Amazon Ads data analysis with Python

If you want more control over your Amazon Ads data than the console gives you, Python is a good way to get it. You can pull data from the Amazon Advertising API, run whatever analysis you want, and automate the parts that are repetitive.

What you can do

The typical workflow looks like this: pull campaign data from the API, clean it up in Pandas, calculate the metrics you care about, and visualize the results. Once you have a script that works, you can schedule it to run daily.

Common use cases include:

• Finding keywords with high spend and low conversions so you can cut waste
• Tracking performance trends over weeks or months to spot seasonal patterns
• Building custom reports that combine Amazon data with data from other sources
• Running statistical analysis to figure out what's actually driving results vs. what's noise

Useful libraries

For data work with Amazon Ads, these are the libraries you'll reach for most:

• Pandas for loading, cleaning, and manipulating campaign data
• NumPy for numerical calculations
• Matplotlib and Seaborn for charts and plots
• Plotly or Streamlit if you want interactive dashboards
• Requests or Boto3 for API calls
• Scikit-learn if you want to build predictive models (bid forecasting, etc.)

Example: campaign performance analysis

import pandas as pd
import matplotlib.pyplot as plt

# Load campaign data
campaigns = pd.read_csv('amazon_campaigns.csv')

# Calculate key metrics
campaigns['CTR'] = campaigns['clicks'] / campaigns['impressions']
campaigns['CPC'] = campaigns['cost'] / campaigns['clicks']
campaigns['ACoS'] = campaigns['cost'] / campaigns['sales']

# Visualize performance
plt.figure(figsize=(12, 6))
plt.scatter(campaigns['CTR'], campaigns['ACoS'])
plt.xlabel('Click-Through Rate')
plt.ylabel('Advertising Cost of Sale')
plt.title('Campaign Performance Scatter Plot')
plt.show()

Example: finding your best keywords

# Identify high-performing keywords
high_performers = campaigns[
    (campaigns['CTR'] > campaigns['CTR'].quantile(0.75)) &
    (campaigns['ACoS'] < campaigns['ACoS'].quantile(0.25))
]

# Calculate bid recommendations
high_performers['recommended_bid'] = high_performers['CPC'] * 1.2

print("Top Performing Keywords:")
print(high_performers[['keyword', 'CTR', 'ACoS', 'recommended_bid']])

Setting up your environment

1. Install Python 3.8 or newer
2. Create a virtual environment: python -m venv amazon_ads_env
3. Install what you need: pip install pandas numpy matplotlib seaborn scikit-learn boto3
4. Set up Amazon Advertising API credentials (OAuth 2.0)

From there, the process is: authenticate, pull the data you want (campaigns, ad groups, keywords, search terms), store it somewhere (CSV, Parquet, or a database), and start analyzing.

Automating it

Once your analysis scripts work, you can schedule them with cron, Apache Airflow, or AWS Lambda. This turns a manual process into something that runs on its own and delivers results to you.

Marketplace Ad Pros also exposes Amazon Ads data through its own API, which can simplify the data extraction step if you're already using the platform.

Learning resources

• Amazon Advertising API Developer Guide
• Pandas and Scikit-learn documentation
• Stack Overflow and Reddit r/Python for community help

FAQ

Do I need to be a Python expert? No. Basic Python is enough to get started. The scripts above are a realistic starting point, not simplified examples.

How much data can Python handle? Millions of rows, easily. If you outgrow Pandas, Polars is a faster alternative for large datasets.

Can I automate this? Yes. Cron jobs, Airflow, or Lambda all work. The point is to write the script once and let it run.

Is the Amazon Advertising API free? The API access itself is free. You need an Amazon Advertising account to use it.

Start analyzing your Amazon Ads data