Comparing Amazon and Shopify revenue in one place means pulling order-level data from both channels into a single dashboard, normalized for fees, refunds, and currency, so you're looking at true net revenue side by side instead of two separate reports that don't speak the same language. Done manually, this is a recurring spreadsheet job. Done right, it's a live view you check in minutes.

Most multi-channel brands hit the same wall: Amazon Seller Central shows one number, Shopify's admin shows another, and neither one tells you which channel is actually more profitable once fees, ads, and returns are factored in.

Here are the seven steps that turn two disconnected dashboards into one number you can actually act on.

DEFINITION: Comparing Amazon and Shopify Revenue in One Place Comparing Amazon and Shopify revenue in one place means consolidating order, fee, refund, and ad spend data from both sales channels into a single view, so revenue, margin, and growth can be measured on the same basis. Without this, Amazon's gross sales figure and Shopify's gross sales figure aren't actually comparable, because each platform reports differently.

Why Doesn't Amazon's Revenue Number Match Shopify's, Even When Sales Are Similar?

Amazon's reported revenue and Shopify's reported revenue don't match because each platform defines "revenue" differently before you even get to analysis. Amazon Seller Central often shows ordered product sales, which can include orders not yet shipped or later cancelled, while Shopify typically shows revenue net of refunds at the point of sale.

Add in Amazon's referral fees, FBA fees, and advertising spend sitting in a separate report entirely, and you end up comparing a gross, pre-fee number on one side against a cleaner, more net-like number on the other. Brands that get this right normalize both channels down to the same definition, usually net revenue after refunds and before channel-specific fees, before making any comparison at all.

How Do You Get Amazon and Shopify Data Into the Same View?

You get both channels into the same view by connecting each platform's order-level data through an integration or export, then mapping both into a shared schema with matching fields: order date, gross revenue, refunds, fees, and net revenue. Without this shared schema, you're not comparing revenue, you're comparing two different spreadsheets that happen to sit next to each other.

Three practical paths, in order of manual effort:

  1. Manual export and merge. Download CSVs from Seller Central and Shopify admin, clean and align columns by hand. Works for a quick one-time check, breaks down as a repeatable weekly process.
  2. Native platform reporting. Some tools offer side-by-side views, but often at the gross revenue level, missing the fee and refund normalization that makes the comparison meaningful.
  3. Automated integration layer. A connected data platform pulls both channels automatically and keeps the comparison current without a manual export each week. This is whatShopify integrationandAmazon integrationconnectors are built to solve, since manually reconciling two channels every week is exactly the kind of task that eats the 10+ hours a week better reporting is meant to give back.

What Fields Do You Actually Need From Each Channel?

You need six fields from each channel at minimum: order date, gross revenue, refunds, channel fees, advertising spend, and net revenue. Skipping any one of these produces a comparison that looks complete but isn't.

  • Order date: Use the date the order was placed, not shipped, for consistent period-over-period comparison.
  • Gross revenue: Total sale value before any deductions.
  • Refunds: Deduct at the period they were processed, not the original order date, to reflect real cash impact.
  • Channel fees: Amazon referral fees and FBA fees; Shopify payment processing fees.
  • Advertising spend: Amazon PPC spend and Shopify-driven ad spend (Meta, Google, TikTok) attributable to that channel.
  • Net revenue: Gross revenue minus refunds, fees, and attributable ad spend. This is the number that should anchor any real comparison.

How Do You Account for Amazon's Fee Structure So the Comparison Is Fair?

You account for Amazon's fees by subtracting referral fees, FBA fulfillment fees, and storage fees from gross revenue before comparing it against Shopify's typically lower, simpler fee structure. Skipping this step is the single most common reason Amazon looks more profitable than it actually is next to Shopify.

A rough fee stack to account for on Amazon:

  • Referral fee: typically 8-15% of sale price, category-dependent
  • FBA fulfillment fee: a fixed per-unit cost based on size and weight
  • Monthly storage fee: charged per cubic foot, higher during Q4
  • Long-term storage fee: applies to inventory sitting more than 365 days

Shopify's comparable cost stack is usually simpler: payment processing (roughly 2-3%) and your own fulfillment cost, which you already control and can compare directly against Amazon's bundled fulfillment fee.

How Should You Handle Currency and Marketplace Differences?

You handle currency and marketplace differences by converting every figure to a single base currency using a consistent exchange rate source, and by separating revenue by marketplace or storefront before combining any totals. Combining a US Amazon marketplace with a UK Shopify storefront without currency normalization produces a number that means nothing.

If you sell across multiple Amazon marketplaces or a US and international Shopify setup, keep each geography visible as its own line before rolling up to a combined total. Collapsing them too early hides which specific market is actually driving growth or dragging on margin.

What's the Best Way to Compare AOV and Customer Behavior Across Both Channels?

The best way to compare AOV (average order value) across channels is to calculate it the same way on both sides, net revenue divided by order count, and to track it separately rather than blending it into one combined average. Amazon and Shopify customers often behave differently enough that a blended AOV hides more than it reveals.

Patterns worth watching separately by channel:

  • AOV by channel: Wholesale or subscription-heavy storefronts often show meaningfully higher AOV than a typical Amazon listing.
  • New vs. returning customer mix: Shopify typically gives cleaner visibility into repeat purchase behavior than Amazon, where customer relationship data is more limited.
  • Category or SKU mix: The same brand can perform very differently by category on Amazon versus its own store, which affects blended margin even when total revenue looks similar.

How Often Should You Update This Comparison, and Who Should Own It?

You should update this comparison weekly, and it should have one clear owner, usually the founder, a finance lead, or an ecommerce manager, rather than being split informally between whoever happens to check each platform. A comparison nobody owns tends to quietly stop happening within a month.

A simple weekly rhythm:

  1. Pull updated net revenue for both channels for the prior week.
  2. Check the growth rate of each channel against the same week last month and last year.
  3. Flag any fee or refund anomaly that's shifted more than 5-10% from the trailing average.
  4. Note the finding in one place, whether that's a shared dashboard or a short weekly summary, so decisions aren't based on memory of "how things felt" that week.

This is also where a live dashboard beats a recurring spreadsheet task. ABI reportingsetup, or an existingPower BIorTableauenvironment fed by a connected data layer, turns this weekly check into something you glance at rather than rebuild from scratch each time. AnAI agentlayered on top can flag the anomaly automatically instead of waiting for the weekly review to catch it.

What Mistakes Do Founders Make When Building This Comparison for the First Time?

The most common first-time mistake is building the comparison once for a board meeting or investor update, then letting it go stale because nobody owns keeping it current. A one-time comparison answers a single question well; it doesn't help you make decisions the following month.

Other mistakes worth avoiding:

  • Blending marketplaces too early. Combining US and international Amazon revenue, or multiple Shopify storefronts, into one number before checking each individually can mask a specific market that's underperforming.
  • Ignoring returns timing. Counting a refund in the month the original sale happened, rather than the month the refund was processed, distorts both months' numbers.
  • Comparing time periods inconsistently. Comparing a 30-day Amazon window against a calendar-month Shopify window introduces a gap before any real performance difference is even considered.
  • Treating advertising spend as separate from revenue. Amazon PPC and Meta or Google spend driving Shopify traffic are both real costs of generating that channel's revenue. Leaving them out of the net revenue calculation overstates both channels equally, but hides which one is actually more efficient.

Brands that get this right build the comparison once with these guardrails in place, then let it run on autopilot rather than rebuilding the same logic from memory every quarter.

How Do You Present This Comparison to a Team, Investor, or Board?

You present this comparison by leading with net revenue by channel, not gross, and by showing the trend over time rather than a single snapshot month. A single month's numbers invite questions about seasonality; a trend line answers most of those questions before they're asked.

A clean format that holds up in a board deck or a Monday team update:

  1. Net revenue by channel, trailing 6-12 months, as a simple line or bar comparison.
  2. Growth rate by channel, month-over-month and year-over-year, since raw dollar figures can look flat while growth rate tells the real story.
  3. Blended margin by channel, after fees and ad spend, to show which channel is actually more profitable, not just which one is bigger.
  4. One sentence of context per channel, noting anything unusual, a stockout, a fee change, a marketplace-specific promotion, so the numbers aren't read in a vacuum.

This is also where a shared, always-current dashboard earns its keep. Instead of rebuilding a deck slide from scratch each time, a liveBI reportingview means the same numbers a founder checks on a Tuesday morning are the ones that go straight into Friday's investor update, with no re-export or re-reconciliation in between.

Original Named Framework

THE CHANNEL PARITY CHECK: A revenue comparison across sales channels is only valid once both sides are normalized to net revenue, using the same fee, refund, and currency treatment.

Most Amazon-versus-Shopify comparisons fail this check without anyone noticing, because gross Amazon sales get compared against near-net Shopify sales, making Amazon look artificially stronger. Running the Channel Parity Check means confirming, before any comparison is drawn, that both channels have refunds deducted at the same point, fees fully accounted for, and currency normalized to one base. Brands that build this check into a recurring habit stop making channel-allocation decisions based on numbers that were never actually comparable in the first place.

Conclusion and CTA

Amazon and Shopify will never report revenue the same way on their own, and comparing them at face value tends to reward whichever platform's fee structure is easiest to overlook. A fair comparison means the same definition of revenue, the same fee treatment, and the same currency base on both sides, checked on a consistent schedule.

Trivas.ai connects all your store data in one place, pulling Amazon and Shopify into a single normalized view instead of two spreadsheets you have to reconcile by hand. See how Trivas.ai makes this effortless:explore the Insights module, check thegetting started guide, ortry Trivas.ai freeand get clarity on which channel is actually winning today. Want a walkthrough first?Get your demo.

FAQ Section

Q: Why does my Amazon revenue look higher than Shopify even though Shopify sells more units? A: Amazon Seller Central often reports gross ordered product sales, which can include orders not yet fulfilled or later cancelled, while Shopify typically shows revenue net of refunds. Comparing the two without normalizing for fees and refund timing makes Amazon appear stronger than its actual net contribution.

Q: What's the best way to compare Amazon and Shopify revenue in one place? A: Pull order-level data from both channels into a shared view with matching fields for gross revenue, refunds, fees, and net revenue, then compare net revenue on both sides. Platforms like Trivas.ai automate this by connecting both channels directly, removing the manual export-and-merge process.

Q: Should I compare gross revenue or net revenue between Amazon and Shopify? A: Always compare net revenue, meaning gross revenue minus refunds, channel fees, and attributable ad spend. Comparing gross figures overstates Amazon specifically, since its fee stack, referral fees, FBA fees, and storage costs, is typically higher and less visible than Shopify's simpler cost structure.

Q: How do Amazon fees affect a fair revenue comparison with Shopify? A: Amazon referral fees (roughly 8-15% depending on category) plus FBA fulfillment and storage fees can meaningfully reduce true profitability compared to what gross sales figures suggest. These fees need to be subtracted before comparing Amazon's performance against Shopify's typically lower and simpler fee structure.

Q: How often should I check my combined Amazon and Shopify revenue numbers? A: Check weekly rather than monthly. A weekly cadence catches fee spikes, refund anomalies, or channel shifts early, while a monthly check often means a full month passes before a meaningful change in channel performance gets noticed.

Q: Can I compare Amazon and Shopify revenue without hiring a data analyst? A: Yes. Tools like Trivas.ai are designed for founders and operators without a technical background, automatically connecting Amazon and Shopify data and normalizing it into one comparable view rather than requiring manual spreadsheet work.

Q: What causes AOV to look different between Amazon and Shopify? A: Customer behavior and pricing dynamics differ by channel, with wholesale or subscription-heavy storefronts often showing higher AOV than a comparable Amazon listing. Tracking AOV separately by channel, rather than blending it into one combined average, reveals patterns a single average would hide.

Ecommerce Analytics Use Case for Beauty Brands