I'm a former Amazon marketplace leader and current 8-figure seller. I write about advanced strategies and tactics for Amazon brands, that you won't read about anywhere else. Not for beginners.
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◼️ The Only Guide To RUFUS That You'll Ever Need
Published 27 days ago • 10 min read
This Issue's TLDR...
THE authoritative guide to Rufus
Factors impacting consumer behavior (I know that one of them will surprise you)
Battle lines have been drawn in the US v China eCommerce war
👉 Did someone forward you this newsletter? First of all, give them a crisp high five when you see them. Second, head over here to subscribe and read past issues.
Why are you paying more than a 10% commission to your FBA reimbursements provider?
Seriously, I want to know.
Are you sticking with your provider because they claim to recover more $?
Are you sticking with your provider because some well-known "Amazon expert" (that might not even sell on Amazon anymore) is always recommending that provider?
Look, man, you do you. But, for me, I'm going to pocket the extra cash from only paying 10% vs 25%.
NBA star Gordon Hayward shares an unforgettable story about Kobe Bryant's legend...
7:24 AM • Jan 6, 2025
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I ask these questions because, I would submit to you that *today* is one of those moments...if you are serious about being an Amazon Expert.
Why?
Because my good friends Danny McMillan, Oana Padurariu, and Andrew Bell just dropped an 8,000 word magnum opus on Rufus, which, if you didn't know already, is the future of product discovery on Amazon.
If you want your brand or products to be found on Amazon, in 2025 and beyond, you need to understand Rufus.
If you can't block off 2 hours to read and immerse yourself in Rufus - The Blueprint, then I challenge you to ask yourself, honestly, where you see yourself in the Amazon space.
And, look, there's nothing wrong with showing up each day, mastering the basics, and getting above-average results.
Some would say that's a honest day's work, and a career to be proud of.
But, if you want to be the best, and believe you are the best, then you need to go deep here, in a corner of the Amazon universe where most won't venture.
I've read The Blueprint twice now. And, I'm going to keep coming back to it.
What will you do?
That Said...
If you want to just dip your toes in the water today, and then dedicate a couple hours this weekend, I have two "gifts" for you.
First, this super helpful TLDR from the authors themselves:
PRO TIP: If you want to advance quickly in your career, get good at writing TLDRs
And, second, an FAQ-style summary from me:
Q: What is Amazon Rufus and how does it differ from traditional search methods?
Rufus is a conversational AI designed to enhance the online shopping experience. It moves beyond traditional keyword-based search by focusing on semantic understanding. This means that Rufus analyzes the context and meaning of user queries, particularly noun phrases, to connect shopper intent to relevant products. Instead of just matching words, it identifies relationships between concepts to provide more accurate and personalized recommendations. Unlike keyword matching where the search engine looks for the exact words you have typed in, Rufus can infer that "pure acetone" is related to "gel nail removal" even if those words are not explicitly stated in the question.
Q: What are "noun phrases" and why are they important for optimizing for Rufus?
Noun phrases are sequences of words or phrases analyzed together to capture context. They typically consist of a noun and its modifiers (adjectives, adverbs). Rufus uses these to understand the core concepts within questions and answers. Instead of only looking at "lamp", Rufus looks at "adjustable brass desk lamp with eye-comfort lighting for bedside reading" to better understand the user's intent. By identifying and ranking noun phrases, Rufus pinpoints the most important information for users and uses that to recommend products that align with user needs. Optimization for Rufus should focus on using detailed noun phrases that incorporate product attributes, materials, and benefits, rather than just using basic nouns or keywords.
Q: How does Rufus use "Click Training Data" to improve product recommendations?
"Click Training Data" refers to the information collected from user interactions with product recommendations. Rufus tracks which products users click on after asking a question or performing a search. This data helps the system learn which phrases and product attributes are most important based on user actions. The system internally generates follow-up questions based on this feedback, allowing it to refine its understanding of user needs. For example, if users consistently click on products emphasizing "cushioning" after searching for running shoes for flat feet, Rufus learns that cushioning is a key preference for this type of query. This leads to better recommendations for future shoppers.
Q: What is "Visual Label Tagging (VLT)" and how does it help with product discovery on Rufus?
Visual Label Tagging (VLT) involves enhancing product images with descriptive overlays and alt-text to provide further context for users and to help Rufus understand what is in the images. This allows Rufus to process visual content alongside the text on a product page. Image alt text should include strong noun phrases, product actions, and context, and text overlays can describe the key features and benefits of a product to make them more discoverable. For example, an image of a knife set can include an overlay that says "Stainless Steel Knife Set in Professional Kitchen Setting" or "Stainless Steel Knife Set for precise and even cuts."
Q: How can sellers use a "Question-Answer (Q&A)" strategy to optimize their product listings for Rufus?
Rufus excels at identifying products through noun phrases within answers to customer questions. Sellers should anticipate common customer questions about their product's use, benefits, and compatibility. Then they should weave natural, conversational answers into their product description and Q&A sections. When creating these questions and answers, prioritize noun phrases, lead with direct answers, include supporting context, and link to related use cases. By framing product descriptions around common questions, sellers provide the type of query-to-context mapping that Rufus uses to make recommendations. For example, a listing for Bluetooth earbuds could answer "Are you tired of your earbuds falling out during workouts?" with "Our Sport X earbuds are designed with a secure, adjustable hook that keeps them locked in place even during intense exercises."
Q: What is "Inference Optimization" and why is it important when optimizing for Rufus?
Inference Optimization involves structuring product content in a way that allows Rufus to infer relationships between product features, benefits, and user needs. The goal is to create clear pathways for Rufus to understand the context behind a product and to surface it as relevant during user interactions. This includes mapping features to benefits, anticipating user questions, and aligning product features with broader use cases. For example, by noting that a "Victorian wall mirror" has "ornate gold frame + beveled glass", one could infer that it "enhances visual appeal & serves as a statement piece in living rooms." Rather than simply listing the features, it is about demonstrating an understanding of why those features are beneficial in certain contexts.
Q: How does Rufus utilize a "Semantic Similarity Model" and what does that mean for how sellers should optimize?
Rufus uses a Semantic Similarity Model to analyze the relationships between phrases and determine how closely they relate to the user's question. This model is trained on data from user queries, answers, and product associations, allowing it to understand the context and meaning behind user questions. The model scores noun phrases based on their relevance, and those scores influence product recommendations. For sellers, this means that content should build semantic bridges between problems and solutions, using varied but related terminology. Instead of simply listing features, sellers should create detailed, natural descriptions that capture what the product is, its key features, how it will be used, and who it's made for. For example, instead of "desk lamp for reading", a description could be, "adjustable brass desk lamp with eye-comfort lighting for bedside reading."
Q: How does the "Noun Stack" approach improve optimization for Rufus and what is an example?
The "Noun Stack" approach involves using increasingly detailed noun phrases to describe a product. Starting from a basic term (Level 1) and adding modifiers to build up to a more optimized, benefit-integrated phrase (Level 5). For example, starting with "Coffee Maker" (Level 1), one could add "Programmable Coffee Maker" (Level 2), then "Programmable Stainless Steel Coffee Maker" (Level 3), and then "Professional-Grade Programmable Coffee Brewing System" (Level 4) and finally "Professional Thermal Brewing System | Programmable Coffee Maker | Premium Bean-to-Cup Machine" (Level 5). This approach helps Rufus better understand the product by ensuring all details are included in a natural language manner. By building a full "noun stack" for a product, sellers can create meaningful, semantically-rich content that Rufus can effectively process.
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FAIR WARNING: Best@Amazon is going to include more DTC-related content in 2025.
Don't worry; I still plan to stick to what I know best (Amazon).
But, as I try to up-skill my own DTC marketing capabilities, I plan to take you all with me.
Sorry, not sorry.
Anyway, and in that vein, I wanted to share the most practical + exhaustive eCommerce CRO guide I've come across.
Read it, file it away, refer back to it.
Click image to access the CRO Playbook
BEST from LinkedIn
My good buddy Demian Lazurko did it again, with this killer infographic on the main factors impacting shopper behavior.
It's perhaps intuitive (Price is #1), but nevertheless a good reminder of some of these durable bits of shopper behavior.
Thanks, Demian!
Click image to see the original LinkedIn post
BEST from X
In case you missed it, battle lines are being draw in the US vs China eCommerce wars.
It all sorted of started about a month ago, in the "Amazon boi" X community, as a bunch of us lamented the structural disadvantages of US sellers relative to China-based sellers, and kicked around possible regulatory solutions.
My good friend, and fellow ex-Amazonian, Shinghi, laid out a regulatory vision:
Provided you're the type of person that is open to changing their mind (which, FYI, is a sign of intelligence), contrarian takes challenge existing thinking.
In my case, I've been vocal -- in this newsletter, and on social media -- that Amazon-native brand owners will need to develop DTC "bro" skillsets to survive.
My thinking behind this belief is:
On Amazon, I see costlier PPC and diminishing marginal returns in many categories.
Amazon's new ad products seem to be elevating the importance of creative assets in the ads (vs the old Sponsored Ads world where it was just about optimizing budgets, bids, and placements).
But...maybe I'm wrong?
Maybe there's more juice to squeeze from Amazon Ads?
I'm a former Amazon marketplace leader and current 8-figure seller. I write about advanced strategies and tactics for Amazon brands, that you won't read about anywhere else. Not for beginners.
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