How to Map E-Commerce Products Taxonomy Using Artificial Intelligence?

Each product on an eCommerce website has a category that shows the product's location in the whole catalog. Usually, these filtrations follow hierarchical patterns to place the product in the category, sub-category, or product type - Electronics > Mobiles > Samsung> black. We call this e-commerce product category hierarchy a product taxonomy. Filtering products logically allows buyers to pick the required product when exploring the retail website easily.

Additionally, with a brand of good category, a product gets more visibility in the search engine on retail eCommerce platforms. Search engines of these retail platforms work by observing user requirements and showing products for those search queries. Search engines rank those products with the best matches to search questions according to relevant fields.

A proper product taxonomy contributed to boosting sales by assisting buyers in searching for exact products while browsing. For example, a search term matching the word in the product title shows a more significant relation to the search query than the product description. Further, a particular term for some products signifies better selectivity, leading to better product ranking. The word choice in the product category indicated did affect the search result relevance for user questions. Since users see relevant search results, the discoverability of products improves, effectively improving user experience.

eCommerce websites organize loads of products in taxonomy that they deem intuitive for their buyers and compatible with brands of their business segments. Considering that we have to deal with hundreds of such websites and go through millions of products daily, we frequently handle different hierarchies for the same product across multiple websites. Several eCommerce platforms could thus use different product taxonomies varying from each other.

We need to formulate these standard product taxonomies for our study. Standard product taxonomies like Google Product Taxonomy and Global Product Classification Taxonomy offer benchmarked ways to represent a product. But, every taxonomy is generic and complete. Therefore with Product Data Scrape, we have to produce our standard taxonomy for every retail e-commerce category, which is generic and comprehensive enough to show products on multiple websites platforms across several geolocations.

A proper benchmark taxonomy for every eCommerce product is essential for a data orchestration funnel. It helps enhance the Retail Knowledge Graph of product data scrape at scale.

Product Data Scrape Retail Knowledge Graph

Most eCommerce websites contain broken and unstructured product information. We handle this broken information, generate structured data from websites, and save it in a standard format. We use the Knowledge Graph in downstream applications for content analysis, attribute tagging, etc. It follows benchmarked hierarchy levels for every e- commerce product.

Plotting eCommerce taxonomies is one of many needs for this graph, but it also has other applications.

Assortment Analytics

Mapping competitors products as per their taxonomies help e-commerce sellers understand their product assortment gap, regardless of competitors' product filtering process.

For example, a retail seller wants to know the product assortment of Scented Candles in a competitor's catalog. The seller may have filtered Home and Kitchen> Interior Design> Scented Candles, but the exact product may be in other categories like Shoes> Home> Candles on the other website. Here, having a scalable and effective process to plot product taxonomies gives a correct analysis of the assortment which retail sellers expect.

Health & Household > Health Care > Alternative Medicine > Aromatherapy > Candles

Fragrance > Candles & Home Scents > Candles