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Article
Publication date: 1 August 2016

Peiman Alipour Sarvari, Alp Ustundag and Hidayet Takci

The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary…

Abstract

Purpose

The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary (RFM) considerations as well as demographic factors. In this study, the impacts of RFM and demographic attributes have been challenged in order to enrich factors that lend comprehension to customer segmentation. Different types of scenario were designed, performed and evaluated meticulously under uniform test conditions. The data for this study were extracted from the database of a global pizza restaurant chain in Turkey. This paper summarizes the findings of the study and also provides evidence of its empirical implications to improve the performance of customer segmentation as well as achieving extracted rule perfection via effective model factors and variations. Accordingly, marketing and service processes will work more effectively and efficiently for customers and society. The implication of this study is that it explains a clear concept for interaction between producers and consumers.

Design/methodology/approach

Customer relationship management, which aims to manage record and evaluate customer interactions, is generally regarded as a vital tool for companies that wish to be successful in the rapidly changing global market. The prediction of customer behaviors is a strategically important and difficult issue because of the high variance and wide range of customer orders and preferences. So to have an effective tool for extracting rules based on customer purchasing behavior, considering tangible and intangible criteria is highly important. To overcome the challenges imposed by the multifaceted nature of this problem, the authors utilized artificial intelligence methods, including k-means clustering, Apriori association rule mining (ARM) and neural networks. The main idea was that customer clusters are better enhanced when segmentation processes are based on RFM analysis accompanied by demographic data. Weighted RFM (WRFM) and unweighted RFM values/scores were applied with and without demographic factors and utilized to compose different types and numbers of clusters. The Apriori algorithm was used to extract rules of association. The performance analyses of scenarios have been conducted based on these extracted rules. The number of rules, elapsed time and prediction accuracy were used to evaluate the different scenarios. The results of evaluations were compared with the outputs of another available technique.

Findings

The results showed that having an appropriate segmentation approach is vital if there are to be strong association rules. Also, it has been determined from the results that the weights of RFM attributes affect rule association performance positively. Moreover, to capture more accurate customer segments, a combination of RFM and demographic attributes is recommended for clustering. The results’ analyses indicate the undeniable importance of demographic data merged with WRFM. Above all, this challenge introduced the best possible sequence of factors for an analysis of clustering and ARM based on RFM and demographic data.

Originality/value

The work compared k-means and Kohonen clustering methods in its segmentation phase to prove the superiority of adopted segmentation techniques. In addition, this study indicated that customer segments containing WRFM scores and demographic data in the same clusters brought about stronger and more accurate association rules for the understanding of customer behavior. These so-called achievements were compared with the results of classical approaches in order to support the credibility of the proposed methodology. Based on previous works, classical methods for customer segmentation have overlooked any combination of demographic data with WRFM during clustering before proceeding to their rule extraction stages.

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Article
Publication date: 12 January 2015

Shimiao Jiang, Shuqin Cai, Georges Olle Olle and Zhiyong Qin

More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only…

Abstract

Purpose

More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be “gently” evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products.

Design/methodology/approach

The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers’ mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations.

Findings

The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation.

Practical implications

The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation.

Originality/value

A new approach for customer segmentation analysis base on OCRs of durable products is proposed.

Details

Kybernetes, vol. 44 no. 1
Type: Research Article
ISSN: 0368-492X

Keywords

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Book part
Publication date: 4 December 2020

Irem Ucal Sari, Duygu Sergi and Burcu Ozkan

Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that…

Abstract

Customer segmentation is an important research area that helps organizations to improve their services according to customer needs. With the increased information that shows customer attitudes, it is much easier and also more necessary than before to analyze customer responses on different campaigns. Recency, frequency, and monetary (RFM) analysis allows us to segment customers according to their common features. In this chapter, customer segmentation and RFM analysis are explained first, then a real case application of RFM analysis on customer segmentation for a Fuel company is presented. At the end of the application part, possible strategies for the company are generated.

Details

Application of Big Data and Business Analytics
Type: Book
ISBN: 978-1-80043-884-2

Keywords

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Article
Publication date: 1 February 1997

Sally Dibb and Lyndon Simkin

Organizations wishing to apply the principles of market segmentation often face problems putting the theory into practice. All too often the required background analysis

Abstract

Organizations wishing to apply the principles of market segmentation often face problems putting the theory into practice. All too often the required background analysis is inadequate or poorly structured or the translation of segmentation strategy into marketing programs is impeded. To be successful, segmentation must lead an organization through a process which undertakes background analysis, determines strategy and develops marketing programs. However, there are a number of points at which the process can break down. Shows how the segmentation program described has tackled these difficulties, leading several management teams through the analysis, strategy and program elements of the market segmentation process. A range of benefits arise from the program. Primary benefits are that the process puts the customer first, maximizes resources and emphasizes strengths over competitors. Secondary benefits relate to the development of a more market‐focussed company culture and the building of inter‐ and intra‐organizational relationships.

Details

Journal of Business & Industrial Marketing, vol. 12 no. 1
Type: Research Article
ISSN: 0885-8624

Keywords

Content available
Article
Publication date: 14 May 2020

Bambang Eka Cahyana, Umar Nimran, Hamidah Nayati Utami and Mohammad Iqbal

The purpose of this study is to apply hybrid cluster analysis in classifying PT Pelindo I customers based on the level of customer satisfaction with passenger services of…

Abstract

Purpose

The purpose of this study is to apply hybrid cluster analysis in classifying PT Pelindo I customers based on the level of customer satisfaction with passenger services of PT Pelindo I.

Design/methodology/approach

Hybrid cluster analysis is a combination of hierarchical and non-hierarchical cluster analysis. This hybrid cluster analysis appears to optimize the advantages of hierarchical and non-hierarchical methods simultaneously to obtain optimal grouping. Hybrid cluster analysis itself has high flexibility because it can combine all hierarchical and non-hierarchical methods without any limits in the order of analysis used.

Findings

The results showed that 72% of PT Pelindo I customers felt PT Pelindo I service was special, while the remaining 28% felt PT Pelindo I service was good.

Originality/value

In total, 117 customers of PT Pelindo I were involved in a study using the non-probability sampling method.

Details

Journal of Economics, Finance and Administrative Science, vol. 25 no. 50
Type: Research Article
ISSN: 2077-1886

Keywords

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Article
Publication date: 30 July 2019

Hossein Abbasimehr and Mostafa Shabani

The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.

Abstract

Purpose

The purpose of this paper is to propose a new methodology that handles the issue of the dynamic behavior of customers over time.

Design/methodology/approach

A new methodology is presented based on time series clustering to extract dominant behavioral patterns of customers over time. This methodology is implemented using bank customers’ transactions data which are in the form of time series data. The data include the recency (R), frequency (F) and monetary (M) attributes of businesses that are using the point-of-sale (POS) data of a bank. This data were obtained from the data analysis department of the bank.

Findings

After carrying out an empirical study on the acquired transaction data of 2,531 business customers that are using POS devices of the bank, the dominant trends of behavior are discovered using the proposed methodology. The obtained trends were analyzed from the marketing viewpoint. Based on the analysis of the monetary attribute, customers were divided into four main segments, including high-value growing customers, middle-value growing customers, prone to churn and churners. For each resulted group of customers with a distinctive trend, effective and practical marketing recommendations were devised to improve the bank relationship with that group. The prone-to-churn segment contains most of the customers; therefore, the bank should conduct interesting promotions to retain this segment.

Practical implications

The discovered trends of customer behavior and proposed marketing recommendations can be helpful for banks in devising segment-specific marketing strategies as they illustrate the dynamic behavior of customers over time. The obtained trends are visualized so that they can be easily interpreted and used by banks. This paper contributes to the literature on customer relationship management (CRM) as the proposed methodology can be effectively applied to different businesses to reveal trends in customer behavior.

Originality/value

In the current business condition, customer behavior is changing continually over time and customers are churning due to the reduced switching costs. Therefore, choosing an effective customer segmentation methodology which can consider the dynamic behaviors of customers is essential for every business. This paper proposes a new methodology to capture customer dynamic behavior using time series clustering on time-ordered data. This is an improvement over previous studies, in which static segmentation approaches have often been adopted. To the best of the authors’ knowledge, this is the first study that combines the recency, frequency, and monetary model and time series clustering to reveal trends in customer behavior.

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Article
Publication date: 26 April 2018

Eugene Wong and Yan Wei

The purpose of this paper is to develop a customer online behaviour analysis tool, segment high-value customers, analyse their online purchasing behaviour and predict…

Abstract

Purpose

The purpose of this paper is to develop a customer online behaviour analysis tool, segment high-value customers, analyse their online purchasing behaviour and predict their next purchases from an online air travel corporation.

Design/methodology/approach

An operations review of the customer online shopping process of an online travel agency (OTA) is conducted. A customer online shopping behaviour analysis tool is developed. The tool integrates competitors’ pricing data mining, customer segmentation and predictive analysis. The impacts of competitors’ price changes on customer purchasing decisions regarding the OTA’s products are evaluated. The integrated model for mining pricing data, identifying potential customers and predicting their next purchases helps the OTA recommend tailored product packages to its individual customers with reference to their travel patterns.

Findings

In the customer segmentation analysis, 110,840 customers are identified and segmented based on their purchasing behaviour. The relationship between the purchasing behaviour in an OTA and the price changes of different OTAs are analysed. There is a significant relationship between the flight duration time and the purchase lead time. The next travel destinations of segmented high-value customers are predicted with reference to their travel patterns and the significance of the relationships between destination pairs.

Practical implications

The developed model contributes to pricing evaluation, customer segmentation and package customization for online customers.

Originality/value

This study provides novel method and insights into customer behaviour towards OTAs through an integrated model of customer segmentation, customer behaviour and prediction analysis.

Details

International Journal of Retail & Distribution Management, vol. 46 no. 4
Type: Research Article
ISSN: 0959-0552

Keywords

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Article
Publication date: 1 October 1998

Claudio Marcus

This paper introduces the concept of the Customer Value Matrix, a customer segmentation approach that is especially well‐suited for small retail and service businesses…

Abstract

This paper introduces the concept of the Customer Value Matrix, a customer segmentation approach that is especially well‐suited for small retail and service businesses. The discussion offers insights into the reasons for the development of this practical approach, a concrete methodology for its implementation, and strategic and tactical applications of the concept. The material is supported with strong evidence from “real‐world” examples featuring a variety of small retail and service businesses. The paper concludes with a discussion of the managerial implications for companies that manage chains of small retail or service businesses as to how they can take advantage of local relationship marketing.

Details

Journal of Consumer Marketing, vol. 15 no. 5
Type: Research Article
ISSN: 0736-3761

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Article
Publication date: 19 October 2015

Elham Akhondzadeh-Noughabi and Amir Albadvi

– The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time.

Abstract

Purpose

The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time.

Design/methodology/approach

A new hybrid methodology is presented based on clustering techniques and mining top-k and distinguishing sequential rules. This methodology is implemented on the data of 14,772 subscribers of a mobile phone operator in Tehran, the capital of Iran. The main data include the call detail records and event detail records data that was acquired from the IT department of this operator.

Findings

Seven different behavioral groups of customer shifts were identified. These groups and the corresponding top-k rules represent the dominant patterns of customer behavior. The results also explain the relation of customer switching behavior and segment instability, which is an open problem.

Practical implications

The findings can be helpful to improve marketing strategies and decision making and for prediction purposes. The obtained rules are relatively easy to interpret and use; this can strengthen the practicality of results.

Originality/value

A new hybrid methodology is proposed that systematically extracts the dominant patterns of customer shifts. This paper also offers a new definition and framework for discovering distinguishing sequential rules. Comparing with Markov chain models, this study captures the customer switching behavior in different levels of value through interpretable sequential rules. This is the first study that uses sequential and distinguishing rules in this domain.

Details

Management Decision, vol. 53 no. 9
Type: Research Article
ISSN: 0025-1747

Keywords

Content available
Article
Publication date: 28 November 2019

Monica Cortiñas, Raquel Chocarro and Margarita Elorz

Consumers are increasingly combining distribution channels, thus displaying so-called omni-channel behavior, both to complete a given purchase and between purchases. The…

Abstract

Purpose

Consumers are increasingly combining distribution channels, thus displaying so-called omni-channel behavior, both to complete a given purchase and between purchases. The authors make a distinction between omni-channel customers, who make use of distribution services in both channels and omni-channel users, who make partial use of the distribution services of one channel to support purchases in another. This paper aims to identify the omni-channel behavior among the customers of a global fast fashion retailer dealing in a wide range of apparel and clothing accessories.

Design/methodology/approach

Using a multinomial logit model, the authors perform a customer segmentation based on observed omni-channel behavior, considering the explanatory roles of demographics, distribution service features and customer service policies across the different retail channels.

Findings

The authors observe that the key retail channel features for explaining omni-channel customer behavior are product accessibility, both in store and online; the assurance that goods purchased online will satisfy the customer’s needs and expectations; and the option to return goods found unsatisfactory.

Practical implications

The results clearly show that the nature of the visits and purchases made by customers is determined by various components of the companýs customer service policy, which can, therefore, be used to guide the retailer’s segmentation strategy.

Originality/value

Future lines of research should explore the economic implications of this customer segmentation. The price perception data emerging from our findings suggest a greater sensitivity to prices in the mono-channel segment, which might be worth exploring in future research.

Future research

Future lines of research should explore the economic implications of this customer segmentation. The price perception data emerging from our findings suggest a greater sensitivity to prices in the mono-channel segment which might be worth exploring in future research.

Propósito

Los consumidores combinan canales de distribución en el denominado comportamiento omni-canal cada vez en mayor medida, tanto para completar una misma compra como entre distintas compras. Distinguimos entre clientes omni-canal, que hacen uso de los servicios de distribución de ambos canales, y usuarios omni-canal, que hacen solo un uso parcial de los servicios de distribución de un canal para apoyar las compras en el otro canal. En este trabajo identificamos este comportamiento omni-canal entre los clientes de una empresa global del sector de la moda que vende un amplio rango de productos de ropa y complementos.

Diseño/metodología/enfoque

Mediante un modelo logit multinomial, realizamos una segmentación de los clientes en base a su comportamiento omnicanal. En esta segmentación, consideramos el papel explicativo, no solo de las características de los individuos, sino también el de los servicios de distribución y las políticas en cada canal.

Resultados

Obtenemos cómo el acceso al producto, tanto en el establecimiento como a la página web, la garantía de que el producto comprado online tendrá las características esperadas y las facilidades para devolver el producto adquirido online si no cumple las expectativas, son rasgos clave de los canales que explican el comportamiento omnicanal de los clientes.

Implicaciones prácticas

Nuestros resultados muestran claramente que diferentes aspectos de la oferta de servicios y de políticas de la empresa determinan las compras y las visitas y estos aspectos pueden ser utilizados para guiar la estrategia de segmentación del detallista.

Originalidad/valor

En este trabajo contribuimos a la literatura sobre el marketing omnicanal presentando un modelo de segmentación, basado en los servicios de distribución ofertados por los minoristas, para las empresas que comercializan productos a través de distintos canales. Aportamos una distinción conceptual entre usuarios de un canal y compradores que tiene un amplio rango de aplicación.

Líneas futuras

Es necesario proseguir con las líneas futuras de investigación para investigar las implicaciones financieras de esta segmentación. La percepción de los precios que se detecta en nuestros resultados puede sugerir una sensibilidad mayor a los precios en el segmento mono-canal lo que puede ser una línea interesante a contrastar en investigaciones futuras.

Palabras clave

Omni-canal, Moda rápida, Trabajo de investigación, Segmentación, Servicios de distribución, Comercio electrónico

Tipo de artículo

Trabajo de investigación

Details

Spanish Journal of Marketing - ESIC, vol. 23 no. 3
Type: Research Article
ISSN: 2444-9709

Keywords

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