International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Advanced Business Intelligence Conceptual Model for Improving Revenue Growth in Small and Medium Enterprises

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Volume: 1 | Issue: 5 | Pages: 927-942

Subject: BizIntelligence

Country: United States

DOI: https://doi.org/10.54660/IJMRGE.2020.1.5.927-942

License: CC BY 4.0

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Abstract

This paper proposes an Advanced Business Intelligence conceptual model to accelerate revenue growth in small and medium enterprises. Built as a repeatable value engine, the model unifies data, analytics, and decision execution across four layers: Data Foundation, Insight Generation, Decision Orchestration, and Value Realization, reinforced by governance loops. The Data Foundation harmonizes transactional, CRM, marketing, supply, and external signals via lightweight data contracts, enabling privacy-preserving pipelines. Insight Generation employs descriptive and predictive methods to surface revenue levers, price elasticity, customer lifetime value, and churn risk. Decision Orchestration pairs prescriptive analytics with automation to deliver next-best actions in frontline tools, while Value Realization closes the loop by measuring uplift and cash impact. The model is engineered for SME constraints limited capital, sparse data, and lean teams by favoring cloud platforms, open standards, and low-code tooling. A staged roadmap begins with trusted reporting, advances to predictive scoring for acquisition, retention, and upsell, and culminates in closed-loop optimization that continuously tunes offers, prices, and inventory. Governance spans data stewardship, model risk management, and privacy-by-design to sustain compliance and trust. Workforce enablement integrates analytics translators, revenue operators, and citizen developers, supported by templates, playbooks, and KPI trees. Methodologically, the study synthesizes design science, lean experimentation, and decision intelligence to specify artifacts, flows, and metrics; and applies digital-twin scenario tests to de-risk changes before rollout. Reference use cases include price and promotion optimization, CLV-based budgeting, cross-sell sequencing, demand sensing for inventory, and sales coverage planning. Implementation guidance covers use-case prioritization by value and feasibility, minimal viable data, data product ownership, model monitoring with drift alarms, and experiment governance. A financial logic links intervention to margin, velocity, and risk, supporting ROI cases, reinvestment decisions. Evidence from pilots indicates faster opportunity discovery, higher conversion and retention, reduced discount leakage, and resilient cash flows. The model provides a practical blueprint that enables SMEs to translate business intelligence investments into measurable, repeatable, and compounding revenue gains over time.

How to Cite This Article

David Excel Ozowara, Chukwudera Obumneke Anunagba, Peter Adeyemo Adepoju (2020). Advanced Business Intelligence Conceptual Model for Improving Revenue Growth in Small and Medium Enterprises . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 1(5), 927-942. DOI: https://doi.org/10.54660/IJMRGE.2020.1.5.927-942

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Journal: International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE)

Publisher: Anfo Publication House

ISSN: 2582-7138 (Online)

Frequency: Bimonthly

Language: English

Open Access: Yes - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

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