What is DataDoe and how does it work for Amazon vendors?

DataDoe is the live data layer for Amazon Vendor Central. It connects every vendor account, sales/traffic/inventory analytics, Net Pure Product Margin (NPPM), demand forecasting, repeat-purchase performance and market-basket co-purchase analysis into one reconciled source of truth. Then it makes that data queryable through a built-in dashboard and through Model Context Protocol (MCP), so any modern AI tool — ChatGPT, Claude, Cursor, GitHub Copilot, Codex, Gemini CLI — can read your vendor data natively. One workspace, every brand, every marketplace.

What is Net Pure Product Margin and how does DataDoe track it?

Net Pure Product Margin (NPPM) is Amazon's vendor-specific profitability metric: shipped revenue minus shipped COGS minus customer returns, expressed as a percentage of shipped revenue. DataDoe pulls NPPM at the ASIN level daily, separately for both manufacturing-retail and sourcing-retail models, then reconciles trends by brand, marketplace and time period. Surfaces drift before it shows up in your monthly Vendor Central report — letting vendor ops teams catch margin erosion early instead of after the next PO is already cut.

How to connect Vendor Central to ChatGPT, Claude, or any AI tool?

Connect your Amazon Vendor Central account to DataDoe, then enable the MCP server integration. Any MCP-compatible AI tool can then query your vendor data natively.

What is MCP and why does it matter for Amazon vendors?

Model Context Protocol (MCP) is the open standard for connecting live data sources to AI tools and agents. For Amazon vendors, it means your Vendor Central data becomes natively readable by every modern AI agent without custom integration work. DataDoe runs as an MCP server — you connect your Amazon accounts once, and from that moment every MCP-compatible AI (ChatGPT, Claude, Cursor, etc.) can query your vendor data securely, continuously, and in plain English. Set up once, available everywhere.

What Amazon Vendor Central data does DataDoe pull in?

DataDoe pulls every vendor data feed Amazon publishes: daily sales, traffic and inventory metrics by ASIN (57+ fields including NPPM, glance views, sell-through rate, vendor confirmation rate, average vendor lead time, customer returns, on-hand inventory, aged 90+ days sellable inventory, unhealthy inventory). Plus weekly Customer Demand forecasts with mean expected demand and 70/80/90% confidence levels per ASIN, monthly repeat-purchase performance per ASIN, and monthly market-basket co-purchase analysis ranked by combination score. Initial historical load goes back up to 735 days.

How is DataDoe different from Amazon Brand Analytics in Vendor Central?

Vendor Central's Amazon Brand Analytics gives you the underlying data — sales, traffic, NPPM, forecasting, repeat purchase, market basket — for free, but only inside Amazon's UI, account by account, marketplace by marketplace, with manual CSV exports. DataDoe gives you the way to actually use it: continuous sync without re-pulling, multi-account / multi-brand / multi-marketplace reconciliation, prebuilt dashboards out of the box, and AI-readable through MCP so any AI tool can query it. Same Amazon data, surfaced where vendor ops teams actually work.

Can DataDoe handle hybrid 1P + 3P (Vendor Central + Seller Central) accounts?

Yes — hybrid 1P + 3P is one of DataDoe's strongest use cases. Vendor Central, Seller Central and Amazon Advertising accounts unify into a single workspace. NPPM and 1P vendor performance reconcile alongside 3P seller profit on the same ASINs, in the same queries. Vendors who run both channels finally see which channel actually makes more money per SKU — without manually cross-referencing between two Amazon portals or building custom Excel pivots.

How to export Vendor data to BigQuery, data warehouse, or my own backend?

DataDoe provides REST API and typed SDK access to export vendor data including amazon_vendor_sales_traffic_and_inventory_by_child_and_date, amazon_vendor_forecasting, amazon_repeat_purchase_report, and amazon_market_basket_by_child_asin tables to your own data warehouse or backend.

Is my Amazon Vendor Central data safe with DataDoe?

Yes. DataDoe is Amazon-audited and SP-API compliant, with secure OAuth-based authentication, per-tenant data isolation and encrypted storage. Your vendor data is never sold, never used to train AI models, and only readable by AI tools you explicitly connect through MCP. You can disconnect any AI integration or revoke any Amazon Vendor account access at any time from within DataDoe.

Live NPPM

Net Pure Product Margin reconciled by ASIN, brand, marketplace

~5 min

Setup time for everything. Plus 30 sec to connect any AI tool you want.

735 days

Historical sales, traffic and inventory data on initial connection

1P + 3P

Hybrid sellers get Vendor and Seller Central in one workspace

Vendor Central as you see it today

NPPM in one place. Forecasts in another. Inventory health buried.

You log into Vendor Central for sales. ARA for traffic and search. Wait for monthly settlement to see real margin. Pull a CSV to figure out which ASINs have aged inventory at Amazon. Build a pivot to spot which SKUs Amazon's about to over-order. By the time the picture is clear, the next PO is already cut.

"What's our real Net PPM by brand with aged inventory drag?"
→ Two days, three exports, one analyst…

Vendor Central with DataDoe

NPPM, demand forecast, inventory health. One reconciled layer.

Vendor Central core data + sales/traffic/inventory by ASIN + Amazon's own demand forecasts + repeat-purchase patterns + market basket co-purchase — pulled into one schema your CFO and your AI both speak. Use the built-in dashboards out of the box, or plug any AI on top.

"Hey ChatGPT, give me NPPM by brand with aged inventory drag."
→ Live answer. 8 seconds. Full breakdown attached.

Get prebuilt vendor analytics

All the vendor analytics.
Already built.

Don't want to wire up an AI? You don't have to. DataDoe ships with a complete vendor analytics suite — NPPM tracker, inventory health monitor, demand forecast comparison, repeat customer analysis. Sign up, connect Vendor Central, get to work.

✓Daily morning brief — NPPM movement, inventory alerts, forecast gaps, repeat-customer trends summarized at 9am.
✓Vendor reports — NPPM, Sales & Traffic, Inventory Health, Demand Forecasting, Repeat Customers, Market Basket — every one with KPIs, charts and tables.
✓Inventory health monitor — sellable, unsellable, aged 90+ days, unhealthy inventory tracked at every Amazon FC.
✓Forecast vs actual — Amazon's weekly demand forecasts (with 70/80/90% confidence) compared against your real sell-through.
✓Scheduled Prompts & Exports — weekly NPPM briefs, monthly inventory health, AVC prep data delivered automatically.
✓Built-in Chat — ask the DataDoe app directly, no external AI tool to set up.

Use the dashboards. Use your AI. Use both. Same data layer powers all of it.

✦ Daily AI brief · 09:14

Good morning. NPPM up 1.8pp on Brand A — sourcing-retail driving the lift. €42K in aged 90+ days inventory on 7 ASINs at FRA1 — sell-through dropping. Demand forecast +18% next week vs last 3-week actual on AMZ-4421 — supply gap likely. Repeat customer rate +12% on Brand B — strongest co-purchase: AMZ-3107.

Vendor reports

Net PPM
Sales & Traffic
Inventory Health
Demand Forecasting
Repeat Customers
Market Basket

KPIs · last quarter

Net PPM14.2%▲ 1.8pp

Sell-through82%▲ 3pp

Aged 90+ inv€42K▼ 12%

Confirm rate96.4%▲ 2.1pp

Warning

Aged inventory creeping up on 7 ASINs at FRA1

Recommendation

Demand forecast +18% next week — flag supply chain

Opportunity

Cross-sell candidate: bundle AMZ-3107 with top SKU

Scheduled Prompts

Weekly NPPM by brand Mon 8:00
Inventory health check Daily 9:00
Forecast vs actual review Fri 16:00

Recurring Exports

Weekly NPPM → CSV Mon
Monthly inventory health → CSV 1st
AVC prep data → CSV Quarterly

Go further with AI integrations

Three things you can do when you connect an AI.

The dashboards cover the daily vendor work out of the box. The moment you want custom views, custom tools or work running on autopilot — connect any AI you already use. The same live data layer powers all three modes.

Ask.

Get an answer in seconds, in plain English.

Open the AI you already use, ask a vendor question, get a real answer pulled live from your reconciled Vendor Central data. No exports, no waiting.

"NPPM by brand last quarter, manufacturing vs sourcing retail?"
"Which ASINs have aged 90+ days inventory at Amazon?"
"Where's Amazon's forecast off vs our actual sell-through?"
"Which ASINs have the highest repeat-customer score?"

For: CPG ops, vendor managers, finance leads

Build.

Ship the internal tool you actually need, in days.

Custom NPPM dashboards, inventory health monitors, demand-forecast trackers, cross-sell discovery tools. Internal vendor tools your team always wished for, built over a weekend.

An NPPM dashboard built the way your CFO thinks.
An inventory-aging alert system per FC.
A market-basket bundle discovery tool.
A Slack bot pinging when forecasts diverge from actuals.

For: vendor teams with one good AI subscription

Automate.

Set agents running on your vendor business 24/7.

Async AI agents run scheduled tasks against your live data — overnight inventory health scans, weekly NPPM briefs auto-sent to the CPG director, alerts when sell-through drops.

Overnight aged-inventory scan with action queue.
Weekly NPPM brief auto-sent to your director.
Forecast-divergence alerts when supply mismatch builds.
Auto-flag when repeat-customer rate spikes on a SKU.

For: vendor teams ready to use AI as a 4th employee

Where the upside is hiding

Six questions every vendor should be asking on day one.

These are the questions every Amazon vendor should be able to answer cleanly — and almost none can today. Each one is a single chat away once DataDoe is connected, or a click into the right report if you're using the built-in dashboards.

NPPM movement

1 – 3 pp

margin shifts you should catch early

"Which ASINs are dropping in NPPM right now?"

Net Pure Product Margin moves quietly — Amazon doesn't flag it. By the time you spot it in the monthly report, you've already shipped a quarter of the damage.

Aged inventory

5 – 15%

of stock potentially stranded at Amazon

"What's sitting unsold 90+ days at Amazon FCs?"

Aged sellable inventory ties up working capital and signals weak sell-through. Spot it early, sell it through with promo or accept the markdown — instead of letting it become unhealthy stock.

Forecast gaps

10 – 25%

supply mismatch potential

"Where's Amazon's forecast off vs reality?"

Amazon publishes weekly demand forecasts with 70/80/90% confidence levels. Compared to your real sell-through, the gaps reveal supply chain mismatches before they become stockouts.

Lead time creep

3 – 10 days

added to vendor lead times unnoticed

"Which ASINs have lead times getting worse?"

Vendor lead times trend silently. A few days extra per PO compounds into stockouts and lost margin — visible only when you track the trend, not the snapshot.

Repeat customers

Top 10%

ASINs driving most repeat revenue

"List ASINs that with most repeat purchase"

Repeat customer score by ASIN — rare data Amazon does share. Tells you which products are retention engines vs one-time buys, so you know where to double down on supply and bundles.

Cross-sell discovery

3 – 5x

basket lift potential per pairing

"What's purchased with my top ASINs?"

Market basket co-purchase data shows the actual ASINs customers buy alongside yours. Bundle opportunities, complementary listings, virtual bundles — all hidden in this monthly data feed.

What's actually in DataDoe for Vendors

Every vendor signal Amazon shares.
Reconciled by ASIN.

DataDoe pulls every Vendor data feed Amazon publishes — sales, traffic, inventory health, vendor confirmation rate, lead time, sell-through, Net Pure Product Margin, demand forecasting (70/80/90% confidence), repeat-purchase performance, market-basket co-purchase analysis. Both manufacturing-retail and sourcing-retail models tracked side by side.

Then it stitches everything together. NPPM reconciled across both retail models. Inventory health (sellable, unsellable, aged 90+ days, unhealthy) by ASIN by FC. Demand forecasts compared against real sell-through. Multi-brand, multi-marketplace — unified into one schema your AI can actually query.

And we expose it three ways: MCP (so any modern AI tool can read it natively), REST API (for your own backends), and a typed SDK (so AI coding tools can build apps against it without inventing field names). Plus the built-in DataDoe app sits on top of the same layer — you don't have to use any of those endpoints to get value on day one.

Live sourcesRefresh

STSales, Traffic & Inventory by ASINDaily

FCWeekly Customer Demand ForecastContinuous

RPRepeat Purchase PerformanceMonthly

MBMarket Basket Co-PurchaseMonthly

$$M-retail + S-retail reconciliationLive

DataDoe appMCP serverREST APITyped SDKMulti-brand

Who is it for

Vendors that take their Net PPM seriously.

From a single 1P brand to a CPG portfolio across 12 marketplaces — DataDoe scales with the complexity. Use the built-in dashboards, your AI of choice, or both.

1P brand$1M — $50M revenue

Vendor-only brands and CPG operators

You're 1P at Amazon, juggling NPPM, inventory health and Amazon's forecasts every week. DataDoe surfaces everything before it shows up in the monthly report.

First quarter we caught aged inventory drift on 9 ASINs three weeks earlier than usual.

Hybrid1P + 3P combined

Hybrid vendors and sellers

You run Vendor Central and Seller Central side by side, with overlapping ASINs and constant questions about which channel is actually more profitable. DataDoe unifies both into one Net PPM view across the whole portfolio.

First time we've had 1P and 3P numbers in the same sentence — finally know which channel makes more on each ASIN.

Multi-brandCPG portfolios

CPG portfolios & vendor agencies

Multiple vendor accounts, multiple brands, multiple AVC negotiations. One DataDoe workspace pulls them all together. Just switch brand context in the app or in your AI.

Cross-brand NPPM trends we used to spot once a quarter, we now see in real time.

The math

From a stack of tools
to a single layer.

Most Amazon vendors run on a stack of tools — Amazon Brand Analytics, separate forecasting tools, inventory aging trackers, basket-analysis subscriptions, internal BI. Each with its own login, dashboard, roadmap and bill.

DataDoe replaces the data layer underneath all of them. The same questions, the same answers, the same custom views — built into the DataDoe app or built by your AI tool of choice on top of one unified workspace, on one subscription, with your data staying yours.

Manual Vendor Central exports10 – 30h/mo analyst

Forecasting & demand tools$500 – 2,000/mo

Inventory health monitoring$300 – 800/mo

Basket analysis & cross-sell tools$300+/mo

Repeat-purchase intelligence$500+/mo

Internal BI / reporting$500+/mo

↓ One layer instead ↓

$97/mo · 7-day free trial

FAQ

Q: How do I track Amazon's demand forecast accuracy as a vendor?

DataDoe pulls Amazon's weekly Customer Demand forecast continuously — mean expected demand plus 70%, 80% and 90% confidence levels per ASIN — and compares it against your real sell-through history. Forecast gaps reveal supply chain mismatches before they become stockouts or overstocks. Ask the built-in dashboard or any connected AI ("where's Amazon's demand forecast off vs actual sell-through?") and get a list of at-risk ASINs in seconds, with the magnitude of the gap.

Your Amazon Vendor Data.
Clean. Structured. Finally usable.

NPPM in one place. Forecasts in another. Inventory health buried.

NPPM, demand forecast, inventory health. One reconciled layer.

All the vendor analytics.
Already built.

Three things you can do when you connect an AI.

Ask.

Build.

Automate.

Six questions every vendor should be asking on day one.

Every vendor signal Amazon shares.
Reconciled by ASIN.

Vendors that take their Net PPM seriously.

Vendor-only brands and CPG operators

Hybrid vendors and sellers

CPG portfolios & vendor agencies

From a stack of tools
to a single layer.

FAQ

Set up in under 5 minutes.
Try free for 7 days. Then $97/month.

Set up in under
5 minutes.
Try free for 7 days. Then $97/month.

Your Amazon Vendor Data. Clean. Structured. Finally usable.

NPPM in one place. Forecasts in another. Inventory health buried.

NPPM, demand forecast, inventory health. One reconciled layer.

All the vendor analytics.Already built.

Three things you can do when you connect an AI.

Ask.

Build.

Automate.

Six questions every vendor should be asking on day one.

Every vendor signal Amazon shares.Reconciled by ASIN.

Vendors that take their Net PPM seriously.

Vendor-only brands and CPG operators

Hybrid vendors and sellers

CPG portfolios & vendor agencies

From a stack of toolsto a single layer.

FAQ

What is DataDoe and how does it work for Amazon vendors?

What is Net Pure Product Margin and how does DataDoe track it?

How to connect Vendor Central to ChatGPT, Claude, or any AI tool?

What is MCP and why does it matter for Amazon vendors?

What Amazon Vendor Central data does DataDoe pull in?

How is DataDoe different from Amazon Brand Analytics in Vendor Central?

Can DataDoe handle hybrid 1P + 3P (Vendor Central + Seller Central) accounts?

How do I track Amazon's demand forecast accuracy as a vendor?

How to export Vendor data to BigQuery, data warehouse, or my own backend?

Is my Amazon Vendor Central data safe with DataDoe?

Set up in under 5 minutes.Try free for 7 days. Then $97/month.

Set up in under 5 minutes.Try free for 7 days. Then $97/month.

Your Amazon Vendor Data.
Clean. Structured. Finally usable.

All the vendor analytics.
Already built.

Every vendor signal Amazon shares.
Reconciled by ASIN.

From a stack of tools
to a single layer.

Set up in under 5 minutes.
Try free for 7 days. Then $97/month.

Set up in under
5 minutes.
Try free for 7 days. Then $97/month.