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Zip code-based price benchmarking and cataloging for retailers

One of USA's popular e-commerce platform look to enhance its pricing strategy based on locations using zip codes.

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A leading e-commerce company in the United States.

Challenge: Being an e-commerce platform, the client sought complete product and pricing data from its outlets spread across various locations in the United States. Before looking to employ web crawling services, the company used to gather relevant data manually to perform the analysis. The requirements included extracting data of the products on their e-commerce platform and store outlets across the nation which were filtered based on zip codes of store locations. Taking the above-mentioned zip code based data extraction, a similar data collection process had to be performed on competitor e-commerce sites to extract price and product data. This data was used for further analysis in product strategy and price benchmarking for a complete product catalog. The client wanted to deploy PromptCloud’s web crawling service to automate the entire data extraction process based on zip codes.

The Solution: Site-specific crawls were employed in this case, which focused on the client’s website. The solution extracted pre-defined data points from the client’s website; important data fields were the unique serial identifier of a product, product name, category, URL link, crawling timestamp, store location, price, and inventory stock availability. Considering the client’s interest in pricing benchmarking, web crawlers were also created for the competitors’ sites. Crawlers collected data from fields such as the unique identifier of a product, URL link, product name, category, crawl timestamp, store, location, price, stock availability in the inventory. The collected data from the above two executions were then classified by zip codes and was used by the client for further analysis. The dataset was delivered to the client in JSON format via PromptCloud’s REST API.


  • Noise-free data is made available to the client based on the requirements.
  • Cut-down on redundancy since the client listed out which stores they wanted to set crawlers for data extraction.
  • No client intervention was required during the crawling procedure.
  • Reduced cost and time delay for the client, since clean data was delivered for analysis.
  • The schema was altered as per client’s request.
  • Periodical updates based on the frequency of crawling was also incorporated.

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