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Ad Campaign Performance Analysis using A/B Testing

Project Overview

This project compares Facebook Ads and Google Adwords to determine which platform performs better in terms of conversions, clicks, and cost efficiency. The project uses A/B testing, statistical analysis, regression, and time-based trends to generate insights.

Business Objectives

  1. Identify which ad platform performs better.
  2. Analyze the relationship between clicks and conversions.
  3. Measure the frequency of high-conversion days.
  4. Conduct A/B hypothesis testing.
  5. Study day-based and time-based trends.
  6. Evaluate how ad spend impacts conversions.
  7. Determine which campaign provides better ROI.

A/B Testing Summary

H0: Both platforms have equal average conversions. H1: Facebook has higher average conversions than Adwords. Statistical results show Facebook performs better, so the null hypothesis is rejected.

Key Insights

Distribution and Outliers

Conversions and clicks show near-normal distribution with few outliers.

Platform Comparison

Facebook has more high-conversion days and stronger engagement.

Correlation

Clicks and conversions have a strong positive correlation (0.87).

Regression Analysis

Ad spend, especially on Facebook, strongly predicts conversions.

Time and Trend Analysis

Conversions remain consistent across weekdays, with a small rise mid-month.

Long-Term Relationship

Statistical results confirm conversions depend significantly on ad spend.

Steps Performed

  1. Loaded and reviewed the dataset.
  2. Cleaned and prepared the data.
  3. Performed descriptive statistics.
  4. Conducted exploratory data analysis.
  5. Compared platform performance.
  6. Performed A/B and hypothesis testing.
  7. Built a regression model.
  8. Checked variable correlations.
  9. Analyzed time-based patterns.
  10. Summarized insights and conclusions.

Tech Stack

Python (Pandas, NumPy, Matplotlib, Seaborn), SciPy, Statsmodels, Jupyter Notebook

Project Structure

project/ data/ ad_data.csv

notebook/ AB_Testing_analysis.ipynb

README.md

requirements.txt