Long-tail search is more valuable than ever, and you can guarantee that your competitors are optimizing both their content and ads for it. But without the usual confinement of one or two keywords and with ads being picked up in more conversational searches, optimization can get lengthy and messy.
There are too many possible aspects of ads for a human marketing team to manage manually (product type, brand, color, size, material, use case, price point, occasion, problem, compatibility, style, customer need; the list goes on).
There is also the possibility that one product could be relevant to dozens of different searches depending on how the shopper describes their need. It can be "going to Scotland in November by train and looking to visit the Highlands" or "I need rainproof hiking boots that match gray trousers" — both searches could lead to the same product.
Even an optimized product title may only capture the obvious searches. A better product description and richer feed can help Google understand the product more deeply (and which customer it is suitable for) without even mentioning the specific product.
Google's own Merchant Center guidance states that product data is used to match products to the right queries, and that incorrect, inaccurate, or missing product information can cause disapprovals, limited eligibility, incorrect displays, or prevent products from showing altogether.
This is why feed quality matters so much. If Google cannot understand the product, it is much harder for that product to appear in relevant long-tail searches.
AI bidding needs strong product data to understand your product, is yours complete?
AI can only work with the data it is given, and is poor at guessing — although it tries its best, we wouldn't leave it to chance. That might sound like a simple point, but it is often overlooked. Retailers often focus on campaign structure, budgets, and bid strategies, but the product feed is really the foundation of Google Shopping performance.
If key product data attributes are missing or incorrect, Google has less information to understand when a product is relevant. This can affect visibility across Google Shopping, Performance Max, Search campaigns, AI Overviews, AI Mode, conversational shopping experiences (ChatGPT, Gemini, Claude), and long-tail product discovery.
Google has also stated that AI-driven shopping experiences are powered by the product data retailers provide, and that if a Merchant Center feed is messy or incomplete, customers may not be able to find those products.
In our experience, a significant proportion of visibility issues stem from missing or incorrect feed attributes. Around 70% of visibility problems can often be traced back to product data that is incomplete, inaccurate, or not descriptive enough, including Gender, Brand, GTIN, variant information, and key specifications, among other things like titles and product descriptions.
In AI-driven search, this is more important than ever. A shopper may not search using your exact product name. They may describe a situation, a use case, or a problem they want to solve and AI will suggest your product. Your feed needs to give Google enough information to make that match.
Good product descriptions are becoming a performance essential for Google Ads
Product descriptions are often treated as a basic ecommerce requirement. But with the rise of AI-driven Google Ads, they are becoming a performance lever. A product description now does more than describe the product to a shopper; it helps Google understand what the product is, who it is for, and what problem it solves.
That does not mean stuffing descriptions with random keywords. It means making the description clear, accurate, and useful for humans first, AI second.
Take this example. A weak description might say: "High-quality jacket suitable for outdoor use." A stronger description would be: "Lightweight waterproof hiking jacket designed for wet-weather walking, commuting, and outdoor activities. Features breathable fabric, an adjustable hood, zipped pockets, and wind-resistant protection."
The second version gives Google far more context and a greater chance of ads appearing in AI Overviews or conversational searches. It can now connect the product to searches around waterproof jackets, hiking, walking, commuting, lightweight outerwear, and wet-weather clothing, and that is exactly the kind of product understanding needed for long-tail and conversational searches.
