Data Mining For Customer Relationship Management

Data mining has various applications for customer relationship management. In this article, we introduce a framework for identifying appropriate data mining techniques for various CRM activities. This article attempts to integrate the data mining and CRM models and to propose a new model of Data mining for CRM. The new model specifies which types of data mining processes are suitable for which stages/processes of CRM. In order to develop an integrated model it is important to understand the existing Data mining and CRM models. Hence the article discusses some of the existing data mining and CRM models and finally proposes an integrated model of data mining for CRM

How do fashion retail customers search on the Internet?: Exploring the use of data mining tools to enhance CRM

This paper seeks to determine the usefulness of data mining tools to SMEs in developing customer relationship management (CRM) in the fashion retail sector. Kalakota & Robinson’s (1999, p.114) model of ‘The Three Phases of CRM’ acts as a basis to explore the use of data mining software. This paper reviews the nature and type of data that is available for collection and its relevance to CRM; providing an advisory framework for practitioners for them to examine the scope and limitations of using data analysis to improve CRM. The data mining tool examined was Google Analytics (GA); an online freeware tool that enables businesses to understand how people find their site, how they navigate through it, and, ultimately, how they do or don’t become customers of it (Google Analytics, 2009). Establishing these relationships should lead to retailer development of enhanced web site aesthetics and functionality to coincide with consumer expectations. The paper finds that the competitive nature and homogeneity of the fashion retail sector requires retailers to improve the ‘reach, richness and affiliation’ (Hackney et al) of their sites by using technology to explore CRM

Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data

An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour

AbstractCRM-data mining framework establishes close customer relationships and manages relationship between organizations and customers in today's advanced world of businesses. Data mining has gained popularity in various CRM applications in recent years and classification model is an important data mining technique useful in the field. The model is used to predict the behaviour of customers to enhance the decision-making processes for retaining valued customers. An efficient CRM-data mining framework is proposed in this paper and two classification models, Naïve Bayes and Neural Networks are studied to show that the accuracy of Neural Network is comparatively better

A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

A Constraint Guided Progressive Sequential Mining Waterfall Model for CRM

CRM has been realized as a core for the growth of any enterprise. This requires both the customer satisfaction and fulfillment of customer requirement, which can only be achieved by analyzing consumer behaviors. The data mining has become an effective tool since often the organizations have large databases of information on customers. However, the traditional data mining techniques have no relevant mechanism to provide guidance for business understanding, model selection and dynamic changes made in the databases. This article helps in understanding and maintaining the requirement of continuous data mining process for CRM in dynamic environment. A novel integrative model, Constraint Guided Progressive SequentialMiningWaterfall (CGPSMW) for knowledge discovery process is proposed. The key performance factors that include management of marketing, sales, knowledge, technology among others those are required for the successful implementation of CRM. We have studied how the sequential pattern mining performed on progressive databases instead of static databases in conjunction with these CRM performance indicators can result in highly efficient and effective useful patterns. This would further help in classification of customers which any enterprise should focus on to achieve its growth and benefit. An organization has limited number of resources that it can only use for valuable customers to reap the fruits of CRM. The different steps of the proposed CGP-SMW model give a detailed elaboration how to keep focus on these customers in dynamic scenarios

Data Mining Techniques with Electronic Customer Relationship Management for Telecommunication Company

Organizations must improve decisional quality, and the continuous usage of data mining techniques is a crucial issue for management. This issue mostly involves an individual's motivation to engage in the behavior. This could perhaps be characterized in terms of the working regimen. technology utilization and employee activity are the two main difficulties that this dilemma revolves around. This study aims to address the aspect associated with data mining and E-CRM in the telecom industry. The methods that are used in the current study,  analysis studies of the data mining techniques are applied to E-CRM that has been identified. Moreover, PHP with the update of the DeLone and McLean methods has been used in the current study. The results show the significance in affecting the continuance used intention of data mining techniques. User satisfaction, technology, and data mining are critical predictors of employment intentions

Churn prediction based on text mining and CRM data analysis

Within quantitative marketing, churn prediction on a single customer level has become a major issue. An extensive body of literature shows that, today, churn prediction is mainly based on structured CRM data. However, in the past years, more and more digitized customer text data has become available, originating from emails, surveys or scripts of phone calls. To date, this data source remains vastly untapped for churn prediction, and corresponding methods are rarely described in literature. Filling this gap, we present a method for estimating churn probabilities directly from text data, by adopting classical text mining methods and combining them with state-of-the-art statistical prediction modelling. We transform every customer text document into a vector in a high-dimensional word space, after applying text mining pre-processing steps such as removal of stop words, stemming and word selection. The churn probability is then estimated by statistical modelling, using random forest models. We applied these methods to customer text data of a major Swiss telecommunication provider, with data originating from transcripts of phone calls between customers and call-centre agents. In addition to the analysis of the text data, a similar churn prediction was performed for the same customers, based on structured CRM data. This second approach serves as a benchmark for the text data churn prediction, and is performed by using random forest on the structured CRM data which contains more than 300 variables. Comparing the churn prediction based on text data to classical churn prediction based on structured CRM data, we found that the churn prediction based on text data performs as well as the prediction using structured CRM data. Furthermore we found that by combining both structured and text data, the prediction accuracy can be increased up to 10%. These results show clearly that text data contains valuable information and should be considered for churn estimation

Critical success factors for the successful customer relationship management: a conceptual case study

Customer Relationship Management (CRM) technology have integrated the latest information technology, including: internet and E-commerce, multi-media technology, data warehousing data mining and artificial intelligence. This is all about the value of customer relationship management. It congregate the scattering data through the process of analysis, it provide a comprehensive and holistic view of certain individual customers. Customer Relationship Management originated and prevailed among western companies, it has already spread in many East Asian countries, such as: Japan, Korean, India and China etc. In order to improve the existing CRM implementation process and enhance the success rate of the CRM implementation, we present the most important Critical Success Factors for the CRM implementation through literature reviews, the chosen CSFs were based on previous studies in the CRM implementation field, focus on the identification of CRM projects, whether they have achieved success or subject to obscure deficiency. Subsequently, the literature study will provide us a group of CSFs which considered to be a comprehensive summarization of those most important factors for CRM implementation projects. It is a challenging work, still some points are summarized