The aim of this study is to know the role of online promotion tools in conducting behavior of the Algerian consumer in the district of Bechar,by reviewing its concept,characteristics and types and measuring its impact...The aim of this study is to know the role of online promotion tools in conducting behavior of the Algerian consumer in the district of Bechar,by reviewing its concept,characteristics and types and measuring its impact on the consumer in studying the intention of buying through Tools of Internet.A sample of Algerian consumers was collected in the district of Bechar,the sample size was 90 individuals distributed to mobile company Ooredoo The descriptive and analytical method was used to obtain statistical data by using a questionnaire and SPSS method for testing hypotheses of the study.The result was the statistical significance through the methods of promotion through Internet and the purchase of the Algerian consumer behavior in the district of Bechar towards the use of mobile services&offers to the operator Ooredoo.展开更多
With the rapid growth of e-commerce and online transactions, e-commerce platforms face a critical challenge: predicting consumer behavior after purchase. This study aimed to forecast such after-sales behavior within t...With the rapid growth of e-commerce and online transactions, e-commerce platforms face a critical challenge: predicting consumer behavior after purchase. This study aimed to forecast such after-sales behavior within the digital retail environment. We utilized four machine learning models: logistic regression, decision tree, random forest, and XGBoost, employing SMOTE oversampling and class weighting techniques to address class imbalance. To bolster the models’ predictive capabilities, we executed pivotal data processing steps, including feature derivation and one-hot encoding. Upon rigorous evaluation of the models’ performance through the 5-fold cross-validation method, the random forest model was identified as the superior performer, excelling in accuracy, F1 score, and AUC value, and was thus deemed the most effective model for anticipating consumer after-sales behavior. The findings from this research offer actionable strategies for e-commerce platforms to refine their after-sales services and enhance customer satisfaction.展开更多
文摘The aim of this study is to know the role of online promotion tools in conducting behavior of the Algerian consumer in the district of Bechar,by reviewing its concept,characteristics and types and measuring its impact on the consumer in studying the intention of buying through Tools of Internet.A sample of Algerian consumers was collected in the district of Bechar,the sample size was 90 individuals distributed to mobile company Ooredoo The descriptive and analytical method was used to obtain statistical data by using a questionnaire and SPSS method for testing hypotheses of the study.The result was the statistical significance through the methods of promotion through Internet and the purchase of the Algerian consumer behavior in the district of Bechar towards the use of mobile services&offers to the operator Ooredoo.
文摘With the rapid growth of e-commerce and online transactions, e-commerce platforms face a critical challenge: predicting consumer behavior after purchase. This study aimed to forecast such after-sales behavior within the digital retail environment. We utilized four machine learning models: logistic regression, decision tree, random forest, and XGBoost, employing SMOTE oversampling and class weighting techniques to address class imbalance. To bolster the models’ predictive capabilities, we executed pivotal data processing steps, including feature derivation and one-hot encoding. Upon rigorous evaluation of the models’ performance through the 5-fold cross-validation method, the random forest model was identified as the superior performer, excelling in accuracy, F1 score, and AUC value, and was thus deemed the most effective model for anticipating consumer after-sales behavior. The findings from this research offer actionable strategies for e-commerce platforms to refine their after-sales services and enhance customer satisfaction.
