Journalist at Promodo
For over 6 years, I have been working as a journalist in the communications and marketing industry. I help brands communicate effectively through written content, engage with market experts, and create professional materials on topics related to business and marketing, sharing insights on working with marketing tools.
Since users increasingly search for products by specific attributes such as material, color, or style, businesses may miss out on relevant traffic if those details are not included in their product feed.
This is a challenge we regularly see in our work with eCommerce clients, especially in consumer electronics and auto parts, where shoppers search by specs and compatibility. In many cases, our analysis shows that potential traffic is being lost due to limitations in the product feed structure.
In this article, we'll tell how we analyze user search behavior, identify opportunities to improve product data structure, and manage approach that significantly improves Shopping campaign performance.
A product feed is a structured file that contains information about products and is uploaded to Google Merchant Center. Google uses this data to create product ads and determine which search queries are relevant for displaying those products.
The quality of a product feed directly impacts the performance of Google Shopping, Performance Max, dynamic remarketing, and other catalog-based campaigns. For these campaign types, the feed has become one of the main sources of signals used by Google's algorithms.
The challenge is that most product feeds are created primarily for website catalogs rather than advertising systems. That’s why product titles and descriptions often lack the attributes that users include in their searches.
For example, someone may search not just for a "laptop" but for a "14-inch laptop 16GB RAM. If these attributes are missing from the feed, Google has fewer signals to determine whether the product is relevant to the search.
This is especially important for non-branded searches, where users are not looking for a specific brand but are searching based on product features and characteristics.
So, the solution is to improve product feed management to boost the performance of advertising campaigns.
The optimization process typically includes three stages:
Product feed enrichment is the process of improving and expanding product attributes to increase the effectiveness of advertising campaigns. At Promodo, we start with how users actually search.
We recommend starting by analyzing the characteristics users most often include when searching for products in relevant categories. Then compare these search patterns with the information available in clients' product feeds.
This analysis helps identify:
In essence, it's a new approach to structuring product data. Targeted not only at website catalogs, but also at Google Shopping algorithms.
The final step is to implement these changes in the product feeds quickly, efficiently, and without requiring additional resources (such as budget or manual effort).
For small product catalogs, updating titles and descriptions manually is manageable. However, when a retailer manages thousands of SKUs, regularly updates its assortment, and runs campaigns across multiple categories, the process quickly becomes too time-consuming and resource-intensive.
Maintaining a consistent feed structure is another challenge. In large catalogs, there are often duplicate entries, inconsistent naming formats, incomplete attributes, and descriptions that don't align with the way users actually search for products.
To solve these challenges, we use AI. This approach significantly reduces the time required to implement feed updates and helps us maintain a consistent and well-structured set of product attributes.
To automate the process with our clients Etnodim and MBM, we used FeedGen, an open-source solution developed by Google and Admixer Advertising. The tool uses generative AI and large language models (LLMs) to optimize product feeds.
It allowed us to apply the enrichment rules we had developed across large product catalogs at scale. Based on the defined attributes, the system generated updated product titles and descriptions and helped fill in missing product information.
In addition, FeedGen can analyze product images and identify attributes that are missing from the source data. For example, if a product's color or style is not included in the feed, AI can detect and automatically add these attributes.
Etnodim is a Ukrainian brand of contemporary embroidered clothing.
Etnodim already had strong visibility for branded searches, but there was an opportunity to attract more non-branded traffic.
Our analysis of Shopping campaigns showed that users were actively searching for products based on attributes such as material, garment type, design details, and style. However, many of these attributes were either missing from the product feed or were insufficiently represented.
We analyzed user search patterns, created title templates that combine key product attributes in a logical and easy-to-understand order, and selected two title-generation formats:
[[TEXT-CENTER-START]]
Material | Gender | Product Type | Color | Title | Brand
Product Type | Gender | Color | Title | Brand
[[TEXT-CENTER-END]]
The Promodo team used FeedGen to build a title-generation framework based on real user search patterns. The AI model generated new product titles and descriptions, added missing attributes, and structured product information to make it easier for Google to understand.
What made this project unique was that FeedGen was used not only to improve existing product data but also to generate new information based on visual content.
As a result:
.webp)
While working with MBM, we faced a different challenge.
MBM is an eCommerce retailer specializing in home goods with a large product catalog. Many products on the site had incomplete attributes, and product descriptions were cluttered with technical symbols and unstructured information. As a result, Google lacked the signals it needed to accurately determine product relevance.
We analyzed the product categories and identified the attributes that had the greatest impact on search relevance. Then we developed a new structure for product titles and descriptions that provided Google's advertising algorithms with more complete and consistent product information.
The AI model generated new title variations that incorporated attributes such as material, brand, color, product type, and other relevant characteristics. At the same time, it cleaned up product descriptions and converted them into a more structured format.
As a result:
An additional benefit was the speed of implementation. More than 500 products were processed in approximately 3 hours without developer involvement or manual rewriting of product listings.
Many eCommerce teams still view the product feed as a technical component of Google Merchant Center rather than an extra tool for improving Shopping campaign performance. At the same time, the Promodo case studies show that efficient product feed management can help businesses reach more relevant search queries and improve Google Shopping results without increasing budgets or launching new campaign types.
This is especially valuable for retailers with large product catalogs, such as consumer electronics or auto parts stores where manually updating titles, descriptions, and attributes can be time-consuming.
If your product feed needs improvement or you'd like to explore an AI-driven approach to feed enrichment, the Promodo team can help you evaluate your current setup and identify opportunities to improve performance.
Journalist at Promodo
For over 6 years, I have been working as a journalist in the communications and marketing industry. I help brands communicate effectively through written content, engage with market experts, and create professional materials on topics related to business and marketing, sharing insights on working with marketing tools.
You may also like
Choose quality and trusted services to improve the presence of your company on the Internet, and feel free to contact our UK team if you have any questions.
While X-Day is soon approaching, every online business should do its best to migrate to Google 4 Analytics (GA4) as soon as possible.
83% of people entrust referrals, relatives, friends, or colleagues and admit that this is the most reliable source of information.
According to the most recent pet industry statistics, American pet owners alone spend more than $103 b. on pet care products.
We at Promodo are ready to help you improve your performance across all digital marketing channels.
Get started
.avif)
.svg){kind=logo}
