Using Machine Learning for Shaping the Future of Advertising for Telco Industry in Indonesia

This paper investigates the enhancement of adver-tising efficiency through data science and machine learning techniques in dynamic business environments. It emphasizes effective advertisement planning and deployment for varied audi-ences using supervised learning methods from machine learning. Analyzing IT sector datasets, the study shows data-driven models guide in optimizing advertisement strategies to bolster market presence and growth. Key results include identifying ‘Success Billing’ and ‘Advertising Networks’ as revenue drivers, with the CatBoost regressors providing precise predictions, evidenced by an R2 Score of 0.82 and the lowest RMSE of 11966 (USD), respectively. This underscores targeted ad spend's impact on revenue and the lesser influence of subscription cycle length. The research also highlights the importance of strategic ad placement and varying effectiveness of ad networks by region, particularly in Indonesia, where a broader ad placement strategy is more effective than relying on a single popular network.