Data-Driven SEO Strategy Optimization to Enhance MSME Sales
Performance on Indonesian E-Commerce Platforms
Dublin Core
Title
Data-Driven SEO Strategy Optimization to Enhance MSME Sales
Performance on Indonesian E-Commerce Platforms
Performance on Indonesian E-Commerce Platforms
Subject
Digital Marketing, Search Engine Optimization (SEO), Data Analytics, MSME, E-Commerce, Indonesia, Ensemble Regression, Random Forest,
Gradient Boosting
Gradient Boosting
Description
The rapid growth of digital commerce in Indonesia has created both opportunities and challenges for Micro, Small, and Medium Enterprises
(MSMEs) seeking to increase their online visibility and sales. This study presents a data-driven approach to Search Engine Optimization (SEO)
strategy optimization aimed at enhancing MSME sales performance on leading Indonesian e-commerce platforms, including Tokopedia and
Shopee. Using a quantitative design, the research integrates Microsoft Excel for preliminary data exploration and Google Colab (Python) for
advanced analysis and predictive modeling. The dataset, comprising over 1,000 transaction entries, includes key SEO-related indicators such as
keyword rank, website traffic, backlinks, social media engagement score, advertising spend, and monthly sales. Ensemble regression models—
Random Forest and Gradient Boosting—were employed to evaluate the predictive relationship between SEO factors and sales outcomes,
validated through RMSE and R² metrics. The findings indicate that advertising expenditure (r = +0.83), backlinks (+0.29), and social media
engagement (+0.25) are the most influential predictors of sales performance, while website traffic shows a weaker positive correlation (+0.13).
These results highlight the critical role of integrated SEO and digital advertising strategies in improving MSME competitiveness. The study
demonstrates that accessible analytical tools can empower MSMEs to make data-driven marketing decisions. Future research should expand
model generalization across industries and explore additional digital variables to improve predictive accuracy
(MSMEs) seeking to increase their online visibility and sales. This study presents a data-driven approach to Search Engine Optimization (SEO)
strategy optimization aimed at enhancing MSME sales performance on leading Indonesian e-commerce platforms, including Tokopedia and
Shopee. Using a quantitative design, the research integrates Microsoft Excel for preliminary data exploration and Google Colab (Python) for
advanced analysis and predictive modeling. The dataset, comprising over 1,000 transaction entries, includes key SEO-related indicators such as
keyword rank, website traffic, backlinks, social media engagement score, advertising spend, and monthly sales. Ensemble regression models—
Random Forest and Gradient Boosting—were employed to evaluate the predictive relationship between SEO factors and sales outcomes,
validated through RMSE and R² metrics. The findings indicate that advertising expenditure (r = +0.83), backlinks (+0.29), and social media
engagement (+0.25) are the most influential predictors of sales performance, while website traffic shows a weaker positive correlation (+0.13).
These results highlight the critical role of integrated SEO and digital advertising strategies in improving MSME competitiveness. The study
demonstrates that accessible analytical tools can empower MSMEs to make data-driven marketing decisions. Future research should expand
model generalization across industries and explore additional digital variables to improve predictive accuracy
Creator
Thosporn Sangsawang1,*, Shuang Li2
Source
https://ijiis.org/index.php/IJIIS/article/view/262/164
Publisher
Rajamangala University of Technology Thanyaburi, Thailand
Date
september 2025
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Thosporn Sangsawang1,*, Shuang Li2
, “Data-Driven SEO Strategy Optimization to Enhance MSME Sales
Performance on Indonesian E-Commerce Platforms,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9732.
Performance on Indonesian E-Commerce Platforms,” Repository Horizon University Indonesia, accessed January 1, 2026, https://repository.horizon.ac.id/items/show/9732.