Business Intelligence, ECBI. IEEE, 2009. http://dx.doi.org/10.1109/ecbi.2009.86.

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Bozkan, Tunahan, Tuna Cakar, Alperen Sayar, and Seyit Ertugrul. "Customer Segmentation and Churn Prediction via Customer Metrics." In 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE, 2022. http://dx.doi.org/10.1109/siu55565.2022.9864781.

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Fathi, Mehdi, Kamran Kianfar, Amir Hasanzadeh, and Amir Sadeghi. "Customers fuzzy clustering and catalog segmentation in customer relationship management." In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2009. http://dx.doi.org/10.1109/ieem.2009.5372997.

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Abidar, Lahcen, Dounia Zaidouni, and Abdeslam Ennouaary. "Customer Segmentation With Machine Learning." In SITA'20: Theories and Applications. ACM, 2020. http://dx.doi.org/10.1145/3419604.3419794.

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A, Razia Sulthana, Anukriti Jaiswal, Supraja P, and Sairamesh L. "Customer Segmentation using Machine Learning." In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2023. http://dx.doi.org/10.1109/icaect57570.2023.10117924.

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Gankidi, Nikitha, Sagarika Gundu, Mohd viqar Ahmed, Tahneeyath Tanzeela, Ch Rajendra Prasad, and Srikanth Yalabaka. "Customer Segmentation Using Machine Learning." In 2022 International Conference on Intelligent Technologies (CONIT). IEEE, 2022. http://dx.doi.org/10.1109/conit55038.2022.9848389.

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Kaur, Sharanjit, and Sarabjeet. "Customer Segmentation Using Clustering Algorithm." In 2021 International Conference on Technological Advancements and Innovations (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai53825.2021.9673169.

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Syaputra, Aldino, Zulkarnain, and Enrico Laoh. "Customer Segmentation on Returned Product Customers Using Time Series Clustering Analysis." In 2020 International Conference on ICT for Smart Society (ICISS). IEEE, 2020. http://dx.doi.org/10.1109/iciss50791.2020.9307575.

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Reports on the topic "Customer segmentation"

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Seybold, Patricia. Designing a Customer Flight Deck(SM) System - Customer Segmentation. Patricia Seybold Group, 2002. http://dx.doi.org/10.1571/fw1-31-02cc.

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