Basic Product Data in E-Commerce: Specifications and Problems of Data Exchange

Abstract

Purpose: This paper summarizes and compares the types and interpretations of the basic attributes necessary to enter product data in selected e-commerce platforms. Design/Methodology/Approach: The research methodology was based on an analysis and selection of a reference group of basic product attributes and identification of appropriate market representatives, platforms and tools commonly used in e-commerce. Furthermore, for each of the selected basic attributes of the product, an analysis was made in terms of the presence, mandatory field, and data input validators. The best practices indicated by the platform developers were also reviewed. Findings: The research results indicate discrepancies in the understanding of the basic attributes of the product. A lack of commonly available, standardized, consistent data describing products for which the manufacturer would take responsibility lead to creating own solutions for the e-commerce market and development of their own meanings of some data.Practical implications: It is necessary to clearly understand e-product data as e-commerce market is relatively young and for which product data has a much greater and often completely different meaning than in a traditional trade. Originality: It provides recommendations for e-commerce platforms for managing e-product core/ basic product data while using a single standard for product master data and common product identifier.

Data Science Challenges of Automated Quality Verification Process in Product Data Catalogues

“…This human-machine interaction provided the opportunity to take a broader view of quality, to include the possibility of prompting, and to validate data that are not 100% certain. Basic industry-independent product attributes [5] were analyzed, enriching them with several additional elements available in the database:…”

“…Validation of product data quality is worth enriching with computer vision techniques. With the help of appropriate algorithms, it is possible to verify the correct placement of the product in the photograph, as well as the color and uniformity of the background, which is important for maintaining appropriate standards [5], [6]. When machine learning models are implemented, objects can be recognized and assigned to a category, confirming the consistency of the product name and description with the photo.…”

“…The European Commission is working on regulations, in the interests of consumer safety, the GS1 Organization (GS1 GDM -Global Data Model) is developing models, which indicate the most important elements from the point of view of B2B and B2C cooperation. However, there are no complete and respected standards in this area for all manufactured products worldwide [5]. The problem is exacerbated by the fact that manufacturers do not universally make information about their products available in digital form, so supply chain participants often build product data sets themselves [6], using various available sources or manually creating them from scratch.…”

“…The problem is exacerbated by the fact that manufacturers do not universally make information about their products available in digital form, so supply chain participants often build product data sets themselves [6], using various available sources or manually creating them from scratch. As a result, commonly used product attributes, such as identification number, name, brand, photo, composition, net content [5], are entered in different, often incorrect ways [7], which can be particularly noticeable for fields that allow the entry of descriptive data or in the form of graphical objects. This, in turn, is very important when shopping on the Internet, where the key to search is not product identifiers, but precisely their names, descriptions, and images [5].…”

“…As a result, commonly used product attributes, such as identification number, name, brand, photo, composition, net content [5], are entered in different, often incorrect ways [7], which can be particularly noticeable for fields that allow the entry of descriptive data or in the form of graphical objects. This, in turn, is very important when shopping on the Internet, where the key to search is not product identifiers, but precisely their names, descriptions, and images [5]. Incorrect data are published on shop websites, search engines, and auction portals, and these are further duplicated and propagated in further databases.…”