Product data enrichment for ecommerce sellers.
Written by
Muhammed Al-Refai
Want to see what it's like to optimize product data at scale?
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AI isn’t assisting discovery. It’s rewriting it.
Explore how AI is reshaping shopper behavior — and what brands must do to stay visible.
Written by
Want to see what it's like to optimize product data at scale?
First impressions are everything—and for online shoppers, those impressions come from your product listings. From titles and descriptions to specs and images, the way your products are presented can make or break a sale.
That’s where product data enrichment comes in.
Enriching your product data means enhancing, optimizing, and completing the information in your listings to make them more compelling, discoverable, and aligned with each sales channel’s unique requirements. Whether you're selling on marketplaces like Amazon and Walmart, or advertising through Google Shopping or Meta, strong product data is your foundation for success.
As online shopping channels increasingly use AI to power discovery, your product feed plays a bigger role than ever. These systems are parsing structured data to match products with real shopping intent. Platforms like Google’s AI Mode and emerging conversational commerce engines rely on structured, enriched listings to surface relevant products.
In this blog, we break down what product data enrichment is, why it matters, and how you can use it to drive better results across your ecommerce channels.
Product data enrichment is the process of enhancing your existing product information to make it more complete, accurate, compelling for shoppers, and usable for sales channels.
At its core, enrichment involves refining key elements of your product listings, such as:
Titles and descriptions: Making them more keyword-rich, informative, and tailored to your audience.
Attributes: Ensuring all relevant product information (like color, size, material, brand) are filled out.
Images and media: Including high-quality image and video links in your product feeds to better showcase the product.
Categorization: Assigning products to the most appropriate and granular categories.
Channel-specific formatting: Adjusting your data to meet the unique requirements of platforms like Google, Amazon, and Meta.
The goal is simple: to improve product visibility, relevance, and conversion potential by polishing the digital presentation of your inventory so it performs better wherever customers discover it.
Enriching your product data improves how your listings appear to potential customers and impacts the performance across your ecommerce channels. Here are some of the key benefits:
Search engines, marketplaces, and ad platforms rely on structured product data to match listings with shopper queries. Enriched data with accurate titles, keywords, attributes, and categorization increases the chances that your products will show up in relevant search results—both paid and organic.
Clear, complete, and compelling product information helps shoppers make faster buying decisions. When customers have all the details they need—like size guides, material specs, and usage info—they’re more likely to trust the listing and complete the purchase.
Platforms like Google Shopping and Meta Ads pull directly from your product feed to generate dynamic ads—meaning the quality of your data has a direct impact on how your products appear in paid placements.
Enriched product feeds with complete titles, accurate attributes, and optimized images help platforms better understand your items, which leads to improved targeting, higher Quality Scores, and more relevant ad placements.
Enriching your product data ensures your listings are machine-readable, semantically accurate, and complete—helping AI systems match your products with the right queries, preferences, and shopper behaviors. As more platforms collapse search, filtering, and even checkout into AI-powered flows, your product data needs to do the talking. These AI tools don’t rely solely on keywords—they depend on rich, structured product data to understand what each listing represents.
Imagine a shopper asks an AI shopping tool, “What’s a good carry-on suitcase under $200 that fits in overhead bins and has spinner wheels?” The AI doesn’t just search for products with those exact words—it looks for listings with detailed, structured attributes like product type, dimensions, price, and features (e.g., spinner wheels).
If your product feed includes all those attributes—accurately labeled and properly formatted—your suitcase is far more likely to be recommended. If the data is missing or inconsistent, you’re invisible to the AI, no matter how great your product is.
Enriched product data reduces friction in the buyer's journey. Shoppers can easily compare items, filter by attributes, and find exactly what they’re looking for—whether browsing on a website or scrolling through a marketplace.
According to a Power Reviews survey, nearly half of customers return items because they do not match the online description.
More detailed product descriptions, specs, and visuals help set accurate expectations. That means fewer unpleasant surprises for customers—and fewer returns or questions for your support team.
Now that you have a good understanding of what product enrichment is and why it’s important for your success online, here are seven powerful strategies that you can implement to enrich your product data effectively:
Your product titles and descriptions aren’t just text—they’re critical levers for visibility, relevance, and conversion. Well-optimized copy helps algorithms understand your product and helps shoppers quickly grasp its value.
Product titles should follow a structured format that prioritizes the most important and searchable information first. This often includes attributes like brand, product type, key features, and variations (e.g., color, size, or material).
Descriptions should go beyond specs and tell the product’s story. Use benefit-driven language that speaks to how the product fits into a shopper’s life. Highlight use cases, key differentiators, and unique value. Include relevant long-tail keywords to improve SEO and search match.
Complete product listings should include all relevant attributes, like color, size, material, gender, and brand. The more attributes you provide, the more ways shoppers (and algorithms) can discover and filter your products.
Consistent formatting helps ensure your data is readable and usable across platforms. For example, using standardized units (“in” instead of “inch” or “inches”) or consistent capitalization improves clarity and makes your brand appear more trustworthy.
While standardization promotes consistency, localization ensures relevance. These two goals can seem contradictory—but they work best when balanced. Localization involves tailoring your product data for different regions by adjusting language, currencies, measurements, and even terminology to match local expectations.
For instance, a product listed as “jumper” in the UK may need to be localized to “sweater” for U.S. audiences, and measurements might shift from inches to centimeters. Localization is essential for brands selling internationally or across multilingual markets.
Visual content plays a major role in purchasing decisions. Include multiple high-resolution images, lifestyle photos, and videos where possible to give shoppers a better sense of the product.
Most platforms support image_link, additional_image_link, or lifestyle_image_link fields for multiple images, which is a foundational part of product enrichment.
Some marketplaces like Amazon and Walmart allow you to include video URLs in specific fields (e.g., video_link or custom fields). These can display product demos, how-tos, or unboxing videos.
Assign products to the most specific category available in a channel’s taxonomy—not just a general one. Granular categorization plays a crucial role in how products are indexed, filtered, and displayed on ecommerce platforms.
Many marketplaces (like Amazon, Walmart, and Target Plus) rely on category-driven navigation and faceted filtering, meaning shoppers often start their journey by drilling down through product categories. If your products are too broadly categorized, they may not appear in the right filtered views or browse paths.
For example:
Instead of assigning a yoga mat to “Sports & Outdoors > Exercise Equipment”, categorize it as “Sports & Outdoors > Exercise & Fitness > Yoga > Yoga Mats.”
A coffee maker should go under “Home > Kitchen Appliances > Coffee, Tea & Espresso > Coffee Makers” rather than just “Appliances.”
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When a product comes in multiple options—like different sizes, colors, or styles—it’s essential to define clear parent-child relationships within your product data feed. Variant structuring helps platforms group related SKUs under a single parent listing, improving the browsing experience and boosting conversion rates.
Instead of displaying each variation as a standalone product (which can clutter search results), your feed should include attributes such as item_group_id for Google Shopping, parent_sku and relationship_type for Amazon.
For example, a t-shirt available in three sizes and four colors should be structured as one parent product with twelve child variants—each with a unique SKU, but grouped under the same product family.
This approach enhances the user experience by allowing shoppers to browse all available options from a single product page, while also reducing duplicate or fragmented listings that can confuse buyers and hurt performance.
It also consolidates reviews under one listing instead of spreading them across multiple variations, helping build stronger social proof and increasing the likelihood of conversion.
If you’re selling across multiple channels or have a large and changing product catalog, enriching that data manually is time-consuming and prone to errors. That’s where feed management platforms come in.
A robust feed management solution like Feedonomics acts as a centralized system for distributing and enhancing your product data at scale.
Here’s how Feedonomics supports and streamlines product data enrichment:
Feedonomics can pull product data from various systems—like your ecommerce platform, PIM, ERP, or spreadsheets—and merge them into one unified feed. This makes it easier to identify missing fields and inconsistencies and to pull the data needed to complete your listings.
Instead of editing listings one by one, you can create rule-based logic to enrich titles, fill in missing attributes, standardize values, and format data to fit each channel’s requirements.
For example, you could optimize the product titles for your blouses using a data transformation formula similar to this:
The platform then parses the values from each bracketed attribute and generates uniform, easy-to-read titles for all blouses across the channel catalog:
Each sales channel has its own specifications, required fields, and best practices. Feedonomics enables you to customize and optimize your data feed for each destination—whether it’s Google Shopping, Amazon, TikTok Shop, or a niche marketplace.
Not sure how to optimize product data for the channels you want to sell on? Don’t sweat the details, our channel experts stay up-to-date on the best practices so you don’t have to.
With Feedonomics, you can group products using custom labels directly within the product feed. These labels make it easy to run controlled A/B tests by segmenting your catalog based on criteria like performance, seasonality, or profitability. You can test variations in product titles, descriptions, or pricing to see what resonates best with your audience, then apply those learnings across your broader catalog.
Feedonomics uses AI to automatically and accurately categorize products at a granular level, reducing manual work and improving data quality. This ensures your products are classified correctly across channels like Google, Amazon, and Meta—boosting visibility and performance.
Whether you have 100 products or 100,000, Feedonomics lets you apply enrichment changes across your entire catalog without manual edits. It’s ideal for merchants with large or frequently changing inventories.
Feedonomics integrates with over 300 channels, with synchronization features that keep your product data up-to-date everywhere you sell.
Product data enrichment gives you a competitive advantage on crowded marketplaces and ad channels. Enriched product data helps your listings stand out, convert better, and perform across every channel.
By implementing smart enrichment strategies—like granular categorization, optimized titles, and the use of optional attributes—you give your customers the information they need to make confident buying decisions. And with the right feed management solution in place, you can automate and scale this process with precision and consistency.
The product data enrichment process involves improving and expanding your ecommerce product data to make listings more complete, relevant, and appealing to shoppers. This includes adding product attributes, refining product details, and filling in gaps across your product catalog. High-quality enriched data helps increase visibility on marketplaces, supports better SEO, and helps customers make more informed decisions.
Yes! By enriching listings with detailed product attributes and related product details, retailers can create smarter product recommendations. This opens the door for effective cross-selling and upsell strategies—especially when combined with advertising platforms that use customer purchase behavior to dynamically recommend products from your product feed. Enriched data provides the context needed to suggest relevant items and increase average order value.
Enrichment tools like Feedonomics help automate and scale the data enrichment process by applying custom logic, merging data from multiple data sources, and identifying missing or incorrect values. These tools are especially useful when working with large volumes of SKUs across different sales channels. They also support ongoing optimization to improve product pages and drive better performance over time.
As search engines and ecommerce platforms adopt AI-driven shopping experiences—like Google’s AI Mode—product visibility depends more on the quality and completeness of product data than on traditional keywords alone. Product data enrichment ensures listings are optimized with accurate titles, detailed attributes, high-quality images, and structured information that AI systems can easily interpret and surface in relevant results. This helps sellers stay competitive as AI reshapes how products are discovered across search, marketplaces, and social commerce.
Retailers can integrate product enrichment software like Feedonomics into their existing workflows using API connections, FTP connections, or web crawls. This data integration ensures that enriched data flows seamlessly between systems, from data sources to the front end of your site. Whether you're managing your catalog internally or working with third-party providers, integration supports synchronization and helps maintain consistency across SKUs.
Yes—enriched product content plays a major role in the success of social media ads. Platforms like Meta and TikTok rely on your product feed to dynamically generate ad creatives. The more complete and compelling your data is—including titles, images, and descriptions—the better your ads will look and perform. High-quality content helps increase metrics like engagement, click-through rates, and ultimately conversions.
Yes! Feed management platforms like Feedonomics are built to support efficient data cleansing as part of a broader data management strategy. Whether your product data comes from multiple sources or varies in structure, Feedonomics can standardize and normalize data quickly, formatting it according to your specifications to ensure consistency across your entire catalog.
This streamlines onboarding for vendors and partners, simplifies internal workflows, and reduces the manual effort required to make raw product data usable. It’s especially useful for businesses in ecommerce, job boards, vehicle classifieds, and marketplaces where incoming data is inconsistent and often incomplete.
Yes! Feed management platforms like Feedonomics play a vital role in validation by automatically reviewing your product datasets for inconsistencies, missing data points, and channel-specific errors.
Feedonomics safeguards your data management operations with built-in systems that flag problems, prevent bad data from publishing, and notify the right people to take action. From automated alerts and hard stops, to inventory buffers, random sampling, and data tracking, our validation tools help ensure your listings remain accurate, compliant, and optimized—maximizing uptime and minimizing disruptions.
Muhammed Al-Refai
