Marketing attribution for e-commerce

Marketing attribution is the process of identifying which marketing touchpoints contributed to a conversion (purchase, signup, lead).

A typical e-commerce purchase journey involves 5 to 8 interactions: a Meta ad seen on Instagram, a Google search, an email click, a display retargeting ad, then a direct purchase. Which channel deserves credit for this sale?

The answer depends on the attribution model chosen. And the model choice directly influences your marketing investment decisions. A poor model can lead you to over-invest in an underperforming channel and under-invest in a profitable one.

The main attribution models:

- Last click: 100% credit to the last click before purchase. The historical and simplest model. Favors conversion channels (brand search, retargeting).

- First click: 100% credit to the first touchpoint. Values discovery channels (Social, Display).

- Linear: credit distributed equally across all touchpoints. Simple but does not reflect the relative importance of each interaction.

- Time decay: more credit to interactions closer to conversion. A good compromise between first and last click.

- Data-driven (Google Analytics 4): algorithmic model based on your actual site data. The most accurate but requires sufficient data volume.

- Position-based (40/20/40): 40% to the first contact, 40% to the last, 20% distributed among intermediaries. Values both discovery and conversion.

Since iOS 14.5 (April 2021), Apple requires explicit user consent for cross-app tracking. Result: 75 to 85% of iOS users decline tracking.

Consequences for attribution: - Meta Ads conversion windows went from 28 days to 7 days by default. - Conversion data reported by platforms is modeled (estimated), not measured. - Multi-touch attribution has become nearly impossible via client-side pixels. - Retargeting audiences are smaller and less precise.

The gradual phase-out of third-party cookies (announced by Google for Chrome) will amplify these trends. Attribution based solely on browser tracking is a model becoming obsolete.

The solutions: Conversions API (server-side tracking), first-party data, and incremental measurement.

First-party data (data you collect directly from your customers) is becoming the backbone of post-cookie attribution.

Your first-party data in e-commerce: - Complete purchase history (orders, products, amounts) - Behavioral data (pages visited, add-to-carts) - CRM data (email, phone, address) - Transaction data (payment method, frequency)

How to leverage it for attribution: 1. Send your conversion data server-side via Conversions APIs (Meta CAPI, Google Enhanced Conversions). The signal is more reliable than the browser pixel.

2. Create Custom audiences based on your customer segments (RFM, cohorts). These first-party audiences outperform interest-based audiences because the signal is based on actual purchase behavior.

3. Measure incrementality by channel by comparing periods with and without ad spend on each channel.

1. Do not rely on a single attribution model. Cross-reference platform data (Meta, Google) with your CMS data for a complete picture.

2. Adopt MER (Marketing Efficiency Ratio) as your primary metric. MER = Total Revenue / Total Marketing Spend. This metric bypasses attribution problems because it does not try to attribute each sale to a channel.

3. Measure profit, not revenue. A channel with a 3x ROAS but low margins can be less profitable than a channel with a 2x ROAS but high margins. POAS (Profit on Ad Spend) is more relevant than ROAS.

4. Invest in server-side tracking. Meta and Google Conversions APIs improve data quality by 20 to 30%.

5. Test incrementality. Cut a channel for 2 weeks and measure the real impact on your total sales. This is the most reliable method for evaluating a channel true contribution.

Fullmetrix complements your attribution stack by adding the profit dimension. You know not only which channel generates sales, but more importantly which generates profit.