Machine Learning–Enhanced Predictive Marketing Analytics for Optimizing Customer Engagement and Sales Forecasting

Md Al Rafi

doi:10.15662/IJRAI.2023.0604004

Authors

Md Al Rafi Washington University of Science and Technology, Alexandria, Virginia, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0604004

Keywords:

Predictive analytics, Machine learning models, Sales forecasting, Customer engagement prediction, Marketing automation, Data mining

Abstract

The high rate of customer interaction data growth in the online market has heightened the need to have smart analytical tools that can facilitate data-driven marketing processes. The current paper provides a sophisticated framework of predictive marketing analytics driven by machine learning that enhances customer engagement prediction and the accuracy of sales forecasting as well. The nature of the proposed framework is the combination of data mining, exploratory analytics, Rand Forest and optimized AdaBoost algorithm to effectively identify multi-channel customer interaction and complex nonlinear behavioral pattern. Advanced feature engineering, normalization and outlier mitigation schemes are pre-processing historical sales transactions, customer engagement logs and campaign performance data before developing the model. To evaluate the model, 80:20 training and test split are performed and cross-validation and hyperparameter optimization are used to guarantee strong and generalizable performance.

Simulation studies have shown that the optimized AdaBoost model has a much better performance compared to the traditional statistical and regression-based models. The optimized AdaBoost is 18-25 percent more accurate in customer engagement prediction and with less error in sales forecasting (RMSE) than baseline models do. Other important predictors of behavior identified by the analytical framework include interaction recency, frequency of purchase and intensity of engagement through multi-channel. The proposed system has been shown to increase customer conversion rate by 14 in a simulated marketing automation environment, which can be attributed to the better identification of high-value and high-propensity customer segments.

Comprehensively, this paper illustrates the transformative nature of optimally structured ensemble learning models, specifically AdaBoost, to predictive marketing analytics to offer a scalable data-intensive tool in the refinement of customer engagement strategies, as well as in improving the quality of sales forecasts.

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