A Multi-Criteria Decision-Support Framework for Optimizing Digital Marketing Strategies in Indonesian SMEs Using Fuzzy AHP–TOPSIS

Bunga Aditi; Tapi Rondang Ni Bulan; Jufrizen

doi:10.31081/dmame8220251671

Authors

Bunga Aditi Fakultas Ekonomi Bisnis, Universitas Harapan Medan, Indonesia
Tapi Rondang Ni Bulan Fakultas Ekonomi Bisnis, Universitas Harapan Medan, Indonesia
Jufrizen Fakultas Ekonomi dan Bisnis, Universitas Muhammadiyah Sumatera Utara, Indonesia

DOI:

https://doi.org/10.31081/dmame8220251671

Keywords:

Decision-Support System, Digital Marketing Strategy, Multi-Criteria Decision Making, SMEs, Marketing Optimization, Fuzzy AHP

Abstract

Digital marketing has emerged as a critical determinant of competitiveness for small and medium enterprises (SMEs) operating within emerging economies. A comparable situation is observed in Indonesia, where the identification of suitable marketing strategies has become increasingly intricate due to multiple interacting constraints. These include limitations associated with financial resources, performance expectations, and other operational restrictions. Despite this complexity, a considerable proportion of SMEs continue to depend on intuition-based judgement rather than adopting systematic and model-driven approaches. To address this shortcoming, the present study develops a multi-criteria decision support framework integrating Fuzzy AHP and TOPSIS for the optimisation of digital marketing strategy selection among Indonesian SMEs. The proposed framework assesses alternative strategies using key evaluation dimensions, including economic outcomes such as return on investment (ROI) and conversion rates, levels of customer engagement, brand equity development, and associated operational expenditures. Empirical evidence was obtained from marketing professionals and SME practitioners located in major urban centres of Indonesia. The findings indicate that strategies centred on influencer-led content, when combined with performance-oriented advertising mechanisms, achieve the highest aggregated evaluation scores under the simultaneous consideration of financial and branding criteria. The developed model offers a systematic and data-oriented mechanism for prioritising marketing alternatives and determining an efficient portfolio under conditions of uncertainty, thereby minimising reliance on subjective judgement. This research advances the domain of decision science by presenting a structured analytical framework that supports improved allocation of strategic resources and strengthens the digital competitiveness of SMEs. Furthermore, it provides actionable insights for both SME managers and policymakers, highlighting the necessity of adopting analytically robust approaches in marketing decision-making processes.

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