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This research delves into the influence of discount strategies on consumer ratings within the e-commerce landscape, particularly on Amazon. A logistic regression model assessed how discount percentages and product categories affect consumer ratings. The study followed a rigorous methodology, beginning with comprehensive data collection across diverse product categories on Amazon. This was succeeded by a detailed exploratory data analysis (EDA), data preprocessing, and subsequent model building. The model was then subjected to an extensive evaluation process, encompassing accuracy, precision, recall, F1-score, and ROC-AUC metrics. The evaluation revealed that the model achieved an accuracy of 74.94%, a precision of 72.69%, and a recall of 74.94%. The F1 score was calculated at 69.26%, and the ROC-AUC score was notably 78.24%. These metrics underscore the model’s capability to accurately predict consumer ratings influenced by discount strategies. Key findings highlighted the significant predictive power of discount percentages and specific product categories, particularly 'Home & Kitchen', suggesting a complex relationship between discounts, product types, and consumer responses. Theoretically, the study enriches the understanding of consumer behavior in e-commerce, highlighting the nuanced impact of discount strategies on consumer satisfaction, especially in online retail contexts. For e-commerce businesses and marketers, the findings underscore the importance of strategically employing discount strategies and tailoring marketing approaches to specific product categories. This study emphasizes managing customer expectations and maintaining product quality alongside discounts. This research provides valuable insights for optimizing e-commerce strategies and paves the way for future investigations. It opens up avenues for further exploration into factors like product quality, brand reputation, shipping times, and the potential of consumer segmentation and sentiment analysis in enhancing marketing effectiveness. The study marks a significant contribution to the field by linking discount strategies with consumer ratings, using advanced data analytics to inform e-commerce practices in the digital age.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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