Data Mining as Support to Knowledge Management in Marketing

Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers\u27 profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability

Applications of Data Mining in Diverse Business Domains

Today, Computers have become the most integral part of human life. Computers are used for various purposes in different business domains which generate huge volume of data related to different business processes. This leads to many challenging problems and issues for computer science and engineering, which includes data storage, data mining, knowledge management, security etc. Various organizations face the challenges in handling the operational data and effective use of these data to generate the knowledge. This knowledge can be used to improve the business processes as well as achieving the customer satisfaction. Data mining can be used to achieve these goals. Different data mining techniques can be used to identify the different patterns in the data. These patterns can be used to understand the outcome of existing business process. In this paper we have briefly discussed the data mining techniques and usefulness of datamining in marketing, education, pharmaceutical and health care and travels and tourism sectors

A New Similarity Measure for Document Classification and Text Mining

Accurate, efficient and fast processing of textual data and classification of electronic documents have become an important key factor in knowledge management and related businesses in today’s world. Text mining, information retrieval, and document classification systems have a strong positive impact on digital libraries and electronic content management, e-marketing, electronic archives, customer relationship management, decision support systems, copyright infringement, and plagiarism detection, which strictly affect economics, businesses, and organizations. In this study, we propose a new similarity measure that can be used with k-nearest neighbors (k-NN) and Rocchio algorithms, which are some of the well-known algorithms for document classification, information retrieval, and some other text mining purposes. We have tested our novel similarity measure with some structured textual data sets and we have compared the results with some other standard distance metrics and similarity measures such as Cosine similarity, Euclidean distance, and Pearson correlation coefficient. We have obtained some promising results, which show that this proposed similarity measure could be alternatively used within all suitable algorithms, methods, and models for text mining, document classification, and relevant knowledge management systems. Keywords: text mining, document classification, similarity measures, k-NN, Rocchio algorith

Business Intelligence Analysis in Small and Medium Enterprises

In order to share knowledge, through discussion and exchange of information, about the technological challenges and management in the digital age, this article discusses in the following sections: First, the mining process - prerequisites and their application to “Small and Medium Enterprises” (SMEs) are discussed. Section two discusses "Using Customer Analytics for Success: The Case of Mexican SMEs." In next Section reviews data management software solutions for business sustainability. Finally, a "marketing analysis" is provided by analysis of SMEs

Data Mining as a tool to Predict the Churn Behaviour among Indian bank customers

The socio economic growth of the country is mainly dependent on the services sector. The financial sector is one of these services sector. Data mining is evolving into a strategically important dimension for many business organizations including b anking sector. The churn problem in bankin g sector can be resolved using data mining techniques. The customer churn is a common measure of lost customers. By minimizing customer churn a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer relationship management (CRM) can be defined as the process of acquiring, retaining and growing profitable customer which requires a clear focus on service attributes that represent value to the customer and c reates loyalty. Customer retention is c ritical for a good marketing and a customer relationship management strategy. The prevention of customer churn through customer retention is a core issue of Customer relationship management. Predictive data mining techniq ues are useful to convert the meani ngful data into knowledge. In this analysis the data has been analyzed using probabilistic data mining algorithm Naive Bayes, the decision trees algorithm (J48) and the support vector machines(SMO)

Identifying Interesting Knowledge Factors from Big Data for Effective E-Market Prediction

Knowledge management plays an important role in disseminating valuable information. Knowledge creation involves analyzing data and transforming information into knowledge. Knowledge management plays an important role in improving organizational decision-making. It is evident that data mining and predictive analytics contribute a major part in the creation of knowledge and forecast the future outcomes. The ability to predict the performance of the advertising campaigns can become an asset to the advertisers. Tools like Google analytics were able to capture user logs. Large amounts of information ranging from visitor location, visitor flow throughout the website to various actions the visitor performs after clicking an ad resides in those logs. This research approach is an effort to identify key knowledge factors in the marketing sector that can further be optimized for effective e-market prediction

Customer lifetime value : an integrated data mining approach

Customer Lifetime Value (CLV) ---which is a measure of the profit generating potential, or value, of a customer---is increasingly being considered a touchstone for customer relationship management. As the guide and benchmark for Customer Relationship Management (CRM) applications, CLV analysis has received increasing attention from both the marketing practitioners and researchers from different domains. Furthermore, the central challenge in predicting CLV is the precise calculation of customer’s length of service (LOS). There are several statistical approaches for this problem and several researchers have used these approaches to perform survival analysis in different domains. However, classical survival analysis techniques like Kaplan-Meier approach which offers a fully non-parametric estimate ignores the covariates completely and assumes stationary of churn behavior along time, which makes it less practical. Further, segments of customers, whose lifetimes and covariate effects can vary widely, are not necessarily easy to detect. Like many other applications, data mining is emerging as a compelling analysis tool for the CLV application recently. Comparatively, data mining methods offer an interesting alternative with the fact that they are less limited than the conventional statistical approaches. Customer databases contain histories of vital events such as the acquisition and cancellation of products and services. The historical data is used to build predictive models for customer retention, cross-selling, and other database marketing endeavors. In this research project we discuss and investigate the possibility of combining these statistical approaches with data mining methods to improve the performance for the CLV problem in a real business context. Part of the research effort is placed on the precise prediction of LOS of the customers in concentration of a real world business. Using the conventional statistical approaches and data mining methods in tandem, we demonstrate how data mining tools can be apt complements of the classical statistical models ---resulting in a CLV prediction model that is both accurate and understandable. We also evaluate the proposed integrated method to extract interesting business domain knowledge within the scope of CLV problem. In particular, several data mining methods are discussed and evaluated according to their accuracy of prediction and interpretability of results. The research findings will lead us to a data mining method combined with survival analysis approaches as a robust tool for modeling CLV and for assisting management decision-making. A calling plan strategy is designed based on the predicted survival time and calculated CLV for the telecommunication industry. The calling plan strategy further investigates potential business knowledge assisted by the CLV calculated

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

Investigating the use of business, competitive and marketing intelligence as management tools in the mining industry

The main objective of this research study is to investigate the extent to which business intelligence, competitive intelligence and marketing intelligence are used within the mining industry. Business intelligence, competitive intelligence and marketing intelligence are the management tools used to mine information to produce up-to-date intelligence and knowledge for operative and strategic decision making. A structured questionnaire is used for the study. A total of 300 mines are randomly selected from a research population of mining organizations in South Africa, Africa and globally. The respondents are all part of senior management. A response rate of 64% is achieved. The results indicat that more than half of the respondents do not have real-time intelligence and proper data mining tools to identify patterns and relationships within a data warehouse. Although a large proportion agrees that their organizations have systematic ways of gathering these different types of intelligence and use them for strategic decision making, there is a significant proportion that did not have any systems. Statistically and practically significant positive relationships with a large effect are found among the dimensions of business intelligence, marketing intelligence, competitive intelligence and perceived business performanc

Business Intelligence from Web Usage Mining

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient