Article: A performance boosting transition in predictive modelling for customer acquisition Journal: International Journal of Applied Management Science (IJAMS) 2025 Vol.17 No.4 pp.400 - 418 Abstract: Improving business performance requires accurate prediction and decision-making, which are informed by historical data insights such as user attention, action, and profit from the products. These insights are generated using statistical techniques that forecast business needs and analyse data to make informed business decisions. Using historical data, a predictive model is built using a statistical approach, commonly employing methods such as regression, time series and cluster analysis. Machine Learning (ML) algorithms enhance this process by automating tasks and uncovering insights. Traditional analysis methods struggle with the complex pattern and dynamic nature of the data, leading to difficulties in interpreting relationships. To address this issue, the Performance Boost - Predictive Model (PB-PM) is proposed, which comprises two levels: level 1 employs Linear Regression (LR) and level 2 employs Ridge regression, Lasso regression, Random Forest (RF) regression and Extreme Gradient Boost (XGBoost) regression. The performance of the PB-PM model is evaluated using Mean Absolute Error (MAE) and R-squared score (R²). The analysis result indicates XGBoost regression algorithm yields the highest R² and lowest MAE for the proposed PB-PM, respectively 0.23 and 0.990. Inderscience Publishers - linking academia, business and industry through research

Title: A performance boosting transition in predictive modelling for customer acquisition

Authors: C. Rajathi; P. Rukmani

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

Abstract: Improving business performance requires accurate prediction and decision-making, which are informed by historical data insights such as user attention, action, and profit from the products. These insights are generated using statistical techniques that forecast business needs and analyse data to make informed business decisions. Using historical data, a predictive model is built using a statistical approach, commonly employing methods such as regression, time series and cluster analysis. Machine Learning (ML) algorithms enhance this process by automating tasks and uncovering insights. Traditional analysis methods struggle with the complex pattern and dynamic nature of the data, leading to difficulties in interpreting relationships. To address this issue, the Performance Boost - Predictive Model (PB-PM) is proposed, which comprises two levels: level 1 employs Linear Regression (LR) and level 2 employs Ridge regression, Lasso regression, Random Forest (RF) regression and Extreme Gradient Boost (XGBoost) regression. The performance of the PB-PM model is evaluated using Mean Absolute Error (MAE) and R-squared score (R²). The analysis result indicates XGBoost regression algorithm yields the highest R² and lowest MAE for the proposed PB-PM, respectively 0.23 and 0.990.

Keywords: business intelligence; cost prediction; lasso regression; linear regression; machine learning; random forest; ridge regression; XGBoost.

DOI: 10.1504/IJAMS.2025.149477

International Journal of Applied Management Science, 2025 Vol.17 No.4, pp.400 - 418

Received: 26 Oct 2023
Accepted: 07 Oct 2024
Published online: 04 Nov 2025 *

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Title: A performance boosting transition in predictive modelling for customer acquisition

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