International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Analyzing AI-Driven Marketing ROI Measurement and Optimization: An Integrated Framework and Power BI Implementation for Causal-Predictive Analysis.

Author(s) Mr. Vineet Kumar
Country India
Abstract The advancement of businesses and growing Artificial Intelligence (AI) patterns in decision making with Digitalization of Marketing activities has created a need for more accurate, real-time analysis and measurement of Marketing Return on Investment (M-ROI). Customary methods such as last-click attribution which only consider the last touch point of customer interaction just before conversion ignores other attributes through which the customer has interacted or have shown interest. Similarly, heuristic budgeting which allocates funds based upon simple, non-scientific methods and apply thumb rule for budget allocation fails to capture and analyse the exact spending as they are not data driven. It can be established that these methods are simple, easy, fast and of low cost but significantly biased which ignores priorities and touch points where these data are not data driven which leads to inefficient budgets. This research will present a unified, AI-Driven and Business Intelligence enabled framework that combines casual –predictive modelling with Interactive Power BI analytics to measure, forecast and optimize marketing ROI. This paper suggest an integrated casual predictive analytical framework for continuous improvement of ROI integrating; First, causal multi-touch attribution (MTA) using shapely-value and Markov-chain methods for revenue allocation across channels, Second Deep-learning customer lifetime value (CLV) modelling to predict long term profitability and Third reinforcement learning based budget reallocation. To bridge academic stringency with managerial application, the framework is functionalized in Microsoft Power BI, enabling dynamic ROI computation, attribution visualization and uplift simulation using DAX and modular datasets. These set of rules along with experimental data was implemented in Power BI creating a modular data model, with DAX Measures for ROI calculations, CLV weighted ROI and uplift analysis. Evaluated with synthetic business like marketing dataset and analysing through visual components demonstrates that casual AI models reduces attribution bias by 25-25% compared to rule based methods and improves CLV forecasting accuracy with MAPE below 10%. Also a calculative experiment reveals that optimized spend reallocation (15% from low impact to high impact channels) increases the ROI from 150% to approximately 180%, representing 19.7% uplift. Result of such experiment shows that integrating AI analytics with BI Visualizations improved measurement accuracy through data-driven causal attribution; enhanced forecasting and optimization capability via predictive CLV and uplift models; and operational interpretability, enabling non-technical managers to act on model outputs through dynamic dashboards. Overall, the research contributes a reproducible, end-to-end system-spanning theory, computation, and visualization for measuring and optimizing marketing ROI. The result demonstrates meaningful ROI improvement and practical feasibility for organizational deployment. Future work will extend this architecture to incorporate privacy-preserving causal inference, real-time data streaming, and federated analytics.
Keywords Artificial Intelligence, Digitalization of Marketing, Marketing Return on Investment (M-ROI), multi-touch attribution (MTA), customer lifetime value (CLV), casual predictive analytical framework, operational interpretability, real-time data streaming
Field Business Administration
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-11
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.63091
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E-ISSN 2582-2160
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