Understanding Email Revenue Attribution: How It Works and Why It Matters

Email marketing is a powerful revenue driver, but attributing revenue to email campaigns isn’t always straightforward. If you’ve ever noticed differences between your email platform’s revenue reports and Google Analytics (GA), you’re not alone. Understanding email revenue attribution is crucial for making data-driven decisions, optimising your campaigns, and accurately measuring your return on investment (ROI).

Why Is Email Revenue Attribution Important?

Revenue attribution helps marketers understand how their email campaigns contribute to overall sales. Without proper attribution, you might undervalue email’s impact, leading to underinvestment in a channel that actually drives significant revenue. By tracking attribution accurately, you can:

• Optimise email performance based on actual revenue impact

• Justify email marketing spend and secure budget

• Align email metrics with business goals

• Improve customer journey analysis by understanding email’s role in conversions

How Email Revenue Attribution Works

Attribution models determine how credit for a sale is assigned to different marketing touchpoints. In email marketing, these models vary based on platform settings and business goals.

Types of Email Attribution Models

1. Last Open Attribution – The last email opened before a purchase gets full credit. This model is useful for engagement tracking but doesn’t account for other influencing factors.

2. Last Click Attribution – The email that was last clicked before the purchase gets full credit. This is common in many ESPs but can overemphasise the final interaction.

3. First Click Attribution – The first email interaction receives full credit. This highlights email’s role in initiating a purchase but ignores subsequent touchpoints.

4. Linear Attribution – Credit is distributed equally across all email interactions leading to a conversion.

5. Time Decay Attribution – More credit is given to interactions closer to the purchase event.

6. Position-Based (U-Shaped) Attribution – Credit is split, with more weight given to the first and last interactions, and the middle interactions receiving less.

Attribution Windows

Attribution windows define the timeframe in which a conversion is credited to an email interaction. Common settings include:

• 1-day window (short, best for impulse purchases)

• 7-day window (common for retail and eCommerce brands)

• 30-day window (useful for longer consideration cycles)

• Custom windows (adjusted based on business needs)

Differences in Attribution Across Platforms

Different platforms use varying attribution models and windows, leading to discrepancies in revenue reporting.

Example:

• Email Platforms (e.g., Klaviyo, Bloomreach, Braze) – Often use last-click attribution with a default 1–5 day window. This means if a customer clicks an email and purchases within that window, the revenue is attributed to email.

• Google Analytics (GA) – Uses a data-driven attribution model, which means it leverages machine learning to analyze a user’s entire interaction path across different channels and assigns credit for a conversion (like a purchase) based on the likelihood that each touchpoint contributed to the sale, considering factors like time between interactions, device used, and ad type, rather than simply giving all credit to the last click

• Paid Ads (Meta, Google Ads, etc.) – May have different attribution settings, such as view-through conversions (crediting impressions) or longer attribution windows (up to 90 days).

These differences can make email look more or less effective depending on the platform you’re analysing.

Why Do Email Revenue Attribution Differences Occur?

Several factors contribute to differences in revenue attribution across platforms:

• Attribution Windows: A 7-day window in your ESP vs. a last-click model in GA can create disparities.

• Cross-Channel Interactions: Customers interact with multiple channels before purchasing, making single-channel attribution models unreliable.

• Direct Traffic Issues: GA may classify some email-driven visits as direct traffic if UTM parameters are stripped or the customer manually enters the website.

• Cookie Restrictions & Privacy Changes: Apple’s Mail Privacy Protection (MPP) and browser tracking limitations impact attribution accuracy.

• Tracking Methodologies: ESPs use pixel tracking, whereas GA relies on session-based tracking, leading to mismatches.
  
  

Best Practices for Accurate Email Revenue Attribution

To improve your attribution accuracy and make informed decisions, follow these best practices:

1. Standardise UTM Tracking – Ensure all emails use consistent UTM parameters to track performance accurately in GA.

2. Compare Data Across Multiple Sources – Use both ESP and GA data to get a holistic view of email performance but keep your reports consistent (i.e. don’t switch between different source data for reporting)

3. Adjust Attribution Windows to Match Sales Cycle – Align your ESP attribution window with typical customer purchase behaviour.

4. Use GA4’s Attribution Reports – Google Analytics 4 provides multiple attribution models; experiment with them to see different perspectives.

5. Monitor Post-Purchase Engagement – Look beyond just the sale and track retention and repeat purchases from email.

6. Educate Stakeholders on Attribution Limitations – Explain discrepancies and set expectations accordingly when presenting data.
   
   

Criticism of Email Revenue Attribution

Despite its usefulness, email revenue attribution is not without its flaws:

• Over-Attribution to Email: ESPs often take credit for conversions that might have happened anyway via other channels.

• Under-Attribution in GA: GA may fail to recognise email’s role if the user interacts through multiple channels before converting.

• Lack of Multi-Touch Visibility: Single-touch models don’t account for the full customer journey.

• Privacy & Data Restrictions: Email tracking is becoming less reliable due to privacy changes, making attribution more complex.
  
  

Final Thoughts

Email revenue attribution is essential for understanding email’s impact on sales, but discrepancies across platforms can create confusion. By knowing how different models work, adjusting attribution settings, and taking a multi-source approach, you can gain more accurate insights into your email marketing effectiveness.

While no attribution model is perfect, the key is to be consistent, transparent, and aware of the limitations. Instead of relying solely on one system, use a combination of data sources and business context to make informed decisions.