Which attribution model should a Shopify store use?
Usually last non-direct click for weekly channel reports, because it names the marketing that brought a buyer back. Keep first click as a second view, and leave Meta and Google Ads on their own settings for bidding. No model shows what a channel caused.
By Joris van Huët, Founder & CEOUpdated 7 min read
Usually last non-direct click for your weekly channel reports, because it names the marketing that brought each buyer back. Shopify's report menus start on last click, which hands those sales to Direct. Keep first click as a second view, and leave Meta and Google Ads on their own settings for bidding.
What one store's data shows
The usual answer is to pick the cleverest model on the menu and move on. One store's export shows that the menu matters less than what sits outside it.
One store's anonymised GA4 export, 1 January 2024 to 21 August 2026. It holds shares of revenue only: no ad spend, no order counts.
| What the export shows | Share of revenue | Source cell |
|---|---|---|
| Direct, in last click, first click and touched views | 57.7% | Channels sheet, Direct row |
| Paid Social, in all three views | 0.0% | Channels sheet, Paid Social row |
| Journeys with 1 touch (0.5 days to buy) | 79.5% | Journeys sheet, 1 touch row |
| Journeys with 2 to 3 touches (12.5 days to buy) | 12.2% | Journeys sheet, 2 to 3 touches row |
On the Channels sheet, Direct holds 57.7% of revenue whether the first click, the last click or every touch gets the credit. When first and last agree like that, the pattern fits journeys that began and ended with Direct. Shopify's last non-direct click leaves a direct-only journey with Direct. So if this store's Shopify data looks the same, no model on the menu would shrink that row much.
On the Channels sheet, Paid Social holds 0.0% in all three views. Whether this store ran social ads at all, the export cannot say. Either way, no model can hand credit to clicks that never reached the record. If an ad platform reports sales that GA4 and Shopify never see, the model menu is the wrong place to look.
On the Journeys sheet, journeys with 1 touch hold 79.5% of revenue, with 0.5 days to buy. Whichever of Shopify's five models you pick, a one-touch journey sends all its credit to that one touch. One click has nothing to share.
The menu earns its keep on longer journeys. The Journeys sheet's 2 to 3 touch row holds 12.2% of revenue, at 12.5 days to buy. The row's 12.5 days sits well inside the 30 days Shopify allows before it resets a first interaction. So first click and last non-direct click can name different channels here, and that gap is worth reading.
What the export cannot show is what any channel caused. It splits revenue across the touches GA4 recorded, and every model only reshuffles that split.
Why does "use the default" mislead a Shopify store?
Because a Shopify store runs on five defaults at once, and they disagree by design.
- Shopify's Analytics reports start on Last click once a report pairs a sales metric with a marketing dimension. Last click counts direct visits.
- Shopify's marketing activity data defaults to last non-direct click, which skips direct visits.
- GA4 uses data-driven attribution for its key event reports unless you change it. Its models give direct visits no credit unless the whole path was direct.
- Google Ads uses data-driven for most conversion actions, and that model steers automated bidding.
- Meta counts conversions inside each ad set's attribution setting, usually 7-day click and 1-day view.
So one order can be Direct in a Shopify report and Email in Shopify's marketing data. GA4 can split it between Email and Paid Search, and Meta can claim it as well. Every tool is following its own help page. Nobody is lying, and nobody agrees.
The fix is not a cleverer model. It is one house model for the weekly report, written on the report, and never mixed with numbers made under another rule.
Last non-direct click usually makes the best house model. It names the marketing that brought a buyer back, instead of crediting the typed visit at the end. It also matches GA4's paid and organic last click, since Google's help calls the two names for the same model. That lets Shopify and GA4 argue on one rule.
Shopify's own help suggests using more than one model to reduce bias in how you read the data. First click is the natural second view: Shopify says it shows which channels generate top-of-funnel interest. Linear spreads credit evenly across every click, direct visits included. Any click hands out more credit than you have orders, so keep it for one channel at a time.
What can no model on Shopify's menu tell you?
It cannot see views. Shopify's models credit channels that customers clicked or visited. Meta can count a purchase made within a day of someone seeing an ad. That view leaves no click for Shopify or GA4 to credit.
It cannot reach past its window. Shopify resets a first interaction when a visitor buys nothing within 30 days of a session, and again after every order. For a repeat buyer, first click shows what brought them back this time, not what found them in the first place.
It cannot tell you what a channel caused. Every model divides the same sales among recorded touches. To learn what a channel adds, pause it in some regions, keep it running in others, and compare total sales. Incrementality testing explains the method.
What to do this week
- Put last click and last non-direct click side by side. From your Shopify admin, go to Analytics > Reports, filter Category to Marketing and open Performance by referring channel. In the Attribution menu, select both models. Pass: Direct shrinks under last non-direct click and you can see which channels fed it. Fail: Direct barely moves, so most direct sales had no earlier click Shopify could see, and tagging matters more than the model.
- Line GA4 up on the same rule. In GA4, click Advertising, then Attribution models under Attribution, and compare paid and organic last click with data-driven for purchases. Pass: paid and organic last click ranks your channels in the same order as Shopify's last non-direct click for that month. Fail: the order differs, so check tagging and lookback windows before you trust either tool.
- Read Meta by attribution setting. In Ads Manager, open the Columns: Performance menu and select Compare attribution settings, then choose 1-day click and 7-day click. Pass: most purchases already show at 1-day click, close to what Shopify credits to Facebook and Instagram. Fail: the total leans on longer windows or views, so judge Meta with a holdout, not with Shopify's menu.
Check the homework. Your GA4 Attribution paths export already holds the evidence. Causality Engine reads that one file and shows what each channel caused next to what last-click gave it, in 1 to 2 minutes, for €99 once (excluding VAT), refundable within 30 days. Check the homework
Sources, 1 October 2026: Marketing reports (Shopify); Measuring marketing performance (Shopify); Get started with attribution (Google); Scopes of traffic-source dimensions (Google); About attribution models (Google); About attribution models and attribution settings (Meta); Results (Meta); Ad Account Insights (Marketing API reference) (Meta); Compare attribution settings in Meta Ads Manager (Meta)
Related answers
Frequently asked questions
Why does Shopify show more Direct sales than GA4?
Usually because they use different rules. Shopify's reports start on Last click, which counts direct visits. GA4's models give direct visits no credit unless the whole path to the purchase was direct. Switch Shopify to last non-direct click and the two read closer, though tracking and windows still differ.When should a Shopify store look at first click?
When you want to know which channels introduce buyers. Shopify says first click shows which channels generate top-of-funnel interest. It resets a first interaction after 30 days without a purchase, and after every order, so for repeat buyers it shows what brought them back.Should Meta and Shopify use the same attribution model?
They cannot. Meta counts conversions inside each ad set's attribution setting, which can include views, while Shopify credits clicks and visits. Use Meta's setting to steer Meta's bidding and Shopify's last non-direct click to compare channels. Settle disputes between them with a holdout.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Causal AttributionCausal Attribution uses causal inference to determine which marketing touchpoints genuinely cause conversions, not just correlate with them.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- Google AdsGoogle Ads is an online advertising platform where advertisers bid to display ads, service offerings, and product listings.
- IncrementalityIncrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.