Connect Amazon Seller Central to Looker Studio: A Guide

Last verified: June 2026

Most Amazon sellers are drowning in data they can't actually see. Seller Central's built-in reporting is functional enough for checking yesterday's orders, but it's nearly useless for spotting trends, comparing ad spend against margin, or sharing performance data with your ops team. Looker Studio changes that, but only once your Amazon data is flowing into it cleanly.

The catch: Amazon Seller Central isn't a native Looker Studio connector. You won't find it in the Google Connectors panel next to Sheets and BigQuery. Every method in this guide involves some kind of intermediary. The decision comes down to your scale, your budget, and how much engineering time you have.

Get this right and you end up with auto-refreshing dashboards showing sales velocity, inventory health, and ad ROI in one place. That means you can actually answer questions like whether to run a lightning deal or reorder a slow-moving SKU without exporting four spreadsheets first. If you're thinking about how this fits into a broader ops stack, the International CPG E-commerce Operations Stack is worth reading alongside this guide.

Before you start

You'll need an active Amazon Seller Central account with primary user or admin permissions, a Google account for Looker Studio, and a subscription to a third-party connector tool if you're going with Method 1. That's it.

Methods at a glance

Method 1: Third-party connector tool (the recommended route)

This is how most sellers actually do it, and honestly it's the right call for most operations. Tools like Power My Analytics, Windsor.ai, and Two Minute Reports sit between Seller Central and Looker Studio, handling the SP-API authentication and data transformation. Configure it once and your dashboard refreshes on whatever schedule you set.

1. Choose your connector tool. Power My Analytics, Windsor.ai, and Two Minute Reports all offer dedicated Amazon Seller Central connectors for Looker Studio. Compare their free tier limits, refresh frequency options, and the specific Seller Central metrics they expose — not all of them surface Inventory reports or FBA fee data by default. Most require a paid tier for scheduled auto-refresh.
2. Create an account with your chosen tool and navigate to its data source or connector setup panel.
3. Select Amazon Seller Central as your data source. The tool will redirect you to Amazon's OAuth login — sign in with your Seller Central credentials. Make sure you're logging in as the primary user or an account with admin permissions, otherwise the authorisation will fail silently.
4. Select your marketplace region carefully. If you sell on Amazon UK, Amazon DE, and Amazon US, these are separate marketplace IDs. Connecting the wrong one (e.g. amazon.com when you want amazon.co.uk) is the most common setup mistake. Select each marketplace you need — some tools let you blend multiple marketplaces into a single data source.
5. Choose your metrics and dimensions. Select the fields you want to pull: units ordered, ordered product sales, sessions, Buy Box percentage, inventory levels, ad spend, and so on. Start with a focused set — you can always add more later without rebuilding from scratch.
6. Set your refresh schedule. Daily refresh is sufficient for most reporting; hourly is available on higher-tier plans if you need near-real-time data for things like flash sale monitoring.
7. Open Looker Studio at lookerstudio.google.com and click + Create → Report.
8. Click "Add data" in the report editor, then scroll to the Partner Connectors section. Search for your chosen tool by name.
9. Authorise the connector to access your Google account, then select the Amazon Seller Central data source you configured in steps 4–6.
10. Click "Add to Report." Your Amazon data fields will now appear in the Looker Studio field panel on the right. Drag fields onto charts to start building.

A practical example: a seller pulling 90 days of sales data by ASIN, with sessions and conversion rate alongside revenue, can spot which listings are getting traffic but not converting — a signal to fix the listing rather than increase ad spend. That kind of analysis is nearly impossible to run manually inside Seller Central.

Method 2: Google Sheets intermediary (for small-scale needs)

No budget for a connector tool right now? This works — with caveats. Manual exports are time-consuming and your data goes stale the moment you download it. So this method is only really defensible for occasional one-off reporting, or if you're testing a dashboard structure before committing to a paid connector. Google Sheets is a native Looker Studio connector, which is the useful part.

1. Log into Seller Central and navigate to Reports → Business Reports (for sales data) or Reports → Fulfillment (for inventory data).
2. Select your report type and date range. The "Sales and Traffic by ASIN" report is usually the most useful starting point for a sales dashboard.
3. Click "Download" to export the report as a CSV file.
4. Open Google Sheets and import the CSV via File → Import → Upload. Use "Replace spreadsheet" if you're refreshing an existing sheet, or "Insert new sheet" if you want to preserve historical tabs.
5. Clean the data. Amazon's CSVs often include header rows with metadata above the actual column headers — delete these so row 1 is your column headers and row 2 is your first data row. Looker Studio needs clean, consistent column headers to map fields correctly.
6. Open Looker Studio and click + Create → Report.
7. Click "Add data" and select Google Sheets from the Google Connectors panel.
8. Authorise access to your Google account, then select the spreadsheet and the specific sheet tab containing your Amazon data.
9. Click "Add to Report." Map your fields and build your charts. Note: you'll need to repeat steps 1–5 every time you want fresh data.

If you're doing this more than once a month, just get a connector tool. The time cost of manual exports adds up fast, and decisions made on two-week-old data aren't really data-driven decisions at all. A seller checking reorder points for 200 SKUs against a spreadsheet from ten days ago is flying blind for a meaningful window of time — and that's exactly the kind of gap that causes stockouts.

Method 3: SP-API to data warehouse (for large-scale operations)

The most powerful method here also happens to be the most involved. If you're processing thousands of orders a day across multiple marketplaces, or you need to join your Amazon data with data from other systems (your ERP, your 3PL, your ad platforms), the Selling Partner API feeding into BigQuery is the right architecture. BigQuery connects natively to Looker Studio, so once your data is in the warehouse, the visualisation layer is relatively straightforward.

1. Register as an Amazon developer by going to Seller Central → Apps & Services → Develop Apps. Complete the developer registration form — this requires a legitimate use-case description. Amazon reviews applications, so allow a few days for approval.
2. Create a new app in the Developer Console and request access to the SP-API roles you need (e.g. Orders, Reports, Inventory). See the SP-API documentation for the full list of available roles and their access requirements.
3. Generate your LWA (Login with Amazon) credentials — client ID and client secret — from the app's credential panel. Store these securely; you'll need them for OAuth token requests.
4. Authorise your selling account to use the app via the Seller Central authorisation flow. This generates a refresh token tied to your account.
5. Set up a Google Cloud project and enable the BigQuery API in the Google Cloud Console.
6. Write or deploy your data pipeline. This is where the real engineering work sits. Your pipeline needs to authenticate with SP-API using your LWA credentials, request the reports or feeds you need (e.g. GET_SALES_AND_TRAFFIC_REPORT, GET_FBA_MYI_UNSUPPRESSED_INVENTORY_DATA), poll for report completion, then download, parse, and write the structured data into BigQuery tables. Python works well for this; Google Cloud Functions handle scheduling, and Secret Manager handles credential storage.
7. Define your BigQuery schema to match the fields coming out of SP-API. Use partitioned tables by date if you're loading daily reports — this keeps query costs manageable as data accumulates over months.
8. Open Looker Studio and click + Create → Report → Add data.
9. Select BigQuery from the Google Connectors panel, authorise access, and choose your project, dataset, and table.
10. Click "Add to Report" and build your dashboard on top of the warehouse data. Because BigQuery handles the heavy lifting, you can now run calculations and joins directly in the data source using custom queries.

This setup makes sense when you're doing the kind of reporting covered in multi-channel inventory forecasting — where you need Amazon data alongside Shopify, eBay, and wholesale order data in a single view. It's also the foundation for serious dynamic safety stock modelling. But if you're a team of five, this is almost certainly overkill. Be realistic about the engineering time you actually have before you start down that path.

Building your first Amazon sales dashboard in Looker Studio

Honestly, most teams overthink the first dashboard. Start with four things and add from there once you know what questions actually come up in weekly ops reviews.

A scorecard row at the top showing total revenue, units ordered, sessions, and conversion rate for the selected date range — with date range controls so anyone can adjust the window without touching the underlying data. Below that, a time series chart of daily or weekly revenue and units ordered over the past 90 days; this is the first place stockouts and sales spikes become visible. Then a table by ASIN covering revenue, units ordered, sessions, Buy Box percentage, and conversion rate per product, sorted by revenue descending — that's where you spot which ASINs are dragging down overall conversion. Finally, if you sell across multiple Amazon marketplaces, a bar or pie chart splitting revenue by marketplace makes clear where growth is actually coming from.

For brands thinking about how this fits into a broader reporting stack, the Sustainable Fulfillment Tech Stack and the B2B Wholesale Operations Stack both cover how reporting layers typically sit within the wider ops architecture.

Automating your Amazon reporting for better inventory management

A report that refreshes daily means your team is looking at today's sell-through rates, not last Tuesday's. That cadence matters more than people realise. One stale spreadsheet at the wrong moment and you're explaining to a brand founder why a top-selling ASIN went out of stock over a bank holiday weekend.

Add an inventory health tab to your dashboard. Units available, days of stock remaining (units available ÷ average daily sales), and a flag for any ASIN with fewer than 30 days of stock. Your ops team gets a daily reorder signal without anyone pulling a report manually. Pair that with proper multi-channel inventory forecasting and you have a real early-warning system for stockouts, not just a prettier version of the same spreadsheet you were already ignoring.

Fashion and apparel brands have an extra layer to think about here — seasonal sell-through rates and return volumes both belong in the reporting stack. The omnichannel fashion tech stack covers where Amazon reporting fits alongside other sales channels for apparel-focused operations.

Common errors and how to fix them

No native connector available in Looker Studio

Amazon Seller Central doesn't appear in Looker Studio's native Google Connectors panel because it simply isn't one. Don't waste time looking for it there. Go straight to the Partner Connectors section and search for your chosen third-party tool, or use the Sheets or BigQuery native connectors as your intermediary layer.

SP-API data is returned but fields are unreadable or mismatched

The raw SP-API response for reports like GET_SALES_AND_TRAFFIC_REPORT is structured JSON or tab-delimited text with Amazon-specific field names that don't map cleanly to what you'd expect. Consult the SP-API report type documentation for the exact schema of each report type before you design your BigQuery table. Skip this step and guess at column mappings, and you'll end up with sessions data in a revenue column.

Manual export data is out of date by the time the dashboard is viewed

This isn't a technical error — it's a structural limitation of the Sheets method. Data in a manually exported CSV is stale the moment you download it. If your team is making restocking decisions on data that's more than a day or two old, switch to a connector tool with automated refresh. The manual method is fine for retrospective analysis; it's not suitable for operational decision-making.

Wrong marketplace connected during setup

Amazon splits its marketplace into distinct regional entities — amazon.co.uk, amazon.de, amazon.fr, amazon.com, and so on. If you're seeing data that doesn't match your actual sales (or no data at all), the most likely cause is that you've authenticated against the wrong marketplace. Go back to your connector tool's data source settings, check the marketplace selector, and reconnect with the correct region. Third-party connector tools handle this at the account level, but you still need to explicitly select each marketplace you want included.

Frequently asked questions

How do I get Amazon Seller data into Looker Studio?

You need an intermediary. The three options are a third-party connector tool (like Windsor.ai, Power My Analytics, or Two Minute Reports), a manually exported CSV loaded into Google Sheets, or a custom SP-API pipeline feeding into BigQuery. For most sellers, a third-party connector is the most practical starting point — setup takes under an hour and refresh is automatic.

Can you connect Amazon Seller Central directly to Looker Studio?

No. Full stop. Amazon Seller Central is not a native Looker Studio connector, and there's no direct one-click connection available from within Looker Studio's standard interface. Every working method requires an intermediary: a paid connector tool, Google Sheets, or a data warehouse like BigQuery.

What are the best connectors for Amazon Seller Central and Looker Studio?

Power My Analytics, Windsor.ai, and Two Minute Reports are the most commonly used third-party connectors for this integration. Each handles SP-API authentication automatically and surfaces Amazon metrics directly inside Looker Studio's Partner Connectors panel. Before committing, compare them on metric coverage (not all surface FBA fee data), refresh frequency, and plan pricing — what's right depends on which Seller Central data types you actually need and how often your team consults the dashboard.

What kind of reports can I build by connecting Amazon Seller Central to Looker Studio?

Quite a lot. Sales performance by ASIN and marketplace. Advertising ROI. Buy Box win rate. Inventory health and days-of-stock-remaining. Session and conversion rate trends. FBA fee analysis. Custom date ranges and filters by product category. Blended multi-marketplace views. None of that is easily achievable inside Seller Central's native reporting, which is the whole point of going through this setup in the first place.

Which method should you pick?

Running a live e-commerce operation and you want a dashboard your team will actually use? Go with a third-party connector. Setup takes under an hour, the data refreshes automatically, and you don't need a developer. The Sheets method is fine for a one-time analysis or for testing a dashboard layout before you commit — but it won't hold up as an operational tool. The SP-API to BigQuery route is genuinely powerful for teams managing hundreds of SKUs across multiple channels, particularly if you're already thinking about how your data flows fit into a wider cross-border e-commerce stack. Just make sure you have the engineering time to build and maintain the pipeline. We've watched teams spend a quarter on this infrastructure when a £99/month connector would have done the job. Know which situation you're actually in before you start.