Optimizing Digital Advertising Budget Allocation Using AHP and Linear Programming: A Case Study of PT ATD, a Wastewater Treatment Service Company

PT ATD, a wastewater treatment service company, faces strategic challenges allocating its digital advertising budget due to inconsistent channel performance and fluctuating cost per lead (CPL), leading to inefficient spending and suboptimal lead acquisition. Although the company consistently meets its monthly target of 30 leads, the overall conversion rate remains below expectations, suggesting an ineffective allocation of the budget across advertising channels. Data for this study were collected from campaign performance records, direct observation, interviews, and Analytic Hierarchy Process (AHP) questionnaires administered to digital marketing practitioners. AHP was applied to determine the priority weighting of advertising channels based on three criteria: Cost per Lead, Conversion Rate, and Number of Leads. The results show that Number of Leads is the most dominant criterion (61.34%), followed by Conversion Rate (27.24%) and CPL (11.42%). Google Ads emerges as the top-performing Channel with a priority weight of 54.56%, followed by Facebook Ads, Instagram Ads, and LinkedIn Ads. These weights are subsequently incorporated into a Linear Programming (LP) model to optimize budget allocation under explicit budgetary constraints. The optimization results recommend allocating approximately 70% of the total budget to Google Ads, which is projected to increase lead acquisition by up to 75% and reduce CPL by about 45% compared to the existing allocation strategy. From a computational intelligence perspective, the integrated AHP–LP approach is a hybrid algorithmic decision-support framework that combines multi-criteria reasoning with mathematical optimization to address complex resource allocation problems. Although validated using a digital advertising case study, the proposed framework is methodologically generic and domain-independent, making it applicable to a wide range of optimization-driven decision-making contexts. This study demonstrates that integrating AHP and Linear Programming provides an objective, data-driven, and scalable solution for intelligent resource allocation and strategic decision support.