Quantitative Assessment of Data-Driven Pricing Optimization Strategies for E-Commerce Platforms in Developing Economies

Abstract

This study examines the effect of data-driven pricing optimization strategies on the performance of e-commerce platforms in developing economies, where firms face intense price competition, high consumer price sensitivity, and persistent market constraints. The problem addressed is that many e-commerce firms in these contexts still struggle to translate customer data, competitor information, demand signals, and real-time analytics into pricing decisions that improve performance consistently. The purpose of the study was therefore to assess whether data-driven pricing optimization functions as a strategic capability that strengthens sales performance, customer satisfaction, customer retention, and competitive advantage. The research adopted a quantitative, cross-sectional, case-based design and collected primary data through structured questionnaires from 210 respondents drawn from cloud-enabled and enterprise-oriented e-commerce platform cases, including marketplace, direct-to-consumer, and hybrid platforms. The key independent variables were customer behavior analytics, competitor price monitoring, demand forecasting, real-time data integration, and dynamic pricing capability, while consumer price sensitivity and developing-market constraints were treated as contextual variables and platform performance as the dependent construct. Data were analyzed using descriptive statistics, Cronbach’s alpha, correlation, and multiple regression in SPSS. The findings show moderate-to-high adoption of data-driven pricing practices, with an overall grand mean of 4.08 and overall instrument reliability of 0.88. Among the predictors, real-time data integration emerged as the strongest positive determinant of platform performance (β = 0.29, p = 0.002), followed by customer behavior analytics (β = 0.24, p = 0.006), competitor price monitoring (β = 0.21, p = 0.011), demand forecasting (β = 0.18, p = 0.019), and dynamic pricing capability (β = 0.15, p = 0.041). The overall model was significant (F = 31.47, p < .001) and explained 57.0% of the variance in platform performance (R² = 0.570). However, consumer price sensitivity (β = -0.17, p = 0.028) and developing-market constraints (β = -0.19, p = 0.015) weakened performance gains. The study implies that firms in developing economies should invest in real-time analytics, integrated pricing systems, and context-sensitive pricing strategies to improve digital competitiveness and sustainable platform performance.