Native Amazon reports are siloed and revenue-focused, which hides profit problems until they've already cost the client money.
Know the core terms cold: ACoS, TACoS, contribution margin per SKU, AMC, share of voice, and Buy Box.
2026 brought real platform shifts: Rufus retired into Alexa for Shopping, Sponsored Prompts became a billable placement, DSP thresholds dropped, and Prime Day moved to June.
Size your actual need before choosing a tool: account count, category expansion plans, time spent reformatting reports, and white label requirements.
Amazon analytics covers a lot of ground: sales data, ad performance, fee breakdowns, competitive intelligence, and increasingly, shopper behavior signals that never used to be visible at all. If your agency is new to the category or catching up after a year of platform changes, here's the context worth having before you pick a tool or promise a client anything.
Why Native Amazon Reports Fall Short
Seller Central and Amazon Ads give you plenty of reports, but they're built to show activity, not profit. Sales, advertising, fees, and refunds each live in a separate place, so by the time a margin problem shows up in a spreadsheet, it's already cost the client money. Native reporting is also revenue-focused by default, and revenue is not the same question as "which SKUs are actually making money after ads, fees, and refunds." That gap is the whole reason a separate analytics layer exists.
The Terms Worth Knowing
A quick glossary, since these terms get used loosely and clients will expect you to use them precisely.
Key Amazon Advertising Terms & Metrics
Term
What It Means
ACoS
Advertising Cost of Sale. Ad spend divided by ad-attributed sales only.
TACoS
Total Advertising Cost of Sale. Ad spend divided by total sales, ad and organic combined.
Contribution margin per SKU
Selling price minus product cost, referral fee, FBA fulfillment, and storage. The actual dollar profit per unit.
AMC
Amazon Marketing Cloud. Amazon's shopper-signal and path-to-purchase data layer.
Share of voice
A brand's visibility in a category relative to competitors, usually measured across organic and paid placements.
Buy Box
The default "Add to Cart" listing on a product page. Losing it is one of the fastest ways to lose sales without any ad metric changing.
What Changed on Amazon in 2026
A few platform shifts changed what "good analytics" needs to cover this year.
Amazon retired Rufus into Alexa for Shopping, Sponsored Prompts became a billable ad placement, DSP entry thresholds dropped, and Prime Day moved to June. The scale of the platform is also part of the picture: Amazon holds an estimated 39.7% of US ecommerce sales in 2026 by one measure, and 35.7% of a $440 billion base by another, depending on how the base is calculated.
Either way, the amount of shopper behavior data flowing through Amazon's own systems keeps growing, which is exactly why AI-powered search and generative shopping assistants are starting to influence purchase decisions before a click ever reaches a listing.
For agencies, the practical takeaway is that ad-placement reporting alone is falling further behind. A client asking "why did conversion drop" increasingly needs an answer that accounts for how Amazon's own AI assistants are surfacing (or not surfacing) their products, not just a bid-and-budget explanation.
How to Know What Your Agency Actually Needs
Not every agency needs the same depth of tooling. A few honest questions help:
Once you know the answer to those, the tool decision gets a lot easier.
If you want a sense of how a market-first tool changes the pitching process specifically, SmartScout's agency guide is worth a read.
FAQs
What Is Amazon Analytics, in Plain Terms?
It's the practice of pulling sales, advertising, fee, and competitive data into one place so you can see profit, not just activity. Native Amazon reports show you what happened in each of those areas separately; analytics connects them.
What’s the Difference Between ACoS and TACoS?
ACoS divides ad spend by ad-attributed sales only. TACoS divides ad spend by total sales, ad and organic combined, which makes it the better gauge of whether the whole account is growing or just shifting where sales come from.
What Is Amazon Marketing Cloud (AMC)?
AMC is Amazon's shopper-signal and path-to-purchase data layer. It shows behavior leading up to a purchase, not just the final ad click, which is increasingly relevant as AI-powered search influences more of that path.
What Changed on Amazon in 2026 That Agencies Should Know About?
Several platform shifts landed this year: Rufus was retired into Alexa for Shopping, Sponsored Prompts became a billable ad placement, DSP entry thresholds dropped, and Prime Day moved to June. Each one changes what a complete analytics picture needs to include.
Does My Agency Need a Dedicated Analytics Tool, or Are Native Amazon Reports Enough?
It depends on scale. A small handful of accounts can often run on manual review. Past that, or once you're pitching new categories, spending significant time reformatting reports by hand, or fielding client requests for white label reporting, native reports stop being enough on their own.
Connect five data buckets before building any dashboard: advertising, retail, shopper intelligence, competitive intelligence, and client-specific data.
Report TACoS and contribution margin per SKU together. TACoS alone can look healthy while margin quietly disappears.
A healthy account-level TACoS runs 10-15% of revenue for an established seller; new launches run 25-40% in the first one to three months.
Set cadence by metric: daily for Buy Box and stock, weekly for ACoS, monthly for TACoS, margin, and share of voice.
Lead every client report with an executive summary and verdict, then back it up with detail, not the other way around.
A good Amazon agency reporting framework does two things at once: it proves the work is driving profit, not just ad clicks, and it gives your team a repeatable structure so reporting doesn't get rebuilt from scratch for every client.
Below is the framework we'd build for 2026: which data sources to connect, which KPIs actually matter, how often to report on each, and how to lay out the report itself.
Why Ad-Level Reporting Isn't Enough Anymore
Native Amazon reports are siloed by design.
Sales, ads, fees, and refunds each live in their own place, and none of them answer the question a client actually cares about: which SKUs are profitable once everything is accounted for.
Advertising cost of sale (ACoS) alone can look healthy while margin quietly disappears. A 28% ACoS can sit next to a 2026 fee stack that includes referral fees, FBA fulfillment, storage, and inbound placement charges, six or more layers deep, and none of that shows up in the ACoS number itself. A reporting framework built only on ad metrics will always miss this.
The Five Data Buckets Your Reports Need to Connect
Before picking KPIs, get the underlying data in one place. Agencies scaling reporting in 2026 are converging on five connected data sources:
Reports built on advertising data alone will always look thinner than reports that connect all five. SmartScout's market and brand data covers the competitive intelligence layer without a separate tool, which is worth factoring in before you go build a custom data pipeline for it.
The Core KPIs
Two numbers should anchor every report:
TACoS (Total Advertising Cost of Sale) divides ad spend by total sales, both ad-attributed and organic, so it shows whether the account is growing overall or just shifting where sales come from. A healthy account-level TACoS runs 10 to 15% of total revenue for an established seller. New launches run higher, often 25 to 40% in the first one to three months, before organic rank compounds and the number comes down. Category sets a floor too: electronics tends to sit near 9%, while beauty and supplements run higher at 15 to 17%.
Contribution margin per SKU is the number TACoS can't give you on its own. It's selling price, minus cost of goods (product, freight, and prep), minus the Amazon referral fee, minus FBA fulfillment, minus storage. Two SKUs can carry the same TACoS and have completely different margins once that math runs. Report contribution margin alongside TACoS, not instead of it, so budget conversations are grounded in dollars, not just ratios.
Reporting Cadence
Not every metric needs the same attention. Daily checks catch problems before the client does. Monthly reports tell the growth story.
Amazon Performance Metrics & Cadence
Metric
Cadence
Primary Owner
Why
Buy Box status, stock levels
Daily
Account manager
Catches issues before they cost sales
ACoS by campaign
Weekly
PPC lead
Keeps bid decisions current
TACoS (account-level)
Monthly
Account lead
Shows whether growth is net-new or shifted
Contribution margin per SKU
Monthly
Account lead
Confirms growth is profitable, not just efficient
Share of voice / competitive position
Monthly
Strategy lead
Frames performance against the category
Building the Monthly Client Report
Structure the report so the client sees the verdict before the detail:
Executive summary. TACoS and contribution margin trend, one paragraph, no jargon.
KPI dashboard. The five metrics above, this month against last month and against the category benchmark.
What changed and why. Tie any TACoS or margin shift to a specific cause: a fee change, a stockout, a new competitor, a bid adjustment.
Next month's plan. Two or three specific actions, not a general strategy recap.
Agencies that skip straight to campaign-level detail lose the client's attention before they get to the part that matters. Lead with the verdict, then back it up. If your team is spending most of a reporting cycle exporting and reformatting data by hand, that's usually a sign the underlying tooling needs a second look rather than the framework itself.
SmartScout starts at $29/mo for two users and takes a market-first approach, built for agency pitching, wholesale, and category expansion work.
DataHawk is the strongest pick for multi-client management and white label reporting, but pricing is custom and billed annually.
Helium 10 and Jungle Scout are built for individual sellers and brands managing their own catalog, not agencies managing outside accounts.
Match the tool to the job: market intelligence for pitching new business, a white label workflow for high reporting volume, PPC depth for listing-focused work.
Most "best Amazon analytics tool" lists are written for individual sellers. An agency's needs look different. You're not researching one product catalog, you're managing a portfolio of client accounts, each with its own login, its own reporting format, and its own idea of what "good" looks like. The platforms below are the ones built (or genuinely adapted) for that reality: SmartScout, DataHawk, Helium 10, and Jungle Scout, plus a note on where a lighter-weight tool like Seller Board still makes sense.
What Agencies Need That Solo-Seller Tools Don't
A single-seller research tool answers "is this product worth selling." An agency tool has to answer that question for dozens of brands at once, then package the answer into something a client can open without a walkthrough call. That means:
Multi-account or multi-client workspaces, so your team isn't juggling separate logins per client.
Role-based permissions, so a junior analyst and a client stakeholder don't see the same thing.
White label or branded reporting, since the client is paying you, not the software vendor.
Market-level data, not just product-level data. Agencies pitch new categories and new clients constantly, and that requires seeing who controls a market before you ever touch a single ASIN.
The Best Amazon Analytics Platforms for Agencies
SmartScout's pitch to agencies centers on two things: winning new clients and then backing up the strategy once hired.
For pitching, the subcategory browser splits Amazon into thousands of narrow niches instead of broad categories. That lets an agency show a prospect their exact market share against specific named competitors rather than industry-wide averages. The brand and seller database supports outreach in a similar way. Agencies can filter by:
Estimated revenue
Product count
Review growth
So an agency offering product photography, for example, can pull a list of high-revenue brands with weak product images and pitch them directly.
The Traffic Graph is built for PPC and ASIN targeting. It maps how traffic flows between products through "frequently bought together" relationships. Enter a competitor's ASIN and it shows which products are sending them cross-sell traffic. Agencies export those ASINs into product-targeting campaigns, which usually run cheaper per click than bidding on broad keywords.
There's also a set of features tied to how Amazon search is shifting toward AI assistants like Rufus:
The AI Scorecard grades a listing against Amazon's newer semantic search model (COSMO) and flags where the metadata doesn't align with how the algorithm reads intent.
The AI Visibility Monitor tracks which outside websites get cited by LLMs like ChatGPT when generating answers within a given product category.
One limitation worth noting: SmartScout isn't built for daily financial reporting or P&L tracking. Agencies typically pair it with Sellerboard or Nova Analytics for that, and sometimes with Helium 10 for more granular keyword-level work. SmartScout's role is market intelligence, sizing up competitors, surfacing targeting opportunities, and giving agencies data to support what they tell clients.
DataHawk
DataHawk's positioning is different from tools like Helium 10 or Jungle Scout. Those platforms keep data locked inside their own interface. DataHawk hands data ownership back to the agency, collecting, cleaning, and standardizing multi-client data so the agency decides how to visualize and present it.
The core draw is uncapped access to raw data. Instead of manually pulling CSV exports, DataHawk's Connections tool streams live Seller Central, Vendor Central, and advertising data straight into an agency's own BI stack, whether that's Snowflake, BigQuery, Looker Studio, or Power BI. The dashboard templates are open source too, so internal developers can rewrite queries and build fully custom visualizations rather than working within someone else's fixed layout.
White-labeling is built in at a deep level:
Branded portals hosted under the agency's own subdomain, with logos and color schemes applied throughout
Role-based client logins, so each client sees their own live dashboard without static PDF exports and without visibility into other clients' data
There's also an AI layer for account monitoring. With dozens of accounts running at once, no account manager catches every issue manually, so DataHawk's machine learning sweeps for anomalies like a sudden keyword rank drop or a reseller undercutting the Buy Box, then flags it with a recommended fix before it hits the client's numbers.
On the channel side, DataHawk isn't Amazon-only. It blends Amazon and Walmart data into one dashboard and tracks organic share-of-voice across more than 20 regional marketplaces, which makes it easier to show a client who's actually winning top-of-search and ad placement over time.
Where it's weaker: deep micro-market research. It tracks keyword share-of-voice but doesn't map cross-traffic patterns the way SmartScout's FBT graph does, so agencies doing heavy market-mapping work often keep SmartScout in the stack alongside it. For pipeline-only needs, Supermetrics is a lighter alternative, though without DataHawk's built-in SEO tracking.
Overall, DataHawk functions less like a seller tool and more like an outsourced data engineering layer: it handles the extraction, cleaning, and multi-account database management so the agency's strategists can spend their time on actual performance work instead of wrangling exports.
Helium 10
Helium 10 positions itself as the tactical, day-to-day execution layer, distinct from SmartScout's market mapping and DataHawk's raw data pipeline. It's the toolset account managers actually use to optimize listings, audit PPC, track rank, and recover lost funds across client accounts.
The keyword and competitor research runs through Cerebro and Magnet. Cerebro is the standard tool for reverse-ASIN lookups, pulling the full organic, sponsored, and Amazon-recommended keyword profile for any competitor listing. Paired with real-time keyword tracking, agencies can show clients a live share-of-voice trend on the search results page rather than just claiming rankings improved.
For managing multiple accounts at once, the Insights Dashboard consolidates:
Total sales, revenue trends, and margins across every client token in one screen
Automatic flags for listing changes, Buy Box hijackers, or sudden rank drops, so managers catch problems before a client does instead of finding out after the fact
PPC is handled natively rather than through a separate tool. Helium 10 Ads maps TACoS (total ACoS) against organic revenue, so a manager can see how ad spend actually connects to bottom-line growth. The built-in AI reads search query performance data continuously and suggests bid adjustments and negative keywords, which cuts wasted spend without someone manually combing through reports.
There's also Market Tracker 360 for competitive intelligence at the market level. Agencies can build a custom competitive set for a client or prospect in under 30 minutes, and it tracks things like a new competitor entering the niche or a price change rippling through category-wide revenue share.
Where Helium 10 is weaker: raw data infrastructure. It has solid dashboards, but it's a closed ecosystem, not something that pipes data into an external BI stack. Agencies that need that go to DataHawk or Supermetrics instead.
Overall, Helium 10 works as the core daily suite: keyword tracking, listing optimization, PPC, and reimbursement recovery, all under one login instead of five separate subscriptions.
Jungle Scout
Jungle Scout's case for agencies rests on scale: once an agency is running dozens or hundreds of client accounts, it needs speed and pitch-ready market data instead of tools built for a single seller.
The core piece is Cobalt, its enterprise engine. It pulls category-level market share, digital shelf visibility, and 1P vs. 3P competitor tracking into one place, so an agency can show a prospective client exactly where they're losing ground to rivals rather than describing it in general terms.
Data accuracy is a big part of the pitch too. Jungle Scout has spent over a decade refining its sales estimates against a large GMV dataset, which matters when an agency is using those numbers for client forecasting or an audit pitch. Estimates that don't hold up under scrutiny undercut the whole pitch.
On the integration side, Jungle Scout connects out through its Model Context Protocol (MCP) and Cloud APIs. That lets agency teams pull marketplace data directly into custom dashboards, internal BI tools, or AI workflows using natural language queries, rather than manually exporting and reformatting data every time.
Day-to-day, a few things sit under one dashboard instead of requiring separate subscriptions:
Keyword Scout for finding keyword opportunities
Supplier verification
Ad performance tracking
Automated review requests
For new client acquisition specifically, Opportunity Finder and Product Tracker let an agency run a quick category audit and surface untapped revenue pockets to bring into a pitch meeting.
Overall, Jungle Scout leans into being the market intelligence and integration layer, useful both for landing new clients and for keeping data flowing into whatever reporting stack the agency already has.
Where a Lighter Tool Fits
Not every agency needs enterprise-grade market intelligence on day one. If your client roster is small and profit tracking is the main gap, a lightweight profit-and-loss tool can hold you over. Just plan to outgrow it once you're pitching new categories or managing more than a handful of accounts, since that's where market-level data starts to matter.
Quick Comparison
Amazon Agency Software Comparison
Platform
Starting Price
Built For
Agency-Specific Strength
SmartScout
$29/mo (2 users)
Brands, wholesale, agencies
Market-first data; Business plan built for small agencies
DataHawk
Custom (annual, credit-based)
Enterprise brands and agencies
Multi-client workspace; white label PDF and live share links
Helium 10
$99/mo
Private label sellers
Deep listing and PPC toolset
Jungle Scout
$49/mo
Brands and wholesale
Easiest learning curve
How to Choose
Match the tool to the actual job. If your agency pitches new categories, wholesale opportunities, or client expansion, market-level data matters more than listing tools, and SmartScout's approach to brands and sellers is built around exactly that. If you're running a high volume of client accounts and need polished, automated reporting on a recurring schedule, weigh DataHawk's white label workflow against the annual commitment. If your core service is PPC and listing optimization for a small number of brands, Helium 10 fits without the agency overhead you won't use. See the full platform comparison for how these tools stack up against the rest of the market.
If you sell on Amazon and you've spent any time in seller research tools, you've run into SellerSprite. It's a strong product, and it's the default choice for a huge number of sellers, especially those shipping in from China. The question isn't "which tool is better." It's "which tool answers the question you're actually asking."
SellerSprite is built to help you find products and keywords. SmartScout is built to help you understand a market: the subcategories inside it, the brands competing for share, and what those brands are spending to win.
Those are different jobs. Here's where each one wins.
What SellerSprite Is
SellerSprite started as a Chinese-first product built for cross-border sellers shipping into the US market, then expanded into English. It claims over a million sellers globally. It's the incumbent tool for a large share of Chinese sellers, and it covers a lot of ground.
Product research, keyword research, reverse ASIN lookups, competitor storefront and ASIN tracking, category insights, Amazon Brand Analytics search terms, ads insights, AI-driven review analysis, and supplier research all live under one roof.
Its buyer is mostly private label sellers, agencies, and PPC specialists. It's a seller-side tool, not a brand-side one, and that shows up in what it does and doesn't measure.
Where SellerSprite Genuinely Wins
Credit where it's due, because a fair comparison has to start here.
Chinese-native experience. Native language product, UI, documentation, and support. SmartScout has a zh-cn site, but support is in English.
Deeper keyword and reverse ASIN data, with weekly Amazon Brand Analytics data built directly into the product.
A free tier and a no-card trial. There's essentially zero friction to start using it.
A browser extension that mirrors most of the web app right on the Amazon product page.
Default incumbent status in China. For a lot of sellers, switching away from it means fighting habit, not features.
Review analysis and supplier research, two categories SmartScout doesn't touch at all.
If your job is finding a product to sell or the keywords to rank it, SellerSprite has real depth there. Its own comparison content even points sellers toward SmartScout for brand mapping, which tells you something about how the two products see themselves.
Where SmartScout Wins
The gap shows up in three places: subcategory data, brand data, and ad spend data. Any research tool can tell you a product looks promising. Few can tell you what percentage of a specific subcategory a brand controls, which named competitors hold the rest, and what each of them is spending to keep it that way.
Subcategory. SmartScout covers more than 40,000 subcategories, sized off the full Amazon catalog, with monthly share and growth history for every brand and seller inside each one. SellerSprite's category insights are sized from the top 100 products in a category, and its extension's BSR analytics cap out around the top 400.
Brand. In SmartScout, a brand is a trackable entity: revenue, share of subcategory, growth over time, catalog coverage, seller footprint, and ad spend, all rolled up together. In SellerSprite, brand functions as a filter or a lookup rather than something you can follow over time. There's no brand-level revenue or share trend.
Ad spend. SmartScout estimates spend at the keyword, product, brand, and seller level, alongside share of voice and organic versus sponsored rank. SellerSprite's Ads Insights covers competitor campaigns and placements, which is useful, but it's campaign-level data, not a brand or seller-wide spend rollup.
Put simply: SellerSprite's data resolves to a product and a keyword. SmartScout's resolves to a named subcategory, a named brand, a named seller, and a dollar figure.
Feature by feature
SmartScout vs. SellerSprite
Capability
SmartScout
SellerSprite
Subcategory coverage
40,000+, full catalog
Top 100 products per category
Subcategory share history
Monthly, by brand
No
Brand revenue and share
Yes, trackable over time
Lookup and filter only
Brand growth analysis
Yes
No
Ad spend by brand
Yes
No
Ad spend by seller
Yes
No
Competitor ad intelligence
Ad Spy: share of voice, organic/sponsored rank, spend
Ads Insights: campaigns and placements
All third-party sellers on an ASIN
Yes, with revenue and coverage
No
Buy Box share by seller
Yes
No
Buy Box by state
Yes
No
Storefront tracking
Yes, plus full seller database
Yes, tracked storefronts
Keyword and reverse ASIN
Yes
Yes, deeper database
ABA search terms
Historical Search Query Performance
Weekly ABA finder
TikTok Shop
Yes, linked to Amazon categories
No
API and data warehouse
Public API plus Snowflake
API packages only
MCP / AI agent access
Yes, Business tier and above
No
Review analysis
No
Yes, AI-driven
Supplier research
No
Yes
Chinese-language product
zh-cn site, English support
Fully native
Tool comparisons like this go stale fast. Both companies ship updates regularly, so treat this table as a starting point rather than a permanent scoreboard.
Price
SmartScout vs. SellerSprite Pricing
Tier
SmartScout
SellerSprite
Entry
Basic: $49/mo, $348/yr
Standard: $79/mo, $790/yr
Mid
Essentials: $119/mo, $1,188/yr
Advanced: $1,290/yr
Core
Business: $299/mo, $2,868/yr
VIP: $1,890/yr
Top
Enterprise, quoted
API packages, quoted
Free option
Trial by arrangement
Free tier plus free trial, no card required
SmartScout wins on entry-level price. At the mid and core tiers, SellerSprite's bundled annual plans carry a lower headline number. That's a real trade-off worth naming: SellerSprite bundles most of its feature set into each tier, while SmartScout's pricing gates by data depth. It's a harder plan to sell on price alone, but it's a more honest reflection of what each tier actually unlocks.
An Honest Read on the Common Objections
"SellerSprite is cheaper." At the entry level, it isn't; SmartScout's Basic plan starts lower. Once the comparison moves to the mid tiers, the more useful question isn't price, it's whether the two tools do the same job. Ask what percentage of a subcategory a brand holds today versus a year ago. Keyword data doesn't answer that.
"We already use SellerSprite." That's fine. Keyword research and product validation are strengths worth keeping. What it won't show is subcategory share, brand-level trends, and ad spend at the granularity described above. Plenty of sellers run both tools side by side rather than replacing one with the other.|
"The data is estimated." True of both tools, and true of the category broadly, since Amazon doesn't publish sales figures directly. SellerSprite's own content puts estimate accuracy in the mid-to-high 80s for high-volume categories, lower for thin niches. The more relevant distinction is what's estimated versus observed. Seller identity, Buy Box percentage, and seller count are observed data in SmartScout, not modeled.
"Does SmartScout support Chinese-speaking sellers?" There's a Chinese-language site at smartscout.com/zh-cn, but support is in English. SellerSprite is fully native. No point pretending otherwise.
Seven Questions Worth Asking Yourself
Whichever tool you're evaluating, these questions tend to expose whether your current research stack actually covers what you need:
What percentage of your subcategory did you own last year, and what do you own now?
Which named brands took the share you lost, and are they new entrants or incumbents?
What are your three largest competitors spending on Amazon ads, at the brand level?
How much of your category's ad spend goes toward your own branded search terms, and who's spending it?
Which adjacent subcategories are growing fastest, and are you positioned in them?
How many third-party sellers are listing your ASINs today, and how many are authorized?
If you wanted this data inside your own BI stack tomorrow, how would you get it?
If your current tool answers all seven, you may not need to add anything. If it doesn't, that gap is usually where subcategory, brand, and ad spend data end up mattering most.
The Bottom Line
SellerSprite is a deep, mature product research and keyword tool with a loyal base, particularly among cross-border sellers. It does that job well, and there's no reason to pretend otherwise. SmartScout is built for a different question: not "is this product worth selling," but "who controls this market, and what are they spending to keep it." If that's the question in front of you, subcategory, brand, and ad spend data are where to look.
SmartScout vs. SellerSprite: Which Amazon Research Tool Do You Need?
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