<![CDATA[Business prospect conversion is a critical process in achieving sales performance within Business-to-Business (B2B) telecommunications services. However, many organizations experience difficulties in converting prospects into customers due to various operational constraints throughout the sales process. This study aims to identify the primary constraints affecting the conversion of business prospects into customers at Telkom Manyar Regional Office by applying the Theory of Constraints (TOC) approach. The study integrates prospect data quality measurement using Tuple Completeness and predictive analytics through a Decision Tree model. A quantitative descriptive approach was employed using 600 prospect records collected from January to June 2025. Tuple Completeness was utilized to evaluate the completeness of prospect data, while Fishbone Diagram analysis was used to identify root causes of conversion constraints. Furthermore, a Decision Tree model was developed using Altair AI Studio to predict conversion outcomes and identify influential variables. The results indicate that prospect data quality represents the primary constraint in the conversion process. Prospects with 100% Tuple Completeness achieved a conversion rate of 79.29%, while prospects with 67% and 33% completeness achieved conversion rates of 25.15% and 0%, respectively. The Decision Tree model achieved an accuracy of 86.11%, identifying Tuple Completeness as the most influential predictor of conversion success. These findings demonstrate that improving prospect data quality can significantly enhance customer conversion performance. The study contributes to the integration of Theory of Constraints, data quality assessment, and predictive analytics in the context of B2B customer acquisition within the telecommunications industry.]]>
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