Amazon, and pretty much every other website in the world, is in the process of a revolutionary transformation.
Since the launch of ChatGPT in 2022, AI tools have grown to become a more visible part of the broader digital experience, with one survey showing that around half of online consumers have used generative AI tools to inform a purchasing decision.
Amazon debuted its AI shopping assistant, Rufus, in February 2024. Since then it’s been rolling the tool out to more territories and releasing additional functions, tentatively making AI integral to the Amazon shopper journey.
As Rufus continues to influence how customers find products on Amazon, it’s crucial that you plan ahead for the impact it’s going to have, and refine your Amazon listing optimisation to get ahead of the AI-first future.
In this guide, we’ll take a closer look at what Amazon Rufus is, how it’s changing the listing optimisation playbook, and how you can optimise your listings for better discoverability by Amazon’s shopping assistant.
Rufus is Amazon’s generative AI shopping assistant tool, a new, conversational layer in the Amazon buyer experience. It’s designed to answer natural-language questions, compare different products, and recommend Amazon products based on signals from the shopper’s unique profile, customer reviews, listing Q&A sections and more.
One of the crucial differences introduced by Rufus is that it doesn’t simply match keywords to listings like Amazon’s normal search engine.
Instead, like all AI tools, it works to understand user intent and context. This allows shoppers to make more specific and conversational enquiries, like they would with a human retail assistant, e.g. “I’m looking for a blender that’s ideal for making smoothies in a small kitchen and is easy to clean.”
Amazon’s own documentation explains that Rufus is trained on “Amazon’s extensive product catalog, customer reviews, community Q&As, and information from across the web”, which is why reviews and what your customers are already saying about your products matter more than ever.
Though Rufus’s rollout has been very gradual since its launch, it’s predicted to be a major part of the Amazon flywheel in the future. Insiders have projected that it will drive an additional $700 million in operating profits by enhancing the customer experience, despite not driving any revenue directly.
In short, Rufus is here to stay, and Vendors will need to accommodate for the changing Amazon shopper experience to keep their listings visible and relevant.
Want to future-proof your Amazon catalogue optimisation? Our Amazon content experts are ready to optimise all facets of your listings, helping you maximise visibility and conversion for greater long-term success.
Historically, Amazon listing optimisation revolved around creating listings that were attuned to the Amazon A10 algorithm.
Amazon listing optimisation has never been exactly easy, but it is made up of a few simple steps: research keywords, match them with your product’s USPs, and create a listing that’s likely to convert.
As Rufus becomes a bigger part of the shopper experience, the Amazon SEO playbook is going to change.
Here’s an overview of some of the most crucial differences between conventional Amazon SEO and Amazon Rufus optimisation to be aware of as we move towards AI-first product discovery.
Emphasis On Exact-Match Keywords: Titles, bullet points, descriptions, and backend keywords are all optimised to include the precise terms that users enter into the Amazon search bar.
Encouraging Conversion Through USPs: The listing content aims to encourage conversions by exploring a product’s key USPs, linking each feature to a distinct benefit for the brand target audience.
Standard Content Hierarchy: Optimisation takes place in the title, bullets, description, and backend keywords, planting high-demand search terms where the A10 algorithm will crawl them.
Focus on Ranking for Search Terms: Though traffic and conversion are the ultimate goals, the main concern of Amazon SEO is making a product visible on a results page for specific terms.
Focus On Shopper Intent and Context: As an AI model, Rufus is focused on what an Amazon user means, rather than simply what they’ve typed in a search bar. This means that listings will have an advantage when they address real-world use cases and needs.
Built for Conversation: Connecting users with listings revolves around a natural, question-and-answer language structure. Phrasing content in a way that targets common questions, e.g. “this blender is great for smoothies”, can improve the product’s chances of visibility.
Expanded Crawling: While the A10 algorithm will only crawl text fields, many thought leaders believe that Rufus goes beyond this, looking at A+ content, Q&A sections, reviews, and other elements to fully understand a product.
Importance of User-Generated Content: Unlike the A10 algorithm, Rufus will look at reviews and Q&A questions when selecting products to serve up in its responses. This difference makes it even more important to align your products and content with customer sentiment, and ensure your audience is getting the best experience possible.
Feeling confused about Amazon Rufus optimisation? Our Vendor+ channel management service covers all facets of your Amazon operation, helping you seize on opportunities in the present and prepare your brand for the future.
As Rufus grows as part of the Amazon shopper journey, the rules for organic discovery will change.
Here are some of the key adjustments you can make to your organic Amazon strategy to prepare your catalogue for the impact of Rufus.
Amazon Rufus uses natural-language processing and its understanding of semantics to interpret what an Amazon user actually means when they ask a question, instead of simply matching keywords to search results.
For example, if a user asks “What’s a good laptop for uni students under £600?”, Rufus will suggest products that match the intent of this question and all its variables, rather than just collating laptops under £600.
To maximise your listings’ discoverability, you’ll need to write content that directly answers use-case questions, and is more closely tailored to the more conversational way people interact with AI tools.
When targeting the example question above, you may want to write product copy including phrases that directly address the needs of university students, for example “ideal for carrying between lectures thanks to its lightweight design and long battery life.”
Like with the A10 algorithm, Amazon hasn’t released a lot of official information about how Rufus crawls and values listings.
However, many thought leaders in the Amazon and AI SEO space have said that Rufus is going beyond a listing’s title, bullets, and other text fields, and drawing from A+ content, images, and even Q&A content.
As Rufus is still a new part of the Amazon shopper journey, it remains to be seen how optimising these parts of a listing will reflect in terms of organic visibility. Still, thinking of your listings holistically and making sure you’re optimising every element beyond text fields will ensure you’ve got all bases covered.
We already know that optimising additional fields on your product listings can contribute to higher conversion rates, so if it also future-proofs your catalogue for AI search, even better!
One of the key things that sets AI tools apart from standard search engine algorithms is their ability to understand longer, more complex content through natural language processing. AI visibility expert Francesca Tabor cites Amazon Rufus specifically while advocating for “Noun Phrase Optimization” (NPO) over more general SEO tactics that focus on individual keywords.
This gives you the freedom to write content that’s more natural and human-oriented, maximising engagement with shoppers while also optimising your products for AI visibility.
If your previous optimisation drives have been more focused on targeting simple keywords, we recommend auditing your listings for opportunities to make your content more natural and human-oriented, ensuring it’s as engaging as possible while taking advantage of Rufus’s advanced language processing.
If you’re an outdoorswear brand, for example, and you have listings with attempts at keyword optimisation like “rugged hiking rucksack waterproof trekking”, you can improve on this with more natural content, e.g. “This durable hiking rucksack helps you trek across rugged terrain while keeping your gear dry”.
Rufus and similar AI tools don't just scan your product pages for their visible content. They’ll also analyse how reviews discuss your product, how Q&A sections frame and solve issues, and what Amazon-specific metadata (e.g. materials, sizes, colours) say about your product.
To ensure your listings are Rufus-friendly, make sure all your metadata attributes are filled in, that Q&A sections are actively managed with clear and useful answers, and that you’re maximising your reviews by enrolling new ASINs in Amazon Vine and always providing a great customer experience.
With Amazon Rufus’s ability to better understand the context of user queries, we’re likely to see a shift in how shoppers find the product they’re looking for.
Instead of entering disparate terms into a search bar, they’ll be having full, in-depth conversations with the tool, explaining exactly what they’re looking for and why. In a way, these tools are moving us away from ecommerce conventions that have been part of the playbook for decades, and back to a more classic retail experience where customers have full, personalised conversations with shopping assistants to find the product they want.
Though monitoring your visibility on Rufus is a lot harder compared to Amazon’s standard search, manually investigating can help you build a clearer idea of how well your listings are meeting Rufus’s search and ranking parameters.
Start with your customer personas, and for each profile have a brain-storming session to come up with:
The product features that are likely to be most important to them.
The real-world use cases they’re likely to talk about when using Rufus to find products.
Common drawbacks in your product niche they’ll want to actively avoid.
A logical chain of follow-up questions they might ask after an initial interaction with Rufus.
By documenting this kind of research, you’ll be able to methodically check how much Rufus is serving up your products in results, and target listing optimisations with greater efficiency.
For now, Rufus is a pretty straightforward AI assistant model; users chat to it about the products they’re looking for, and the model connects them with results that match their needs.
However, with Amazon projecting such a long-term investment in AI, you should expect to see some significant changes in a short space of time.
One example of this is Rufus’s image search feature, where users can upload a picture and have the assistant help them find products featured in the content.
Amazon thought leader Andrew Bell demonstrated this capability by uploading a complex image of a bedroom, and asking Rufus to find all the items in the photo, which Rufus was able to do in a matter of seconds.
While Rufus remains a new, experimental technology, make sure you’re following the right thinkers and news sources to keep a close eye on new developments. When new AI search features are rolled out, you’ll have an opportunity to act on them before the competition and seize a major advantage.
Amazon’s AI shopping assistant hasn’t made standard Amazon SEO obsolete. However, it has raised the bar for organic discovery, and made variables like contextual content, customers’ tone of voice, and meta data more important than ever.
As a Vendor, the rise of Amazon Rufus will present an opportunity to shape a more complete narrative across every element of your listing, and connect with your audience in a more natural, conversational manner.
For more support with ensuring your listings are engaging and future-proof, be sure to check out our other blog posts, or find out more about how our Amazon content services can maximise visibility for your entire catalogue.
Amazon Rufus is Amazon’s generative AI shopping assistant. It was first deployed on Amazon US in 2024, and is gradually being rolled out across other territories. The tool allows customers to browse products using natural, conversational language, asking detailed questions about products’ real-world uses, comparing the features of competing items, and getting recommendations based on reviews, Q&A sections, and user preferences.
No. At least, not for now. Rufus is an additional discovery layer that offers another dimension to traditional Amazon search, rather than replacing it completely. Traditional A10 Amazon SEO is still essential for getting your products seen by shoppers using standard Amazon search, while Rufus optimisation covers AI searches using context-rich and conversational prompts. As there’s a lot of crossover in the best practices for both disciplines, you should try to optimise for both rather than viewing organic visibility as a “one or the other” situation.
As Rufus becomes a bigger part of the shopper journey, it’s likely to change how customers discover and choose products. As an LLM AI model, it’s able to evaluate Amazon product listings based on customer reviews and Q&A sections, and contextualise product features using the natural, conversational questions that Amazon users ask it.
What’s the main difference between Amazon SEO and Rufus optimisation?
While Amazon SEO is aimed at ranking for exact-match keywords on traditional Amazon search results, Rufus optimisation is focused on creating a more complete, context-rich listing that can be cited when answering real-world questions and use cases, rather than appearing for simple search terms.
When optimising for Amazon Rufus, aim to write content that:
Answers natural, common questions about your products.
Focuses on benefits and real-world use cases.
Is written in a conversational and human style.
Includes complete product attributes and Amazon metadata.
Includes complete reviews, Q&A sections, and A+ content.