Real-Time Statistical Method for Marketing Profit of Japanese Cosmetics Online Cross-Border E-commerce Platform

Huang, Jingxian

doi:10.1007/978-3-030-97124-3_48

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Although this study had important reference value for improving the performance of CBECSC-EM, it lacked targeted research on cross-border e-commerce enterprises, and the overall applicability of this method needed further investigation. Cassia and Magno [7] explored the connection between enterprises' international marketing strategy, export operation capability, and financial performance from the perspective of resources. The experiment used partial least squares structural equations to model and analyze exporter data in the food and beverage industry.…”

Section: Related Workmentioning

confidence: 99%

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

Tan

2023

Intell. and Converged Netw.

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In recent years, with the support of national policies, Cross Border E-Commerce (CBEC) has developed rapidly. This business model not only brings significant benefits to the national economy, but also has many unique challenges, especially at the level of supply chain management. Therefore, to enable CBEC enterprises to develop sustainable supply chain, this study discusses the performance evaluation model of supply chain and proposes a CBEC Supply Chain Performance Evaluation Model (CBECSC-EM) based on the Levenberg-Marquardt Backpropagation (LMBP) algorithm. This experiment constructs performance evaluation indicators for the supply chain of CBEC enterprises. On this basis, the LMBP algorithm is introduced, and improved in the experiment to make the overall performance of the evaluation model more scientific and reasonable. In the verification set, the maximum F1 values of LMBP, DEA, SBM, and BP are 98.46%, 93.78%, 87.29%, and 78.95%, respectively. The MAPE value of LMBP model is 0.102%, which is lower than the other three methods (0.282%, 0.343%, and 0.385%) selected in the experiment. The maximum standard deviation rates of importance and operability of the evaluation indexes are 0.1346 and 0.1405, respectively, and there is a significant consistency between the expert scores. Therefore, the LMBP algorithm has broad application prospects in supply chain performance evaluation of CBEC enterprises.

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Section: Related Workmentioning

confidence: 99%

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

Tan

2023

Intell. and Converged Netw.

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“…In the last decade, many scientists present the application results of various machine learning methods and algorithms for classification (Bandam et al, 2022) and prediction of churn behavior of the most valuable part of the current clients (Günesen et al, 2021), searching for more efficient approaches of customer churn prediction. Among the used methods and models for solving the tasks of data processing for telecommunication industry are logistic regression, decision tree and random forest models for churn prediction (Kiguchi et al, 2022;Vezzoli et al, 2020;Kuznietsova et al, 2022), K-means, SVM (Sánchez et al, 2022), the combination of k-means customer segmentation and SVM prediction (Xiahou et al, 2022), the multi-level classification using SVM in the SLS-SVM algorithm (Huang, 2022), the Naïve Bayes for prediction of loyal or disloyal customers and their behavior (Jayadi et al, 2020;Rabiul Alam et al, 2021), an ECHAID (exhaustive Chi-square automatic interaction detector) classification tree for consumers segmentation (Kelley et al, 2022) and so on. used Fisher discriminant equations and logistic regression analysis to develop a customer churn prediction model through customer segmentation on the telecommunication market.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer churn prediction model: a case of the telecommunication market

et al. 2022

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The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

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Real-Time Statistical Method for Marketing Profit of Japanese Cosmetics Online Cross-Border E-commerce Platform

Cited by 2 publications

References 18 publications

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

Customer churn prediction model: a case of the telecommunication market

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