- Our client consults brands on product attributes and price points for maximum performance.
- They use product attributes data as well as customer reviews to build models that correlate these factors with sales and product performances.
- They needed to scale their data extraction to cover 1500 product categories from various ecommerce platforms.
- Grepsr provided the freshest and high quality data across these 1500 product categories multiple times every day.
As a consulting firm, our client is in a unique position to be able to advise some of the largest global brands on what specifications to include and avoid in their products. This could include anything from whether lace-up shoes perform better than slip-ons or Velcros, or if items at lower price-points deliver better qualities than their mid or higher price-point counterparts.
To be able to provide such insights and build trust, our client needs access to large amounts of product data on a daily basis. With an in-house team, they were able to make do for a couple of years. As the business grew, they needed to scale their data extraction efforts, and were on the lookout for a sustainable solution that could automate extractions multiple times a day, integrate well with their own filesystems, and, more importantly, understand their requirements and respond quickly.
Grepsr’s experience with ecommerce data extraction allowed our client to get product data for hundreds of keywords with ease. Our platform’s scheduling feature also enabled customized crawl run times, ensuring the availability of the latest datasets multiple times every day.
In addition, after each extraction was completed, the resulting dataset was seamlessly fed to their local storage, resulting in a smooth workflow.
Similar challenges faced across the industry:
Lack of technical know-how to automate routine data extractions
Businesses need fresh data to gather the best insights. To that end, one or two data extractions a day does not suffice. They need a system that can easily schedule crawl runs at specific intervals as well as on demand.
Lack of resources - time, money and manpower - for data sourcing at scale
Data extraction is extremely tedious and highly error-prone. Most businesses lack the infrastructure to perform high volumes of data sourcing, and at a quality that yields the best results.
Overcoming data source restrictions
Most websites place limits on how many requests can be made in a set time period, and regularly block bots from accessing their content.