Segmentation Approach for Athleisure and Performance Sport Retailers Based on Data Mining Techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 71-85
Author(s):  
Sunčica Rogić ◽  
Ljiljana Kašćelan
Keyword(s):  
Data Mining ◽  
Direct Marketing ◽  
Business Environment ◽  
Customer Segmentation ◽  
Data Sets ◽  
Data Mining Techniques ◽  
The Future ◽  
Segmentation Approach ◽  
Promotional Materials ◽  
And Performance

This paper seeks to compare certain customer segments from two sport footwear, apparel, and equipment retailers and to examine an objective market segmentation method, based on the recency, frequency, monetary (RFM) and the decision tree (DT) models. The case study is based on two data sets, aiming to compare the different customer segments, both from sport retail industry, and represents an application of data mining techniques in a business environment. The customer segmentation enables the customer selection for the future direct marketing campaigns based on the previous purchasing behavior. Analyzing the customers' purchasing history can help the company determine the value of each customer and therefore target or not target such customers in the future with promotional materials, based on both the customers' interests and their value. Thus, based on the results, personalized offers can be created for each of the defined customer groups, which may increase the efficiency of the overall campaign, reduce costs, and increase profitability.

scholarly journals Customers Grouping Using Data Mining Techniques in the Food Distribution Industry (A Case Study)

2020 ◽  
Keyword(s):  
Data Mining ◽  
Customer Segmentation ◽  
Food Distribution ◽  
Data Mining Techniques ◽  
Customer Lifetime ◽  
Rfm Model ◽  
Marketing Activities ◽  
Using Data ◽  
Production Company

Significant data development has required organizations to use a tool to understand the relationships between data and make various appropriate decisions based on the information obtained. Customer segmentation and analysis of their behavior in the manufacturing and distribution industries according to the purposefulness of marketing activities and effective communication and with customers has a particular importance. Customer segmentation using data mining techniques is mainly based on the variables of recency purchase (R), frequency of purchase (F) and monetary value of purchase (M) in RFM model. In this article, using the mentioned variables, twelve customer groups related to the BTB (business to business) of a food production company, are grouped. The grouping in this study is evaluated based on the K-means algorithm and the Davies-Bouldin index. As a result, customer grouping is divided into three groups and, finally the CLV (customer lifetime value) of each cluster is calculated, and appropriate marketing strategies for each cluster have been proposed.

Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

2021 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Maulida Ayu Fitriani ◽  
Dany Candra Febrianto
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Direct Marketing ◽  
Class Imbalance ◽  
Processing Technique ◽  
Customer Segmentation ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Mining Methods ◽  
Bank Marketing

Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.

A Study of XML Models for Data Mining

Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran
Keyword(s):  
Data Mining ◽  
Data Model ◽  
Data Representation ◽  
Data Models ◽  
Data Mining Techniques ◽  
Xml Documents ◽  
The Future ◽  
Future Data

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

Symbiotic Data Miner

2010 ◽  
pp. 1903-1908 ◽  
Author(s):  
Kuriakose Athappilly
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Evolutionary Approach ◽  
Complex Data ◽  
Data Mining Techniques ◽  
New Knowledge ◽  
Technical Professionals

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.

A Survey on Building Recommendation Systems Using Data Mining Techniques

2022 ◽  
pp. 24-56
Author(s):  
Rajab Ssemwogerere ◽  
Wamwoyo Faruk ◽  
Nambobi Mutwalibi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Recommender Systems ◽  
Performance Measures ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Learning Hypothesis ◽  
Depth Study ◽  
And Performance ◽  
Using Data

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

Symbiotic Data Mining

2011 ◽  
pp. 1083-1086
Author(s):  
Kuriakose Athappilly ◽  
Alan Rea
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Business Environment ◽  
Evolutionary Approach ◽  
Complex Data ◽  
Data Mining Techniques ◽  
New Knowledge ◽  
Technical Professionals

Symbiotic data mining is an evolutionary approach to how organizations analyze, interpret, and create new knowledge from large pools of data. Symbiotic data miners are trained business and technical professionals skilled in applying complex data-mining techniques and business intelligence tools to challenges in a dynamic business environment.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

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