Anand Chhatpar

They argue that when software shops on a person’s behalf, it filters for confidence rather than responding to a keyword, image, price, or review.

• Those confidence signals include return rate, review sentiment, complaint patterns, listing clarity, Q&A, use-case specificity, repeat purchase rate, and brand reputation.
• They put the window to build those signals at roughly 18 to 24 months, warning that brands treating it as a 2028 problem will spend 2027 trying to work out why conversion is falling.

The post lists the signals an agent weighs and recommends investing in listing quality, review velocity, complaint resolution, Brand Store and A+ content, and Amazon Marketing Cloud data to reveal the path to purchase before agentic features absorb it.

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They describe turning down a seven-figure client on that basis and say that even hitting a 10% TACoS on a negative-margin product would only optimize the rate of loss.

Chhatpar models a $30 product: $18 product cost plus inbound shipping, $4.50 FBA fulfillment, a $4.50 referral fee, and 18–22% TACoS of $5.40 to $6.60, for a total cost of $32 to $34 per sale. They set four conditions before advertising makes sense: 50% or more gross margin before ad spend, 8% or more listing conversion without paid traffic, a supply chain that can scale three to five times, and customer lifetime value that justifies acquisition cost.

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A higher ACoS driving more sales can leave more actual profit than a lower ACoS driving less, they argue. The goal is profitable scale, not a smaller percentage.

Chhatpar compares a 15% ACoS driving $50,000 in sales, which spends $7,500 on ads and leaves a $42,500 profit margin, with a 10% ACoS driving $20,000, which spends $2,000 and leaves $18,000. Their framework tracks PPC performance, organic ranking as a market-share indicator, total revenue, and profit dollars.

Chhatpar's unit-economics argument favors durable margin over a headline rate: a low TACoS on a losing product is still a losing product.

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Colburn argues that one-to-one attribution has gone too far now that AI mediates product discovery, and that the next model is probabilistic contribution and commercial influence.

• Chhatpar counters that Amazon is the only major platform that closes the loop in one place, from ad impression to product search to purchase, with a real sale verifiable in the same dashboard.
• They argue that when Google or Meta attribution becomes harder to trust, budgets should move toward the platform with closed-loop measurement, not away from accountable platforms.

The post contrasts what breaks elsewhere — an AI Overview intercepting a purchase journey, Rufus surfacing a competitor at the top of search, or a shopper researching in ChatGPT while last-click credit lands on Amazon — with what Amazon supplies: first-party data, actual purchase signals, and verified incrementality testing.

Chhatpar calls Amazon the most defensible ad investment while arguing that the business is squeezing the sellers who fund its ad growth.

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Amazon announced on April 15 that it would switch ad payments from credit card billing to proceeds-deduction.

• Sellers organized, seven-figure accounts publicly threatened to pause spend, and after a Modern Retail story landed before Amazon's May 11 Upfront, Amazon backed down and delayed the change to August 1.
• Chhatpar argues that the $69 billion ad business, growing 23% year over year, is more fragile than it looks: internal models showed real downside from even a 1 to 2% spend cut by top accounts.
• They also note that Amazon has compressed seller margins for years through fee increases, rising fulfillment costs, return policy shifts, and billing changes.
• On policies that directly threaten the ad revenue line, they argue, Amazon will blink.

The nine-day reversal and the move to August 1 anchor Chhatpar's advice: sellers should use the following months to get organized, not feel relieved, because the policy is still coming.

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Demand-side platforms receive millions of bid requests per second and must process them all, even though most will never win, inflating infrastructure costs and compressing margins.

• Dynamic Traffic Engine is a file-based framework that lets DSPs communicate bidding priorities to SSPs so irrelevant requests are filtered before reaching the DSP; TripleLift integrated first.
• Chhatpar argues that Amazon is betting industry-wide modernization will benefit its own DSP, which holds first-party retail signals — helping build the road because they have the best car.
• Agency operators running Amazon DSP should expect lower CPMs and more efficient delivery as adoption spreads, without bidding differently.

Chhatpar describes a file-based priority signal from DSP to SSP and predicts it will reduce wasted impressions reaching buyers.

Two posts cover the same announcement in slightly different wording, underscoring Chhatpar's emphasis on the infrastructure change.

Read original · Original 2

Chhatpar argues that agentic shopping is not a future problem.

• Rufus handles product discovery, “Buy for Me” handles reorders, and AI completing a full purchase without a human in the loop is already in testing.
• The post explains that AI agents assess confidence differently from human shoppers, filtering for return rate, review sentiment, listing clarity, Q&A, use-case specificity, repeat purchase rate, and brand reputation.
• They put the window to build those trust signals at roughly 18 to 24 months.

For brands planning a campaign

For a retail media agency or Amazon-focused brand, this is the strongest opening for a commentary or advisory angle on agentic commerce. The practical angle is not "AI is coming" but which trust signals brands need to build now: listing quality, review velocity, complaint resolution, A+ content, and Amazon Marketing Cloud path-to-purchase data.

Chhatpar walks through an Amazon profit-and-loss example.

• Product cost plus inbound shipping is $18, FBA fulfillment is $4.50, Amazon’s 15% referral fee on a $30 selling price is $4.50, and advertising at 18% to 22% TACoS adds $5.40 to $6.60.
• Total cost to land one sale is $32 to $34 on a $30 product.
• The post lists four conditions that must be met before Amazon advertising makes sense: gross margin of 50% or more before ad spend, listing conversion of 8% or more without paid traffic, a supply chain that can scale three to five times without breaking fulfillment, and customer lifetime value that justifies the cost of acquisition.

For brands planning a campaign

This is a strong starting point for an advisory session on marketplace profitability before scaling ad spend. It also supports a thought-leadership piece on why advertising cannot fix a negative-margin product and why TACoS targets are meaningless when the underlying unit economics lose money.

Chhatpar explains that Amazon open-sourced its Dynamic Traffic Engine and donated it to IAB Tech Lab.

• It addresses programmatic waste: demand-side platforms receive millions of bid requests per second and process them all, even though most will never win a bid.
• Dynamic Traffic Engine is a file-based framework that lets DSPs send bidding priorities directly to SSPs, which filter irrelevant requests before they reach the DSP.
• TripleLift was the first to integrate publicly.
• Chhatpar says Amazon is betting that a faster, less wasteful supply chain benefits its own DSP because it has first-party retail signals.
• For agency operators running Amazon DSP, they expect lower CPMs and more efficient delivery as adoption spreads.
• A second post repeats the announcement in slightly shorter form.

For brands planning a campaign

Use this for expert commentary on retail media infrastructure, Amazon DSP efficiency, and how programmatic supply-chain changes affect media buyers. The practical question: what changes for Amazon DSP campaigns when irrelevant bid requests are filtered before they reach the buyer?

Chhatpar quotes Collin Colburn, IAB VP of Commerce and Retail Media, saying, “We need to blow attribution up.”

• The post explains that one-to-one attribution is becoming fictional as AI mediates product discovery through Rufus, ChatGPT, and Google AI Overviews.
• The next model, they say, is probabilistic contribution and commercial influence.
• Their counterpoint: Amazon is the only major platform that closes the loop in the same environment, from ad impression to product search to purchase.
• When Google’s AI Overviews intercepts a purchase journey or Rufus surfaces a competitor, attribution elsewhere gets harder.
• Chhatpar argues that this makes Amazon DSP and Amazon first-party purchase signals the most defensible measurement environment at scale.

For brands planning a campaign

This is a strong angle for a thought-leadership piece on Amazon DSP as a measurement play in an AI-mediated discovery world. It gives retail media agencies a concrete argument for moving budget toward platforms with closed-loop measurement, rather than away from accountable platforms.

Chhatpar describes Amazon switching ad payments from credit card billing to proceeds deduction on April 15.

• Sellers organized, seven-figure accounts publicly threatened to pause spend, and after a Modern Retail story, Amazon backed down and pushed the change to August 1.
• The more revealing takeaway, they argue, is that Amazon’s $69 billion ad business is more fragile than it looks.
• The post notes that Amazon Ads is growing 23% year over year on that base, while Amazon has also been compressing seller margins through fee increases, rising fulfillment costs, return policy shifts, and billing changes.
• Sellers have more power than they think on policies that directly threaten the ad revenue line, Chhatpar says.

For brands planning a campaign

Use this for commentary on seller-Amazon power dynamics, policy risk, and ad budget leverage. The advisory question is how brands and sellers can organize around policies that directly threaten their spend—and why Amazon responds when top accounts credibly threaten to pause.

Chhatpar argues that the most common Amazon PPC mistake is obsessing over low ACoS.

• The example compares a 15% ACoS that drives $50,000 in sales with a 10% ACoS that drives $20,000.
• The first scenario spends $7,500 on ads and leaves a $42,500 profit margin; the second spends $2,000 and leaves an $18,000 profit margin.
• Most sellers choose the lower ACoS and leave money on the table, they say.
• The framework shifts focus from PPC performance to organic ranking, total revenue, and profit dollars rather than percentages.

For brands planning a campaign

This is a useful hook for an advisory session or piece on profitable scale versus vanity metrics. It offers a simple, concrete reframe for Amazon sellers optimizing for lower ACoS while shrinking total profit.

They argue that AI agents filter for confidence signals rather than respond to keyword, image, price, or review tactics.

• The signals listed are return rate, review sentiment, complaint patterns, listing clarity, Q&A, use-case specificity, repeat purchase rate, and brand reputation.
• Suggested preparation areas include listing quality, review velocity, complaint resolution, brand store and A+ content, and Amazon Marketing Cloud data, with an estimated 18-24 month window before agentic commerce scales.

For brands planning a campaign

A retail media agency or Amazon-focused brand could turn this into thought leadership on preparing product pages and trust signals for AI-mediated shopping, or an advisory session on which signals to prioritize before agentic features affect conversion.

Examples: Source 1

As AI Overviews, Rufus, and ChatGPT mediate product discovery, the post argues, deterministic impression-to-conversion paths become less reliable, and the next model is probabilistic contribution and commercial influence. Chhatpar’s counterpoint is that Amazon becomes more defensible because it closes the loop from ad impression to product search to purchase in one environment, with first-party data, purchase signals, and incrementality testing.

For brands planning a campaign

A brand or agency could commission a piece on reallocating budget toward Amazon DSP as attribution weakens on other platforms, or use the argument to prompt debate in a retail media strategy session.

Examples: Source 1

They model a $30 product with $18 product and inbound cost, $4.50 FBA fulfillment, $4.50 referral fee, and 18-22% TACoS, landing at $32-$34 total cost per sale.

• The conclusion: advertising can scale healthy unit economics but cannot fix a business that loses money per order.
• Four conditions are listed before Amazon advertising makes sense: 50%+ gross margin before ad spend, 8%+ listing conversion without paid traffic, a supply chain that can scale 3-5x, and customer lifetime value that justifies acquisition cost.

A modeled per-unit P&L and a four-condition checklist make the argument concrete.

For brands planning a campaign

This supports advisory content or a workshop on Amazon P&L diagnostics, margin repair, and when to decline or pause ad spend—not another campaign-optimization offer.

Examples: Source 1

The problem is programmatic waste: DSPs receive millions of bid requests per second and must process many that will never win.

• DTE is a file-based framework that lets DSPs send bidding priorities to SSPs, filtering irrelevant requests before they reach the DSP; TripleLift integrated first.
• The post argues that a more efficient supply chain, combined with Amazon’s first-party retail signals, strengthens Amazon’s position as a dominant DSP.
• Agency operators running Amazon DSP could see lower CPMs and more efficient delivery at full adoption.

Two posts cover the same announcement with minor wording differences, suggesting the topic was a priority.

For brands planning a campaign

Agencies could use this to explain retail media infrastructure to clients or review DSP efficiency assumptions before setting programmatic budgets.

Examples: Source 1 · Source 2

Amazon had announced a switch from credit card billing to deduction from proceeds for ad payments, then delayed the change to August 1 after seller organizing, threats from seven-figure accounts to pause spend, and a Modern Retail story ahead of Amazon’s May 11 Upfront.

• They argue the reversal shows Amazon’s $69B ad business is more fragile than it appears and that sellers have leverage on policies that directly threaten ad revenue.
• The post also notes Amazon has compressed seller margins through fee increases, fulfillment costs, return policy shifts, and billing changes.

For brands planning a campaign

A brand or agency could use this for commentary on seller-Amazon power dynamics and policy risk, or to plan ad budgets around platform changes.

Examples: Source 1

They compare a 15% ACoS driving $50K in sales with a 10% ACoS driving $20K, showing that the higher-ACoS scenario produces more profit dollars.

• The recommended framework tracks PPC performance, organic ranking as a market-share indicator, total revenue, and profit dollars rather than percentages.
• The closing prompt asks readers for their target ACoS and why.

For brands planning a campaign

This angle suits PPC education content, sales-team training, or a client workshop on shifting from percentage-based targets to profit-based goals.

Examples: Source 1

Examples include the agentic commerce warning, the DTE open-source explainer, the unit-economics teardown, and the ACoS comparison. The concise LinkedIn commentary translates platform changes into decision points for brands and agencies.

Pattern across all seven full posts.

For brands planning a campaign

For a campaign, this style suits timely thought leadership, newsletter commentary, or a moderated discussion where a short, argument-led post can frame a strategic shift and invite responses. For a long-form research report, ask for additional evidence.

Examples: Source 1 · Source 2 · Source 3 · Source 4 · Source 5 · Source 6 · Source 7

They analyze agentic shopping features Rufus and Buy for Me, Amazon’s open-sourced Dynamic Traffic Engine, the reversal of its ads billing policy, closed-loop DSP measurement, and Amazon PPC economics. No paid or contractual relationship with Amazon is stated.

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It targets agency operators running Amazon DSP. No relationship between Chhatpar and IAB Tech Lab is stated.

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Each writes in plain first-person prose about diagnosing what is holding a product back.

Nader works inside the account, tallying line-item fees, fulfillment costs, and per-child-SKU profit to decide how aggressively to advertise each variation.

• They also write about telling a client that PPC cannot fix the situation.
• Chhatpar makes the same point strategically, using modeled per-unit costs and platform-level arguments about agentic shopping and attribution rather than an account teardown.

Read original · Original 2

They share an interest in where AI sits between sellers and their own data.

Aggarwal writes from a vendor-services position about marketplace margin enforcement—ASINs pushed into Direct Fulfillment, search suppression, and margin targets—and Amazon's AI assistants from the tooling side, with an explicit plug for their own firm's services. Chhatpar frames the same shifts as strategic arguments for brands and agencies, focusing on budget allocation and measurement.

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Fishman walks through what each new Amazon tool does—Prime Air, Amazon Quick, Seller Flex, Profit Analytics, and scenario modeling—and what it could save a seller. Chhatpar uses announcements to launch arguments about strategy, such as why closed-loop measurement or trust signals matter more as AI mediates shopping.

Read original · Original 2

The posts show account-level profit diagnosis in detail: one child SKU produced $29,519 in sales at 24.79% TACoS and still lost $4,078, while a sibling SKU on the same parent listing made $3,087 on $19,587 in sales.

• Nader separates parent-level fees into FBA fulfillment ($24,461.88), referral fees ($9,275.65), storage ($3,463.84) and FBA inbound defect fees ($5,287.68), then asks which lever would actually remove the loss.
• Lowering bids, Nader notes, does nothing to fix $5,287.68 in inbound defect fees.
• A second post describes ending an engagement by telling a client that PPC could not fix the business, even though CPC, ACoS, CTR and TACoS were improving.
• For a brand that suspects its ads are being blamed for an unprofitable SKU, that operational framing is more useful than a forward-looking AI story.

The work stays inside ad-account and fee mechanics.

• These posts do not cover agentic shopping, programmatic infrastructure, or how budgets shift as attribution weakens—the core of Chhatpar's posts here.
• For a commissioned thought-leadership piece on Amazon's AI-driven shopping shift, their agentic-commerce and Amazon DSP arguments map more directly.
• For a hands-on advisory session on why one SKU is losing money, Nader's profit-first PPC approach is the closer match.
• Two posts dated 24 and 28 September 2026 are discussed.

Examples: Source 1 · Source 2

One post walks through Amazon Accelerate 2026's Seller Assistant: persistent memory of a seller's pricing patterns, inventory cycles and growth goals across sessions; a canvas for modeling pricing scenarios against live data; and automated workflows that keep every action behind approval with an audit trail.

• The other covers Amazon's margin strategy, arguing that profitability now outranks selection: SKUs missing contribution-margin targets are pushed into Direct Fulfillment, items below profitability thresholds face search suppression, and Gross Margin Guarantee pressure can leave vendors writing a check back.
• For an agency briefing clients on what changed in Seller Central and how Amazon's margin calculus affects listings, that pairing is practical.

The margin post doubles as agency positioning, listing services from AdOrbix, so it reads as advocacy rather than neutral analysis.

• It is dated 24 October 2025, roughly ten months before the Accelerate coverage, making its marketplace-margin claims older than the tooling discussion.
• The Accelerate post is candid about limits: the change is in how fast a seller sees data, not yet in whether they know what to do about it.
• Neither post predicts the AI-shopping shift covered in Chhatpar's agentic-commerce and attribution posts.
• Choose Aggarwal for a session on adopting seller AI or responding to margin enforcement; choose Chhatpar for a forward-looking agentic-commerce narrative.
• Reviewed two posts dated 24 September 2026 and 24 October 2025.

Examples: Source 1 · Source 2

The two posts summarize Accelerate announcements for sellers: day one covers Prime Air 30-minute drone delivery, Amazon Quick's direct connection into Seller Central, and Seller Flex, cited as a 17% conversion lift from keeping bulky inventory in the seller's own warehouse.

• Day three covers Profit Analytics pulling revenue and costs across advertising, fulfillment, refunds and returns into one view; scenario modeling before a price or inventory decision; better inventory visibility; and AI supply-chain planning.
• Each item is tied to an operator action, and Fishman states a method: start audits at the SKU-level P&L, find which SKUs actually make money, then put more budget behind those.
• That makes the posts useful for a newsletter, internal briefing, or client-facing summary of the news itself.

These roundups prioritize speed and shareability over deep strategic argument.

• They report what Amazon announced but do not offer the trust-signal checklist for AI agents, the argument about attribution eroding outside Amazon, or the unit-economics gates in Chhatpar's posts.
• For a webinar or piece built on strategic implications rather than announcements, their material carries more weight.
• For immediate circulation of the Accelerate news with a practical seller takeaway, Fishman's summaries are more directly usable.
• Reviewed two posts dated 23 and 25 September 2026.

Examples: Source 1 · Source 2