Data Science and Analytics Tools - A/B Testing for Marketing Campaigns

 Notes:

• What problem does it solve?
  Helps businesses evaluate the performance of different marketing campaign versions to determine which one yields better results.

• How can businesses or users benefit from customizing the code?
  Users can customize the metrics for success (e.g., clicks, conversions) and modify the experiment's parameters.

• How can businesses or users adopt the solution further, if needed?
  Can be expanded to run multiple tests simultaneously, helping refine marketing strategies in real time.

Actual Python Code:

import pandas as pd

from scipy import stats

# Load A/B test data (assumed to have 'Group' and 'Conversion_Rate' columns)

data = pd.read_csv('ab_test_results.csv')

# Split data by groups (A and B)

group_a = data[data['Group'] == 'A']['Conversion_Rate']

group_b = data[data['Group'] == 'B']['Conversion_Rate']

# Perform t-test to compare the means of the two groups

t_stat, p_value = stats.ttest_ind(group_a, group_b)

# Interpret results

if p_value < 0.05:

    print("There is a significant difference between groups A and B.")

else:

    print("No significant difference between groups A and B.")