Amazon product data analysis using Helium 10 exports and Keepa API
This project involved building a system to process large product datasets exported from Helium 10 and enrich them with data from the Keepa API.
The goal was to enable deeper analysis of Amazon marketplace products by combining multiple data sources into a single analytical pipeline.
Due to NDA restrictions, some operational details cannot be disclosed.
Project context
Amazon sellers often use tools like Helium 10 to export large datasets containing product information such as:
- product identifiers
- estimated sales data
- keyword performance
- product rankings.
However, Helium 10 exports alone do not provide the full historical and pricing context needed for deeper analysis.
The client needed to combine these datasets with Keepa market data to generate more accurate product insights.
Scope and goals
The system needed to support:
- processing large Helium 10 export datasets
- retrieving additional product information through the Keepa API
- matching and merging datasets
- enabling large-scale product analysis.
Tech stack
- Amazon marketplace data exports
- Helium 10 product datasets
- Keepa API integration
- data processing pipeline
- product data matching algorithms
Helium 10 data processing
Helium 10 exports often contain thousands of product records.
The system was designed to:
- parse export files
- extract product identifiers
- prepare datasets for analysis.
Data normalization was required to ensure consistent formatting across records.
Keepa API integration
To enrich the dataset, the system retrieved additional product information from the Keepa API.
This included:
- historical price data
- sales rank trends
- marketplace performance indicators.
The integration allowed the platform to collect deeper insights about each product.
Data matching and comparison
One of the key challenges was matching product data across multiple sources.
The system implemented logic to:
- identify products using standardized identifiers
- merge Helium 10 and Keepa datasets
- compare metrics across different data sources.
This enabled more accurate product analysis.
Large-scale data analysis
Once the datasets were combined, the system allowed large-scale analysis of Amazon products.
The resulting dataset could be used for:
- product opportunity research
- competitor analysis
- pricing strategy evaluation
- marketplace trend monitoring.
Result
The final solution enabled efficient processing of large Amazon product datasets.
The system delivered:
- automated Helium 10 export processing
- Keepa API data enrichment
- structured product data comparison
- scalable marketplace analysis.
What this case demonstrates
Modern ecommerce analytics often requires combining data from multiple sources.
By building custom data processing pipelines and API integrations, businesses can unlock deeper insights from marketplace data.
If you need marketplace data analysis
Many ecommerce businesses rely on multiple tools that generate fragmented datasets.
Custom data processing systems can combine these sources and enable more powerful analytics.
FAQ
What is Helium 10?+
Helium 10 is a popular tool used by Amazon sellers for product research, keyword analysis, and marketplace data exports.
What is the Keepa API used for?+
The Keepa API provides historical pricing and sales rank data for Amazon products, allowing deeper marketplace analysis.
Why combine Helium 10 data with Keepa data?+
Combining these datasets allows more accurate analysis of product performance, pricing trends, and market opportunities.