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Zipcode based price benchmarking for retailers

Here's our case study of a popular e-commerce platform based out of the United States, seeking data to be extracted from the web to enhance its pricing and product strategy.

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Zipcode based price benchmarking for retailers

  1. 1. Zip code-based price benchmarking for retailers A leading ecommerce platform from United States turned up to PrompCloud’s cost-effective DaaS solution from manual in-house data extraction and analysis.
  2. 2. The problem Company A leading ecommerce platform in the United States. Along with having an ecommerce presence, the firm also has physical outlets across the nation. Context Client wanted to aggregate data from their own ecommerce platform, and competitors’ platforms to improve their pricing strategy based on locations. Problem statement Provide product and pricing data extracted from their website for the stores based on zip codes. A similar requirement was also provided by the client for their competitor platforms.
  3. 3. Challenges deep-dive Challenge 1 Being an ecommerce platform, the client sought complete product and pricing data from its outlets spread across various locations in the United States. Challenge 2 Before looking to employ web crawling services, the company used to gather relevant data manually to perform analysis through various store outlets and website using in-house capabilities. Challenge 3 The requirements included extracting data in an automated manner for the products on their ecommerce platform associated with the stores across the nation which were filtered based on zip codes.
  4. 4. Challenges deep-dive Challenge 4 Taking zip code-based data extraction, a similar data collection process had to be performed on competitor ecommerce sites to extract price and product data. Challenge 5 This data was used for further analysis in product strategy and price benchmarking for the complete product catalog. Challenge 6 The client wanted to deploy PromptCloud’s web crawling service to automate the entire data extraction process based on zip codes which should be scalable with high volume data requirements.
  5. 5. Solutions Site-specific crawls were deployed which were based on the client’s website, pre-specified frequency and data fields. Some of the key fields are unique serial identifier of a product, product name, category, URL, crawling timestamp, store location, price, and inventory stock availability. 1 2 Same process was repeated for competitor websites. It included data collection for the corresponding fields from competitor e-commerce platforms. 2 The extracted data from the two executions was delivered to the client in JSON format via PromptCloud’s REST API.3
  6. 6. Benefits Customized noise-free data Noise-free data was made available to the client which helped them expedite analysis process and focus on the improvement of the pricing strategy 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 as this was completely automated based on pre-defined requirements. Reduced overhead in web crawling Unbiased crawling procedure
  7. 7. Benefits Reduced Total Cost of Ownership Reduced cost and data delivery latency by 86% which helped client roll out new pricing in shorter time period. The schema was altered as per client’s request. Periodic updates and reports based on the frequency of crawling was also delivered which helped client streamline their requirement without additional charges from our end. Flexible deliverables as per client’s interest Room for changing requirements for deliverables
  8. 8. A Pioneer in Data as a Service www.promptcloud.com |sales@promptcloud.com

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