This project investigates the relationship between spending on various marketing channels and overall product sales using linear regression analysis. The goal was to identify the most effective marketing channels for driving product sales by analyzing the impact of TV advertisements, billboards, Google Ads, social media, influencer marketing, and affiliate marketing. After applying linear regression models to the data, we concluded that affiliate marketing has the highest impact on increasing product sales.
Ceci, Victoria Williams, Spenser
The goal of this project is to identify which marketing channels have the most significant effect on product sales, enabling businesses to optimize their advertising budget allocation for maximum return on investment.
The dataset used contains 300 rows of transaction data, including spending on various marketing channels and the corresponding product sales.
Linear regression models were applied to each marketing channel to predict product sales. The analysis revealed that affiliate marketing had the highest impact on sales, based on R-squared and Root Mean Squared Error (RMSE) values.
Affiliate marketing was the most effective channel. TV advertising, billboards, and Google Ads also showed significant influence on sales.
Our analysis concludes that businesses should prioritize affiliate marketing for the highest impact on product sales. Further work could involve segmenting the affiliate marketing category into specific types for more granular insights.