Optimizing Customer Acquisition Through Business Buying Behaviour Analysis for AI Deployment to Enhance Competitiveness: A Case Study from The Perspective of an AI Provider (Blue Core)
DOI:
https://doi.org/10.55927/ijar.v5i7.16814Keywords:
B2B Marketing Strategy, Business Buying Behaviour, Generative Artificial Intelligence, Enterprise AI Adoption, Indonesia, Marketing Mix 7P, TOWS MatrixAbstract
This study investigates the key factors influencing business-to-business (B2B) decision-making in Indonesian financial institutions when adopting enterprise generative artificial intelligence (GenAI) platforms and proposes strategic marketing initiatives for an AI provider (BlueCore, pseudonym) to strengthen competitive positioning and enhance financial performance. A mixed-methods research design was employed, combining semi-structured interviews and structured questionnaires with nine respondents from small, medium, and large-scale financial institutions. External analysis utilized the PESTEL framework, competitor grid analysis, and the Buygrid Framework (Robinson et al., 1967). Internal analysis applied Segmentation-Targeting-Positioning (STP), the 7P Marketing Mix, Internal Factor Evaluation (IFE), External Factor Evaluation (EFE), TOWS Matrix, and Quantitative Strategic Planning Matrix (QSPM). B2B purchasing decisions for enterprise AI in Indonesian banking are universally consultative, driven predominantly by environmental stimuli including regulatory compliance and competitive pressure. QSPM analysis identifies "Maximizing Proprietary Platform Capabilities through Live Demonstrations and Tailored AI Solutions" as the top-priority strategy (TAS = 6.79), followed by Governance-First Positioning (TAS = 6.65) and Modular Adoption Staging (TAS = 6.61). This study provides an empirically grounded analysis of enterprise AI procurement behaviour in an emerging market context, offering actionable strategic guidance for AI providers navigating Indonesia's rapidly expanding but governance-sensitive technology landscape.
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