Suppose you have a flight to catch, and you know the destination and where you will board the flight from, as well as the airline, but you do not have any other information. When is your flight? Which gate do you board from? What is your seat number? So many questions would arise, and to solve them might lead you to miss your flight! Though this is a hypothetical scenario, it does represent the need for accurate and complete information. Companies that deal with big data, too, require a similar “enriched” form of data, that which provides all the details, contains no errors, and enhances the raw data that might have existed initially. This is known as data enrichment, the aim of which is to transform basic and limited data into insightful and more well-rounded data.
Such enrichment is usually of two kinds– Customer Relationship Management (CRM) Enrichment, and Product Data Enrichment. But what is the difference between the two?
If Company A, for instance, is viewing data gathered from their products on an eCommerce website, there can be two sets of data that they could infer from. One would be about their customers– what are the age groups that are investing in their products? What is their location? Loyalty status? Purchase frequency? And much more. Another kind of data would be about the products itself. What is the brand, size, color, model, etc., of the washing machine? Therefore, both kinds of enrichment would play a critical role in the functioning of Company A, and how they make decisions regarding their products.
dataX.ai’s focus is Product Data Enrichment, also known as SKU Build. This would ensure that our clients’ product discoverability, conversion rates, as well as operation efficiency, all improve significantly. We achieve this through collating data from a variety of sources, such as datasheets, supplier feeds, and any other disparate data sources within the system. This enrichment would entail enhanced product titles, marketing descriptions, and usage guidelines, along with specifications of the products (such as weight, material, size, color, etc.). We also ensure accurate product categories, tags, and attributes for better filtering of products, such as “Color: Blue” through product matching, auto classification, and other features.
Therefore we can help our clients achieve-