In the world of data analysis, "econometrics" and "machine learning" are two terms you'll often hear. While they share tools and techniques, they have fundamentally different goals: explanation versus prediction.
Econometrics: The Science of "Why"
The primary goal of econometrics is to understand causal relationships. An econometrician wants to know: If I change X, how does Y change? For example, "If I increase my advertising spend by 10%, *by how much* will my sales increase?" To answer this, econometricians build models based on economic theory. They are obsessed with isolating the causal effect of one variable, controlling for all other confounding factors. The interpretability of the model is paramount. The goal is to get an unbiased estimate of a specific coefficient.
Machine Learning: The Science of "What Next"
The primary goal of machine learning (ML), especially in a business context, is prediction. An ML model is judged on one main criterion: its predictive accuracy. For example, "Given a customer's past purchase history and browsing behavior, *will they* churn next month?" ML models, particularly deep learning models, can be incredibly complex "black boxes." While they might be highly accurate at predicting an outcome, it's often difficult or impossible to understand exactly *why* they made a particular prediction. The relationship between inputs and outputs can be non-linear and incredibly complex.
When to Use Which?
- Use Econometrics when... you need to understand the effect of a specific action to make a strategic decision. (e.g., "Should we lower our price? We need to know the price elasticity of demand.")
- Use Machine Learning when... you need to make a large number of automated predictions at scale. (e.g., "Which of our 10 million users should we show this ad to? We need to predict the click-through rate for each one.")
The Best of Both Worlds
The lines are blurring. Modern data science often involves a combination of both. For instance, you might use an econometric model to identify the key causal drivers of customer retention. Then, you could use those drivers as features in a more powerful machine learning model to predict which specific customers are most at risk. At Sterling Contractors, we are experts in both disciplines, allowing us to choose the right tool for your specific business problem.