Quality Product Data Is the Foundation of E-Commerce
Many merchants run their stores and marketplaces with incomplete product information. The result is predictable:
- Conversion suffers
- Returns climb
- Shoppers lose trust before they reach the checkout
The fix is not more content for the sake of content. The fix is finding the right data, from the right source, and validating it before it goes live.
Before you start, three decisions shape the entire process:
- Where will you look?
- What data do you already have to use as search parameters?
- What information are you missing? Descriptions, images, or both?
Each decision affects the quality of the result. The clearer the answers, the better the output.
Where to Look
You have three options:
- Search the internet broadly
- Limit your search to a brand or manufacturer's website
- Go directly to a specific product page
The more specific the source, the better the results.
Best Option: Extract from a URL
If you have a link to the supplier product page for each item, use the Extract from URL workflow step in Dyver. Key points:
- It analyzes the page and pulls relevant information for each product
- Dyver can construct these links automatically when the manufacturer's site follows a consistent URL pattern
- This is the most reliable method
No Product Page Link? Use Find Product Data
If you do not have individual product URLs, the Find Product Data workflow step gives you two options:
- Limit your search to known manufacturer or brand websites
- Run a generic internet search
Note: Generic searches work well for popular products sold across many stores. They tend to be less effective for niche or uncommon items.
What Search Parameters to Use
Good search results depend on choosing the right identifiers.
If You Have a Unique ID
Use it. The strongest options include:
- EAN or GTIN, when your industry typically includes them on product detail pages
- Manufacturer or brand assigned codes
If You Do Not Have a Unique ID
Combine identifiers to narrow the search:
- Product name plus brand name
- Product name plus category
- Product name plus color or other attribute
There is no universal formula. Experiment with different combinations and see which one returns the best results for your catalog.
What Information Are You Missing
Your products may be lacking images, descriptions, structured attributes, or all three. The right approach depends on what is missing.
Text Data: Descriptions, Titles, Metadata, Attributes
Searching for textual content lets you:
- Create comprehensive product descriptions
- Write metadata that supports SEO
- Populate attribute fields with structured values
This is the layer that powers search relevance, filtering, and discoverability.
Images
Visual content is no longer a finishing touch. It is part of the data layer itself. Additional images:
- Improve conversion rates
- Make listings look more professional
- Can feed into AI to generate product descriptions or extract embedded text
Always Validate the Results
Regardless of which type of data you are retrieving, validate the results before publishing. Dyver supports two validation paths:
- AI-based checks for scale across large catalogs
- Human in the loop review for accuracy on critical fields
Most catalogs benefit from a mix of both.
Takeaways for E-Commerce
- Missing product data is a sales and returns problem before it is a content problem.
- The most reliable source is a direct supplier URL. Generic searches are a fallback for popular items.
- Unique identifiers such as EAN or GTIN deliver the strongest match rates when they are available.
- Text and images are both part of the product data layer, and both affect conversion.
- Always validate, whether through AI checks, human review, or a combination of the two.
Filling product data gaps is not a one-time project. It is an ongoing part of running a healthy catalog. The brands that treat it that way are the ones that keep their listings sharp, their search results relevant, and their shoppers confident enough to buy.