Machine Learning–Driven Insights into Pricing and Review Dynamics of iPhone Listings in the Indian E-Commerce Market
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ID: 312678
2025
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Abstract
Apple iPhones hold a high-end niche in the Indian smartphone industry but sales performance indicators reveal a high degree of dispersion in sales of the products, according to model, price, and consumer trends. Although it has a well-established brand reputation across the globe, Apple still has not exploited the fast growing digital market in India. This paper analyses the most important pricing and review-related variables related to the commercial attractiveness of iPhone ads in the major e-commerce websites including Amazon and Flipkart using business analytics and machine learning. Since the dataset does not provide the direct results of sales such as (units sold or revenue), pricing, discount rates, customer rating, and the number of reviews are considered to be proxy outcome measures of consumer interaction and customer sale signals. In line with this, the guided learning exercise is also presented as a binary classification problem (1 = iPhone, 0 = non-iPhone), which allows to examine the ways in which these proxy-based features separate Apple products among other smartphone listings. After evaluating K-Nearest Neighbors, Decision Tree, Random Forest and AdaBoost classifiers, a sampled dataset including product attributes: such as model type, storage options, color, price, discounts, customer ratings, and counts of reviews were used as the input to run the algorithm. The measures of performance were achieved with AUC, accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient. Random Forest and AdaBoost models, which are ensemble-based models, showed a better discriminative capacity, with values of AUC ranging between 0.99 and 0.998. As the findings indicate, moderate pricing, employed discounts strategically, and high customer ratings are the most significant drivers of engagement signals in the determination of consumer interest in the iPhone products. These learnings can offer workable, data-inspired advices to both Apple and online retailers, achieving a more successful pricing, marketing proposals, and positioning of products in the fiercely competitive and price-conscious smart phone market of India.
| Reference Key |
imported_1777055976_69ebb8e80b366
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|---|---|
| Authors | Murad Ali |
| Journal | Journal of Computing & Biomedical Informatics |
| Year | 2025 |
| DOI |
10.56979/1001/2025/1118
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| URL | |
| Keywords | Keywords not found |
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