Optimizing Marketing Strategies: Integration of Al-Biruni Earth Radius Algorithm for Feature Selection and Pipeline Regression Model | ||||
| Journal of Artificial Intelligence in Engineering Practice | ||||
| Volume 1, Issue 1, April 2024, Page 18-33 PDF (1.66 MB) | ||||
| DOI: 10.21608/jaiep.2024.354005 | ||||
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| Authors | ||||
Khaled Sh. Gaber1; Ahmed Mohamed Zaki 1; Marwa M. Eid  2, 3; Doaa Sami Khafaga4; Amel Ali Alhussan4; Mahmoud Elshabrawy Mohamed1
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| 1Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA | ||||
| 2Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt | ||||
| 3Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt | ||||
| 4Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia | ||||
| Abstract | ||||
| With the current business environment becoming increasingly ferocious, the effectiveness of digital marketing strategies is no longer a matter of debate as many organizations have realized the need to gain an edge over competition and improve the ROI with their marketing efforts. This study looks into the specifics of digital marketing effectiveness by, in the process, analyzing true indicators and key metrics. Demonstrating an understanding of the complexity of online marketing operations and the diversity of the variables involved, econometric techniques provide feature choice that affects campaign outcomes the most. At first, the variety of performance between different algorithms from feature selection gave the average error ranging from 0.38264 to 0.44194. However, following the optimization provides the tendency to see a decrease in mean errors and an improving performance. Afterward, the step of predictive modeling is implemented, employing diverse machine learning algorithms including ExtraTreesRegressor, GradientBoostingRegressor, SVR, and CatBoost to assess the effectiveness of foreshowing marketing outcomes. Before the optimization, the recommendations made by the predictive modeling are not too accurate and uniform for each algorithm. That being said, however, once the optimization is done, enhancement in prediction accuracy to the tune of substantial improvement is observed with metrics indicating the same as less MSE, RMSE, and R2. Contributing to a more thorough comprehension of the issue of selecting features and models for predicting as well as efficiency of digital marketing, the study also offers an understanding of the opportunities and obstacles that are present in the process of building digital marketing strategies. A thorough evaluation of top metrics and KPIs gives decision-makers data-driven tools to define their marketing activities, deliver tangible results, and stay relevant in the fast-paced digital environment of today. | ||||
| Keywords | ||||
| Digital marketing; Feature Selection; Predictive Modeling; Metrics; KPIs; Optimization | ||||
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