Product information management (PIM) is the process of managing all the information required to market and sell products through distribution channels. This product data is created by an internal organization to support a multichannel marketing strategy. A central hub of product data can be used to distribute information to sales channels such as e-commerce websites, print catalogues, marketplaces such as Amazon and Google Shopping, social media platforms like Instagram and electronic data feeds to trading partners. Moreover, the significant role that PIM plays is reducing the abandonment rate by giving better product information.[1]
PIM solutions are most relevant to business-to-consumer and business-to-business firms that sell products through a variety of sales channels in a range of industries.[2] The use of PIM is generally influenced by a company's:
PIM manages customer-facing product data required to support multiple geographic locations, multilingual data, maintenance and modification of product information within a centralized product catalogue. PIM can act as a centralized hub for storing product information and from every channel.[3] Product information kept by a business can be scattered throughout departments and held by employees or systems, instead of being available centrally; data may be saved in various formats, or only be available in hard copy form. It also helps businesses to improve their conversion rate optimization (CRO) by displaying consistent branding and reducing abandonment rate. Moreover, PIM allows the automation of most of the processes of product creation. All in all PIM provides a centralized solution for media independent product data maintenance, efficient data collection, data governance and output.[4]
PIM systems consolidate all product information onto a single platform. It helps connect retailing and manufacturing channels to counter complex challenges in managing and maintaining product data quality. In terms of company information technology infrastructure, this means having a PIM platform running over or alongside a database with an application server, and/or XML-based exchange of product information. This forms a foundation upon which to build sales and procurement business processes. With PIM solutions, access and user authorizations for all database information, ordering processes linked with inventory management systems and the mechanisms for modular expansions are managed via a web-based administration interface.
Point of sales systems and online shopping platforms such as online marketplaces are based upon electronic catalogues. PIM systems can load descriptive product information as content into a catalogue management solution, where products are grouped and managed for specific target markets. Data exchange interface standards such as Open ICEcat allow seamless interchange of electronic catalogues between vendors on the one hand and purchasing firms and marketplace operators. E-procurement solutions are closely related, which automate the procurement process for purchasing goods and services. These create transparency for the product data of multiple vendors to support the centralized management of multi-supplier catalogues and facilitate price and quality research.
Data management systems are often not interoperable meaning that data exchange without PIM can lead to severe repercussions for a business. Some companies use master data management as an information technology resource in lieu of PIM. But master data management systems are not a business application and often lack usability, product data management capability including data enrichment, validation and workflow rules, which impact return on investment.[9] [10]
PIM has witnessed a transformative synergy with Artificial Intelligence (AI), offering enhanced capabilities in the realm of product data management. AI serves as a virtual assistant, supporting product enrichment and decision-making across various business processes within PIM. A few high-impact use cases include auto attribution and writing product copy. AI plays a crucial role in auto attribution, employing neural networks and computer vision to validate attributes applied by team members while suggesting potentially overlooked or missed attributes. This not only ensures the accuracy of product data but also enables automated product categorization, enhancing assortment variety per category and powering search, navigation, and personalization/recommendation systems. Additionally, AI contributes to auto copywriting by swiftly generating product descriptions based on enriched attributes and user prompts. This aids in efficiently crafting diverse romance copy for product categories that share similar descriptions, allowing for rapid content creation and enrichment.[11]
Enterprise content management is a term encompassing technologies, methods and tools used for gathering, imaging, storing, archiving and providing electronic content. Distinction can be made between four separate sub-areas. Document management systems are deployed for archiving, and product data management involves the management of structured, technical data for such applications as parts diagrams and lists. Content management systems are more commercially oriented and provide a framework for knowledge management or informational service offerings through the management of unstructured, document-type content. PIM systems are used to manage structured data in a business context for feeding into any kind of distribution channel, from electronic catalogues to online shops to print catalogues.