Author
D. Kalaivani
Bio: D. Kalaivani is an academic researcher. The author has contributed to research in topics: The Internet & Market segmentation. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Papers
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TL;DR: Multi process prediction model is proposed to analyse customer behaviour using logistic regression method and the proposed model result is validated and compared with many existing online shopping customer models.
Abstract: Online purchase is one of the big changes to the retail marketing. As the lifestyle changed, the people are not going to shop for purchasing required items like gifts, accessories and any electronic items. Everyone started to use online and saving their time and money by getting a good offer through online shopping. Online shopping helps the customer to know the price of the item in advance and able to compare the price with different vendors. It helps the customer to buy the item from the vendor who offers the item with low-cost and good quality. The customer behaviour analysis always depends upon the usage of the internet and service provided by the multi vendor for the various products. Customer behaviour analysis is very much needed to help the vendors to define their strategy for online shopping, advertising, market segmentation and so on. The idea behind this work is to predict the customer behaviour based on their internet usage for various online shopping activities. Multi process prediction model is proposed to analyse customer behaviour using logistic regression method. The proposed model result is validated and compared with many existing online shopping customer models.
2 citations
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TL;DR: A practical CoP implementation framework is proposed that adopts the Benefits, Tools, Organisation, People and Process (BTOPP) model in addressing the key questions surrounding each of the BTOPP elements with a structured approach and identifies key challenges such as organizational culture and performance measurements.
19 citations
03 Oct 2022
TL;DR: In this paper , the authors used supervised machine learning algorithms such as Decision Tree (DT), KNN, Support Vector Machine (SVM), and Naıve Bayes (NB) to predict preferences on online shopping of buyers.
Abstract: E-commerce business has become a prominent entity of global retail as online transaction saves time and cost at the same time. COVID-19 pandemic and lockdown accelerated the growth of e-commerce. The new e-business companies are booming rapidly whereas insincerity in customer concern is noticeable. As a result, the purchasers are facing numerous problems while buying online. The main objective of this study is to predict preferences on online shopping of buyers and based on that analysis, the pattern can be observed. While doing the study, we used some popular Supervised machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naıve Bayes (NB) algorithm. Amongst those, best accuracy was delivered by the Decision Tree algorithm. The output clearly demonstrates that, people are more likely to participate in online shopping if the obstacles could be alleviate which means, buyers are still not satisfied and confident about the online platform. Hopefully, the result of this study can be a great asset for improving the E-commerce sector of Bangladesh if it is optimized wisely.
1 citations