Market Metrico: An In-Depth Analysis of Correlating ROI, CPC, and Acquisition Costs Across Advertising Channels

Description

Market Metrico is an advanced analytical project designed to explore the intricate relationships between Return on Investment (ROI), Cost Per Click (CPC), and Customer Acquisition Costs across various advertising channels. Utilizing data from prominent social media platforms such as Facebook, Google, Email, and YouTube, this project aims to uncover actionable insights that can enhance marketing strategies and optimize advertising spend.

Built with R, Market Metrico leverages powerful data analysis and visualization techniques to provide a comprehensive understanding of advertising metrics.

Features

• Data Aggregation: Collects and integrates advertisement data from multiple social media platforms.
• Data Cleaning: Ensures data accuracy through thorough preprocessing.
• Exploratory Data Analysis (EDA): Offers detailed EDA to identify patterns and trends.
• Visualization: Presents data insights through various visualization techniques.
• Statistical Analysis: Conducts correlation analysis to explore relationships between key metrics.
• Reporting: Generates detailed reports to summarize findings and recommend optimizations.

Requirements

• R (version 4.0.0 or later)
• RStudio (optional but recommended)
• R packages:
  • dplyr - Data manipulation
  • readr - CSV file reading
  • DataExplorer - Data exploration
  • DT - Displaying tables in Rmarkdown
  • ggplot2 - Plotting graphs
  • heatmaply - Creating heatmaps
  • kableExtra - Managing markdown content
  • tidyr - Enhancing text visibility
  • caret - Handling regression models
  • e1071 - Building Support Vector Models
  • cluster - Building clustering models
  • forecast - Time series analysis

Installation

1. Clone the repository:

   git clone https://github.com/wizaye/Market-Metrico.git
   cd Market-Metrico

2. Install the required R packages:

   install.packages(c("dplyr", "readr", "DataExplorer", "DT", "ggplot2", "heatmaply", "kableExtra", "tidyr", "caret", "e1071", "cluster", "forecast"))

Usage

1. Load the data: Place your advertising data files (e.g., CSV files from Facebook, Google, Email, YouTube) into the datasets/ directory.

2. Run the analysis:

   • Load and preprocess the data.
   • Perform exploratory data analysis.
   • Visualize key metrics and relationships.
   • Conduct correlation analysis.

3. Generate the report: Create a report.Rmd and knit the file with suitable code snippets from the repo to produce a comprehensive report in HTML or PDF format, summarizing the findings and insights.

Project Structure

• datasets/: Directory to store raw advertisement data files.
• code/: Contains R scripts for data processing and analysis.
  • models/: Contains R scripts for building and evaluating models.
  • Preprocessing.R: Script for data cleaning and preprocessing.
  • final_modified_data.R: Script for deriving additional columns.

Contributing

Contributions are welcome! Please fork the repository and submit pull requests for any enhancements or bug fixes.

Documentation

The Detailed explanation of the code is published on the Rpubs website.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Authors

• @khizarsait
• @Vijayendher Gatla
• @Sai_Karthikeya