A/B testing aims to determine the most effective ad by comparing the performance of different ad variants. This analysis is used to optimize advertising content and design. A/B testing allows us to understand how users react and develop more effective advertising strategies based on this data. Evaluating A/B test results using statistical methods increases the reliability of the findings.
Dataset Description
The dataset we created for this project includes the metrics necessary to analyze the performance of different ad variants used in A/B tests:
TestID: Unique ID of each test
Variant: A/B testing variant
Impressions: How many times the ad was viewed
Clicks: How many times the ad was clicked
Conversions: Number of conversions from the ad
Revenue: Income from advertising
Cost: Cost of advertising
Project Steps
Data Loading and First Look: We will load the dataset and get an overview. We will examine the structure of the data and detect missing or incorrect data.
Cleaning of Missing and Erroneous Data: We will fill in the missing values and correct the incorrect data.
Analysis of A/B Test Results: We will compare the performance of different variants.
Data Analysis and Visualization: We will analyze the A/B test results and visualize the results.
Statistical Tests and P-Value Calculation: We will perform statistical tests to evaluate the significance of A/B test results.
Ad Content Optimization: We will optimize ad content by identifying the highest performing variant.
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