price-scraping-in-ecommerce

Price scraping or price data extraction is carried out by setting up custom web crawlers to fetch the product data from competitors’ E-commerce portals. The number of competitors to be crawled can be decided by evaluating your market and close competitors.

Once a crawl is done, the extracted product data would contain various fields like site name, brand name, product name and the price. The product data is then run through a normalization system to prepare it for matching. This normalization is important as the data fields might often contain special characters, symbols and numbers – all of which would cause problems during matching.

At PromptCloud, we deliver the scraped price data via multiple formats like JSON, XML or CSV depending upon your preferences. The frequency of crawls can be defined as per your specific requirements and crawls can even be done in near real-time.

The price data provided is in a clean and ready-to-use format and all you have to do is connect it with your system to match it with your own pricing. It’s up to you to decide how you want to tweak your pricing with respect to your competitors’. The pricing process can be automated by developing an algorithm which will consider your competitors’ prices as input and makes changes to your prices as per a given set of rules.

Price matching is the next and immediate step after price scraping and extraction. It makes it possible to automate the whole pricing process. A strong matching system is essential here as every other ecommerce portal will have some minor differences when it comes to the product descriptions including product name and brand name.

The price matching process starts with indexing of the scraped data. Indexing can be done on a search engine like Elasticsearch. Here, data form the different sites are given unique IDs so that they can be identified separately by the matching algorithm. The matching algorithm performs text-matching techniques on the available input, which are the scraped product data and reference site data. The reference site would be your own ecommerce site as the matching should be done against products on your own catalog.

After matching is done, the algorithm assigns a match score to every match it could find. If the score is closer to 1, it means the match is very strong and the matched product data is sent to the output dump file.

The matches with poor scores are sent to a stage 2 matching algorithm where a much comprehensive matching is done. If the output turns out to be satisfactory, the algorithm sends them to the output dump file. Other matches are discarded as they aren’t reliable enough. However, the matching process is based on approximation and cannot handle instances where the product/brand names coincide or listings have incorrect data.

There are 3 major applications for ecommerce price scraping:

1. Competitive pricing

Ecommerce companies need price data from their competitors to keep their pricing strategy up to date and competitive.

2. Building a price comparison site

Price comparison sites are the go-to destination for most ecommerce shoppers before making the purchase decisions online. Price data extraction can be used to power a price comparison site.

3. Research and analytics

Market research firms can use price data scraped from multiple ecommerce websites to derive insights on different niche markets about pricing, demand and availability.