Most PIM software looks alike on a feature list. Nearly every vendor will say it handles variants, connects to your ERP, supports several channels and has AI built in. The differences show up in daily work: how a system handles your messiest supplier file, how much of the AI's output your team has to fix, and what the system costs once integrations and usage are added.
So the best way to choose is to write requirements you can test, ask vendors to show how they meet them, then run a proof of concept on a sample of your own products. If you're still deciding whether you need a PIM at all, start with our guide to whether you need a PIM.
What should you decide before you look at PIM vendors?
Write down the facts every vendor will ask for. They narrow the field before any demo:
- How many SKUs you have and how often new products get added
- How many attributes each product carries
- Which locales and channels you publish to today, and which you plan to add
- Where product data comes from: your ERP, suppliers, spreadsheets or a PLM
- Who edits and approves product data in each team
Also be clear about what kind of business you are. Brands, retailers and distributors look for different things in a PIM. A brand cares most about rich content for its own products. A department store or distributor cares most about taking in data from hundreds of suppliers and mapping it to one taxonomy.
Then pick the selection team. Include the people who will work in the PIM every day, usually merchandising and ecommerce, along with IT for integrations and whoever owns the budget.
What belongs in a PIM requirements checklist?
Group your requirements by area and mark each one as a must-have, should-have or nice-to-have. Write each requirement as something a vendor can demonstrate, such as "imports a supplier spreadsheet with different column names and maps it to our attributes".
The last column gives an example of what to ask each vendor to show you in a demo. These are starting points to adapt to your own catalog, not a complete RFP.
| Area | What to include | Ask the vendor to show you |
|---|---|---|
| Data model | Product types and families, parent and variant structure, bundles and related products, attributes required per category, and room to grow well beyond today's catalog. Check how easily your team can add or remove attributes later. | How they'd set up one of your more complex products, such as one with many variants, and how attributes get added or removed later |
| Supplier and source data | Imports supplier data in the formats you receive today, maps supplier fields to your attributes, standardizes units and values, and re-imports without creating duplicates. Clear rules for which fields the ERP owns. | A supplier file they haven't seen before, loaded and mapped to your attributes |
| Classification | Your own taxonomy plus each channel's, such as Google product categories or a retailer's schema. Industry standards where they apply to you. | Your products sorted into your taxonomy and into one channel's categories |
| Enrichment and content | Fills missing attributes with a source for each value, flags values it's unsure of for review, writes descriptions from approved attributes in your brand voice, and handles translation if you sell in several languages. | Missing attributes filled on your products, with where each value came from |
| Quality and governance | Completeness and validation rules per category and per channel, roles and approvals, and a history of who or what changed each value. | A product held back from publishing because a required field is empty |
| Channels | Connectors to your ecommerce platform, marketplaces and retail partners, with one master record and a version for each channel. | One product sent to two channels that need different fields |
| Usability | Daily work that non-technical users can do after basic training, and bulk editing that holds up at your catalog size. | Your own team making a bulk edit after a short walkthrough |
| Security and commercials | SSO, access controls, security certifications and whether your data is used to train shared AI models. Pricing model, implementation approach, support terms and how you get your data out if you leave. | Their security documentation, and how you'd export your data and images if you leave |
Keep the must-haves short. A focused list is easier to test, and a vendor that fails one of them is out.
Which PIM requirements matter most for distributors and multi-brand retailers?
Distributors and multi-brand retailers both take in product data from many sources. A distributor gets it from hundreds of suppliers. A department store or multi-brand retailer gets it from the brands it carries, and each brand sends it in its own format and describes products in its own way. So how a PIM handles incoming data usually decides whether it works for them.
Weight these requirements higher:
- Supplier and brand data in any format. Spreadsheets, PDFs, spec sheets and feeds, without asking suppliers or brands to reformat.
- Units and value lists. Two suppliers describing the same spec as "10 cm" and "100 mm" should end up as one value.
- One taxonomy across brands. A dress from one brand and a dress from another should land in the same category with the same attributes, so your filters work across brands.
- Rules per brand. Some brands set their own rules for product names, claims or images. The PIM should apply those rules to that brand's products only.
- Classification standards. Technical distributors often need ETIM or eCl@ss classes, GTINs from GS1, or UNSPSC codes for procurement customers. Ask which versions a PIM supports and how it handles a new release.
- Many outbound formats. Distributors often send data to their own site, to marketplaces and to customers' procurement systems, each with its own schema.
Our guide to the best PIM software for distributors compares vendors on these points.
How do you evaluate AI features in a PIM?
Every PIM vendor now says it uses AI. What matters is how good the output is on your products and how much control your team keeps over it. When you evaluate AI in a PIM, look at:
- What a reviewer sees for each value the AI fills, such as where it came from and how sure the AI is
- What the AI does when it can't find a value: leave it blank, flag it or guess
- Whether you can choose which fields the AI writes to, and protect values your team has already approved
- Whether it keeps your value lists clean, without adding several spellings of the same material
- Whether AI output is checked against each channel's requirements before it publishes
- How it learns your brand voice and terminology, and whether your team's edits improve it
- Whether your data is used to train shared AI models
- How AI usage is priced
Our guide to AI in PIM covers how to measure data quality once AI is involved.
What should you include when you compare PIM costs?
Compare vendors on the total cost, not the licence alone. Ask each one to price:
- The licence, and what it's based on: users, SKUs, channels or connectors
- Implementation, whether by the vendor or a partner
- Connectors and any custom integration work
- AI usage, including limits and overage charges
- Extra locales and channels you plan to add
- Training and support tiers
Also count your own team's time. A system that leaves more manual work, or more AI output to review, costs more each month even if the licence is lower.
How do you run a PIM proof of concept on your own data?
A demo shows you the vendor's data in the vendor's best workflows. A proof of concept shows you your own data in your workflows. Run one with your top two or three vendors before you decide.
- Write your pass criteria first. Decide what a pass looks like for each workflow and share the same criteria with every vendor.
- Build one sample and send it to every vendor on the same day. Use a few dozen products across two or three product types, including the products with the most variants. For a furniture retailer, that might be sofas with fabric and leg options, rugs in several sizes and one supplier's spec sheets exactly as they arrived.
- Include a supplier file as it arrived. Send it in its original format, without cleaning it up first, so you see how each PIM handles real supplier data.
- Test workflows, not features. Run three to five end-to-end tasks, such as importing and mapping a supplier file, filling and reviewing attributes, categorizing to your taxonomy, writing descriptions and exporting to one channel.
- Have your own team drive. Ask the people who'll use the PIM daily to run at least one workflow themselves.
What should you measure in a PIM proof of concept?
Use the same sheet for every vendor, and record your reviewers' numbers rather than figures the vendor reports.
| Measure | What it tells you |
|---|---|
| Fill rate | The share of empty required attributes the system filled |
| Accuracy | The share of filled values your team checked and confirmed were correct |
| Values flagged for review | How many of the values that turned out to be wrong the system had flagged |
| Review time | How long it took your team to check and approve each product, and how many values they changed |
| Categorization | The share of products placed correctly in your taxonomy |
| Channel checks | The share of products that passed the target channel's rules the first time |
| Descriptions | The share approved without edits, and any claims that weren't in the source data |
How do you score PIM vendors and make the final decision?
Use a weighted scorecard. Score each area, weight it by how much it matters to your team and add up the total. Treat must-haves as a gate: a vendor that fails one is out, whatever its total.
Give the most weight to what you measured in the proof of concept, since it shows how each system handles your own products. Set the other weights by your priorities, such as integrations or time to go live.
Also judge each system as a whole. A vendor can score well on individual features and still be hard to work with day to day if those features don't fit together. Look at how smoothly one product moves from a supplier file to a live listing.
Before you sign, speak to a reference customer with a similar catalog and check the contract's exit terms. If you're replacing an existing PIM, our PIM migration guide covers moving your data across. For vendor-by-vendor comparisons, see the best PIM software for ecommerce.
How does Hypotenuse AI handle these requirements?
Hypotenuse AI is an AI-native PIM. Enrichment, content generation and image editing run inside the same system your product data lives in. It can work as a standalone PIM or alongside the PIM you already have.
| Requirement | How Hypotenuse AI handles it |
|---|---|
| Supplier data | Suppliers submit products in the format they already use, or your team onboards them on their behalf. |
| Filling gaps | Fills missing attributes and shows where each value came from. |
| Review | Flags uncertain data and gives confidence scores, so your team can approve changes quickly. Every suggestion stays editable. |
| Standardization | Keeps units and values consistent across every supplier. |
| Classification | Classifies products using your internal taxonomy or external standards like Google Shopping and Amazon. |
| Content | Writes product descriptions, titles and bullets in your brand voice, following your guidelines. |
| Channel checks | Validates every field against your internal rules or channel requirements before publishing. |
| Audit trail | Traces every value back to the input it came from, including supplier data. |
| Integrations | Connects with your ERP, CMS and ecommerce platforms like Shopify, with bi-directional sync. |
| Security | Meets SOC 2 requirements, with strong access controls and secure, compliant infrastructure. |
FAQs
What is the difference between a PIM demo and a proof of concept?
A demo is the vendor showing its system, usually with its own sample data. A proof of concept is your team testing the system on a sample of your own products, against pass criteria you set in advance.
How many products should you use in a PIM proof of concept?
A few dozen products across two or three product types is usually enough to see the differences between vendors. Include the products with the most variants and at least one supplier file in its original format.
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