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Increasing attendance of retailers online and big data soaring new heights calls for a quick look at what’s trending these days in the context of the e-commerce landscape.

Amazon Price Comparison Ad for Kindle

Requirements that we receive from our enterprise clients with respect to crawling and extracting products can by far be categorized into 3:

  1. Collecting product information from specific categories of interest.
  2. Collecting product prices from all categories on a retailer site.
  3. Collecting the entire product catalog including title, images, rating, sale price, original price and all possible specifications.

What’s to be done with all of this collated information? Again, these are the popular trends-

1. Comparison Shopping

The simplest of all is price comparison engine that all of us are so addicted to these days. The consumer is ready to jump from one retailer on to another even for a $1 saving (besides, reduced affinities is another trend with comparison shopping). With such trends, businesses want to be involved with facilitating such information that influences consumer behavior by helping them discover lower prices for a product. Looks like the craze will be ON for some more time.Extract product data- comparison_shopping

2. Semantic Analysis

The idea behind collecting the entire product catalog is to run analyses on rich product attributes. For instance, how was a black IPad case priced over the blue one; or how fast are the i7 processors performing over i5 in the demand vs. supply chain. This technique is turning social too by providing assistance to buyers in order to make informed decisions. Possibly the next big thing in E-commerce.

Collecting Product informations from catalog

Collecting product information’s

3. Demographic based cataloging cum pricing

You should believe when we say that there are million dollar businesses that function out of studying how pricing schemes change with geographies. Analyses are run on seasonal variations of prices or product customizations (or their unavailability) based on geographies.

Since the above use cases are the core, competencies are built in-house around it and data handling is often given away to DaaS providers like us, especially because they do not want to be limited by scale and gather as many products as they can. Here’s a simplified version of the data in fashion. The complicated versions have a pretty involved schema.
Product Information Snippet
With so much going around scraping products off the websites to aggregate and analyze relevant information, a lot of product-centric businesses are shaping up in the e-commerce market. What are your thoughts?

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