Purchase Behavior Data Research Articles

Deep Mining Algorithm of Online Purchase Behavior Data Based on Decision Tree Model

Abstract It is difficult to mine online buying behavior data by ignoring the classification of online buying behavior data, and the precision and recall are both on the low side. The training set of online buying behavior data is processed by top-down recursion, and a single decision tree is created recursively, and a decision tree classification model is constructed. Based on the classification results of behavior data, the regular estimation of online shopping features is calculated by preprocessing customer behavior features, and the deep mining algorithm is designed. Experimental results show that the decision tree model has good data clustering effect. Based on this, the precision and recall of online shopping behavior data mining are high, and the application performance is ideal.

Design of Rural E-commerce Customer Data Mining System

Customer relationship management based on data mining helps to strengthen the cultivation of high-quality customers and improve customer loyalty and loyalty. China’s e-commerce industry market is becoming more and more prosperous, with the data volume increasing exponentially. Mining the purchasing behavior data of e-commerce industry customers has become a major way for major e-commerce platforms to increase sales volume. Traditional mining algorithms cannot meet the needs of big data analysis. According to different purposes, we will combine mining algorithm and cloud computing platform to mine the potential information of these data and provide beneficial guidance for enterprise operation. This paper starts with the impact of big data on rural e-commerce customers. At the same time, this paper analyzes the current situation of rural e-commerce and proposes the design of rural e-commerce system based on data mining [1].

Panic Buying and Consumption Displacement during COVID-19: Evidence from New Zealand

Panic buying and hoarding behavior is a significant component of crisis- and disaster-related consumption displacement that has received considerable attention during the COVID-19 pandemic. Understanding such purchasing and stockpiling behavior provides critical information for government, disaster managers and the retail sector, as well as policy makers to adjust crisis response strategies and to better understand disaster management, including preparedness and response strategies. This study examines consumer purchasing behavior, retail spending and transactional data for different retail sectors between January 2017 and December 2020 using data for the greater Christchurch region in New Zealand. Once COVID-19-related panic buying began, overall spending increased sharply in anticipation of lockdowns. Transactional spending increased and subsided only slowly to a level higher than pre lockdown. The magnitude of the panic buying event far exceeded historical seasonal patterns of consumer spending outside of Christmas, Easter and Black Friday, although daily spending levels were comparable to such consumption events. The results of the study highlight the importance of comparing panic buying to other events in terms of purchasing motivations and also considering that so-called panic buying may contribute to greater individual and household resilience. The volume of sales alone is not adequate to define panic buying. Instead, the extent of divergence from the normal daily spending value per retail transaction of a given population provides a much more accurate characteristic of panic buying.

Autoencoder-Enabled Potential Buyer Identification and Purchase Intention Model of Vacation Homes

A trend of purchasing a lakeside, seaside, or forest vacation home has been raised in China. However, such purchase behavior has received limited attention from the research community in emerging markets. This study aims at investigating the factors behind vacation home purchase behavior and helping identify potential buyers. Specifically, factors, such as air quality, enduring involvement, place attachment, and destination familiarity, are examined via a proposed integrative model, which links these factors to purchase intention. The total number of potential buyers of vacation homes is increasing but remains small, compared to the whole consumers' population, resulting in imbalanced purchase behavior data when validating a model. To address this problem, this study proposes an autoencoder-enabled and k-means clustering-based (AKMC) method to identify potential buyers. The proposed methods tested on a dataset of 309 samples, collected through a questionnaire-based survey, and achieves a model accuracy of 82% in identifying potential buyers, outperforming other traditional machine learning methods, such as decision trees and support vector machines. This study also provides explainable results for the vacation home purchase behavior and a decision-making tool to identify potential buyers.

Effects of customer personal characteristics on the satisfaction-loyalty link: a multi-method approach

This study aims to examine the moderating effects of customer personal characteristics on the satisfaction-loyalty link in order to overcome potential response bias and common-method variance in the link by using both real-life purchasing behavior data and survey in a cross-method and panel data. Two separate data collection procedures dealt with survey and customer relationship management (CRM) data. A total of 391 members of restaurant loyalty program participated in the survey. Also, additional data were gathered on restaurant CRM. Data were analyzed using SEM and multi-group analysis. This study confirmed the nonlinear relationship between customer satisfaction and brand loyalty due to the significant moderating effects of customers’ personal characteristics on subsequent stages of the link.

Can Purchase Behavior Predict Relationship Perceptions and Willingness to Donate?

ABSTRACTAs arts organizations frequently seek purchases and donations from the same consumers, one of their challenges is identifying which consumers are likely to become donors in the future. With all of the data currently available, many organizations are looking to their databases to predict future consumer behavior. In the customer relationship management literature, models have been developed that allow organizations to use consumer purchase data to predict future purchase behavior. This goal of this research is to extend this literature to determine whether it is possible to predict consumer willingness to donate using purchase behavior data. It is not expected that purchase behavior data will be nearly as accurate a predictor of future donation behavior as past donation behavior would be. Rather, the goal is simply to determine whether organizations would be able to use purchase behavior data as a proxy for relationship perceptions to improve their ability to identify prospective donors among their consumers. Three commonly used methods of prediction based on past behavior are tested using a combination of purchase behavior data and consumer surveys: a segmentation approach, a recency/frequency/monetary value model, and a Pareto/NBD customer relationship management model. Across all three methods, purchase behavior is found to be a poor predictor of both customer relationship perceptions and willingness to donate to the organization.

Exploding the Legend of TV Advertising and Price Promotions

<h3>ABSTRACT</h3> The advertising and marketing communities traditionally have understood television advertising effectiveness and its relationship to in-store marketing tactics through small-market research or marketing-mix modeling. Although these studies have improved the quality of advertising-effectiveness research, they have done little to improve the tools marketers need to translate those insights on a larger scale and optimize their marketing allocations. With the proliferation of “big data,” researchers now have the ability to refine those tools and give marketers a more granular understanding of brand-purchase behavior and the impact of multiple marketing levers on in-store brand sales. This paper leverages the anonymous household-level purchase behavior data from 60 million households across the United States and the second-by-second measurement of television-viewing habits from more than 2 million set-top box households, and the current study applies actual (non-modeled) single-source, household-level data to demonstrate a methodology for optimizing the mix of television advertising and in-store marketing.

Analysis Online Shopping Behavior of Consumer Using Decision Tree

Trading failure is the main reason for a dispute of C2C e-commerce. So predict the behavior of transactions can assist buyers and sellers negotiated transactions, helps to reduce transaction disputes. Separate the success and failure purchase record, then establish decision-making model through the C5.0 decision tree and RFM(Recency, Frequency, Monetary) model on consumer purchase behavior data, quantify the importance of the decision variables, the demonstration experiment shows the prediction accuracy is more than 80%.

The ‘third class’ of medications: Sales and purchasing behavior are associated with Pharmacist Only and Pharmacy Medicine classifications in Australia

ObjectiveNonprescription (over-the-counter) medications in Australia are classified as Pharmacist Only Medicines, Pharmacy Medicines, or unscheduled medications. This report characterizes these medication classifications using key sales and purchasing behavior variables. DesignDescriptive, nonexperimental, cross-sectional study. Setting15 pharmacies in southeast Queensland, Australia, with data recorded over 36 hours per pharmacy in mid-August, 2006. ParticipantsEligible purchasers (n = 3,470 medication purchases) of all nonprescription medications (including nutritional supplements). InterventionResearchers documented details of all observed nonprescription medication sales and interviewed all available patients following the transaction. Main outcome measuresIncidence of product-related consultation, products purchased (brand, dosage form, classification), and purchasing behavior data (including previous purchase, intended use, intended user, and intention to purchase a particular brand). ResultsMore restrictive classification of the purchased medication was significantly (P < 0.01) associated with younger purchasers, purchase of a single nonprescription medication, intent to self-use the medication, intent to purchase a particular brand, repeat purchase, brand-switching interventions by pharmacy staff, pharmacy staff influence on first-time purchases, and observed consultation by pharmacists. Legislative compliance issues were identified: Pharmacists consulted in only 54.7% of Pharmacist Only Medicine sales and 13 cases (1.7% of observed sales) occurred in which Pharmacist Only and Pharmacy Medicines had been sourced from publicly accessible areas of the store. ConclusionPharmacist Only Medicines received greater levels of professional involvement during their sale than Pharmacy Medicines and unscheduled medications, despite higher levels of product familiarity among patients. To optimize the benefits of this classification system, emphasis on professional guidelines is recommended.

Relationship Quality and the Theory of Planned Behavior models of behavioral intentions and purchase behavior

Using real-life purchase behavior data of apparel and survey information, this study compares the Relationship Quality and the Theory of Planned Behavior models. The attitude towards the buying behavior, the subjective norm and perceived behavioral control (antecedents of the buying intention in the Theory of Planned Behavior) are better predictors of behavioral intentions than Relationship Quality. In both models intentions fully mediate the impact of attitudinal antecedents on behavior, both in terms of purchase incidence and purchase behavior (amount spent, number of visits, and types of products bought). Frequency and recency of prior buying behavior and, to a lesser extent, its monetary value, predict subsequent purchase incidence, above and beyond the impact of attitude and intention. Attitudinal antecedents of behavior significantly predict buying behavior, but they become insignificant when buying behavior is included in the model.

A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents

Are trust and risk important in consumers' electronic commerce purchasing decisions? What are the antecedents of trust and risk in this context? How do trust and risk affect an Internet consumer's purchasing decision? To answer these questions, we i) develop a theoretical framework describing the trust-based decision-making process a consumer uses when making a purchase from a given site, ii) test the proposed model using a Structural Equation Modeling technique on Internet consumer purchasing behavior data collected via a Web survey, and iii) consider the implications of the model. The results of the study show that Internet consumers' trust and perceived risk have strong impacts on their purchasing decisions. Consumer disposition to trust, reputation, privacy concerns, security concerns, the information quality of the Website, and the company's reputation, have strong effects on Internet consumers' trust in the Website. Interestingly, the presence of a third-party seal did not strongly influence consumers' trust.

Analysis of Purchase Intent Scales Weighted by Probability of Actual Purchase

The authors show how to incorporate actual purchase behavior data (usually from diaries) into the calculation of standard errors for making inferences about the proportion of purchasers from self-reported purchase intent. The case considered is for a single sample and two products, where interest is in the difference between the two groups in probability of purchase.