CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns

Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall

Vip-Focused Crm Strategies In An Open-Market

Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers’ behaviours in an open-market is absolutely important to increase its revenue. Therefore, we propose CRM strategies focused on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness to VIP customers and identify association rules among the types from the transactions of VIP customers. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions such as customer identification, attraction, retention and development for the open-market to thrive

Análise preditiva de churn em um e-commerce

Orientador : Denise Fukumi Tsunoda.Trabalho de Conclusão de Curso (graduação) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Curso de Gestão da Informação.Inclui referênciasResumo : Estudo sobre a aplicação de machine learning em análises preditivas de churn em e-commerce. Busca entender o conceito de clientes churn dentro de organizações onde não há assinatura de um serviço e/ou produto, bem como o impacto da tecnologia na evolução do marketing e das novas formas de comércio. Analisa documentos recuperados sobre o tema na base de dados Scopus, fazendo uma revisão sistemática e análise bibliométrica dos documentos levantados. Há uma série de técnicas de mineração de dados aplicadas para a previsão de churn, tais como redes neurais artificiais, árvores de decisão e máquinas vetoriais de suporte. Apresenta a relação entre machine learning e a predição de churn. Conclui-se na primeira etapa a recência do assunto e a falta de estudos aprofundados sobre o tema, além de que os algoritmos mais utilizados envolvem árvore de decisão, Support Vector Machine, Rede Neural Artificial, Random Forest e Regressão Logística. Comenta que a maioria dos estudos existentes relacionados com a previsão do churn de clientes são análises estáticas e não estão bem adequados à realização de monitorizações individuais e dinâmicas, já que a análise de dados estáticas não fornecem monitoramento dinâmico do churn do cliente. No segundo momento do estudo, foram selecionados três métodos e aplicados em uma base de um e-commerce na área de varejo. Os métodos escolhidos foram: Árvore de Decisão (J48), Rede Neural Artificial (Multilayer Perceptron) e Support Vector Machine (SMO). Concluiu que os três modelos apresentaram resultados similares quando analisados a taxa de acertos do modelo, porém o que apresenta melhor tempo de execução do modelo é a Árvore de Decisão J48

e-Business management assessment: framework proposal through case study analysis

Purpose – This paper proposes an e-Business assessment framework for organizations that aim to enhace the effectiveness of their online presence and maximise the benefits that result from it. The framework is based on three main pillars derived from academic literature research: emarketing strategies, CRM strategies, and business model strategies. Design/methodology/approach – This paper reviews literature from e-Marketing, CRM and business model strategies, leading to the generation of a e-Business assessment framework. Secondly it takes 19 case studies and analysis them using Atlas.TI, through qualitative content analysis, to validate that framework. Findings – Pragmatic advice for practitioners derives from research results considering that this framework enables managers to characterise the company in terms of its e-Business approach, making it possible to determine the level of depth of competitive online strategies. Lessons for an improved e-Business approach can be derived from this paper. Originality/value – This study proposes a novel e-Business framework to assist organizations that want to to have an on-line presence. Its original since it is comprised of the factors identified in a literature review that contribute to define and scope that on-line presence. The framework is then validated through the collection of 19 case studies of companies that have this on-line presence, validating the theoretical findings.info:eu-repo/semantics/acceptedVersio

Measuring Customers Satisfaction of E-Commerce Sites Using Clustering Techniques: Case Study of Nyazco Website

Today the use of modern technologies in the daily life for satisfying the needs is unavoidable. Follow the news and searching through the internet has affected organizations to provide platform on the Internet for availability of information for the customers. With the development of e-commerce, online shopping plays an increasingly important role in people’s life. With the use of data mining technique prospect, managers of this site can analyze preferences and purchasing patterns of online customers in order to custom product recommendations. Data mining helps to provide services in accordance with customers’ requirements. The aim of this research is to identify the customers’ requirements in online shopping and cluster these customers based on independent attributes such as gender, product classification, recency, frequency and monetary. For this purpose, the data related to Nyazco website that is an e-commerce website with a variety of products, were examined as a case study in the period of 7 months. The authors of this paper will define four clusters by using k-means algorithm and RFM model by IBM SPSS Modeler 14.2 software. Customers in the third cluster and fourth cluster will be identified as the most important customers. Therefore, providing the demands of these customers should be prioritized

Log-Based Session Profiling and Online Behavioral Prediction in E-Commerce Websites

Improvements to customer experience give companies a competitive advantage, as understanding customers' behaviors allows e-commerce companies to enhance their marketing strategies by means of recommendation techniques and the customization of products and services. This is not a simple task, and it becomes more difficult when working with anonymous sessions since no historical information of the user can be applied. In this article, analysis and clustering of the clickstreams of past anonymous sessions are used to synthesize a prediction model based on a neural network. The model allows for prediction of a user's profile after a few clicks of an online anonymous session. This information can be used by the e-commerce's decision system to generate online recommendations and better adapt the offered services to the customer's profile

Different Prices for Different Customers – Optimising Individualised Prices in Online Stores by Artificial Intelligence

Today’s information tracking technology and Big Data open up new opportunities for e-commerce. Online stores can collect personal information to estimate customers’ willingness-to-pay. This enables the application of price differentiation where different customers are charged different prices for the same product. Lower prices offered to customers who share the word have an advertisement effect, while higher prices have adverse effects. In this paper we develop a decision model for individualised prices in online stores that considers the sharing of prices by word of mouth which is mostly neglected by current literature. Complex decision models in e-commerce are caught between the need of adequately representing the reality and the demand of being solvable within reasonable time limits. We use various artificial intelligence solution methods to solve the decision model for numerical examples. Our results indicate that despite word of mouth differential pricing can be financially worthwhile