Every manufacturer organizes product data around its own products and systems. When a retailer or software platform combines several brands, the information may arrive through spreadsheets, portals, data feeds, or PDF specification sheets. Field names, value formats, and category structures can all vary from one source to another.

Color is a simple example. Several furniture manufacturers may sell brown sofas, but one feed may use “Brown,” another “Chocolate,” and another “Espresso.” Shoppers can recognize them as similar colors, but a website filter may treat each name as a completely different option.

The problem grows with every brand added to the catalog. Product pages, filters, and comparison tools may all need separate rules for how each manufacturer names and formats its data.

Product data normalization brings that information into one consistent structure. The process identifies comparable fields, converts values into shared formats, and preserves meaningful differences between products. Turning those different feeds into one usable catalog begins with creating a shared structure for product information.

Common Ways Product Data Is Normalized Across Brands

Product data normalization can involve many attributes, formats, and product types. The examples below show common ways to organize information from different manufacturers into one consistent catalog structure.

• Organize Products by Category and Product Type

Normalization starts with a shared catalog structure. Every product can have common fields for its brand, product name, model number, category, description, images, and supporting documents. Keeping those details in predictable fields gives every product record the same basic foundation.

Products also need consistent categories. A washing machine might arrive under “Washers,” “Laundry Appliances,” or “Home Appliances,” depending on the manufacturer. A shared category structure can place each model under “Washers & Dryers” and then “Washers,” regardless of the category used in the original feed.

Within those categories, each product type can keep the fields that make it different. Refrigerators may include capacity and configuration, sofas may include upholstery and seating capacity, and mattresses may include comfort level and construction.

After every product and attribute has a place in the catalog, the next step is to connect the different labels manufacturers use for the same information.

• Separate Width, Depth, and Height

Manufacturers don’t always list product dimensions in the same order. One feed may use “width × depth × height”, another may use “height × depth × width”, and another may arrange the measurements differently. Even when the numbers describe the same product type, each measurement's position can vary across feeds.

Normalization identifies which value represents the width, depth, and height, then stores each measurement in its own field. Retailers can therefore filter products by width, depth, or height without accounting for the original order used by every manufacturer.

Separating the measurements establishes what each number represents. The units and number formats may still need to be standardized before products can be compared accurately.

• Standardize Measurements and Warranties

Manufacturers may provide the same measurement in different units. One brand might list a refrigerator width as 91.44 centimeters, while another lists it as 36 inches.

Normalization converts the measurements and provides both values in the product data: 91.44 centimeters and 36 inches. Retailers can then display the unit their customers use, or show both, while keeping the underlying product information consistent.

Warranty periods can follow the same approach. A warranty listed as 24 months can also be provided as two years. Both values describe the same coverage period, but consistent presentation makes warranty information easier to display and compare across brands.

Measurements and warranties are only two examples of values that may arrive in different formats. Product identifiers can also vary between sources.

• Standardize SKU and Model Number Formats

A Stock Keeping Unit (SKU) or model number may include slashes, hyphens, spaces, or other formatting. Problems can occur when one system keeps that punctuation while another removes it. The same product may be treated as two different records or fail to match across connected systems.

For example, Samsung lists its over-the-range microwave as ME17F7021MT/AA, while Furniture of America lists the Lauritz Chair as CM6088DG-CH. Standardizing these identifiers as ME17F7021MTAA and CM6088DGCH removes the punctuation and gives connected systems one consistent format to recognize.

The original SKU or model number can still appear on the product page, while the standardized version is used behind the scenes to make products easier to search, match, and update across systems.

• Group Similar Colors Together

Not every product value should be standardized by removing its differences. Color names, for example, may need to keep the manufacturer’s original wording while still grouping them under a shared color.

Several furniture manufacturers may sell brown sofas, but one feed may use “Brown,” another “Chocolate,” and another “Espresso.” If each name remains a separate filter value, shoppers may need to select several options to find similar products.

Normalization can keep the manufacturer’s original color name while assigning each product to a broader color group. The product page can still show “Chocolate” or “Espresso,” while a shopper filtering for brown can find both products.

The product information that requires normalization varies by manufacturer and product type. These examples show some of the most common applications, but the same process can also organize other specifications, attributes, and values found across manufacturer feeds.

What Happens After Product Data Is Normalized

After categories, dimensions, identifiers, colors, and other attributes follow the same structure, retailers and software platforms can use the data more consistently across their catalogs.

Help Shoppers Filter and Compare Across Brands

Website filters can become fragmented when manufacturers use different field names and values for similar product information. A website may treat “Overall Width” and “Width,” “Black Stainless Steel” and “Black SS,” or “French Door” and “French-Door” as separate options, even when shoppers expect to find those products together.

The table below shows how normalization places those variations under shared attributes.

Manufacturer A

Manufacturer B

After normalization, both refrigerators can appear under the same width class, color family, and configuration filters. The original manufacturer values can still remain in the product record, including the exact width shoppers may need when checking whether a refrigerator will fit.

Consistent Information Across Connected Systems

eCommerce websites, Point of Sale (POS) systems, Enterprise Resource Planning (ERP) systems, and other applications can read the same product fields. Retailers are less likely to find one format on the website and another in their internal systems.

Easier Catalog Expansion

When a retailer adds a new manufacturer, the incoming data can be mapped to the existing catalog structure. Product pages, filters, and connected systems can keep using the same fields instead of rebuilding for every brand.

These benefits depend on accurately organizing product information across many brands and product types. Businesses can build that process internally or work with a product data provider that handles the normalization for them.

Build Multi-Brand Catalogs With Normalized Data From Skulytics

Normalized product data remains useful only when you maintain the structure as manufacturers release new products and revise existing information. That ongoing work is where Skulytics fits.

Skulytics is a product data platform that collects, normalizes, and maintains information from more than 400 appliance, furniture, and mattress brands. Through the Skulytics Product Data API, retailers and software platforms can retrieve that information through one connection instead of processing each manufacturer’s data separately.

Skulytics handles normalization before the product data reaches the API. The result is a catalog that follows one consistent structure across brands without removing the manufacturer-specific details retailers may still want to display.

Ready to see how normalized product data could work for your catalog? The Skulytics Individual plan starts at $100 per month and includes 100,000 API calls. There’s also a 14-day free trial with 5,000 API calls, so you can try the API before choosing a plan. 

Explore Skulytics pricing to compare your options, or book a demo to discuss your catalog and integration needs.