The rise of generative AI doesn’t render keyword research obsolete-it expands your toolkit. Chatbots interpret context by linking user questions to underlying product attributes. For example, a prompt like 'recommend a camping tent for rainy season' implies waterproof material, durable seams and easy setup. Use those implied attributes in your copy. Label your tent as 'waterproof' and mention 'quick‑pitch design' so the AI can confidently recommend it. Create bullets that answer common scenarios such as 'withstands heavy rain' or 'fits two adults comfortably'.

In generative search, authority matters as much as relevance. Build authority by structuring your content clearly: use headings that mirror the questions people ask, such as 'What’s included?' or 'How to clean'. Include data points like material specifications, warranty length and compliance certifications. When other websites link to your product (e.g., review blogs), it reinforces the credibility that generative engines seek. Although you can’t control every external source, you can encourage bloggers and influencers to feature your product by providing media kits and story angles.

Don’t forget the backend search terms. Use this hidden field to cover alternate spellings ('ruksak' for backpack), plural forms and even competitor products that your audience might search for. These variations help generative models widen the net when they match user queries to products. Periodically review your Search Query Performance report in Seller Central to see which queries drive traffic and conversions, and update your backend keywords accordingly. This agile approach keeps your listing aligned with emerging trends and voice‑assistant phrasing.

Finally, don’t treat AI search and Amazon SEO as separate efforts. The same practices that improve your ranking in A9 - clear titles, persuasive bullet points, high‑quality images and a steady stream of reviews - make your product easier for generative engines to evaluate. Keep iterating your listing based on data from Search Query Performance reports, and watch how often AI assistants include your products in their suggestions. Each update is an opportunity to expand your visibility across both human and machine readers.

Because AI assistants synthesise information from multiple sources, make sure your listing aligns with content on your brand’s website, social profiles and press coverage. Consistent messaging reinforces authority and reduces the risk of outdated or conflicting information influencing recommendations. Where possible, publish detailed buying guides and comparison charts on your own site and link them in your product description under the ‘From the manufacturer’ section. This not only helps the shopper but also provides additional signals to generative models about your expertise.

Generative AI search assistants act like knowledgeable friends, filtering the entire catalogue down to a handful of relevant options. To earn a place in their recommendations, you need to optimise beyond keywords alone. Structure your listing so key information is easy for language models to extract: use short, clear sentences; break complex features into bullet points; and avoid jargon. When you describe your product, focus on benefits (e.g., ‘keeps drinks cold for 24 hours’) rather than generic descriptions (‘double‑walled stainless steel’).

Optimise your listing for voice and conversational search by embedding phrases like ‘what’s the best …’ and ‘top‑rated’ in your copy. Shoppers increasingly ask assistants for recommendations using natural language, so include common question formats in your bullet points and description. Think about your product from the perspective of questions: ‘What size backpack fits as a personal item?’ or ‘Which power bank can charge a laptop?’ – then answer those directly.

Remember the first time you used a search engine? You probably typed a few keywords and hit enter. Today’s AI assistants behave more like concierge staff. You describe what you’re looking for in complete sentences-“I need a durable backpack for a three‑day hiking trip”-and they understand context, intention and nuance. Rather than handing you ten blue links, they synthesize information from trusted sources to deliver a single, authoritative answer.

Generative search changes the rules:

  • Questions replace keywords. Instead of short phrases, people ask full questions. Your content must answer who, what, where, when and why.
  • Answers replace link lists. The AI summarizes multiple pages into one coherent response, citing sources along the way. Being included in that summary is more valuable than a traditional ranking.
  • Credibility beats repetition. Quality, structure and authority weigh more than mere keyword density.

Generative Search Optimization (GSO) is the practice of making sure your brand appears in conversational AI apps. It still relies on SEO fundamentals-crawlable pages, descriptive headings, helpful content-but you must craft copy that an AI can reinterpret without losing meaning.

And here’s the twist: while ChatGPT and similar assistants are redefining how people discover products, most product searches still happen on Amazon itself. That’s where the A9 algorithm comes in. A9 aims to maximize revenue per customer, rewarding listings that drive sales and delight shoppers. It draws on keywords, conversion rates, reviews and seller performance. Mastering both GSO and A9 means your listings can surface in AI conversations *and* dominate Amazon search results.

Generative search invites shoppers to talk to AI assistants as if they were trusted friends, but the marketplace itself remains governed by Amazon’s A9. The algorithm analyzes keyword relevance in titles, bullet points and descriptions while measuring performance through clicks, conversions and sales. In other words, you must craft copy that answers questions and persuades the shopper to act. We’ll explore how to frame high-intent phrases that satisfy both conversational queries and the scoring criteria that A9 uses to rank products.

For Amazon sellers, this shift doesn’t mean abandoning keyword research; it means evolving it. Amazon’s A9 algorithm is more than a keyword matcher-it predicts which products shoppers will buy. It still looks at the keywords in your title, bullets and backend search terms, but it also weighs performance signals like sales velocity and conversion rates. That’s why mixing broad and long‑tail keywords in your listing, then monitoring how those terms convert, remains essential.

Double down on keyword research: Start with a list of broad phrases for your niche, then drill into longer, more specific queries. Incorporate those terms naturally into your title, bullet points, description and Search Terms. Stay within Amazon’s limits: roughly 200 characters for titles, around 500 characters per bullet and 2,000 characters in the description. Avoid repeating the same word in multiple fields and don’t waste space on filler words; every character should move a customer toward the buy button.

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