Amazon Ads Natural-Language Analytics 2026: Can Sellers Finally Stop Pulling Reports Manually?

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Amazon advertisers have spent years doing the same repetitive work.

Download reports.

Open spreadsheets.

Combine Sponsored Products, Sponsored Brands, display and video data.

Calculate changes.

Find anomalies.

Build charts.

Then finally ask the question that mattered in the first place:

What actually changed—and what should we do about it?

Amazon is trying to reverse that workflow.

On September 28, 2026, Amazon Ads announced a new AI-powered natural-language analytics experience inside Amazon Ads Agent. Eligible advertisers can ask questions such as:

“Which campaigns drove the most new-to-brand customers last month?”

or:

“How does my ROAS compare across campaigns?”

and receive answers supported by campaign context, visualizations, trends and performance data instead of manually building every analysis from reports. Amazon Ads

Amazon says the system is built on its Unified Reporting foundation and first-party shopping signals and can incorporate full-funnel measurements such as new-to-brand purchases, Long-Term Sales and category benchmarks. Amazon Ads

At Adorbix, we see this as much more than a reporting shortcut.

The real shift is:

REPORT → QUESTION → INSIGHT → DECISION

instead of:

REPORT → EXPORT → CLEAN → PIVOT → CHART → QUESTION

Amazon can remove a lot of the mechanical work.

But sellers still need to know what questions are worth asking.

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What Exactly Did Amazon Launch?

Natural-language analytics is a conversational analytics experience inside Amazon Ads Agent.

Instead of navigating several reporting screens, advertisers can type questions in ordinary language and receive analysis directly inside the advertising platform.

Amazon says the experience combines:

• conversational analysis,
• campaign context,
• key metrics,
• trends,
• visualizations,
• benchmarking,
• and an interactive workspace where advertisers can refine their questions. Amazon Ads

The feature is designed to analyze advertising performance across Amazon ad products rather than forcing the advertiser to inspect each channel separately.

That means the workflow can move from:

“Download Sponsored Products.”

“Download display.”

“Download video.”

to:

“What caused our customer-acquisition efficiency to decline last month?”

That's a much more useful starting point.

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Is the Feature Available to Everyone?

Not yet.

Amazon currently describes natural-language analytics as a closed-beta capability available to hands-on-keyboard advertisers through enrollment.

Eligible users access it through Amazon Ads Agent, and Amazon says advertisers can request access through an Amazon Ads representative or join the waitlist. Amazon Ads

Amazon currently lists beta availability across markets including:

• United States
• Canada
• Mexico
• Brazil
• United Kingdom
• Germany
• France
• Italy
• Spain
• Netherlands
• Belgium
• Poland
• Türkiye
• Sweden
• Saudi Arabia
• UAE
• Egypt
• Australia
• India
• Japan
• Singapore. Amazon Ads

So sellers should not assume the new conversational interface is automatically visible in every account today.

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Why This Matters for Amazon PPC Teams

Amazon PPC reporting has historically been easy to access but difficult to synthesize.

A campaign manager may have all the data required to diagnose a problem.

The problem is finding the right combination.

Imagine sales decline 18%.

Possible causes include:

• lower impressions,
• lower CTR,
• higher CPC,
• budget limitations,
• reduced conversion,
• inventory problems,
• weaker new-to-brand acquisition,
• seasonal demand.

Traditionally, someone investigates those variables manually.

Natural-language analytics can make the first pass much faster.

A marketer could ask:

“Why did sales decline despite stable ad spend?”

Then refine the analysis:

“Was the decline driven more by traffic or conversion?”

Then:

“Which five campaigns contributed most?”

That is much closer to how a senior analyst works.

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Amazon Says Nearly 1,000 Metrics Are Becoming One Question Away

At unBoxed 2026, Amazon said Amazon Ads Agent puts nearly 1,000 advertising metrics within reach through natural-language interaction.

Advertisers can ask about areas such as:

• campaign drivers,
• new-to-brand performance,
• Long-Term Sales,
• full-funnel trends,
• and peer benchmarks,

and receive business reviews with visualizations rather than manually assembling those metrics from multiple reports. Amazon Ads

That is important because the challenge in Amazon advertising is increasingly not lack of data.

It is:

too much data with too little time to interpret it.

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Can Sellers Finally Stop Pulling Reports Manually?

For many routine questions:

Potentially, yes.

Amazon specifically positions the feature as a way to reduce manual report pulling and spreadsheet analysis for common performance questions. Amazon Ads

But sellers should not delete their reporting infrastructure tomorrow.

There are still situations where raw reports matter.

You may need them for:

• custom profitability models,
• financial reconciliation,
• historical archiving,
• offline analysis,
• custom attribution,
• agency/client reporting,
• BI dashboards.

The new AI experience is best understood as:

ANALYTICS ACCESS

not necessarily:

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A COMPLETE REPLACEMENT FOR EVERY REPORTING SYSTEM

At Adorbix, we'd use conversational analytics to accelerate investigation while maintaining structured reporting for financial and historical analysis.

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The Real Win Is Faster Diagnosis

Imagine a brand has 400 active campaigns.

Monday morning, someone needs to determine what deserves attention.

Old workflow:

Export → sort → filter → compare → calculate → investigate.

New workflow:

“Show me campaigns where spend increased more than 20% but sales declined.”

Then:

“Rank them by wasted spend.”

Then:

“Explain whether CPC or conversion caused the biggest change.”

Instead of spending the morning locating the problem, the team can spend more time solving it.

That's where the productivity gain becomes meaningful.

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New-to-Brand Analysis Becomes Much Easier

Amazon specifically uses new-to-brand customers as an example of what natural-language analytics can analyze. Amazon Ads

That's strategically important.

A campaign may have excellent ROAS while mostly selling to people who already know the brand.

Another campaign may have weaker immediate ROAS but bring in far more first-time buyers.

If the business is trying to expand its customer base, the second campaign may deserve more budget.

Instead of only asking:

“Which campaigns generated the most sales?”

brands can ask:

“Which campaigns generated the most new-to-brand customers?”

Then:

“Which had the lowest cost per new-to-brand purchase?”

That creates a better customer-acquisition conversation.

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Amazon Benchmark Data Adds Context to the Answer

Knowing your CTR is:

0.55%

doesn't automatically tell you whether that's good.

Amazon's benchmark reporting provides category-specific comparisons for measures such as:

• CTR,
• CPC,
• CPM,
• new-to-brand purchase rate,
• new-to-brand percentage,
• cost per new-to-brand purchase,
• video completion rate,
• cost per completed view. Amazon Ads

Natural-language analytics can bring benchmark context into the conversation.

Instead of asking:

“What's our CTR?”

the better question becomes:

“How does our CTR compare with similar brands in our category?”

That distinction helps teams decide whether a number is actually worth fixing.

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Benchmarking Can Prevent Bad Optimization Decisions

Imagine your campaign CTR is:

0.6%.

Someone internally decides the target should be:

1%.

They begin aggressively changing:

• creative,
• targeting,
• bids.

But suppose the relevant category median is:

0.42%.

Your campaign was already outperforming the peer group.

Without contextual data, the team may optimize a problem that doesn't exist.

Amazon benchmarks make performance more meaningful because the advertiser can understand:

Where are we relative to similar brands?

rather than:

Did this metric go up or down?

At Adorbix, we'd combine both.

Trend tells us what changed.

Benchmark tells us whether it matters.

Long-Term Sales Changes How Upper-Funnel Campaigns Are Evaluated

Amazon's natural-language analytics also supports analysis involving Long-Term Sales. Amazon Ads

Long-Term Sales estimates future sales value associated with new-to-brand customer engagement over a 12-month horizon.

Amazon's methodology looks beyond an immediate transaction by considering engagement such as:

• product detail-page visits,
• branded searches,
• add-to-carts,
• purchases,

and historical downstream customer behavior. Amazon Ads

That matters because upper-funnel campaigns can look weak under immediate attribution.

A streaming-TV campaign may introduce thousands of new shoppers who buy weeks later.

Conversational analytics makes it easier to ask:

“Which campaigns have the strongest Long-Term ROAS?”

rather than judging everything through immediate purchase ROAS.

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ACoS Still Matters—But It Shouldn't Be the Only Question

Many Amazon sellers live inside ACoS.

And for Sponsored Products, it remains extremely useful.

But natural-language analytics makes it easier to move beyond:

“What's our ACoS?”

toward:

“Why did ACoS increase?”

Then:

“Did CPC rise or conversion fall?”

Then:

“Which search campaigns account for most of the increase?”

Then:

“Did total sales or new-to-brand acquisition improve enough to justify it?”

That's how reporting becomes analysis.

At Adorbix, we'd encourage sellers to think in three levels:

Metric

↓

Cause

↓

Business outcome

Natural Language Doesn't Mean the AI Automatically Knows Your Profit

This is one of the biggest limitations brands should remember.

Amazon understands:

• ad spend,
• advertising sales,
• traffic,
• new-to-brand,
• retail activity,
• media signals.

But your true profitability may also depend on information outside the advertising platform.

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For example:

COGS

freight

FBA fees

coupons

returns

agency costs

overhead

So if Amazon Ads Agent says:

“Campaign A has the best ROAS.”

Adorbix would still ask:

“Does Campaign A generate the best contribution?”

Those are not always the same thing.

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Example: The Highest ROAS Campaign Might Not Be the Most Profitable

Consider two ASINs.

Product A

ROAS: 5.0

Contribution margin before ads: 14%

Product B

ROAS: 3.4

Contribution margin before ads: 42%

Amazon advertising data may make Product A look superior.

But Product B may generate much more money after all costs.

Natural-language analytics makes Amazon data easier to analyze.

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It does not eliminate the need to connect Amazon data to financial data.

That's where Adorbix's profitability layer becomes important.

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Conversational Analytics Can Improve Budget Meetings

One of the best use cases may be the weekly advertising review.

Imagine someone asks:

“Where should we put another $10,000 next week?”

Today, answering that well may require multiple reports.

With conversational analytics, teams can explore:

“Which campaigns are budget constrained?”

Then:

“Which of those outperform category benchmarks?”

Then:

“Which also have strong new-to-brand performance?”

Then:

“Which products have enough inventory?”

The first three questions may increasingly happen inside Amazon Ads Agent.

The last question still requires business context.

That's the ideal relationship:

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AI FINDS THE OPPORTUNITY → HUMANS VALIDATE THE BUSINESS CASE

Natural-Language Analytics Is Not the Same as the AMC Expert Tool

Amazon announced several conversational analytics tools at unBoxed 2026, so the terminology can get confusing.

Natural-language analytics focuses on letting advertisers conversationally explore Amazon Ads performance, metrics, benchmarks and visualizations.

The AMC Expert Tool tackles more advanced Amazon Marketing Cloud analysis by interpreting a natural-language request and generating appropriate AMC SQL. Amazon Ads

Think of the difference as:

Natural-Language Analytics

= easier campaign and business-performance analysis.

AMC Expert Tool

= easier advanced clean-room analytics and SQL workflows.

Brands can benefit from both.

They solve different levels of analytical complexity.

Unified Reporting Is the Foundation Underneath the Experience

Amazon says natural-language analytics is built on its Unified Reporting infrastructure. Amazon Ads

That's important.

Amazon has spent much of 2026 consolidating advertising reporting across:

• accounts,
• countries,
• ad products,
• placements,
• audiences.

Conversational analytics is essentially the intelligence layer sitting on top of that standardized data.

The progression is:

Fragmented reports

↓

Unified Reporting

↓

CONVERSATIONAL ANALYSIS

This explains why Amazon can now answer broader questions across multiple campaign types.

The Analytics Workspace Still Lets You Inspect the Data Visually

Natural-language analytics is not designed to provide one sentence and end the analysis.

Amazon says the experience includes an interactive visual workspace where advertisers can:

• view visualizations,
• explore trends,
• inspect context,
• refine questions. Amazon Ads

That's a good design choice.

Sometimes the AI's answer tells you where to look.

A chart tells you:

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what actually happened.

At Adorbix, we'd use conversation for exploration and visual analysis for validation.

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Don't Blindly Trust Every AI Explanation

This matters especially for high-spend accounts.

Generative AI can produce plausible explanations.

Advertisers should still verify material recommendations.

Suppose Ads Agent says:

“Sales declined because CTR fell.”

Check:

• Did impressions also change?
• Did inventory go out of stock?
• Did price rise?
• Did conversion change?
• Did the event period end?

AI should help narrow the investigation.

It should not eliminate analytical skepticism.

For major decisions, Adorbix would verify the underlying data before changing large bids or budgets.

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Better Questions Will Become a Competitive Advantage

When reporting becomes easier, report access is no longer the advantage.

Question quality becomes the advantage.

Weak question:

“What's my ROAS?”

Better:

“Which campaigns increased ROAS the most month over month?”

Stronger:

“Which campaigns increased new-to-brand acquisition without increasing cost per customer?”

Even stronger:

“Which of those campaigns promote products with enough margin and inventory to justify another 20% in spend?”

The final version combines:

advertising data

with:

business strategy.

That's where Adorbix adds value.

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The Adorbix Natural-Language Analytics Framework

At Adorbix, we'd structure conversational analytics around five steps.

1. ASK

Start with the business question.

Example:

Why is customer acquisition getting more expensive?

2. DIAGNOSE

Use Amazon Ads Agent to inspect:

• spend,
• CPC,
• conversion,
• new-to-brand,
• campaign drivers.

3. BENCHMARK

Determine whether the metric is weak:

relative to peers,

not just:

relative to last month.

4. CONNECT

Add external context:

• margin,
• inventory,
• seasonality,
• promotions.

5. ACT

Then decide whether to:

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SCALE → CUT → TEST → FIX

That keeps AI from becoming a recommendation machine disconnected from economics.

How Adorbix Can Use Natural-Language Analytics

The feature can accelerate several recurring agency workflows.

Weekly Account Reviews: Identify the biggest performance changes without building the entire analysis manually.

PPC Waste Audits: Ask which campaigns increased spend without corresponding sales growth.

New-to-Brand Analysis: Find campaigns actually expanding the customer base.

Budget Allocation: Identify high-performing campaigns constrained by spend.

Benchmarking: Compare campaign efficiency against relevant category peers.

Full-Funnel Analysis: Review Long-Term Sales and upper-funnel contribution.

Client Reporting: Use AI to surface the story behind the numbers before analysts build the final business recommendation.

The objective is not to eliminate analysts.

It is to eliminate analyst time spent doing repetitive extraction.

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A Better Weekly Amazon Ads Review

A practical Monday workflow could look like this.

Ask:

“What were the five biggest performance changes last week?”

Then:

“Which changes had the largest revenue impact?”

Then:

“Which were driven by CPC versus conversion?”

Then:

“Which campaigns are below category benchmarks?”

Then:

“Which campaigns produced the strongest new-to-brand results?”

Now the human team applies:

• profitability,
• inventory,
• client priorities,
• promotional plans.

That is significantly faster than beginning with a blank spreadsheet every Monday.

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A Practical 30-Day Test

If your account receives closed-beta access, don't immediately abandon existing reports.

For the first two weeks, run the new experience alongside your normal reporting process.

Ask the same questions your analysts already answer manually.

Compare:

Accuracy.

Speed.

Depth.

Missing context.

During weeks three and four, identify analyses that no longer need manual preparation.

Good candidates may include:

• campaign comparisons,
• trend summaries,
• new-to-brand breakdowns,
• benchmark questions.

Keep structured reports for:

• accounting,
• advanced financial analysis,
• custom BI,
• historical storage.

The objective is selective automation.

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Natural-Language Analytics Readiness Checklist

Before relying heavily on conversational reporting, confirm:

• Beta access is available
• Existing KPIs are clearly defined
• Team understands new-to-brand
• Long-Term Sales methodology is understood
• Benchmark eligibility is confirmed
• Profitability data exists outside Amazon Ads
• Analysts validate major recommendations
• Current reporting baselines are preserved
• Inventory context is available
• Actions are measured after implementation

AI becomes more useful when the team already knows how it defines success.

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Frequently Asked Questions

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What is Amazon Ads natural-language analytics?

It is Amazon's new conversational analytics experience inside Amazon Ads Agent. Advertisers can ask campaign-performance questions in natural language and receive answers supported by metrics, trends, visualizations and advertising context. Amazon Ads

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When did Amazon announce it?

Amazon announced the experience on September 28, 2026, around unBoxed 2026. Amazon Ads

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Can it analyze new-to-brand performance?

Yes. Amazon specifically uses questions involving new-to-brand customers as examples of the feature's capabilities. Amazon Ads

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Can it analyze Long-Term Sales?

Yes. Amazon says the experience includes full-funnel metrics such as Long-Term Sales alongside more traditional advertising measures. Amazon Ads

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Does it include benchmarks?

Amazon says conversational analytics combines advertising data with retail signals and benchmarks. Amazon's benchmark system currently includes metrics such as CTR, CPC, CPM and new-to-brand performance. Amazon Ads

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How many Amazon Ads metrics can advertisers ask about?

At unBoxed, Amazon said Amazon Ads Agent places nearly 1,000 metrics within reach through natural-language analytics. Amazon Ads

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Is the feature generally available?

No. Amazon currently lists natural-language analytics as a closed beta for eligible hands-on-keyboard advertisers. Amazon Ads

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Where do advertisers access it?

Eligible advertisers access the feature through Amazon Ads Agent. Amazon Ads

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Does it replace Amazon Marketing Cloud?

No. Natural-language analytics is designed for easier advertising-performance analysis. AMC remains a deeper analytics environment, with the separate AMC Expert Tool helping translate natural-language questions into AMC SQL. Amazon Ads

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Can sellers stop downloading reports entirely?

Probably not in every workflow. Conversational analytics can replace substantial manual investigation, but financial reconciliation, custom BI, advanced modeling and historical reporting may still require structured exports.

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Can Adorbix help brands use natural-language analytics?

Yes. Adorbix can combine Amazon's conversational analytics with PPC expertise, benchmark analysis, inventory, contribution margin and account strategy so brands move faster from AI-generated insight to financially sound action.

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Key Takeaways

Amazon Ads is moving reporting away from:

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DOWNLOAD → SPREADSHEET → ANALYSIS

toward:

QUESTION → ANSWER → VISUALIZATION → ACTION

Amazon's new natural-language analytics experience can help advertisers explore campaign performance, new-to-brand behavior, benchmarks and Long-Term Sales through conversational questions inside Amazon Ads Agent. Amazon Ads

It is built on Amazon's Unified Reporting foundation and first-party shopping signals.

Amazon says nearly 1,000 metrics are becoming accessible through the conversational experience. Amazon Ads

But the feature remains in closed beta and should be treated as an analytical accelerator—not unquestioned decision authority.

The biggest productivity gain will come from reducing:

repetitive reporting,

dashboard navigation,

spreadsheet preparation,

so marketers can spend more time on:

interpretation,

strategy,

and:

action.

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Final Takeaway: Sellers May Pull Fewer Reports—But They'll Need to Ask Better Questions

The Amazon advertiser's advantage used to include knowing:

which report to download,

which column to filter,

which pivot table to build.

Natural-language analytics makes those mechanics less valuable.

Amazon increasingly lets the advertiser say:

“Tell me what changed.”

“Why?”

“Which campaign matters most?”

“How do we compare with competitors?”

That's powerful.

But the AI still does not automatically know:

your real product margin,

your inventory constraints,

your working-capital priorities,

or your broader business strategy.

At Adorbix, we see the winning workflow as:

AMAZON AI → FASTER INSIGHTS → ADORBIX CONTEXT → BETTER DECISIONS → PROFITABLE ACTION

So, can sellers finally stop pulling reports manually?

For many routine questions, increasingly yes.

But the future isn't a world with no analysts.

It's a world where great analysts spend less time collecting numbers—and more time deciding what those numbers actually mean for the business.

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