Customer Behavior Analysis Towards Online Shopping using Data Mining

2018 ◽  
Author(s):  
Muqaddas Gull ◽  
Arshi Pervaiz
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Online Shopping ◽  
Customer Behavior ◽  
Using Data

scholarly journals Analysis on Insurance Policy and Related Customer Behavior using Data Mining Technique

2016 ◽  
Vol 139 (6) ◽  
pp. 46-47
Author(s):  
M. Ashrafa ◽  
D. Asha ◽  
D. Radha ◽  
M. Sangeetha ◽  
R. Jayaparvathy
Keyword(s):  
Data Mining ◽  
Customer Behavior ◽  
Insurance Policy ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Using Data

Customer Relationship Management Using Data Mining Model

2019 ◽  
Vol 118 (7) ◽  
pp. 95-100
Author(s):  
S. Balamurugan ◽  
Dr.M. Selvalakshmi
Keyword(s):  
Data Mining ◽  
Customer Behavior ◽  
Customer Relationship ◽  
Customer Profitability ◽  
Different Types ◽  
Classification Prediction ◽  
Using Data ◽  
The Right ◽  
Mining Model ◽  
Common User

The paper describes marketing insights from Data Mining about new promotions to create, focus on profitability and emphasis on the most profitable promotion that could be sent. The paper shows about the development of predictive modeling, from data mining which provides insights into future customer behavior and customer profitability. Data Mining provides a blueprint and how to define and use customer profile. It shows how to acquire new customers in the most profitable way possible and retain profitable customers. Data mining is an effective method to target at risk-customers with the right marketing promotion and services to keep them loyal. The paper discusses the number of data mining techniques with reference to customer retention for mobile phones (CART, Rule inductions, Ann etc) with a common user interface that the tool can support, an ability to support a number of different types of analysis including classification, prediction, and association detection.

Study on Group Differences of Online Shopping Based on Data Mining

2015 ◽  
Vol 713-715 ◽  
pp. 2482-2485
Author(s):  
Yang Zhao ◽  
Lin Wang
Keyword(s):  
Data Mining ◽  
Online Shopping ◽  
Implementation Intentions ◽  
Large Data ◽  
Implementation Intention ◽  
Mobile Internet ◽  
Shopping Behavior ◽  
Group Differences ◽  
Using Data ◽  
Online Shopping Behavior

In the face of complex problems of implementation intentions predict the online shopping behavior, we conducted a preliminary exploration of group differences intention on the online shopping behavior using data mining method. The research results show, different types of temperament, personality, gender and living area in online shopping experience and behavior intention have common characteristics of groups, and different groups have obvious difference in online shopping behavior . Finally, this paper combined with large data and the mobile Internet era characteristic, put forward to large data comprehensive online shopping search behavior index, O2O business model as the foundation, the prospect of research on the construction of implementation intention theory of online shopping behavior.

Analyzing Customer Behavior Using Online Analytical Mining (OLAM)

2015 ◽  
pp. 894-910 ◽  
Author(s):  
Thanachart Ritbumroong
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Customer Behavior ◽  
Future Research ◽  
Technical Performance ◽  
Data Mining Algorithms ◽  
Integration Data ◽  
Mining Algorithms ◽  
Provided Examples

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter reviews the capabilities of OLAM and discusses the well-known concept encompassing the analysis of customer behavior. The underlying motivation of this chapter is to present the opportunities for the development of OLAM to support the customer behavior analysis. Three main directions of the advancement in OLAM are proposed for future research.

Analyzing Customer Behavior Using Online Analytical Mining (OLAM)

2015 ◽  
pp. 98-118
Author(s):  
Thanachart Ritbumroong
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Customer Behavior ◽  
Future Research ◽  
Technical Performance ◽  
Data Mining Algorithms ◽  
Integration Data ◽  
Mining Algorithms ◽  
Provided Examples

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter reviews the capabilities of OLAM and discusses the well-known concept encompassing the analysis of customer behavior. The underlying motivation of this chapter is to present the opportunities for the development of OLAM to support the customer behavior analysis. Three main directions of the advancement in OLAM are proposed for future research.

Service Level Comparison for Online Shopping using Data Mining

2016 ◽  
Vol 5 (1) ◽  
pp. 19-22
Author(s):  
K.Subhasree Chandini ◽  
◽  
A. Roshini ◽  
A. Kokila ◽  
B. Aishwarya ◽  
...  
Keyword(s):  
Data Mining ◽  
Online Shopping ◽  
Service Level ◽  
Using Data
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