International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 7 | Year 2026 | Article Id. IJETT-V74I7P118 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I7P118

Optimizing Marketing Operations with Artificial Intelligence and Information Technologies

Hassan Ali Al-Ababneh, Ibrahim Alkhaldy, Mohammed Abd-alkarim Almomani, Maher Ibrahim Tawdrous, Noor Ahmad Alkhudierat, Jameel Ahmad Khader

Received Revised Accepted Published
28 Apr 2026 12 Jun 2026 18 Jun 2026 28 Jul 2026

Citation :

Hassan Ali Al-Ababneh, Ibrahim Alkhaldy, Mohammed Abd-alkarim Almomani, Maher Ibrahim Tawdrous, Noor Ahmad Alkhudierat, Jameel Ahmad Khader, "Optimizing Marketing Operations with Artificial Intelligence and Information Technologies," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 7, pp. 263-280, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I7P118

Abstract

Digital marketing is more volatile than traditional marketing, making deterministic marketing planning to be inadequate for allocating resources across technology-mediated channels. The existing AI-powered marketing research has largely been focused on the accuracy of the predictions, with fewer studies focusing on the operational implications of learning that are constrained and risk-aware. This research proposes a stochastic optimization model for marketing operations, considering the risk level of budget allocation, information technology constraints and estimating the revenue using Artificial Intelligence. The following model is proposed in which marketing operations are regarded as a stochastic resource allocation problem, where expected revenue, revenue volatility and saturation effects, as well as budget constraints, are taken into account simultaneously. Corporate-level marketing and financial metrics for Amazon, Walmart, Procter & Gamble, Coca-Cola and Nike are used for empirical testing for the period 2019-2023. The nonlinear revenue response functions are estimated using gradient boosting regression, and the stochastic optimization problem is solved using sample average approximation with 1,000 Monte Carlo scenarios. The findings indicate that the proposed framework can improve ROIs for marketing from 1.00 to 1.23, decrease the volatility of revenue by 26.0%, reduce the 10% VaR-based downside revenue loss from -18.2% to -10.4and enhance scenario robustness by 55.8%. The improvement is validated through statistical testing, sensitivity analysis and ablation analysis. The study makes a contribution to engineering-oriented marketing research by offering a clear, repeatable model for decision support in the context of uncertainty and uncertainty of marketing operations.

Keywords

Artificial intelligence, Stochastic optimization, Marketing operations, Information technologies, Risk-Aware decision support, Digital marketing.

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