The analysis aims to assess the effectiveness of Campaign Type C specifically for the departments that contribute to the bottom 25% of revenue in the Regork company. This evaluation is crucial as it can provide valuable insights into the return on investment (ROI) for Campaign Type C and help the Regork CEO make data-driven decisions. By identifying the impact of this campaign on departments with lower revenue contribution, the CEO can optimize marketing strategies, allocate resources efficiently, and improve overall profitability.
To address this problem, historical sales and campaign data were utilized. Upon identifying the departments falling in the bottom 25% of revenue contribution, households associated with these departments and their participation in Campaign Type C were filtered. The redemption rates, participation rates, and sales impact of this campaign on these departments were analyzed.
The analysis provides a comprehensive understanding of the effectiveness of Campaign Type C within departments that are lagging in revenue contribution. This information can be invaluable to the Regork CEO for several reasons:
By understanding the impact of Campaign Type C on low-performing departments, the CEO can decide whether to allocate resources differently or modify the campaign strategy to improve ROI.
Improving the performance of underperforming departments can contribute to overall profitability and company growth.
pandas: Used for data manipulation and analysis. Provides DataFrames to work with tabular data efficiently. seaborn: Simplifies creating informative statistical graphics. Enhances the aesthetics of plots. matplotlib.pyplot: Provides a versatile way to create a wide range of plots and charts for data visualization. plotly.express: Useful for interactive and visually appealing plots, particularly for web-based visualizations. numpy: Fundamental for numerical computing and mathematical operations on arrays.