scholarly journals Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms

2022 ◽  
Vol 30 (3) ◽  
pp. 1-23
Author(s):  
Zongxiao Wu ◽  
Cong Zang ◽  
Chia-Huei Wu ◽  
Zilin Deng ◽  
Xuefeng Shao ◽  
...  
Keyword(s):  
Customer Value ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Entropy Weight ◽  
Rfm Model ◽  
Consumption Data ◽  
Entropy Weight Method ◽  
Customer Profiling ◽  
Weight Method ◽  
Rmf Model

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

Improving Customer Value Index and Consumption Forecasts using a Weighted RFM Model and Machine Learning Algorithms

2022 ◽  
Vol 30 (3) ◽  
pp. 0-0
Keyword(s):  
Customer Value ◽  
Marketing Strategies ◽  
Machine Learning Algorithms ◽  
Entropy Weight ◽  
Rfm Model ◽  
Consumption Data ◽  
Entropy Weight Method ◽  
Customer Profiling ◽  
Weight Method ◽  
Rmf Model

Collecting and mining customer consumption data are crucial to assess customer value and predict customer consumption behaviors. This paper proposes a new procedure, based on an improved Random Forest Model by: adding a new indicator, joining the RFMS-based method to a K-means algorithm with the Entropy Weight Method applied in computing the customer value index, classifying customers to different categories, and then constructing a consumption forecasting model whose RMSE is the smallest in all kinds of data mining models. The results show that identifying customers by this improved RMF model and customer value index facilitates customer profiling, and forecasting customer consumption enables the development of more precise marketing strategies.

A fire risk assessment method based on the combination of quantified safety checklist and structure entropy weight for shopping malls

2021 ◽  
pp. 1748006X2098737
Author(s):  
Quanle Zou ◽  
Tiancheng Zhang ◽  
Wei Liu
Keyword(s):  
Evaluation Method ◽  
Research Result ◽  
Fire Risk ◽  
Living Standard ◽  
Shopping Malls ◽  
Entropy Weight ◽  
Essential Components ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Laws And Regulations

In recent years, various large- and medium-sized shopping malls have been essential components of each city with the speed-up of China’s urbanization process and the improvement of residents’ living standard. A method for evaluating fire risk in shopping malls based on quantified safety checklist and structure entropy weight method was proposed according to related literatures as well as laws and regulations by analyzing the characteristics of fires occurring in shopping malls in recent years. At first, the factors influencing the fire risk in shopping malls were determined by carrying out on-site survey and visiting related organizations to construct an evaluation index system for fires occurring in shopping malls; afterwards, a quantified safety checklist composed of four parts (i.e. safety grade, grade description, scoring criterion and index quantification) was established based on related laws and regulations; subsequently, index weights were determined by utilizing structure entropy weight method, thus putting forward a method for assessing fire risk in shopping malls based on quantified safety checklist and structure entropy weight method. Eventually, the applicability of the evaluation method was validated exampled by Wal-Mart. The research result provides a theoretical basis for further improvement of the theoretical system for fire risk evaluation in shopping malls, and also exerts practical and guidance significance on timeous and effective early warning as well as prevention and control of building fires.

Research on Degree Evaluation of Rural Waste Recycling of Heilongjiang Province Based on the Integrated Evaluation Method

2011 ◽  
Vol 347-353 ◽  
pp. 1735-1739
Author(s):  
Jie Shang ◽  
Yuan Yao
Keyword(s):  
Index System ◽  
Agricultural Waste ◽  
Waste Recycling ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Heilongjiang Province ◽  
Entropy Weight ◽  
Development Direction ◽  
Entropy Weight Method ◽  
Weight Method

This paper has analyzed the degree of agricultural waste recycling utilization, and problems existing in current rural calculated degree of waste recycling in Heilongjiang province, using AHP and entropy weight method integrated and construct the rural waste recycling system, and points out that the evaluation index system of agricultural waste recycling after the development direction.,This paper has analyzed the degree of agricultural waste recycling utilization, and problems existing in current rural calculated degree of waste recycling in Heilongjiang province, using AHP and entropy weight method integrated and construct the rural waste recycling system, and points out that the evaluation index system of agricultural waste recycling after the development direction.

Application of Combination Evaluation for Academic Title Evaluation

2011 ◽  
Vol 50-51 ◽  
pp. 756-760
Author(s):  
Bao Feng Li ◽  
Jing Guo Qu ◽  
Pu Yu Hao
Keyword(s):  
Associate Professor ◽  
Topsis Method ◽  
Entropy Weight ◽  
Base Points ◽  
Subjective Weight ◽  
Evaluation Models ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Double Base

In this paper, using the relevant data of 34 teaching staffs who participate in the academic title evaluation of associate professor in 2010, firstly it introduces the entropy weight method, Topsis method with subjective weight, Topsis method with objective weight and double base points method with subjective weight to evaluate and sort the performance of 34 teaching staffs. Secondly, two combination evaluation models are constructed to do the same work and the conclusions are more science and rational.

Sign in / Sign up

Export Citation Format

Share Document