Prediction of users online purchase behavior based on selective ensemble learning

A probabilistic multi-dimensional selective ensemble learning method and its application in the prediction of users' online purchase behavior are studied in this work.Firstly, the classifier is integrated based on the dimension of predicted probability, and the pruning algorithm based on greedy forward search is obtained by combining the two indicators of accuracy and complementarity.Then the pruning algorithm is integrated into the Stacking ensemble method to establish a user online shopping behavior prediction model based on the probabilistic multi-dimensional selective ensemble method.Finally, the research method is compared with the prediction results of individual learners in ensemble learning and the Stacking ensemble method without pruning.The experimental results show that the proposed method can reduce the scale of integration, improve the prediction accuracy of the model, and predict the user's online purchase behavior.

Consumer Purchase Behavior based on product concept learning

Form consumer learning perspective, a theoretical analysis of the structure of brand knowledge is gave. Product concepts will be transformed into knowledge nodes, association links and affective response which are stored in consumer memory. Consumer brand knowledge is composed of brand awareness, brand image and brand attitude. Data are collected from questionnaire. The structure of brand knowledge is confirmed by exploratory factor analysis. Form a binary logistic regression analysis of brand knowledge and consumer purchase behavior, it found that brand attitude is the main factor to predict and explain the purchase behavior based on product concepts.

A Study on the Influence of External Stimuli and Psychological Processes on Stationery Purchase Behavior Based on SOR

Based on the SOR(stimulus-organism-response)model to obtain 534 survey data from the MTurk platform,the relationship between external stimuli,psychological processes,and consumers’stationery purchasing behavior was empirically analyzed using a multivariate ordered logistic regression model.In terms of marketing stimuli,consumers’recognition of product quality and price as well as the convenience of purchasing channels significantly and positively influenced purchasing behavior;in terms of social stimuli,self’s opinion of needs and the positive evaluation of peers significantly and positively influenced purchasing behavior;in terms of psychological process,consumers’knowledge,trust,willingness to know,and preference for stationery products significantly and positively influenced purchasing behavior.Accordingly,it is proposed that,in order to promote the purchase of stationery products,the production and operation links should ensure the quality of stationery products,promote price control in a reasonable range,guarantee a healthy,orderly,and convenient stationery market environment,as well as strengthen the multi-sensory promotion of stationery products.

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

Inter-Purchase Time Prediction Based on Deep Learning

Inter-purchase time is a critical factor for predicting customer churn.Improving the prediction accuracy can exploit consumer’s preference and allow businesses to learn about product or pricing plan weak points,operation issues,as well as customer expectations to proactively reduce reasons for churn.Although remarkable progress has been made,classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-,medium-,and long-term data are likely to interfere with each other sequentially.Different from literature,this study proposed a hybrid inter-purchase time prediction model for customers of on-line retailers.Moreover,the analysis of differences in the purchase behavior of customers has been particularly highlighted.The integrated self-organizing map and Recurrent Neural Network technique is proposed to not only address the problem of purchase behavior but also improve the prediction accuracy of inter-purchase time.The permutation importance method was used to identify crucial variables in the prediction model and to interpret customer purchase behavior.The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches on the transaction data provided by a leading e-retailer in Taiwan.This study provides a valuable reference for marketing professionals to better understand and develop strategies to attract customers to shorten their inter-purchase times.

Consumer Reaction to the Great East Japan Earthquake

Following the Great East Japan Earthquake in March 2011, the demand for bottled water increased sharply. In this paper, the authors analyze who purchased more bottled water after the earthquake using Quick Purchase Report data. The results are as follows： first, consumers who before the earthquake tended to purchase less bottled water tended to increase the volume purchased after the earthquake; second, the motives for purchasing bottled water after the earthquake differed between consumers in the Tokyo and Osaka metropolitan districts.

Research on the Influencing Factors of Purchasing Internet Financial Wealth Management Products based on AHP

Internet financial wealth management product(IFWMP)has recently been one of the most popularity.There is limited quantitative research on IFWMP which can helps customers to choose the products based on the significance of each factor.In the paper,a multi-criteria decision-making model of IFWMP was developed,namely analytic hierarchy process(AHP)which is used to make decisions to the unstructured problems through quantifying weights of each criterion.This paper investigated ten influential factors relevant to the purchase of IFWMP and analyzed the frequency of collected responds to show the significance of factors.Based on the quantified weights,the result of the research indicated that compatibility,product liquidity,perceived ease of use and perceived usefulness affect the investors purchasing behaviors most that every investors should pay great attention to.

Research on the Influencing Factors of Purchasing Internet Financial Wealth Management Products Based on AHP

Internet financial wealth management product(IFWMP)has recently been one of the most gained popularity.There are limited quantitative research on IFWMP which can help customers to choose products based on the significance of each factor.In this paper,a multi-criteria decision-making model for IFWMP was developed,namely the analytic hierarchy process(AHP)which is commonly used to make decisions for unstructured problems through quantifying weights of each criterion.This paper investigated ten influential factors relevant to the purchase of IFWMP and analyzed the frequency of collected response to show the significance of the factors.Based on the quantified weights,the results of the research indicated that compatibility,product liquidity,perceived ease of use,and perceived usefulness affected investors purchasing behaviors and that every investor should pay great attention to.

Identifying the key purchase factors for organic food among Chinese consumers

The current study primarily aims to identify the critical purchase factors that affect Chinese consumer purchase intention and purchase decision with regard to organic food consumption,in accordance with a modified theory of planned behavior and the alphabet theory.Specifically,this study builds a conceptual research framework by which to delve into the relationships between purchase factors and purchase intention,and elucidate the mediating roles of purchase factors in the relationships between purchase intention and purchase decision.Moreover,by leveraging a modified theory of planned behavior and the alphabet theory,the current study also determines the critical roles of subjective norms and reveals the information and knowledge that impact consumer attitude toward the purchase of organic food.The current study leverages the purposive sampling method and captures 310 records within Beijing,China.The results indicate that purchase attitude correlates positively with subjective norms and knowledge,while purchase intention correlates positively with purchase attitude,perceived behavior control,and food therapy culture.Furthermore,purchase intention can significantly mediate relationships between each of purchase attitude,perceived behavior control,food therapy culture,and purchase decision.Finally,we discuss the theoretical and practical significance of the framework,and propose subsequent research directions regarding organic food purchase behavior.