Which analytical method would you use to establish a causal relationship between marketing expenditures and sales?

Using regression analysis is an effective way to establish a causal relationship between marketing expenditures and sales. This method allows you to quantify the impact of changes in marketing spend on sales outcomes by analyzing historical data.

In regression analysis, you can use various forms of regression, such as linear regression, to determine how much sales are expected to increase (or decrease) with each unit increase in marketing expenditure. This analysis also helps control for other variables that might influence sales, allowing a clearer picture of the direct relationship between marketing efforts and sales performance.

Moreover, regression analysis can indicate the strength and significance of the relationship through statistical measures such as the coefficient of determination (R-squared) and p-values, which provide insights into how reliably marketing expenditures can be linked to changes in sales.

While alternative methods like A/B testing are useful for understanding the effects of specific marketing strategies in controlled experiments, they do not typically provide a comprehensive statistical model for analyzing historical data over time or for estimating future sales based on various marketing expenditure scenarios. Data mining focuses on discovering patterns within large datasets rather than establishing causal relationships. Time series analysis is beneficial for forecasting based on historical trends but does not directly address the causality between marketing spend and sales.