Enhancing Product Management Strategies: A Case Study of Indonesian E-Commerce Using a Data-Driven Machine Learning Approach

Rari Fitri (Master of Management Science Program, Faculty of Economics and Business, Universitas Padjadjaran)
Samidi (Universitas Padjadjaran and Universitas Budiluhur)

TIJAB (The International Journal of Applied Business)
Vol. 10 No. 1 (2026): MARCH 2026

Background: In online marketplaces where thousands of products compete with similar prices and ratings, sellers struggle to decide which products deserve promotion, retention, or removal. As competition in Indonesian e-commerce intensifies, data-driven approaches are needed to manage product performance. Objective: This study aims to formulate a product management strategy using a two-stage data-driven approach. It focuses on performance-based product segmentation and classification and identifies the key variables that show the strongest relationships with performance segments and sales outcomes. Method: This study uses a census-type sample of 2,547 household products in the “Rumah Tangga” category on Tokopedia, collected via web scraping in April 2025. K-Means clustering segments products based on product rating, total product ratings, store rating, and total store ratings; Random Forest classifies products into the identified segments; and correlation analysis examines relationships between attributes, performance segments, and sales outcomes. Results: The segmentation analysis produced three product performance segments: low, medium, and high. The Random Forest classifier categorized products into these segments with 99.4% accuracy. Correlation analysis indicates that product and store ratings play a central role in differentiating performance segments, while the number of product ratings is more closely associated with sales outcomes. Conclusion: The findings support strategies such as targeted promotions for high-performing products and inventory adjustments by segment, while strengthening customer rating engagement and store reputation to improve product performance signals. This study extends the literature on data-driven product management by demonstrating how a combined segmentation-classification approach can operationalise performance-based portfolio decisions in an emerging market context. Keywords: data-driven strategy; e-commerce; machine learning; product performance; segmentation