Ecommerce Marketing Attribution: Which Model Is Right for Your Store?

If you're relying on last-click attribution to make marketing decisions, you're probably underspending on the channels that are actually building your business and overspending on the ones that just happen to show up at the end.

Attribution is one of the most misunderstood areas of ecommerce analytics — and one of the most consequential. The model you use determines which channels get credit for a sale, which shapes your budget allocation, and ultimately drives your growth decisions. Getting it wrong is expensive.

This guide explains how the main attribution models work, what their trade-offs are, and how to think about attribution for your ecommerce store.

What Is Marketing Attribution?

When a customer buys from your store, they've typically had multiple interactions with your brand before converting. They might have:

1. Discovered you through a Google Shopping ad

2. Come back the next day via organic search

3. Received a promotional email

4. Clicked a Facebook retargeting ad

5. Come back directly and purchased

Which of those five interactions gets credit for the sale? That's what attribution is answering.

The model you use dramatically changes what the data tells you — and therefore what decisions you make.

The Main Attribution Models

Last-Click Attribution

The last touchpoint before the purchase gets 100% of the credit. In the example above, direct traffic gets all the credit.

Where it's still used: Google Ads historically defaulted to this. Many platforms still use it.

The problem: Last-click systematically undervalues upper-funnel channels (SEO, paid social, display) that initiate discovery, and overvalues bottom-funnel channels that capture intent that was already built elsewhere. It makes Google Shopping and paid search look more effective than they often are, and makes social and organic look less effective.

First-Click Attribution

The first touchpoint gets 100% of the credit. In the example above, the Google Shopping ad gets all the credit.

Where it's useful: Understanding which channels are best at driving discovery and acquisition.

The problem: Ignores all the touchpoints that influenced the decision between discovery and purchase. A customer might discover you through social, but if they only convert after an email flow nurtures them, first-click misses the conversion mechanism entirely.

Linear Attribution

Credit is distributed equally across all touchpoints. In a five-touchpoint journey, each gets 20%.

Where it's useful: A more balanced view that at least acknowledges multiple interactions matter.

The problem: Treats all touchpoints as equally important, which isn't accurate. The ad that triggered initial discovery and the remarketing ad that brought them back to convert probably don't deserve equal weighting.

Time-Decay Attribution

Touchpoints closer to the conversion get more credit. The last interaction before purchase gets the most credit; the first gets the least.

Where it's useful: Short buying cycles where recency is genuinely more predictive of conversion intent.

The problem: Similar to last-click, it undervalues the channels that build awareness and initiate the buying process. For stores with longer consideration periods, this model skews toward bottom-funnel.

Position-Based (U-Shaped) Attribution

40% of credit goes to the first touchpoint, 40% goes to the last, and the remaining 20% is distributed evenly across the middle interactions.

Where it's useful: Balances the importance of acquisition (first touch) and conversion (last touch) while acknowledging middle interactions. Often a good starting point for ecommerce.

The problem: Still somewhat arbitrary — why 40/20/40? The weightings aren't based on your actual customer data.

Data-Driven Attribution

Uses machine learning to analyse your actual conversion paths and assign credit based on which touchpoints statistically increase the probability of conversion.

Where it's available: GA4 (for stores with sufficient data), Google Ads (for accounts with sufficient conversions).

Why it's the best option (when available): The credit allocation is based on your real customer behaviour, not a predetermined formula. It's the closest thing to understanding how your marketing actually works.

The Attribution Model Google Ads Uses (And Why It Matters)

Google Ads has moved to data-driven attribution as the default for most accounts, replacing last-click. This is a meaningful change — it means Google now assigns credit for conversions across multiple touchpoints in the customer journey rather than just the last ad click.

The practical implication: channels that influence the path to conversion (Display, YouTube, upper-funnel Shopping) now get credit in Google Ads reporting, whereas before they would have appeared to drive zero conversions under last-click. This changes how you should interpret your campaign performance data.

If your Google Ads account is still using last-click attribution, switch to data-driven under the Conversion Actions settings.

The GA4 Attribution Picture

GA4 uses data-driven attribution by default for its reporting. But here's something many ecommerce marketers miss: the attribution model used in GA4 and the attribution model in your individual ad platforms are different views of the same reality.

Your Facebook Ads manager counts a conversion differently from GA4, which counts it differently from Google Ads. This is why your reported numbers rarely match across platforms — each is applying its own attribution logic and counting window.

To get a coherent picture:

• Use GA4 as your source of truth for cross-channel performance

• Accept that platform-reported numbers will always be higher than GA4 (they all over-report because they each claim credit)

• Focus on trends and relative performance rather than absolute numbers when comparing channels

Multi-Touch Attribution: The Practical Reality

For most ecommerce stores in NZ and Australia, the most practical approach is:

1. Use GA4 with data-driven attribution as your primary reporting layer

2. Look at assisted conversions (in GA4: Advertising → Attribution → Model comparison) to see which channels influence the path even when they're not the last touchpoint

3. Don't make budget decisions based on platform-reported ROAS alone — Facebook's reported ROAS includes conversions that GA4 attributes elsewhere

The assisted conversion report is particularly valuable. It often reveals that a channel you're considering pausing (because its direct conversion numbers look weak) is actually touching 40% of all converting paths before a purchase happens elsewhere.

Practical Attribution by Channel

Paid Search (Google Shopping / Search Ads) Typically the last or second-to-last touchpoint for high-intent purchases. Strong at capturing existing demand. Data-driven attribution is appropriate. Track ROAS in Google Ads but cross-reference with GA4.

Paid Social (Meta / Instagram) Primarily an upper-funnel and remarketing channel. It will almost always look weak under last-click. Look at 7-day view-through conversions plus click conversions together. Compare assisted conversion data in GA4 to understand its real contribution.

Organic Search (SEO) Often the first or middle touchpoint. SEO drives awareness and consideration-stage traffic that converts via other channels later. Don't evaluate SEO by direct conversions alone — look at what percentage of all converting paths included an organic visit.

Email Typically a mid-to-late funnel channel for stores with an engaged list. Email tends to look strong under any attribution model because it directly triggers purchases. Monitor revenue-per-recipient in Klaviyo and compare attributed revenue in GA4.

Setting Up Attribution Correctly in GA4

If you haven't set up GA4 ecommerce tracking properly, no attribution model will give you accurate data. The foundation matters more than the model.

Before thinking about attribution, confirm:

• GA4 is installed and firing on all pages

• Ecommerce events are being tracked (view_item, add_to_cart, begin_checkout, purchase)

• The purchase event is passing the correct revenue and transaction ID values

• You have Google Ads linked to GA4 and conversion actions imported

Without accurate purchase tracking, attribution is an exercise in reading the wrong map.

One Attribution Model Is Never the Full Story

The honest reality is that no single attribution model perfectly captures how your marketing drives revenue. Customers don't follow linear paths. They discover your brand, forget about it, see a remarketing ad months later, sign up for your email list, and eventually buy when the timing is right.

What attribution gives you is a practical framework for making resource allocation decisions — not a perfect truth. Use it to directionally understand which channels are contributing, apply some healthy scepticism to any platform's self-reported numbers, and make decisions based on trends and relative performance rather than absolute credit assignment.

If you're not confident that your analytics setup is giving you an accurate picture of where your revenue is actually coming from, our analytics and attribution service starts with an audit of your tracking implementation before we touch any of the marketing interpretation.