Application of artificial neural network in market segmentation: A review on recent trends

Despite the significance of Artificial Neural Network (ANN) algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000-2010 and proposed a classification scheme for the articles. One thousands (1000) articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.Comment: 24 pages, 7 figures,3 Table

Clustering Methods for Electricity Consumers: An Empirical Study in Hvaler-Norway

The development of Smart Grid in Norway in specific and Europe/US in general will shortly lead to the availability of massive amount of fine-grained spatio-temporal consumption data from domestic households. This enables the application of data mining techniques for traditional problems in power system. Clustering customers into appropriate groups is extremely useful for operators or retailers to address each group differently through dedicated tariffs or customer-tailored services. Currently, the task is done based on demographic data collected through questionnaire, which is error-prone. In this paper, we used three different clustering techniques (together with their variants) to automatically segment electricity consumers based on their consumption patterns. We also proposed a good way to extract consumption patterns for each consumer. The grouping results were assessed using four common internal validity indexes. We found that the combination of Self Organizing Map (SOM) and k-means algorithms produce the most insightful and useful grouping. We also discovered that grouping quality cannot be measured effectively by automatic indicators, which goes against common suggestions in literature.Comment: 12 pages, 3 figure

COMBINING VISUAL CUSTOMER SEGMENTATION AND RESPONSE MODELING

Customer Relationship Management (CRM) is a central part of Business Intelligence and sales campaigns are often used for improving customer relationships. This paper explores customer behavior during sales campaigns. We provide a visual, data-driven and efficient framework for customer segmentation and campaign-response modeling. First, the customers are grouped by purchasing behavior characteristics using a self-organizing map. To this behavioral segmentation model, we link segment migration patterns using feature plane representations. This enables visual monitoring of the customer base and tracking customer behavior before and during sales campaigns. In addition to the general segment migration patterns, this method provides the capability to drill down into each segment to visually explore the dynamics. The framework is applied to a department store chain with more than one million customers

Customer Portfolio Analysis Using the SOM

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

SEGSys: A mapping system for segmentation analysis in energy

Customer segmentation analysis can give valuable insights into the energy efficiency of residential buildings. This paper presents a mapping system, SEGSys that enables segmentation analysis at the individual and the neighborhood levels. SEGSys supports the online and offline classification of customers based on their daily consumption patterns and consumption intensity. It also supports the segmentation analysis according to the social characteristics of customers of individual households or neighborhoods, as well as spatial geometries. SEGSys uses a three-layer architecture to model the segmentation system, including the data layer, the service layer, and the presentation layer. The data layer models data into a star schema within a data warehouse, the service layer provides data service through a RESTful interface, and the presentation layer interacts with users through a visual map. This paper showcases the system on the segmentation analysis using an electricity consumption data set and validates the effectiveness of the system

Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns.This work was supported by European Union Sixth Frame work Program under Project EU-DEEP SES6-CT-2003-503516.Paper no.TPWRS-00633-200

Study about customer segmentation and application in a real case

The hospitality industry generates a huge variety of data that grows by the day, becoming incrinsingly difficult to analyse this data manually in order to build a good data model. A thorough understanding of current customer profiles enables better resource allocation and leads to better definition of product and market development strategies. Dividing customers into similar groups to help develop more objective and focused marketing messages for each of the segments. Thus, in the present dissertation methods of classification and segmentation of existing data in the literature review are studied. Then, a real case study is presented, using data from Property Management Systems of eight Portuguese hotels, four city hotels and four resort hotels. This data set consists of fortyone attributes but, after selection of the most predictive variables, only a subset of attributes is used for data modeling. Next, the classification and segmentation methods studied in the literature review are applied for extracting the relevant information. The results are analyzed and discussed to understand their suitability to study the particular characteristics of hotel reservations.O setor de hospitalidade gera uma enorme variedade de dados que crescem a cada dia, tornando-se fisicamente impossível analisar esses dados manualmente a fim de construir um bom modelo de dados. Um profundo entendimento dos perfis dos atuais clientes permite uma melhor alocação de recursos e leva a uma melhor definição das estratégias de desenvolvimento de produtos e mercados. A divisão dos clientes em grupos semelhantes para ajudar a desenvolver mensagens de marketing mais objetivas e focadas para cada um dos seus segmentos. Desse modo na presente dissertação são estudados métodos de classificação e segmentação de dados existentes na revisão da literatura. De seguida, procede-se à apresentação de um estudo de um caso real, usando dados pertencentes a Sistemas de Gestão de Propriedade de oito hotéis portugueses, quatro hóteis de cidade e quatro hóteis de resort, este conjunto de dados é composto por quarenta e um atributos, mas, após uma selecção das variáveis com maior poder preditivo, apenas um subconjunto de atributos é utilizado para a modelação dos dados. Em seguida, são aplicados os métodos de classificação e segmentação estudados na revisão de literatura de modo a extrair informação relevante. Os resultados são analisados e discutidos para entender sua adequação ao estudo das características particulares das reservas de hotéis