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eTrade's challenges with Extracting Amazon data, validating, and improving data accuracy.

Standardizing Data Sources at eTrade With Rubick.ai

Etrade

Introduction

eTrade is known for reducing complexity by offering brands a unified channel to be present on all eCommerce platforms. The client faced issues in scraping the data of automobile products from Amazon using the ASIN codes. Rubic.ai uses cutting-edge AI solutions to deliver the best results.

Goals and Objectives

Identifying products on Amazon

Identifying products on Amazon

Ecommerce product qc

Provide quality check

ecommerce

Ensure data correctness

Ecommerce data

Improved 
fill rate

ecommerce product mapping

Mapping data to category-specific template

Data scraping from the web

Data scraping from the web

The Problem

The challenges that eTrade faced revolved around the following:

Extract product information

Extract Product Information

Efficiently extracting existing product information from Amazon based on ASIN codes.

Category specific template

Category-Specific Templates

Validating and mapping this information to category-specific templates.

Ecommerce data

Data Accuracy

Enhancing data accuracy and fill rates.

The Solution

The solution toolkit we employed included the following strategies:

  • Based on the provided ASIN codes, Rubick effectively retrieved product information using cutting-edge web scraping algorithms. Regularly monitoring and updating the scraping process were carried out to adapt to any changes on the Amazon website.
  • Rubick.ai carefully designed a validation method to cross-verify the retrieved product data against Amazon’s product listings and other reliable sources. This verification assisted in finding any inconsistencies or information gaps. After being verified, the data was next mapped to category-specific templates that complied with ETrade’s specifications. 
  • Our team also closely checked any inconsistencies and missing data in the retrieved product information. This was aimed at improving the data correctness. 
Etrade

We used data enrichment strategies, such as locating missing data from reliable sources and amending faulty attributes where specific attributes were either missing or inaccurate.

What We Achieved

We achieved great results for the client. It included a high-quality, standardized catalog with better data accuracy and fill rates, allowing for more efficient operations and catalog management procedures at ETrade.

etrade

Accuracy and Completeness Delivered Timely

Our meticulously crafted toolkit of strategies helped ETrade improve the overall quality of processes and, thus, have a satisfied customer base. Our services optimized the fill rate process, ensuring accuracy and speed simultaneously.

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