A Neural Network Application to Identify High-Value Customers for a Large Retail Store in Japan

2002 ◽  
pp. 55-69 ◽  
Author(s):  
Edward Ip ◽  
Joseph Johnson ◽  
Katsutoshi Yada ◽  
Yukinobu Hamuro ◽  
Naoki Katoh ◽  
...  

The data mining activities studied in this chapter concern the early identification of potential high-value customers. Member stores can use this information to establish a close relationship with this select group of customers, thus reducing the chances of losing them. Traditionally, Japanese drugstore chains, unlike their American counterparts, have enjoyed close ties with their customers. For example, cash register clerks at Pharma stores may have substantial interactions with customers using online market research questionnaire forms. By closely monitoring the purchasing behavior of relatively new visitors to the store and applying data mining tools to pertinent data, the company can provide decision support to clerks and to the marketing department to aid in relationship building. For instance, sales campaign information, customized coupons, and free samples can be directly mailed to members of the targeted group or given to them at the checkout counter.

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.


Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh

Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.


2015 ◽  
Vol 64 (1) ◽  
pp. 49-64 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Katalin Takács ◽  
Gábor Szatmári ◽  
Endre Dobos ◽  
...  

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