Abstract

Predictive analytics is pivotal in shaping strategic decision-making in today's business environment. Analytical projects, however, can be broadly categorized by two distinct objectives: explanation and prediction. This paper contrasts the two primary approaches of data modeling – statistical modeling for inference and machine learning for prediction – through a practical application in marketing analytics. First, a traditional multiple linear regression model is constructed using a publicly available advertising dataset to explain the relationship between different advertising channel expenditures and sales. The model's coefficients and statistical significance are interpreted to derive actionable insights for budget allocation. Second, a suite of machine learning models is developed and evaluated to identify the most accurate predictive engine for forecasting future sales. Lastly, by direct comparison, the study recommends employing explanatory statistical models to predict unseen data and advocates evaluating models’ predictive accuracy using machine learning models designed explicitly for prediction tasks. This highlights the inherent trade-off between model interpretability and predictive performance, offering practical criteria for analysts to consider when selecting the most suitable modeling approach.

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