Assessing Ad Clicks for Significance

Project Overview

This project analyzes the effectiveness of different text colors in a company's online advertisements. The goal is to determine if specific colors have significantly different click-through rates than the current blue text color. To read more about the process, check out my blog post The Color of Success: Optimizing Ad Click-Through Rates.

Table of Contents

1. Installation
2. Project Motivation
3. File Descriptions
4. Results
5. Licensing, Authors, and Acknowledgements

Installation

This project requires Python 3.x and the following Python libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scipy
• statsmodels

You can install these packages using pip:

pip install pandas numpy matplotlib seaborn scipy statsmodels

Project Motivation

The stakeholder wants to optimize online advertisements by identifying the most effective text color. The specific questions addressed are:

1. Does blue have the highest click-through rate?
2. If not, which color does?
3. Are the differences in click-through rates statistically significant?

File Descriptions

• assessing_ad_clicks.ipynb: Jupyter notebook containing the primary analysis
• ad_click_table.csv: Dataset used for the analysis (not included in repo)

Results

Key findings from the analysis include:

1. Blue does not have the highest click-through rate.
2. Ultramarine has the highest click-through rate, followed by Sapphire, Aquamarine, and Teal.
3. The difference in click-through rates between Ultramarine and Blue is statistically significant.

Licensing, Authors, and Acknowledgements

• Data source: Provided by the stakeholder (simulated for this project)
• This project is licensed under the MIT License - see the LICENSE file for details
• Connect with me on LinkedIn!