Research on the enterprise online marketing mode based on data mining

2021 ◽  
pp. 1-10
Author(s):  
Yulan Zheng
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Online Shopping ◽  
Popular Science ◽  
Online Marketing ◽  
Marketing Mode ◽  
Book Sales ◽  
Sales Company ◽  
Traditional Marketing ◽  
Real Estate Company

With the development and popularity of mobile networks, online shopping has gradually become a trend. For enterprises, the traditional marketing mode has been difficult to play an effective role when facing the emerging online shopping mode. This study aims to improve the revenue benefits of online shopping. This paper first introduces the traditional marketing mode and then selects the data mining model used for consumption preference segmentation to build an online marketing mode. An example analysis was conducted on a book sales company and a real estate company. The results showed that more users in this community preferred five types of books, and the percentages from high to low were teaching and learning materials, modern novels, popular science books, historical literature, and classical novels; more customers preferred online platforms among the channels for collecting information on home purchase. No matter it was the book sales company or the real estate company, compared with no fluctuation in the company’s turnover under the traditional marketing mode, the turnover of the company increased month by month after adopting the online marketing mode.

scholarly journals The Influence of E-Marketing on Performance of Real Estate Enterprises: Based on Super-Efficiency DEA and Grey Entropy Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhong-Huan Wu ◽  
Hong-jie Chen
Keyword(s):  
Real Estate ◽  
Business Performance ◽  
Corporate Performance ◽  
Marketing Channels ◽  
Online Marketing ◽  
Super Efficiency ◽  
Entropy Methods ◽  
Real Estate Enterprises ◽  
Real Estate Company

E-marketing is an important tool for real estate enterprises. We evaluate 3 online marketing channels of 44 Chinese real estate companies. Super-efficiency DEA and grey entropy methods are applied to analyse the influence of E-marketing on the performance of real estate enterprises. We find that E-marketing will affect the business performance of real estate companies. Real estate company managers should adopt more strategies to improve corporate performance.

scholarly journals Application of Data Mining Technology in Evaluating Real Estate Investment Plan Based on GRA-AHP

2019 ◽  
Vol 1284 ◽  
pp. 012037
Author(s):  
Zhuang Meier ◽  
Wen-Tsao Pan ◽  
Shi Zhuohong ◽  
Zhou Yingying ◽  
Zhong Zuchang
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Real Estate Investment ◽  
Mining Technology ◽  
Investment Plan

scholarly journals The Impact of Corporate Performance on Mental Accounting and Loss Aversion in Financial Decisions

2021 ◽  
Vol 12 (4) ◽  
pp. 259
Author(s):  
Mona Hassabelrasoul Mohammad ◽  
Dalal Mohamed Ebrahim Mohamed ◽  
Elsaid Abd Elazim Tolba Elsharkawi
Keyword(s):  
Graduate Students ◽  
Real Estate ◽  
Loss Aversion ◽  
Corporate Performance ◽  
Mental Accounting ◽  
Organization Performance ◽  
Financial Decisions ◽  
Real Estate Company ◽  
The Impact

This study investigates the effect of the organization performance on two psychological biases, mental accounting and aversion to loss, on financial decisions to both investors and managers. To achieve this, two experiments are conducted. The first experiment consists of 40 graduate students as investors, while the second one consists of 40 accountants in a real estate company as managers. The results of the study indicate that the performance of companies impacts both mental accounting and aversion to loss of investors, whereas the performance of companies affects the mental accounting of managers in making their financial decisions but does not affect the aversion to loss.

scholarly journals CONSUMER BUYING BEHAVIOR TOWARDS ONLINE SHOPPING

2016 ◽  
Vol 4 (8(SE)) ◽  
pp. 60-65
Author(s):  
Lakshmi
Keyword(s):  
Social Class ◽  
Online Shopping ◽  
Business Environment ◽  
Online Marketing ◽  
Buying Behavior ◽  
Group Relation ◽  
Potential Consumer ◽  
Buying Behaviors ◽  
Internet Literacy ◽  
Consumer Buying Behavior

With the increasing internet literacy, the prospect of online marketing is increasing. There are millions of people online any time and they all are a potential consumer in the online market. Since there are so many providers, the most important thing for organizations is to understand what are consumer wants and needs in this competitive business environment. Customer buying behaviors are influenced by different factors such as culture, social class, references group relation, family, salary level and salary independency, age, gender etc. and so they show different customer behaviors. These studies explain online shopping important and consumer buying behavior in online shopping.

Applying data mining algorithms to real estate appraisals: a comparative study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Thiago Cesar de Oliveira ◽  
Lúcio de Medeiros ◽  
Daniel Henrique Marco Detzel
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Support Vector ◽  
Predictive Capacity ◽  
Content Type ◽  
Data Mining Algorithms ◽  
Wide Range ◽  
Very Large Databases ◽  
Mining Algorithms ◽  
Statistical Results

Purpose Real estate appraisals are becoming an increasingly important means of backing up financial operations based on the values of these kinds of assets. However, in very large databases, there is a reduction in the predictive capacity when traditional methods, such as multiple linear regression (MLR), are used. This paper aims to determine whether in these cases the application of data mining algorithms can achieve superior statistical results. First, real estate appraisal databases from five towns and cities in the State of Paraná, Brazil, were obtained from Caixa Econômica Federal bank. Design/methodology/approach After initial validations, additional databases were generated with both real, transformed and nominal values, in clean and raw data. Each was assisted by the application of a wide range of data mining algorithms (multilayer perceptron, support vector regression, K-star, M5Rules and random forest), either isolated or combined (regression by discretization – logistic, bagging and stacking), with the use of 10-fold cross-validation in Weka software. Findings The results showed more varied incremental statistical results with the use of algorithms than those obtained by MLR, especially when combined algorithms were used. The largest increments were obtained in databases with a large amount of data and in those where minor initial data cleaning was carried out. The paper also conducts a further analysis, including an algorithmic ranking based on the number of significant results obtained. Originality/value The authors did not find similar studies or research studies conducted in Brazil.

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