For years, the product feed was treated as a problem to solve once and forget. You\u2019d export it from your management system, upload it to Merchant Center, verify that your products were approved. Done. The real work \u2014 the strategic work \u2014 was happening elsewhere: in bids, audiences, and creatives.<\/p>\n
That era is over. Understanding why has become one of the most important tasks for anyone working in performance marketing today.<\/p>\n
Until a few years ago, the logic behind advertising campaigns was linear and relatively transparent. In Google Shopping, you specified negative keywords, set bids by category or product, reviewed the search term report, and optimized accordingly. The feed was there to populate ads with accurate product data \u2014 title, price, image, availability. It was a prerequisite, not a lever.<\/p>\n
On Meta, the structure was similar: you built audiences, wrote copy, produced creatives. The product catalog \u2014 the feed\u2019s equivalent \u2014 served Dynamic Product Ads, meaning retargeting. It was a tactical tool, not a strategic one.<\/p>\n
In this context, investing in feed optimization had limited value. It just needed to be accurate. Campaign performance depended on other variables.<\/p>\n
The turning point came with the progressive automation of advertising platforms. Google introduced Smart Shopping, then evolved it into Performance Max. Meta developed Advantage+ Shopping Campaigns. In both cases, the logic is the same: the algorithm takes over targeting, bidding, and creative decisions \u2014 based on the signals it receives.<\/p>\n
The primary signal, in both cases, is the feed.<\/p>\n
Performance Max doesn\u2019t accept keywords. It has no required audience segments. It has no manually selected placements. It receives assets \u2014 headlines, images, videos \u2014 and it receives the product feed. From these inputs, it decides who to show what to, where, and when. The feed is no longer the container for ads: it\u2019s the instruction set the algorithm uses to build them.<\/p>\n
Meta Advantage+ follows the same logic on the social side. The product catalog no longer exists just for retargeting \u2014 it powers prospecting campaigns, drives creative personalization, and informs automated bidding decisions. Here too, the quality of catalog data equals the quality of the signal the algorithm receives.<\/p>\n
The practical consequence is clear: traditional optimization levers \u2014 manual bids, defined audience segments, explicit keywords \u2014 have lost their influence. The feed has gained it.<\/strong><\/p>\n This isn\u2019t just a theoretical argument. The numbers back it up directly.<\/p>\n Google measured the impact of adding product feeds to Demand Gen campaigns: consolidated campaigns with a broad product selection see an average +33% in conversions<\/strong> and +18% in clicks<\/strong> compared to campaigns without a feed, at the same cost. This isn\u2019t a marginal improvement \u2014 it\u2019s the difference between a campaign that scales and one that stalls.<\/p>\n On the operational side, a documented case study from paid media practitioners describes an automotive client with over 340,000 products in their catalog: disorganized feed, missing titles, absent images on a significant portion of listings. The result: dynamic campaigns that underperformed and cost-per-lead out of control. After a systematic effort to reorganize and optimize the feed \u2014 without touching bids or budget \u2014 the client grew to over 30,000 leads per month with a 55% drop in cost per lead.<\/p>\n The feed wasn\u2019t a technical detail in that case. It was the core problem.<\/p>\n If the feed has become the primary signal for algorithms, optimizing it means something different than it used to. It\u2019s not just about meeting platform technical requirements \u2014 though that remains the baseline. It\u2019s about building a feed that speaks the algorithm\u2019s language and maximizes relevance in the eyes of Google and Meta.<\/p>\n In practice, this means working across multiple dimensions simultaneously.<\/p>\n Quality of text attributes.<\/strong> Product titles are the most direct signal for query matching in Shopping and for relevance in PMax. A title built with the right structure \u2014 brand, product type, key attributes in the first 70 characters \u2014 is worth more than any bid adjustment for a product struggling to earn impressions.<\/p>\n Completeness of structured data.<\/strong> GTINs, Google product categories, variant attributes (color, size, material) \u2014 these are not optional fields. They are the signals Google uses to connect a product to its Shopping Graph, the global database that aggregates pricing, availability, and review data. A product with a correct GTIN is a product Google recognizes. One without a GTIN is an unverified entity.<\/p>\n Strategic segmentation with custom labels.<\/strong> Custom labels don\u2019t affect search relevance, but they are the tool marketers use to maintain control over algorithm behavior in PMax. Segmenting the catalog by margin, seasonality, or historical performance \u2014 and building separate asset groups for each segment \u2014 is the only way to prevent PMax from concentrating all budget on already-strong products while ignoring those with untapped potential.<\/p>\n Real-time updates.<\/strong> The frequency of feed updates \u2014 prices, availability, variants \u2014 directly impacts the data quality the algorithm perceives. A feed with frequent errors or delayed updates accumulates penalties to the account\u2019s trust score that propagate across the entire catalog.<\/p>\n If the transition from classic Shopping to Performance Max has already redefined the role of the feed, what\u2019s happening in the world of large language models could redefine it further.<\/p>\n ChatGPT, Gemini, and Perplexity are integrating product results into their conversational interfaces. When a user asks Gemini \u201cwhat\u2019s the best winter coat under $200\u201d or asks ChatGPT \u201cwhere can I buy a compact laser printer,\u201d the responses include products \u2014 with image, price, availability, and a purchase link. The source of those products is the structured feed: Google Merchant Center first, but also dedicated feeds that individual LLMs are developing or adopting.<\/p>\n The logic is the same as in Shopping and PMax: the LLM reads the feed data and decides which products to surface in response to a query. Brands with an optimized feed \u2014 complete attributes, relevant titles, accurate structured data \u2014 will be visible in these new touchpoints. Brands with sparse or low-quality data won\u2019t, regardless of the quality of the underlying product.<\/p>\n It\u2019s too early to quantify the impact of this channel on eCommerce traffic and conversions. But the direction is clear: the feed is already the data layer that connects a brand\u2019s catalog to every digital discovery and purchase channel. And the number of those channels is only going to grow.<\/p>\n The product feed marketing<\/a> is not a new name for an old practice. It\u2019s the recognition that the feed \u2014 the data structure describing a brand\u2019s product catalog \u2014 has become the most cross-cutting and highest-impact asset in the modern performance marketing ecosystem.<\/p>\n It doesn\u2019t replace campaign strategy, creative development, or performance data analysis. It sits alongside all of that as the prerequisite that determines whether those investments can actually work.<\/p>\n An optimized feed doesn\u2019t guarantee results. A poor feed prevents them.<\/p>\n \u2192 Discover Product Feed Marketing suite by Highstreet.io<\/a><\/span> Per anni il product feed \u00e8 stato trattato come un problema da risolvere una volta e dimenticare. Si esportava dal gestionale, si caricava su Merchant Center, si controllava che i prodotti fossero approvati. Fine. Il lavoro vero \u2014 quello strategico \u2014 stava altrove: nelle offerte, nelle audience, nelle creativit\u00e0. Quel tempo \u00e8 finito. E capire […]<\/p>\n","protected":false},"author":4,"featured_media":11914,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[83,81,79],"tags":[],"class_list":["post-11898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-guide","category-marketing","category-product-feed"],"acf":[],"yoast_head":"\nThe data confirming the paradigm shift<\/h2>\n
What \u201coptimizing the feed\u201d means in the algorithmic era<\/h2>\n
The next frontier: product feeds in LLMs<\/h2>\n
From technical requirement to strategic asset<\/h2>\n
\n\u2192 AI Enrichment: automatic optimisation of feed attributes<\/a><\/span>
\n\u2192 Request a demo<\/a><\/span><\/p>","protected":false},"excerpt":{"rendered":"