Structuring Customer Data Requirements for Sales Enhancement through Big Data Analytics

Authors

Muhsin Nor Paizin Pusat Pungutan Zakat, Kuala Lumpur, Malaysia Author https://orcid.org/0000-0001-7332-9673

DOI:

https://doi.org/10.66277/nmr.1.1.155

Keywords:

Big Data Analytics, Business Sales, Customer Data Management, Sales Performance, Single Customer View, Strategic Decision-Making

Abstract

Businesses increasingly face pressure to enhance sales performance while managing the rapid expansion of customer data generated from diverse digital sources. This data proliferation necessitates more structured and strategic Customer Data Management (CDM) approaches to fully leverage big data analytics. Accordingly, this study aims to develop a conceptual framework that identifies the essential customer data requirements for improving strategic decision-making and sales outcomes. Employing an integrative literature review, this research synthesizes contemporary studies on big data analytics, customer engagement, and data management systems. The findings reveal that effective data utilization requires the systematic classification of customer information into four key dimensions: identity, quantitative, descriptive, and qualitative data. This framework extends the conventional “five Vs” of big data by translating abstract data characteristics into actionable analytical domains. The study argues that organizations adopting this structured approach can construct a comprehensive single customer view, enabling more precise segmentation, personalized marketing, and improved customer lifetime value. This study contributes to the literature by offering a domain-specific model for CDM that bridges theoretical abstraction and practical implementation in data-driven sales strategies.

[Bisnis saat ini menghadapi tekanan yang semakin besar untuk meningkatkan kinerja penjualan sekaligus mengelola pertumbuhan pesat data pelanggan yang dihasilkan dari berbagai sumber digital. Proliferasi data ini menuntut penerapan pendekatan Customer Data Management (CDM) yang lebih terstruktur dan strategis guna memaksimalkan pemanfaatan analitik big data. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan suatu kerangka konseptual yang mengidentifikasi kebutuhan data pelanggan yang esensial dalam rangka meningkatkan pengambilan keputusan strategis dan hasil penjualan. Dengan menggunakan pendekatan integrative literature review, penelitian ini menyintesis berbagai studi kontemporer terkait analisis big data, keterlibatan pelanggan, dan sistem manajemen data. Hasil penelitian menunjukkan bahwa pemanfaatan data yang efektif memerlukan klasifikasi sistematis informasi pelanggan ke dalam empat dimensi utama, yaitu data identitas, kuantitatif, deskriptif, dan kualitatif. Kerangka ini memperluas konsep konvensional “five Vs” dalam big data dengan menerjemahkan karakteristik data yang bersifat abstrak ke dalam domain analitis yang operasional. Penelitian ini berargumen bahwa organisasi yang mengadopsi pendekatan terstruktur ini mampu membangun single customer view yang komprehensif, sehingga memungkinkan segmentasi yang lebih presisi, pemasaran yang terpersonalisasi, serta peningkatan customer lifetime value. Studi ini berkontribusi terhadap literatur dengan menawarkan model CDM yang bersifat spesifik domain, yang menjembatani abstraksi teoretis dengan implementasi praktis dalam strategi penjualan berbasis data.]

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