Optimizing Subscription Package Selection Using a Business Intelligence-Based Decision Support System on Customer Payment Data
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Abstract
Subscription-based business models generate large amounts of customer payment data that has the potential to be used to support strategic decision-making. However, in many companies, the use of this data is still limited to presenting descriptive information through Business Intelligence dashboards, thus failing to produce objective decision recommendations. This condition often results in the selection of superior subscription packages being based on subjective assessments. This study aims to design a Decision Support System (DSS) integrated with Business Intelligence to determine the optimal subscription package. The Analytic Hierarchy Process method is used to determine the criteria weights, Simple Additive Weighting is used to rank alternatives, and the Technique for Order Preference by Similarity to Ideal Solution is used to validate the ranking results. The research data comes from customer payment data including the number of customers, number of transactions, total revenue, and average transaction value. The results show that the New Hit package consistently ranks highest in the SAW and TOPSIS methods. These findings demonstrate that the integration of Business Intelligence and Decision Support System (DSS) can produce objective, stable and reliable data-driven recommendations to support a company's strategic decisions.
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