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Case Study
Data Capturing for Amazon US
Data Capturing & Product Comparison for Amazon US
“The combination of structured workflow for data capturing, intelligent product matching, and human verification proved the best decision we ever made to build a clean, accurate, and scalable product database.“
A project from Amazon US required large-scale product data capturing for comparative analysis, helping to manage voluminous product information efficiently. It involved everything from removing duplicate product entries to matching similar listings and maintaining standardized records to trust. Eminenture relied on automation with human-driven quality checks to clean, deduplicate, standardize, and verify data before organizing it. This sorted approach helped us create a consistent product database while reducing overall operational cost & effort. This case also showed enhanced productivity because of hygienic product records, which accelerated decisions faster than ever.
Challenges
Handling a massive set of product information while aligning premium quality with consistency was critical. It required a structured approach to navigate these complexities:
- Capturing large volumes of product data efficiently.
- Managing the availability of product information across sources.
- Identifying and removing duplicate product records.
- Matching products seamlessly for comparison.
- Maintaining product attributes and formats consistently.
- Infallible data quality across a large product database.
- Meeting the timeframe of the project within the required turnaround time.
Solutions Provided
Addressing the aforesaid challenges became effortlessly seamless via our structured data-capturing solutions and unbiased product comparison workflows. We used automation and human verification methods together to deliver these solutions:
- Capturing and organizing high-volume product data, which were 40K+.
- Applying product matching techniques for accurate comparisons.
- Identifying and removing duplicate product records.
- Standardizing product information across datasets.
- Automating repetitive data-handling activities.
- Conducting human quality checkups to ensure data accuracy.
- Creating a clean and consistent product database.
Process Used
We tailored the case-specific workflow, encompassing how to capture, compare, validate, and even organize product information. This process required technology and data specialists to work together for maintaining accuracy with consistency throughout the project. So, we covered these steps:
- Product data capturing.
- Data availability checks.
- Product matching and comparison.
- Duplicate identification and deduplication.
- Data standardization.
- Automated data processing.
- Human QA verification.
- Final data validation and delivery.
Result
The project delivered a scalable and reliable product database by leveraging automation, standardized processing, and expert-driven quality assurance:
- 40K+ product records successfully processed.
- 99% accuracy achieved across processed data.
- Duplicate products identified and removed.
- Product information matched for reliable comparison.
- Standardized records created for consistent database management.
- Expert-driven quality checks strengthened data reliability.
- On-time TAT supported continuous operations.
- Reduced manual effort and improved productivity.
- Cleaner product data enabled better business insights.
Presenting Performance with Facts
Figures to Measure the Accelerating Rate of Improvement
40
K+Data Processed
0
%Accuracy
0:1
AI+Human
0
/5C-SAT
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Eminenture-The globally acclaimed most trusted BPM company
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