Enhancing Customer Acquisition Strategies Through Look-Alike Modelling with Machine Learning Using the Customer Segmentation Dataset

Ankit Bansal

Authors

Ankit Bansal Manager Consumer Health, 2921 birdcall path, leander, TX 77641, USA Author

Keywords:

Customer Segmentation, Lookalike Modeling, Lifetime Value, Machine Learning, Customer Acquisition

Abstract

In today's highly competitive B2C e-commerce landscape, companies are in a relentless battle to both retain their existing customers and attract those of competitors. The escalating cost of customer acquisition due to increased market saturation and aggressive marketing tactics necessitates a focus on customer retention. The paper outlines a methodology for enhancing customer Acquisition by leveraging ML techniques. This study's objective is to contribute in a special way to the field of enhancing customer Acquisition. The experiments are done using the customer segmentation datasets obtained from Kaggle and data mining techniques, Decision Tree and linear regression. It measures the model performance by using regression metrics that include Mean Square Error, Mean Absolute Error, and R² Score. From the results presented above, it is clear that DT model yields lower MSE of 1.1785 and higher R² score of 89.42% compared to LiR model which scored comparatively low MSE of 3.6896 and low R² score of 78.52% hence proving DT better in explaining variance of target variable. This study offers a strong foundation on how customer acquisition can be done through data analysis and predictive modelling.

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