This repository hosts the GoodBelly Marketing Analytics Project, which aims to dissect the impact of various marketing strategies on the sales volume and average retail price (ARP) of Good Belly products. Using a dataset encompassing sales, promotions, and demographic information across multiple regions, this project employs causal analysis and multiple linear regression to provide insights into the effectiveness of marketing activities. Project Objective The primary objective of this project is to evaluate the return on investment (ROI) for Good Belly's marketing demos and promotions, determining their justification and suggesting improvements. This analysis aims to aid marketing strategists in making data-driven decisions to enhance sales performance and optimize marketing spend.
The dataset includes weekly sales data for Good Belly products, detailed as follows: • Date: Week ending date for the sales data. • Region: Geographic region of the store. • Store: Unique identifier for each store. • Sales: The number of ounces sold. • ARP: Average retail price per ounce sold. • Sales_Rep: Presence of a sales representative (0 for no, 1 for yes). • Endcap: Indication of endcap promotion (0 for no, 1 for yes). • Demo: Indication of in-store demonstration (0 for no, 1 for yes). • Natural: Count of natural retailers within a 5-mile radius. • Fitness: Count of fitness centers within a 5-mile radius. Methodology
The dataset underwent rigorous cleaning and preprocessing to ensure the integrity of the analysis. This included handling missing values, encoding categorical variables, and normalizing the data where necessary. Exploratory Data Analysis (EDA) Comprehensive EDA was conducted to uncover underlying patterns and relationships within the data, focusing on sales trends, price elasticity, and the geographical distribution of marketing activities.
A key component of our methodology, causal analysis was utilized to ascertain the effect of marketing strategies on sales outcomes. This involved comparing sales performance with and without the implementation of specific marketing activities, such as demos and endcap promotions.
Multiple linear regression models were developed to quantify the relationship between marketing activities (independent variables) and sales performance (dependent variable), allowing for the identification of significant predictors of sales volume and ARP.
Our analysis revealed several key insights: • The presence of a sales representative significantly enhances the effectiveness of endcap promotions. • In-store demonstrations have a positive impact on sales volume, with an increased effect when conducted in conjunction with other marketing activities. • Regional variations in the effectiveness of marketing strategies suggest the need for tailored marketing approaches.
The GoodBelly Marketing Analytics Project highlights the importance of strategic marketing planning and the potential for data-driven approaches to significantly improve sales outcomes. Recommendations for Good Belly include: • Increasing the frequency and visibility of in-store demonstrations in high-performing regions. • Tailoring marketing strategies to regional preferences and demographics. • Continuously monitoring and analyzing sales data to inform future marketing initiatives.
To replicate this analysis or explore the dataset:
Clone this repository to your local machine. Ensure that R and necessary libraries (dplyr, ggplot2, caret, lmtest, and others as required by the analysis) are installed. You can install these packages using install.packages("packageName") in R. Open the R Markdown files provided in RStudio or another R environment to view the analysis and results. Contributions to this project are welcome. Please refer to the contributing guidelines before making a pull request. License This project is licensed under the MIT License - see the LICENSE file for details.