ANALISIS REGRESI UNTUK MENGOPTIMALKAN ANGGARAN PEMASARAN DIGITAL

(STUDI KASUS ROI PADA 5 PLATFORM MEDIA SOSIAL)

Abstract

Digital transformation has shifted the marketing paradigm, yet optimizing budgets across various social media platforms remains challenging for companies in Indonesia. This research aims to develop a regression analysis model to optimize digital marketing budget allocation across five major social media platforms. Adopting a quantitative approach with correlational analytical design, this study involved 120 medium and large companies in Indonesia actively implementing digital marketing strategies. The results show significant positive correlations between budget allocation and ROI for all platforms, with the strongest correlations found in Instagram (r = 0.68) and TikTok (r = 0.64). Non-linear regression models identified diminishing returns patterns on several platforms, with optimal budget allocation for Instagram at approximately 43.75%. Cluster analysis yielded four company groups with distinct profiles and performance, with the highest ROI (7.5%) in the cluster prioritizing TikTok and Instagram. The optimization model developed recommends allocations of: Instagram (32.5%), TikTok (28.7%), YouTube (18.8%), Facebook (10%), and LinkedIn (10%), projecting a 27.3% ROI increase. Model validation through case studies confirmed an average ROI increase of 21.5%. This research provides an analytical framework for digital marketing budget optimization that can be adapted to company characteristics and platform dynamics.

Keywords: digital marketing, regression analysis, return on investment, social media, budget optimization