Selling on Amazon and Your Own Store: Your ads sell on Amazon and your store, but each only sees half the picture. That gap is where budget leaks.
Read the full article below for detailed insights and actionable strategies.
The attribution problem
One sale. Four channels. 400% credit claimed.
Reported revenue: €400 · Actual revenue: €100 · Gap: €300
Two Storefronts, One Confused Ledger
When you sell on both a marketplace and your own store, an ad can drive a sale on the surface it does not report, so each channel undercounts or overcounts and the true picture falls into the gap. That blind spot is expensive and quiet.
A shopper sees your ad, then buys on Amazon because they trust the checkout, or the reverse. Your own-store attribution never sees the marketplace order. The marketplace never sees the ad. Each system is honest and each is half-blind.
Why Blended Metrics Miss It
Brands paper over this with blended reporting, but blended numbers hide exactly the interaction that matters: cross-surface sales caused by a channel that neither surface credits. It is a bigger version of the disagreement we unpack in Meta ROAS versus Shopify revenue, except now a whole storefront is off the books.
The danger is scaling a channel that looks weak on-site while it quietly drives marketplace orders, or killing one that looks strong on-site while it cannibalizes sales you already had.
Measure Total Incremental Revenue, Not Per-Surface Credit
The way out is to stop asking each storefront to grade the ads and instead measure total incremental revenue across both. A causal read on your combined analytics estimates what your marketing actually caused at the brand level, not what each surface managed to see.
You cannot allocate budget well across two storefronts while each one is looking at half the field.
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Key Terms in This Article
Analytics
Analytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
Attribution
Attribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
Incrementality
Incrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
Metrics
Metrics are quantifiable measures that track and assess business process status. They evaluate campaign performance and inform attribution analysis.
Revenue
Revenue is the total income generated by the sale of goods or services related to a company's primary operations.
Shopify
Shopify is an ecommerce platform for creating online stores and selling products. Attribution modeling shows which marketing channels drive traffic and conversions within Shopify.
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Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Frequently Asked Questions
Why is attribution harder when selling on both Amazon and your own store?
When you sell on both a marketplace and your own store, an ad can drive a sale on the surface it does not report, so each channel undercounts or overcounts and the true picture falls into the gap.
How do you measure it?
Upload your Google Analytics export and a causal attribution read estimates each channel's incremental contribution with a confidence score, so you can see total incremental revenue across both storefronts, not per-surface credit instead of guessing.