Matching products across platforms
Peter Andersen worked as a technical consultant for a number of companies. Always ready to lend his expertise in the area of technology, Peter received a new project assignment to help build a prototype for a new cutting edge mobile app that could analyse user behaviour and suggest the best product to buy based upon their search history. In order to develop such a prototype, it was essential for him to accumulate data from multiple ecommerce sites with varied catalogs to compare and contrast product between them and choose a winner.
While researching for ways to crack his latest project problems, Peter came across PromptCloud and saw an immediate fit between his requirements and our offering. He requested heavy schema data from the top e-commerce sites to help him build his prototype and was impressed with the support and data quality he received.
How did PromptCloud Help?
- PromptCloud helped discover the appropriate data fields Peter needed to build his prototype
- Each record within a dataset had all details i.e. product name, product price, availability status, short and long descriptions, all image URLs, SKU, dimensions, category, brand, source and the source URL from where it was fetched. This in turn allowed logical comparisons at various levels in his design. For instance, first compare the price, then the attributes and so on.
- The extracted data was further processed according to the client’s’ instructions and the schema discussed, so it was easy to absorb data at client end
Benefits to the client
- Parallel collection of data from multiple sources to enhance the client’s database
- Any site changes within the source was taken care of and client was provided with clean, structured data
- The client was abstracted from any crawling processes so he could focus on his prototype
- The data delivered helped the client’s project ultimately leading to an early successful completion of his prototype
- Low turnaround time and a scalable solution assured the client of further project capacity if the prototype went into production
Thus we helped Peter acquire high quality data required to build his project in no time and leading to a bigger order once the prototype went into production.
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