Mining the Web Data for Classifying and Predicting Users’ Requests

Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client’s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands

Retail Shop Sales Forecast by Enhanced Feature Extraction with Association Rule Learning

Sales is a basic standpoint for business growth. Demand for consumer products decides the success rate of every business resulting in a profit. Proper analysis of the consumer interest in a particular product decides future sales. The ordinary tactics for sales and promotion objectives no longer help businesses keep up with the speed of a challenging market because it goes out with no knowledge of consumer buying habits. As a consequence of technological developments, significant changes can be seen in the domains of marketing and selling. As a result of such developments, multiple important factors such as consumers' buying habits, target people, and forecasting sales for the coming years can be readily determined, assisting the sales crew in developing strategies to achieve an upsurge in their company. This paper investigates the use of Association Rule Learning with Feature Extraction to forecast sales performance in order to recognise buyers. The consumer's related goods are identified using the association framework. Data on buying activities are derived from purchase invoices provided by the business. The outcome of both is utilized to create a company strategy. Support, Confidence, and Lift are the metrics used for evaluating the quality of association rules produced by the model. Based on the buyers’ preferences this paper forecasts retail shop sales and predicts the association relation between the products by feature extraction with Association rule learning to improve future sales. The suggested approach is employed to discover the most common pairings of items found in the business. This will assist with promotion and revenue. This method can help you find intriguing cross-selling and connected goods. The WEKA tool was used to evaluate the correctness of the Association rule that was created

How do consumers overcome ambivalence toward hedonic purchases ? a typology of consumer strategies

Purchase decisions for hedonic products and services are often characterized by ambivalence -sensory benefits make them attractive, but consumers may feel guilty about bying them. To overcome this ambivalence, consumers frequently adopt strategies that allow them to enloy hedonic benefits while limiting their negative feelings. Combining an extensive literature review with an interpretive study, the authors identify 23 consumer strategies and propose a typology in four groups on the basis of strategy antecedents: two groups of objective strategies (obtaining consumption benefits without purchasing, objectively contining purchasing costs) and two groups of subjective strategies (manipulating the mental accounting of costs and benefits, relinquishing responsability).consumer behavior; hedonic purchase; consumer strategies

Clustering Customer Shopping Trips With Network Structure

Moving objects can be tracked with sensors such as RFID tags or GPS devices. Their movement can be represented as sequences of time-stamped locations. Studying such spatio-temporal movement sequences to discover spatial sequential patterns holds promises in many real-world settings. A few interesting applications are customer shopping traverse pattern discovery, vehicle traveling pattern discovery, and route prediction. Traditional spatial data mining algorithms suitable for the Euclidean space are not directly applicable in these settings. We propose a new algorithm to cluster movement paths such as shopping trips for pattern discovery. In our work, we represent the spatio-temporal series as sequences of discrete locations following a pre-defined network. We incorporate a modified version of the Longest Common Subsequence (LCS) algorithm with the network structure to measure the similarity of movement paths. With such spatial networks we implicitly address the existence of spatial obstructs as well. Experiments were performed on both hand-collected real-life trips and simulated trips in grocery shopping. The initial evaluation results show that our proposed approach, called Net-LCSS, can be used to support effective and efficient clustering for shopping trip pattern discovery

The Impact of Demographic Variables and Consumer Shopping Orientations on the Purchasing Preference for Different Product Categories in the Context of Online Grocery Shopping

This study examines the impact of demographic variables and consumer shopping orientations on the purchasing preference for different product categories in the context of online grocery shopping within the UK. The data for this study was primarily collected from a web-based survey of consumers in the UK using a questionnaire. The quantitative data was enhanced by qualitative data in form of semi-structured interviews to enhance the quantitative results. A structural equation model (SEM) was used to analyse the quantitative data and to measure the relationships between the respective constructs. The findings show that the purchasing preferences vary by product category. Keywords: Demographic Variables, Consumer Shopping Orientations, Product Categories, UK, and Structure Equation Modelling. DOI: 10.7176/JMCR/52-0