PIM Requirements Checklist for Essential Features and Selection Criteria

Key Terms to Know

Many PIM projects go wrong before the contract is signed. A team picks the best demo, then learns the platform cannot model its variants or connect cleanly to its ERP.

A PIM (product information management) system keeps product data in one place and sends it to every channel you sell through. Choosing one well means writing down what you need and making each vendor prove it. Below is a requirements checklist and a simple way to score finalists.

Digital transformations have accelerated across industries, but many stall because teams skip the requirements stage and spend months evaluating the wrong tools for their product information management system.

Start with the problems the PIM must fix, such as slow launches, rejected distributor files, or five versions of one product description. Record your product count, users, and channels, and agree on how to measure success. Specific requirements knock poor fits out early.

List every type of data you manage, too. McKinsey research from September 2024 found that structured data is only about 10 percent of the data available, and that generative AI has opened up the other 90 percent, which is unstructured. Catsy’s overview of what PIM manages splits product data into seven categories, which makes a handy starting list.

Then map each team, from sales to finance, to the features it needs. A 2018 McKinsey analysis found that fewer than 20 percent of companies had maximized the potential of a data transformation and implemented advanced analytics at scale. Only about 30 percent of the banks surveyed had a data strategy, which McKinsey named as the first step.

“Must-have” means a vendor that fails the requirement leaves your shortlist. “High” should lower a vendor’s score. “Depends” applies only to some catalogs, industries, or channels. If you cannot see a requirement working in a demo or proof of concept, treat it as unmet.

Not sure which platform type you need? Catsy’s PIM platform comparison for 2026 explains when each type fits best.

Data modeling is the requirement most worth testing early. A rigid model forces you to simplify your catalog to fit the software. Plan for three times today’s volume. Cloud-native platforms grow with you, while older systems often need costly hardware upgrades.

Once a PIM is live, spreadsheet routines give way to automatic checks, approval workflows, and quality scores. At scale, data governance keeps this working. Validation rules block bad entries, permissions control who can publish, and audit trails record every change. Custom integration work can cost more than the license, so check for ready-made connectors.

Rules matter as much as software. Successful data transformations can yield enormous benefits. In McKinsey’s 2018 research, banks that captured benefits across risk, costs, and revenues raised their bottom line by 15 to 20 percent. One bank found that only about 20 to 30 percent of its data quality problems came from system faults. The rest came from human error.

As a baseline, a complete PIM should offer five core capabilities:

• Workflow, data modeling, and hierarchy management
• Rich product content authoring
• Multichannel publishing and contextualization, ideally from one omnichannel product database
• Digital asset management (DAM)
• Product variant management

Catsy’s guide to product data quality standards shows how to turn these capabilities into measurable completeness targets.

Your ERP should sync product and pricing data, your storefronts need automatic catalog updates, and DAM systems must keep every file tied to the right product. Real-time sync also needs an API with error handling and automatic retries. Most teams start with a one-way sync from ERP to PIM and add two-way fields later, as Catsy’s PIM ERP integration guide explains.

Migration drives much of the cost. Clean data moves easily, while scattered spreadsheets need cleanup first.

AI shopping assistants add a newer requirement. OpenAI’s product feed specification asks merchants for structured catalog data with accurate pricing, availability, and seller context. Google’s Universal Commerce Protocol covers merchants in the United States, Canada, and Australia and lets select ones offer checkout in AI Mode and Gemini. As of late 2026, onboarding is rolling out in the US first, with Canada and Australia expected to follow in 2027. Merchant Center stays the hub for product data either way. Your PIM should export complete, current feeds through channel templates that fit each destination.

Catsy’s PIM software buyer’s guide covers integration planning in more depth.

PIM sits inside a large and growing technology budget. In October 2024, Gartner forecast worldwide IT spending of $5.74 trillion for 2025, up 9.3 percent from 2024. Its July 2026 forecast puts 2026 spending at $6.37 trillion, up 14.2 percent from 2025.

Judge vendors on implementation, support, and industry experience. Catsy’s article on PIM implementation myths shows what a typical SaaS rollout covers. Work out total cost of ownership over three to five years, and leave room for adoption. McKinsey’s rule of thumb is to plan at least one more dollar for process change and training for every dollar spent building a digital solution.

Compliance shapes the shortlist as well. Healthcare needs HIPAA support, manufacturers need safety standard support, and financial services need audit trails. In the EU, the Commission’s 2025 to 2030 ecodesign working plan will share product information mainly through the Digital Product Passport. Certain large batteries come first, from February 2027, according to an EU Council document. Catsy’s PIM for manufacturers page shows compliance attributes such as REACH, RoHS, and DOT approvals stored directly in the product record.

Decide on proof, not promises. Cloud and on-premises deployments place different demands on your IT team, so match the deployment model to your capacity. Run a proof of concept on your own data, and involve both IT and daily users.

AI features need the same scrutiny. Gartner predicts that through 2026, organizations will abandon 60 percent of AI projects that are not supported by AI-ready data. Ask each vendor where AI-generated content waits for human approval. Catsy explains how a PIM acts as a harness for AI through permissions and approval steps.

The cheapest platform is rarely the best value.

First, drop any vendor that fails a must-have, refuses a proof of concept on your data, or will not put pricing and scope in writing. Aim for three or four finalists. Then score each one from 1 to 5 on the dimensions below, using weights you agree on before the first demo.

8-Dimension PIM Evaluation Framework with example weights

Multiply each score by its weight, add the results, and divide by 5 for a score out of 100. If every other score is 4, a vendor rated 5 on data modeling and 3 on integrations scores 81, while one with the reverse ratings scores 79. Setting weights first means the result reflects your priorities, not the best demo.

Catsy combines PIM and DAM with unlimited attributes, completeness scoring and validation, custom approval workflows, and ERP connectors such as SAP, Oracle NetSuite, and Microsoft Dynamics 365. It publishes to channels including Amazon, Walmart, Shopify, and BigCommerce.

Book a Catsy demo and score it against your checklist.