Skip to content
English
Contact us
  • There are no suggestions because the search field is empty.

Using PIM Data in Other Systems

This article explains how approved Proton PIM data is used in downstream systems, including ERP, CRM, and e-commerce platforms, along with common use cases and best practices for maintaining data consistency across tools.

Proton PIM is designed to act as a central source of truth for product data that feeds other tools and workflows.

Common Downstream Use Cases

Approved PIM data is often used in:

  • Enterprise Resource Planning (ERP) systems

  • Customer Relationship Management (CRM) tools (ex: Proton CRM)

  • Ecommerce platforms

  • Internal analytics or reporting tools

Using PIM as the source helps ensure product data stays consistent everywhere it appears.

Using PIM Data in ERP Systems

In ERP systems, PIM data is commonly used for:

  • Product master records

  • Item descriptions and specifications

  • Manufacturer and part number alignment

This helps reduce mismatches between operational data and product content.

Using PIM Data in CRM Systems

In CRMs, such as Proton, enriched product data can support:

  • Accurate product visibility for sales teams

  • Cleaner product selection during quoting

  • Better product context in customer conversations

Consistent product data helps sales teams sell with confidence.

Using PIM Data in E-Commerce Platforms

For e-commerce teams, PIM data supports:

  • Product listings with consistent descriptions

  • Complete and standardized specifications

  • Improved search and discovery

  • Faster product onboarding

Using approved PIM data reduces manual rework and improves customer experience.

Best Practices for Using PIM Data Downstream

  • Treat Proton PIM as your system of record for product content

  • Avoid manually editing exported data in multiple places

  • Re-export updated products when changes are approved

  • Align teams on where product data should be maintained

Once you’re exporting and using PIM data across systems, advanced concepts like attributes, categories, and standardization can help further improve data quality at scale.