Most companies don’t set out to build a product data strategy. They set out to reduce returns, speed up product launches, improve the digital shelf, or keep retailers happy.
The problem? Those challenges often share the same root cause.
In our recent webinar, Why Product Data Is Costing You More Than You Think, experts from Syndigo, GS1 US, and Pilgrim’s explored how small product data issues can quietly create major business costs. From retailer chargebacks to delayed launches and customer returns, poor product data has a way of showing up where you least expect it.
Here are the five biggest takeaways.
1. Returns Don’t Create Problems. They Reveal Them.
One of the webinar’s most memorable examples involved something seemingly insignificant: an incorrect package dimension.
The mistake started during item setup. The data was syndicated downstream. Retailers published it. Customers saw incorrect information on the product detail page (PDP). Orders were placed. Returns followed.
The return wasn’t the problem.
It was the symptom.
This is why businesses often spend time investigating what’s visible instead of fixing what caused it. By the time a return, complaint, or retailer issue surfaces, the problem has already moved through multiple systems and teams.
Takeaway: If you’re only looking at the outcome, you’re already late. Improving product data quality means tracing issues back to the source and fixing them before they impact the customer experience.
2. Bad Product Data Travels Faster Than Most Teams Realize
A single inaccurate attribute rarely stays in one department.
It impacts ecommerce teams managing the digital shelf. It creates work for customer service. It frustrates retailers. It slows down sales teams. It forces operations and product teams to investigate and correct errors.
As Helen Grimster explained during the webinar, bad data isn’t shy. It introduces itself to half the organization.
Organizations often think they have separate issues:
- Product returns
- Retailer fines and chargebacks
- Slow item setup
- Manual rework
But these business challenges frequently start with the same thing: incomplete, inaccurate, or disconnected product information.
Takeaway: Product data isn’t a back-office problem. It’s a business performance issue with implications across revenue, operations, compliance, and customer experience.
3. Trusted Product Data Requires More Than Accurate Data
According to GS1 US, trusted product data must pass four tests:
- Complete – The required information exists.
- Accurate – The data matches the product.
- Trusted – Teams and partners can rely on it.
- Synchronized – Everyone works from the same version of the truth.
This is where standards matter.
James Krajewski from GS1 US highlighted the critical role of Global Trade Item Numbers (GTINs), GDSN, and standardized data sharing in creating a trusted foundation.
Without common identifiers and synchronized product data, every retailer, marketplace, and partner risks working from a different version of the product record.
And when that happens, product content syndication becomes difficult to scale.
Takeaway: Trusted product data starts long before a product reaches the digital shelf. It begins with identification, validation, governance, and synchronization.
4. Spreadsheets Are Helpful. They Are Not a Strategy.
Many organizations start their product information management journey in spreadsheets.
That’s normal.
What’s not sustainable is running an entire product content ecosystem through spreadsheets indefinitely.
Pilgrim’s Digital Marketing Manager Aaron Conkey shared how his team struggled to manage growing retailer networks, digital shelf requirements, and brand expansion without a scalable system.
As new retailers, channels, and product lines were added, manual processes became increasingly difficult to maintain.
Instead of allowing retailer requirements to dictate every workflow, the team shifted its focus upstream. They created repeatable processes and implemented systems capable of supporting growth.
The lesson wasn’t that spreadsheets are inherently bad.
The lesson was that spreadsheets shouldn’t become the operating model.
Takeaway: Growth eventually exposes gaps in manual processes. To scale effectively, brands need governance, validation, workflow, and technology working together.
5. Great Product Data Is Becoming Even More Important in the AI Era
One of the strongest themes throughout the discussion was how product data now influences far more than retailer websites.
Today’s product content is consumed by:
- Search engines
- Retail algorithms
- Ecommerce marketplaces
- AI shopping assistants
- Generative AI search experiences
As Aaron noted, if a product detail page doesn’t answer customer questions, it’s unlikely to perform well with consumers or AI systems.
That’s a significant shift.
Historically, product content was created for shoppers. Now it’s also being evaluated by machines that determine visibility, recommendations, and discoverability.
As agentic commerce and AI shopping assistants continue to evolve, complete and structured product data becomes a competitive advantage.
The brands that win won’t simply have the best products.
They’ll have the products that can be accurately understood, trusted, and recommended by AI systems.
Takeaway: The future of commerce depends on AI-ready product data. If your data isn’t complete, structured, and consistently distributed, your products risk becoming invisible in emerging shopping experiences.
Final Thought: The Best Product Should Win
The biggest lesson from the webinar was simple:
Small product data issues create expensive business problems.
Returns, retailer complaints, delayed launches, and digital shelf gaps often begin with a missing attribute, an incorrect dimension, or a disconnected process somewhere upstream.
Organizations that invest in product information management (PIM), product experience management (PXM), master data management (MDM), and product data syndication are not just improving data quality. They’re creating the foundation for faster launches, stronger retailer relationships, better customer experiences, and greater visibility in an increasingly AI-driven marketplace.
Because the goal isn’t better data for data’s sake.
It’s helping great products get chosen.
