Ritu Java is the CEO of PPC Ninja, an agency offering Amazon advertising and creative services: PPC management, listing creation and optimization, storefronts, and AI video. An engineer by training, she spent about 17 years in Japan working in tech before starting an e-commerce business on the side, then moved to the US for a year-long data science program.

That's when she "discovered Amazon" and never looked back. Today she runs Rufus and Alexa audits at scale for large brands and writes the "AI for E-Commerce" newsletter, distilling what she's learning as the space shifts under everyone's feet.

Listen/Watch Our Interview:

Episode 2 is a tactical deep dive into how brands stay visible on Amazon as AI reshapes search. Andrew sits down with Ritu to map how the old keyword world (A9) and the new conversational one (Alexa for Shopping) actually work together rather than replacing each other. It's part strategy, part myth-busting, and full of concrete moves you can apply to your listings this week.

Subscribe now

Highlights

• The dual flywheel: Ritu’s core framing, keyword search (A9) and conversational search (Alexa) are two flywheels you have to spin at the same time. “We’ve gotta spin both these flywheels simultaneously.”

• A9 isn’t going anywhere: “If there’s one thing Amazon cares more about than AI, it’s its bottom line.” They won’t torch years of proven search science overnight.

• Cosmo isn’t A9: Cosmo is one knowledge graph among many at Amazon, it adds semantics, inference, and personalization, but it did not “replace” A9. Different animals running in parallel.

• Three step-ups from A9: What Alexa does that keyword search can’t: semantics (”cozy blanket” = soft blanket), inference (”best shoes for mountain climbing” → hiking boots), and personalization (a gamer and a commuter searching “headphones” get different results).

• Noun phrase optimization: Ritu’s five-part recipe for a title that AI understands determiner, adjective (pre-modifier), noun adjunct, head noun, and prepositional phrase (post-modifier). “The ultra-lightweight, waterproof hiking boots with durable rubber soles” beats a comma-stuffed keyword string.

• The Prompts Report: There’s already a report in the advertising console showing which prompts triggered which campaigns, plus impressions, clicks, and attributed sales, though data is still sparse with a short look-back window.

• 66% and above the fold: More than 66% of sales happen on page one, and the majority above the fold, which is why organic alone won’t cut it and the organic-to-ads (OA) ratio matters.

• Page one is the gate: “You have to be on page one in A9 to even be considered in the pool” Alexa draws from and personalizes.

• “Ambient” and ubiquitous: Ritu’s prediction: Alexa for Shopping becomes ambient, reachable from every surface (phone, watch, product page, Lens Live), not just a search box.

Ritu is one of the sharpest minds working in Amazon today, and this conversation is a masterclass in staying visible as search gets smarter. Enjoy the conversation.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

We’re here to make sense of brand strategies for Agentic Commerce on Amazon, Walmart and beyond. Subscribe, share, and stay tuned!

TL;DR

Why Noun Phrases Sit at the Center of Agentic Commerce Optimization

In my previous article, I laid out a clear definition of Amazon ACO

I also laid out seven distinct strategies for putting it into practice.

I argued, Amazon shopping now runs on two discovery systems working together. A9, the search algorithm that has powered Amazon’s marketplace for years, still decides whether a product can be found at all: it reads listing words and assembles the candidate pool a query pulls from. Sitting on top of that pool is Alexa for Shopping, powered by Rufus, which interprets what the shopper is actually trying to accomplish, reasons through their context and memory, applies constraints, weighs the evidence on each product page, personalizes the result, and selects the item that best fits the person, the occasion, the budget, and the moment. Agentic Commerce Optimization (ACO) is the practice of engineering an ASIN to succeed across both layers at once.

Good ACO is great SEO, but great SEO alone is not enough for ACO.

The new objective is to become eligible across the whole family of query pathways a shopper mission generates. A keyword is what a person types; a mission is what they are trying to get done. When someone asks an assistant to “find something for my kids and our home,” that one prompt can fan out into many searches by product type, room, style, use case, recipient, budget, material, size, and constraint. Query Planning Optimization (QPO) is the layer that asks a harder question than keyword coverage ever did: of all the queries this mission may generate, how many can my product be found by, and how many can it honestly win?

Part I: Foundations

What Is a Noun Phrase?

A noun phrase is a group of words centered around a noun that functions as a single unit to identify, describe, or refer to a person, place, thing, idea, or concept. The noun serves as the head of the phrase, while surrounding words such as adjectives, determiners, numerals, or prepositional phrases provide additional meaning. Examples include wall art, metal wall art, large abstract metal wall art, and large abstract metal wall art for living room. Although these phrases vary in length and specificity, each functions as one complete noun phrase because it names a single, coherent concept.

Noun phrases are fundamental to human language because they allow meaning to become progressively more precise. Starting with a simple noun such as chair, additional words can narrow its meaning into office chair, ergonomic office chair, or ergonomic mesh office chair for home office. Each added modifier reduces ambiguity by introducing attributes such as material, size, color, style, function, audience, or context. Rather than creating separate ideas, these modifiers work together to refine the same underlying concept into a more specific and informative noun phrase.

Figure 1. Amazon suggestions for table lamp show how a parent product concept can fan out into related phrases by room, format, constraint, and style. Each suggestion adds a modifier that makes the original concept more specific.

In commerce and information retrieval, noun phrases are especially important because they closely reflect how people search, think, and communicate about products. Shoppers rarely search using isolated keywords. Instead, they naturally use descriptive noun phrases such as stainless steel water bottle, modern farmhouse dining table, or organic tomato ketchup. Search engines, recommendation systems, and AI shopping assistants increasingly rely on these complete semantic units to understand intent, match products to customer needs, and distinguish one product from another. As a result, well-constructed noun phrases have become one of the foundational building blocks of modern search, semantic retrieval, and AI-driven commerce.

What Is Noun Phrase Optimization?

Traditional keyword work asks, “Which search terms should the ASIN rank for?” That objective still holds, but within Alexa for Shopping a deeper strategy is required. Now, we ask,

If Amazon search, Rufus, Alexa for Shopping, and Amazon Ads prompts rely on queries, products, catalog content, reviews, Q&A, attributes, prompts, and comparison behavior, then complete product phrases are more useful operational units than isolated keywords.

While You Wait, It Searches. Behind “Thinking…”, the agent searches using noun phrases: the kinds of queries a shopper may have typed manually. One prompt, such as “find me stuff for my kids and home,” can lead the agent into a broader family of related searches by product type, room, style, audience, use case, and constraint.

How personalization shapes each search. Household context, prior behavior, stated preferences, style signals, budget, location, and product constraints may all shape the assistant’s search path. The point is not that every shopping journey will use every possible signal. The point is that the search path is no longer limited to the exact words the shopper typed.

The Burden of Search. Without the agent, every one of these moves belongs to the customer, for every single item: (1) find the words, turning a vague need into a searchable phrase; (2) type the search, thumbing out the parent query; (3) scan the suggestions, reading autocomplete options and judging which child phrase fits the mission; (4) open and compare, weighing price, rating, size, style; (5) second-guess, hit back, start over; (6) refine the phrase, adding room, size, material; (7) repeat for the next item as the loop restarts; (8) settle or abandon. That whole loop can run again and again across a shopping mission. One prompt changes who carries the work.

Figure 3. Amazon suggestions for camping gear show how a parent product concept can expand through audience, use case, and complementary-product language. The phrase environment around a parent query often reveals the broader mission behind it.“Need help deciding?” prompts show the query plan extending into AI-assisted paths.

Part II: The Noun Phrase System

The Anatomy of a Noun Phrase

A noun phrase can include:

1. Head noun: art, statue, lamp, ketchup.

2. Product type: wall art, horse statue, floor lamp, tomato ketchup.

3. Material: metal, bronze, brass.

4. Style or theme: coastal, equestrian, mid-century, industrial.

5. Use case: for living room, for reading nook, for bedside table.

6. Constraint: outdoor-safe, lightweight, dimmable, energy-efficient.

7. Audience: horse lover, interior designer, home decorator.

8. Proof layer: official seller, UL-listed, ETL-certified floor lamp.

Figure 4. Amazon suggestions for large table lamp show noun phrase anatomy in the wild: room, format, material, style, and constraint all appear as natural refinements of the parent product concept.

The NPO Taxonomy for Amazon Listings

NPO needs a taxonomy because different phrases perform distinctly across categories, but broadly speaking, we can understood through six broad categories: identity, attributes, context, audience, constraints, and proof.

Figure 5. Amazon suggestions for artwork wall decor show how modifiers can attach before or after the head noun, refining the product concept by style, room, material, or audience.

Figure 6. Amazon suggestions for disney princess show how a parent query can expand across commercial product forms. The same head concept can fan out across dolls, toys, apparel, decor, books, and other product categories.

How to Build Phrase Evidence

Operators should build phrase evidence from the market, not imagination. Useful phrase evidence can come from query behavior, advertising signals, product-page content, customer language, reviews, Q&A, catalog data, and brand-owned product facts.

Part III: Measuring & Mapping

How to Think About Signals

Marketplace reports do not all answer the same question. NPO requires operators to understand that different signals reveal different parts of the search and shopping journey.

Some signals speak to visibility. Some speak to attention. Some speak to shopper confidence. Some speak to product-page fit. Some speak to conversion. Some speak to competitive context. Some speak to whether the product’s language is becoming clearer across relevant demand clusters.

Different marketplace, catalog, advertising, and customer-language signals can help teams understand whether product language is improving visibility, attention, confidence, and conversion. No single report tells the whole story. The goal is not to imply perfect attribution from every phrase; it is to understand whether the product is becoming easier to find, understand and select.

How to Measure NPO Without Reducing It to One Metric

Measure NPO as a visibility and confidence system, not as a single content change. Across the improvement loop, operators should evaluate whether product language appears to improve across visibility, attention, confidence, product-page fit, conversion, demand capture, and learning from controlled changes.

Across the improvement loop, evaluate whether product language appears to improve across these areas:

1. Visibility: Is the ASIN being considered in relevant demand environments?

2. Attention: Is the product language earning the shopper’s interest?

3. Confidence: Does the product page reduce uncertainty?

4. Fit: Does the PDP satisfy the shopper’s constraint?

5. Conversion: Does the product complete the mission?

6. Demand capture: Is the brand or ASIN gaining strength across relevant demand clusters?

7. Learning: Do controlled content changes suggest measurable improvement?

Figure 7. Amazon suggestions for metal wall art show that a parent query can refine into room, constraint, style, placement, and category language. The parent phrase matters, but much of the demand lives in the refinements beneath it.

Part IV: NPO in Practice

NPO Rules for Amazon Operators

1. Use accurate noun phrases.

2. Prioritize phrases that represent real product fit.

3. Do not force irrelevant terms.

4. Separate parent phrases from child phrases.

5. Use mega phrases only when natural.

6. Use noun stacks to organize meaning

7. Interpret organic and paid signals together, because they reveal distinct forms of demand, relevance, and content-market fit.

8. Understand where the product is already visible and where relevant demand may still be underrepresented.

9. State attribution carefully.

But not every phrase should be used. Horse memorial statue should only be used if memorial intent is genuinely accurate. NPO requires discipline. It does not force irrelevant phrases.

Figure 8. Amazon suggestions for large metal wall art for living room show that even a mega phrase is still a parent. Shoppers may keep refining by size, color, motif, and format. Mega phrases should be used only when natural.

Improve Product Language and Evidence

Teams should improve the product’s language, evidence, and presentation across the surfaces they control. The goal is not to stuff longer phrases into every available field; it is to make the product’s meaning easier to retrieve, compare, verify, and select.

NPO requires teams to connect product language, marketplace evidence, content quality, and performance signals into a disciplined improvement loop.

A traditional Amazon strategy might ask, “Do we rank for metal wall art?” QPO asks a better question: “What search paths would a shopper or AI shopping assistant likely use to satisfy the buying mission behind metal wall art?”

QPO identifies the paths. NPO builds the language and proof that allows the ASIN to win those paths. Operators can evaluate whether marketplace signals improve across relevant demand clusters

.

Figure 9. One prompt vs. the query plan: the searching still happens; the question is who carries the cognitive load. Before, the shopper has to think up, type, compare, refine, and re-search. After, the shopper gives the assistant the mission, an

d the assistant carries more of the search burden. The cognitive load doesn’t disappear; it moves off the customer and onto the agent.

Part V: COSMO and the Relational Layer

COSMO Relations, Noun Phrases, and the Incomplete Bridge Between Search Language and Product Meaning

Noun Phrase Optimization becomes much more important when viewed through the lens of Amazon’s COSMO research. COSMO is strategically useful because it shows that Amazon reads beyond product attributes, category labels, and product titles, extending to the commonsense relations that connect products to human intentions.

The COSMO paper states that existing ecommerce knowledge graphs often contain large volumes of concepts or product attributes but fail to discover user intentions. COSMO is presented as a system that mines user-centric commonsense knowledge from massive behaviors, including search-buy and co-buy behavior, to construct large-scale ecommerce knowledge graphs for downstream services such as search relevance, recommendation, and search navigation.

It matters for NPO because noun phrases often presuppose exactly the kinds of relations COSMO tries to model.

For example:

large coastal metal wall art for living room

carries the weight of a more specific keyword phrase and presupposes several relations at once: that the product is made of metal, has a coastal style, is sized large, and belongs in a living room.

This is the deeper reason NPO is more than specificity or query expansion. A noun phrase is a compact relation statement. It expresses what the product is, what it is made of, where it belongs, who it is for, what function it serves, what event or situation it supports, and what proof must be checked before purchase.

Figure 10. Amazon suggestions for marvel legos for show how a parent phrase can refine through audience language. That does not mean sellers can directly control Amazon’s internal relation graph, but it does show why audience, context, and product fit matter in AI-assisted commerce.

However, COSMO also shows why a purely COSMO-inspired optimization framework would be incomplete for operators.

The COSMO relation taxonomy is powerful, but it is not perfectly clean or categorically distinct. Multiple relation types can point toward similar commercial meanings, such as function, usage, audience, event, activity, time, or intent-like contexts. That means COSMO’s relations are useful as a signal of how ecommerce systems may organize intent, but they should not be treated as a final operator taxonomy.

One caveat matters here. NPO should not simply copy COSMO relation names and turn them into a listing checklist. The relation layer is messier than that. COSMO itself is built from behavior, generated knowledge, relation-aware prompts, human feedback, plausibility, typicality, filtering, instruction tuning, and downstream serving. The paper explicitly discusses noisy generations, generic or unfaithful knowledge, filtering, human annotation, and typicality judgments. In other words, even Amazon’s own research treats relation generation as a probabilistic, quality-controlled process. It behaves nothing like a simple deterministic catalog rule.

The right way to frame this is a balance of usefulness and limits:

COSMO makes NPO strategically useful because it confirms that ecommerce search and recommendation systems benefit from user-intention relations on top of product attributes. But COSMO also makes NPO operationally incomplete because sellers do not receive a COSMO relation report, cannot directly inspect Amazon’s internal relation graph, and cannot know with certainty which relations Amazon has inferred for a given ASIN.

That means NPO should be described as relation-aware product-language optimization. It stops short of direct COSMO optimization.

We cannot directly optimize COSMO.

But.. we can structure product content around noun phrases that presuppose commercially important relations, then evaluate whether marketplace signals improve across relevant demand clusters.

It gives operators a useful framework without overstating control. COSMO explains why noun phrases matter: they encode the relational logic of e-commerce intent. NPO turns that logic into controllable product language. QPO predicts where those relations may be tested by search paths, prompts, comparisons, and fact-checking. Marketplace signals then show the external traces of whether that language may be working.

Taken together, the layers stack cleanly:

COSMO reveals the relational layer of ecommerce search. QPO predicts the query paths where those relations may matter. NPO expresses those relations through accurate noun phrases in operator-controlled content (the product detail page). But because COSMO relations are overlapping, partially inferred, and not exposed directly to sellers, NPO remains an inference-based strategy rather than a complete control system.

That is why NPO is strategically useful now, but operationally incomplete. It gives Amazon operators a way to align content with the relational structure of modern ecommerce AI, while still respecting the limits of what marketplace data and public research can actually prove.

One Prompt. The Whole Query Plan. An AI shopping agent can run the search paths a shopper would have typed, shifting the burden from customer to agent. “Find me things for my kids and our home” can fan out into a broader family of product searches shaped by audience, room, style, use case, category, and constraint.

Yesterday, shoppers carried the whole plan themselves: typing a parent query, scanning suggestions, refining by room, material, style, format, and audience, then comparing, backing out, and re-searching across child noun phrases, one query at a time. With AI shopping, the agent expands one prompt into the broader query plan and does the searching. Every path still runs, invisibly. None of that work vanishes; it simply shifts off the customer and onto the agent, and that quiet handoff is what shoppers are really trading for when they hand a mission to an assistant. For the ASIN, winning now means being eligible, trusted, and selected across the paths the agent runs: query-plan coverage that goes beyond keyword coverage.

Figure 11. The full landscape: one prompt can expand into many related search paths, shifting the burden of refinement from the shopper to the assistant.

Noun Phrase Optimization is how Amazon operators translate product reality into the language of modern search, AI shopping, and retail media.

Query Planning Optimization predicts where the customer or agent may search. NPO ensures the product is named, described, shown, and proven in the noun phrases required to win those search paths.

Together, QPO and NPO create a practical system for understanding where a product is visible, where relevant demand may still be underrepresented, and how AI shopping visibility may improve without overclaiming what the data can prove.

The winning ASIN carries query-plan coverage: visible across the parent query, relevant across the child clusters, persuasive enough to earn clicks, clear enough to earn cart adds, trusted enough to earn purchases, and structured enough to be understood by both shoppers and AI shopping systems.

Leave a comment

Subscribe now

How Amazon Is Splitting Titles

The 75-character Product Name carries product identity, while the 125-character Item Highlights layer carries product relevance.

A lot of people are reacting to the shorter titles, but the change makes far more sense once you look at how it plays out on Alexa for Shopping,

Alexa for Shopping Compatibility

In an Alexa for Shopping product card, 75 characters seem ideal for product identity because shoppers need to recognize what the item is immediately. The 125-character summary then seems ideal for relevance and comparison because it provides enough room to explain a use case, feature, material, or buying reason without crowding the name. Shorter Product Names also sound more natural when Alexa reads them aloud on Echo devices, and the Item Highlights create a clean follow-up layer that gives Alexa quick language around use case, feature, material, compatibility, buying reason, or shopper fit. I am already seeing these title changes inside Alexa for Shopping experiences, paired with summarized product data from Item Highlights, and personalization factors. Echo voice shopping has updated on mine as well, and it is worth trying.

Mobile-First Readability

Amazon’s own language points to mobile display as a major reason for the title change, and that matters because mobile shopping rewards clarity rather than title density. Detail density shifts into the 125-character layer. A shopper scanning a small screen needs a clean image, a readable title, clear pricing, offer context, a delivery promise, and a short reason to care. Long titles may index terms, but they often weaken comprehension within compact mobile layouts, which is exactly why the shorter Product Name works better on a phone. These will be instrumental in displaying across mobile shopping cards, Alexa Shopping recommendations, Echo voice responses, AI comparison systems, agentic commerce workflows, and product-card commerce surfaces.

Consistency Across Online Commerce Surfaces

Amazon also says the shorter title format is meant to be consistent with other online stores, and that line matters because commerce is moving beyond isolated product detail pages. Product information increasingly needs to work across marketplaces, storefronts, shopping assistants, and agentic commerce systems. I would connect this carefully to the Universal Commerce Protocol, not as a direct cause, but as evidence of a broader shift toward cleaner, more structured, and more portable product data across commerce surfaces. Product pages are becoming structured inputs rather than standalone destinations.

Amazon SEO Is Not Dead

The wrong takeaway is that shorter titles eliminate Amazon SEO. They do not. A9-style relevance still depends on indexing, product type, attributes, browse classification, conversion, price, reviews, availability, offer quality, and content depth. What changes is title dependency. Titles are not storage containers for every keyword. Product identity now has to work harder in the Product Name, and product relevance has to work harder in the Item Highlights. Everything else has to support the broader product graph, including bullets, backend terms, attributes, images (some), A+ content (some), reviews, pricing, availability, and offer signals. Amazon Title optimization is now more structured, requiring our Amazon SEO to become cleaner in our content architecture across all facets of product detail pages.

Here is an example of the interface that actually changes keyword phrases you would have otherwise counted on to give you sales. I would not trust Amazons listing enhancer outright. If you do, make sure to split test low priority products now that way when you have to update your hero products, you will be prepared (if you havent done that already).

Where This Is Heading

Amazon is shortening titles on paper, but strategically the change reshapes how products are packaged for mobile shoppers, AI comparison, and assistant-led commerce. Agentic Commerce Optimization is increasingly becoming an imperative for brands, and as the examples below show, that is no less true on Amazon.

Subscribe now

TL;DR

• Good ACO is great SEO. Agentic commerce does not replace Amazon SEO. It makes SEO more important because retrieval is still the foundation of recommendation.

• A9 determines whether the product can be found. Alexa for Shopping (Rufus) determines whether the product can be understood, trusted, personalized, and selected.

• The evolution is from keyword ranking to mission eligibility. A shopper may say one thing, but Alexa may generate many searches from that one request.

• A keyword is what the shopper types. A mission is what the shopper is trying to accomplish.

• Query Planning Optimization is a new tactical layer. QPO asks whether the ASIN can be found across the likely query branches Alexa may create from a shopper mission.

• The new unit of optimization is the query plan. One shopper prompt can branch into searches by product type, room, style, use case, recipient, budget, material, size, constraint, and desired outcome.

• Noun Phrase Optimization turns keywords into natural product language. Instead of stuffing isolated terms, brands should build clear phrases that preserve keyword value while helping Amazon understand the product.

• Semantic Bridging connects the product to adjacent meanings. A listing should connect the product to rooms, styles, occasions, recipients, use cases, and shopper intents that Amazon may infer.

• Inference Optimization maps features to outcomes. Brands need to show how product attributes translate into benefits, buyer personas, real-world use cases, and final purchase confidence.

• Attributes are structured product truth. Titles, bullets, images, A+ content, and reviews help explain the product, but attributes tell Amazon what the product actually is.

• Missing attributes create silence. If size, material, style, finish, compatibility, room, use case, or category-specific fields are blank, Alexa has less evidence when those details matter to the shopper mission.

• Product Page Coverage asks whether the ASIN can answer the questions customers are likely to ask. The product page should clearly cover durability, dimensions, material, installation, style, room fit, gifting, cleaning, compatibility, value, and any category-specific concerns.

• The strongest ASIN is not merely keyword-rich. It is clear, complete, specific, current, attribute-rich, review-supported, visually understandable, and semantically connected to real shopper missions.

• The bottom line is simple. Brands need to make every ASIN findable by A9, understandable to Alexa for Shopping (Rufus), provable through PDP evidence, and selectable for a specific shopper in a specific situation.
  
  
  
  Let's dive in.
  
  Everything begins with a definition.
  

  So let’s start there:
  

  What is ACO on Amazon?1

A playbook is not built around using the same offensive and defensive strategy against every opponent. It is built around understanding the specific composition of each team, then designing a strategy that exploits its weaknesses, protects against its strengths, and gives your team the best chance to win.

The same principle applies to marketplace optimization.

What we do for Amazon is not the same as what we do for Walmart. The fundamentals may overlap. You still need strong content, clean data, relevant keywords, persuasive creative, operational readiness, and conversion discipline. In football terms, both teams still pass the ball, run the ball, and put eleven players on the field. But winning depends on more than fundamentals. Winning depends on adapting the strategy to the environment.

For this reason, marketplace distinctiveness matters. A brand wins because it is distinct. Its products, positioning, proof points, customer promise, and category authority separate it from everyone else. But in a world shaped by AI assistants, retrieval systems, recommendation models, and agentic shopping layers, the question changes:

That is the heart of agentic commerce optimization.

Amazon should not be treated like Google. Walmart should not be treated like Amazon. ChatGPT should not be treated like either one. Each marketplace has its own data structure, ranking system, retrieval logic, shopper behavior, recommendation surfaces, and AI interpretation layer. Meaning, each platform requires its own optimization science.

The same is true for ACO.

Amazon ACO should not be copied from traditional SEO, Google SEO, Walmart SEO, or generic AI optimization. It must be built around Amazon’s own marketplace mechanics, product data architecture, ranking systems, recommendation logic, customer review intelligence, advertising signals, and Alexa for Shopping (Rufus) retrieval behavior.

At the same time, ACO does not replace SEO. In fact, one of the central arguments of RetailPlaybook is simple:

What follows is a practical breakdown of the strategies I have developed through a deep study of Amazon patents, scientific papers, marketplace behavior, and an optimization portfolio of more than 4,000 ASINs, including 1,500 optimized by hand and the rest improved through human-in-the-loop systems.

Earlier I published Alexa for Shopping: the Blueprint. If you are new here, that piece lays the foundation for everything below. Here is the quick recap.

Now, let's recap Alexa for Shopping: the Blueprint

Recap of Part 1:

“Optimization is now missional. Each shopping mission contains constraints, attributes, memories, preferences, budgets, timing, and context signals that shape both the query plan and the final product selection. Winning listings will need to function like structured evidence systems: clear, complete, specific, current, and trustworthy….Alexa for Shopping extends that pattern into a more personalized and agentic interface. Its advantage is that it can use Amazon’s search infrastructure to assemble a stronger candidate pool across multiple related queries, then refine that pool through context. The ranking does not begin from nothing. It begins inside a product selection pool shaped by search relevance, query expansion, product attributes, sales signals, availability, price, and customer intent. Alexa can then apply another layer of judgment: which product fits this person, this occasion, this budget, this preference set, and this shopping mission?

We also learned that Position 1 in Alexa for Shopping is ( after factoring in “eligibility gates”)making a fit decision.

Position 1 appears to reflect overall fit across multiple signals, including relevance, reviews, price, velocity, discount, delivery, and shopper intent.

Interestingly enough, a brand new study I did based on an evaluation of 15,000+ Product Cards revealed an eligibility floor of 4.4 stars on product positions 2-8. Reviews and ratings are not as hard of gates other research leads us to believe and as we will explore in later series, category specific rules are imperative.

For some more examples from part 1, see the appendix.

Before we dig into the tactical strategies for your brand, I want to go over key agentic capabilities of Alexa for Shopping.

Agentic Capabilities of Alexa for Shopping

Reasoning

Alexa for Shopping reasons through the customer memory graph, interprets the intent of the prompt, and then constructs a search plan designed to bring in the right product.

That matters because a shopping prompt is rarely as simple as the words the customer says. Alexa may reason across several pieces of context at once: purchase history, stated preferences, household context, room-level needs, style patterns, brand preferences, and prior shopping behavior.

For example, imagine a customer asks for wall decor. Alexa may see that the customer has purchased large wall decor before and also loves dogs. A simple system might assume, “She probably wants dog wall decor.” But deeper reasoning may complicate that. Her past wall decor purchases appear abstract, not pet-themed. That could lead Alexa to prioritize abstract wall decor instead. Then another piece of context appears: the only room left to decorate is a dog room. Now dog-themed decor becomes relevant again. Then Alexa sees that the dog is a German Shepherd, so it may include at least one search path for German Shepherd wall decor while still keeping abstract wall decor in the candidate pool.

The point is not that every shopping journey will be this complex. The point is that real-life shopping intent can be messy because people are messy. Their preferences, rooms, occasions, recipients, budgets, and constraints overlap. Query Planning is the result of deeper reasoning capabilities applied to that messy intent.

On product pages specifically, Alexa can pull from almost every facet of the product detail page to decide whether a product fits the mission. Titles, bullets, images, A+ content, reviews, ratings, price, delivery promises, variations, brand information, product attributes, and customer language all become potential signals.

For brands, this means optimization is not just about ranking for one keyword. It is about giving Alexa enough structured, visual, textual, and review-based evidence to understand where the product fits, who it fits, what mission it solves, and why it deserves to be included in the search plan.

Remembering

• Search history (actual terms you have used)

• Browsing history (products overall)

• Purchasing history (recency and historical)

• Cart Awareness (everything in your cart currently)

• Price Preferences

• Brands you prefer (even ones you do not)

Everything about you cultivated from your Amazon interactions shapes the searches and selections Alexa for Shopping makes on your behalf. Plus, directly applies to reasoning as well.

In the example below, Alexa knows my age, the fact I have a wife (even her own interests), information about my kids, my nine different friends and their interests, my sweet white German Shepherd Sophie, how I like my macbook set up and what skill level of a pickleball player I am (and we all know that matters!).

Researching

Alexa sometimes researches the web to inform decisions on products, especially if those products are mentioned in third party review sites, such as techradar, Toms Guide, Cosmopolitan and the like.

However, with Alexa AI overviews on broader terms, sources are provided for each of the answers

As an example below, researched by Alexa includes citations of third-party sites,

If the third-party web describes your product better than you do, the web wins the citation. Earned coverage has become off-page authority you cannot edit so the work is to make the off-Amazon story match the on-Amazon one.

Find ways to get yourself in these publications, since they inform AI Alexa overviews and answers in the conversational interface.

Retrieval

After reasoning through intent and remembering who you are, Alexa still has to go and get products, a step called retrieval. Alexa does not run one search. Alexa builds a query plan, a family of searches fanning out across the catalog from a single human request. "Metal wall art for my living room" does not become one query; one query becomes many: one for the core phrase, others for "industrial metal wall sculpture brass gold," "oversized abstract metal wall decor," "wrought iron wall sculpture living room," and so on. Each reaches a different region of the catalog, including products listed under language a shopper never typed. Those queries fire in parallel against Amazon's search index, and results are pooled, deduplicated, and cleaned into a single candidate set. From there in theory a larger set of products are chosen from. One signal matters more than any other here: frequency. A product surfacing across four of nine queries reads as broadly relevant, far stronger evidence than a product appearing in only one. Retrieval assembles the candidate pool a winning product gets chosen from. And a product no query searched for can never be chosen.

In future articles, we will explore the Anatomy of a Product Card for Alexa for Shopping.

Subscribe now

Ranking

Retrieval decides what can be found. Ranking decides what gets shown. And ranking inside Alexa is not a single sort but a stack of layers. First come hard filters, binary and unforgiving: in stock, genuinely deliverable, sitting in a correct category, past rating and review floors, inside any stated budget, and clear of anything a shopper has ruled out. A product with ten thousand five-star reviews still gets cut for failing a single hard constraint. Structured attributes act as gates. A product missing a size or material field gets excluded before scoring even begins, never penalized inside scoring. What survives gets scored across many weighted signals at once: style and aesthetic fit, quality-confidence (rating shaped by review count and distribution, not a headline number), price-value, brand trust, color and finish compatibility, size and scale, actual review text, and listing completeness.

No single signal wins. Power lives in convergence, and when signals disagree, Alexa may weight toward negatives. Because disappointed buyers are often the most honest voices in any set. One move separates Alexa from a results page. she does not return a top eight by score, because a top eight tends toward near-duplicates. Alexa selects a best set, spanning style, price tier, brand, size, and finish, then re-scores against you specifically: your room, your history, your stated preferences. Hence Position 1 in Alexa for Shopping reflects a fit decision, not a pure relevance score. A winning product did not match the most words. A winning product best completes the person's mission.

How do we Optimize for Alexa for Shopping?

1. NOUN PHRASE OPTIMIZATION

According to the patent I broke down in January 2025, one strong recurring theme throughout the paper was this idea of Noun Phrases, that is, a set of natural language phrases that moved keyword stuffing titles to searching terms far more human, WITHOUT sacrificing years of proven search science. At that time I coined the term, Noun Phrase Optimization. What is NPO?

Simply, NPO is the science and practice of taking the search terms with highest sales volume and relevant overlap to produce natural language phrases. As with anything, methods were developed, for example noun stacking.

“metal wall art”

“Large metal wall art”

“Large metal wall art for bedroom”

“Large modern metal wall art for bedroom”

“Large modern metal wall art for contemporary bedroom.”

“Large modern style metal wall art for contemporary bedroom, living room and entryway”

Especially for titles and bullets, noun phrases make far more sense than previous strategies and I will argue throughout RetailPlaybook, noun phrases are the future of PRODUCT TITLES too. In my estimation, 75 characters is plenty of room to capture the true identity of a product without sacrificing the fundamentals of keyword research.

To help with Noun Stacking, try this GPT: Noun Stacking GPT

2. SEMANTIC BRIDGING

Semantic Bridging is the practice of connecting your product to the adjacent meanings Alexa may infer: the rooms it fits, the occasions it serves, the recipients it suits, and the intents a shopper never typed but clearly meant.

Alexa for Shopping absorbing Rufus's product intelligence did not erase its semantic richness or superior product understanding. I have access to billions of data points actually pointing to semantic-based autocomplete search, however due to costs, Amazon has yet to fully operationalize, despite having the technology with proven gains from their own patents and science. With that said, the technology does exist in Alexa for Shopping, even if not fully in A9 (traditional/evolving amazon search methods).

The key difference:

• Old way: String keywords together

• New way: Create detailed, natural descriptions that capture:
  What the product is
  Its key features
  How it will be used
  Who it is made for

Take a single product: a large black metal wall sculpture. The literal keyword is “metal wall art.” But Alexa can bridge that product into meanings the shopper never typed:

Room: living room above the couch, entryway, primary bedroom accent wall.

Occasion: housewarming gift, new-home decor, anniversary present.

Style: industrial, modern farmhouse, transitional.

Use case: large statement piece for a tall blank wall, conversation starter for an entertaining space.

Each connection is a separate door into the catalog. The product never changes. The number of shopper missions it can answer multiplies.

The more adjacent meanings your listing legitimately connects to, the more query branches it can answer, and the more often it shows up as the right answer to a mission the shopper only half-described.

3. INFERENCE OPTIMIZATION

One reality about Amazon AI you will hear often from me is inference. Similar to its technical definition, inference is making a determination of something to be true (or possibly true) based on reasoning and evidence. In the end, inference time connects all relevant signals, fills gaps and then surfaces what product truly fits.

I came to the realization (inference!) based on all the evidence from patents, science papers and operating experience, Alexa for shopping (Rufus), operates across several dimensions, lexically (the words the customers use), syntactically (the structure from the search + prompt), semantically (underlying meaning of intent), contextually (the situation the customer is in). And most IMPORTANTLY, what distinguishes Amazon and places them above all others in the AI shopping agent world, RAW SIGNALS, Queries, clicks, carts, lists, reviews, history or purchases. Then Alexa+ provides personalization at a scale unheard of, prime video watch history with timestamps, connections between shows and merchandise, datapoints ChatGPT, Claude and Gemini could only dream about possessing. Quite literally, Alexa for shopping works off a treasure trove of personalization and product intelligence.

In a sense, “inference” has become the ultimate keyword. It was in Rufus: The Blueprint that the art of inference pathways was born.

Inference pathways map product features to benefits and outcomes, helping Amazon Rufus and customers connect product attributes to their real-world applications. Connecting the dots between your product features reveals the benefits of what the product does, why it matters, real-world outcomes, and who it is most ideal for.

Inference is the ultimate keyword. The goal is no longer to rank for “metal wall art.” It is to be selected for inference-rich situations “large black metal wall art under $100 for a living room,” “modern metal wall decor above a fireplace,” “outdoor metal wall art that won’t rust.” Clearly, these are shopping situations your listing has to be inferable into.

EXAMPLE OF AN INFERENCE PATHWAY

Inference Pathways GPT

4. A9 OPTIMIZATION (Yes, Keyword Optimization)

Unfortunately, not everything written about Amazon algorithms that ends up in LLM training data is accurate. For example, the mythological idea of an A10 algorithm had been perpetuated (no pun intended) so long, the training absorbed the content from ad hoc sources, well-intentioned companies and others flat out desperate to produce content at scale.

I bet you can guess, A9 is the real algorithm behind Amazon search technology currently deployed on Amazon. There was never an A1, A2… etc. Simply A9.

Think of the A9 Algorithm as the force of ranking behind what products show up in Amazon search result pages (among other things). Or, even better, A9 is the Amazon version of SEO.

How exactly does A9 connect to AI search optimization? Instead of Amazon creating a distinct method of search, why not use the most proven method of search to purchase then Amazons search philosophy. Their team likely posed the question years ago: how do we make search intelligent to the user's context and preferences?

keep the proven engine, then layer reasoning on top. Amazon did not throw out twenty years of search science to chase a chatbot. A9 still forms the candidate pool, fast and at scale, answering the old question well: does a listing carry the words a shopper used? Alexa for Shopping then sits on top and asks the harder one: does a product answer what a shopper is trying to accomplish? Same retrieval foundation, a new layer of judgment.

Good ACO is Great SEO. A9 and Alexa do stack together in symbiotic union. A9 decides whether a product can be found. Alexa decides whether a product gets understood, trusted, and selected. Optimize for one and ignore the other, and you either win retrieval but lose the recommendation, or read beautifully while never entering a pool A9 builds first.

5. SHOPPING MISSIONS and QUERY PLANNING OPTIMIZATION (Brand new to the playbook)

The difference between a keyword and a mission becomes clear through the example of a mother shopping for Christmas gifts. Her objective may be simple: find something meaningful for each of her children. On a traditional search interface, however, she must repeatedly reformulate that objective. “LEGO for boys” may become “Marvel LEGO,” then “Marvel LEGO gift,” “Marvel LEGO for adults,” or “Marvel LEGO under $100.” A beauty search for one daughter may evolve from “makeup” into searches involving lipstick, preferred brands, ingredient standards, cruelty-free requirements, shades, gift sets, and price limits. The mission remains stable while the search language changes. Traditional search requires the customer to perform that reformulation manually. An agentic shopping assistant can perform it on the customer’s behalf by considering recipients, interests, product categories, brands, constraints, delivery needs, prior behavior, and contextual preferences. The customer experiences one conversation, but Amazon executes a family of searches underneath it. Sellers must therefore optimize not only for what the shopper says, but also for the queries the assistant is likely to generate from what the shopper means.

What QPO measures, and where it sits

Strip away the acronym and QPO measures one thing: whether Amazon’s system covers the primary intents and the routing logic for your product, whether every major way a shopper could come looking has a door, and whether that door leads somewhere. Coverage and routing, surfaced across both layers of the system: A9 search and Alexa for Shopping.

QPO asks a harder question: of all the queries this mission will generate, how many can my product be found by, and how many can I truthfully win? It does not replace keyword work, it sits on top of it, because the keyword is now one branch of a tree the shopper never sees.

A NEW UNIT OF MEASURE

One new unit of optimization is the query plan.

A query plan contains multiple branches representing different interpretations, contexts, recipients, constraints, and product needs.

For the wall art mission, the plan could contain several dimensions simultaneously:Alexa can combine these dimensions into more specific retrieval queries:

• Large black abstract metal wall art for living room.

• Minimalist three-piece metal line art for bedroom.

• Floral metal wall accent for kitchen.

• Weather-resistant dragonfly wall art for patio.

• Personalized metal family name sign.

• Lightweight statement wall decor that is easy to mount.

• Metal wall art under $50

EXAMPLE: “I need Metal Wall Art for my Whole Home”

Breakdown of Example

Mission Mapping:

A keyword is what the shopper types. A mission is what the shopper is trying to accomplish.

For example, “blender” is a keyword with no background of the mission. “Quiet blender for daily smoothies in a small apartment under $100” is a mission

Tactical Exercise: Build a Mission Map

For every ASIN, create a mission map with these fields:

Here is an example: Counter Top Blender

But “Andrew, what does this look like for me”, to help you get started I developed a GPT. Millions of products out there, I cannot describe them all here.
Mission Mapping GPT

6. Optimization for All Amazon Attributes

I say optimize for all attributes, going far beyond “Cosmo relations”2 to a deeper categorically aware attribute field guide. I encourage you (the brand) to provide all possible fields, because truthfully, most brands are not optimizing to the truest depth within their category. Which means, making requests crystal clear to Amazon you need EVERYTHING for attribute guide as a 1P brand.3 (Make sure you are Brand Registered). For 3p brands, you already have full control without Amazons approval (assuming you have Brand registry).

In point of fact, Amazon recently published a Level 54 Science Paper regarding LLM guided attribute graphs. Essentially, modern commerce search is becoming a three-stage system where retrieval finds candidates, attribute graphs represent product reality, and LLMs reason over those attributes to determine which products best satisfy the shopper's mission (we will tackle papers such as these in future newsletters).5 Going beyond Cosmo it brings deeper categorical relationships.

I proved the importance already in my two-part deep analysis of an Alexa+/Rufus patent where attributes were described as one of the more important aspects to include in your product detail page to prepare for retrieval and rank (another one had been relevance filtering based on profile information and more).

For this one, no GPT or Claude Skill can truly capture the essence of a category attribute graph I recommend new tools coming to ReFiBuy. I believe you will wish you had sooner.

Example of a Category Specific Attribute “Graph”

7. Product Page Coverage

This section covers optimization for whether your listing answers the prompts customers are typing and likely typing. I have already proven, via the first Rufus patent, that click training data heavily influences prompt pills. In other words, the prompts you see on a product detail page are the results of customer click data (with the exception of templated ones like “compare with similar” or “show price history”). In essence, we are asking here, are you the answer? 6 We know for sure, Alexa for Shopping/Rufus will index almost all information on the product detail page for answering, IF it is on the product detail page.7

• Title (now will be product name + item highlights)

• Bullet Points (1P brands should have access to up to 10 bullets, indexable all the way through the 10th bullet.)

• Product Description

• NATIVE A+ Content Text

• Lifestyle Photographs with AND without text (more often though with text)

• Video indexing is in the works, computational expense at scale too vast for now.
  

Everything above is the written playbook. The podcast is where we talk it through. In the launch episode, sat down with me — Andrew Bell, now ReFiBuy’s VP of Research to introduce RetailPlaybook and go deep on Alexa for Shopping, Amazon’s patent playbook, and what agentic commerce optimization looks like across Amazon, Walmart, and Target. We get into why good ACO is a different playbook for every marketplace, the ~100 million shoppers now reaching Alexa for Shopping, and the conversion lifts I’m seeing on optimized product pages. If you’d rather listen than read, start here.

RetailPlaybook Podcast

Part of RetailPlaybook is our new podcast!

In our first launch episode, we’re announcing two things at once: Me joining ReFiBuy as VP of Research, and the debut of RetailPlaybook. Going forward, I’ll host RetailPlaybook, but for episode one Scot Wingo jumped in to introduce the channel and interview me in person.

🎧 Listen wherever you get your podcasts: Spotify, Apple, etc.

Or, we have a video version on YouTube (make sure to subscribe!) →

FINAL THOUGHTS

Fundamentally, I am obligated to say, the rise of agentic shopping does not make traditional Amazon search irrelevant. It makes search more important because retrieval becomes the foundation from which the assistant constructs a smaller, more personalized recommendation set. A9 forms candidate pools for individual searches. Alexa for Shopping can interpret the mission, generate and prioritize those searches, apply hard constraints, evaluate evidence, compare qualified products, personalize the result, and support action. For sellers, this changes the objective from winning one typed keyword to becoming eligible across the correct family of query pathways. It also changes the definition of a successful product page. The strongest ASIN is not merely the one with the most keywords, the highest rating, or the most attractive creative. It is the product Amazon can retrieve, verify, compare, explain, personalize, and confidently select for a specific shopper in a specific situation. The product must become the right puzzle piece inside the larger mission. Query Planning Optimization provides the retrieval strategy, while Agentic Commerce Optimization provides the structured data, evidence, governance, and feedback system. The final objective is clear: become eligible, remain qualified, prove fit, and earn selection.

Alexa for Shopping reasons over a graph of typed relationships between products and needs, and precise noun phrases populate strong, specific edges in that graph. The more exactly you describe the product, the more specific the edge that routes a matching shopper to you and the more separate queries in step 2 your listing answers, which is the frequency signal that triangulates you as broadly relevant. Attributes create structured truth, noun phrases create retrievable meaning, and context creates inference. Keywords are not dead. The way we used to use them (bare, isolated, stuffed) is the part that is dying.

Over time we will tackle these tactics with increasing depth, exploring nuances that would otherwise go unaddressed.

Some of this is inferred from Amazon’s public AI shopping materials, Amazon Science research, patents, and field testing, not from a disclosed production ranking document

APPENDIX + FOOTNOTES

Alexa for Shopping works within the context of your spot in the journey. Amazon breaks this into Exploratory (Home Page/Search Bar), Comparison (SERP) and Decision (PDP).