Data aktualizacji: 18.11.2025

Discrepancies in data collected by Google Analytics 4 (GA4) and affiliate tracking systems have raised concerns among advertisers, publishers, and affiliate networks. The new attribution model in GA4, based on Data-Driven Attribution (DDA), may favor Google-owned channels, potentially compromising the objective evaluation of affiliate campaign performance.

In this article, we analyze data discrepancies and the specific reasons why affiliate channels are missing from GA4 reports in the customer purchase journey (Customer Journey). Let’s start with the fundamentals — what we know about attribution and its significance in marketing.

What Is Attribution and Why Does It Matter in Online Marketing?

Attribution in marketing is the process of evaluating and assigning value to marketing activities to understand their impact on a potential customer’s response.

Attribution helps identify the most effective channels and touchpoints that contribute to a conversion. In e-commerce, a conversion typically means a completed purchase in the online store.

Attribution plays a crucial role in marketing, especially in the age of advanced technologies and artificial intelligence. Sophisticated algorithms can process large amounts of data, detect patterns, and assign conversion value to specific sources with greater accuracy.

Understanding different attribution models and how they are used across marketing platforms is essential in building a strong marketing strategy. Since attribution modeling is complex, many models exist to help marketers understand how users interact with a seller’s website and content.

Basic Attribution Models

1. First-Click Attribution

This model assigns all conversion value to the first interaction a customer has with the brand across campaigns and marketing activities. It focuses on the first point of contact as the critical moment that initiated the conversion path.

This model is used when marketers want to understand which channels are best at capturing user attention at the beginning of the customer journey.

2. Last-Click Attribution

This model allocates all conversion value to the final touchpoint before the conversion. While popular, it often neglects the impact of earlier interactions in the customer journey.

For example, if a user clicked a Google ad, later followed a link from social media, and finally made a purchase after clicking a Google search ad again, this model assigns 100% of the conversion credit to the last Google ad interaction. In practice, this can lead to severely undervaluing channels like affiliate networks.

3. Linear Attribution

Distributes conversion value evenly across all touchpoints in the customer journey.

4. Time-Decay Attribution

Prioritizes touchpoints that occur closer to the conversion, gradually reducing the value of earlier interactions.

5. Algorithmic or Data-Driven Attribution

This model uses advanced algorithms to analyze how each touchpoint contributes to the conversion, assigning value based on actual influence. It’s the most advanced method and adjusts to unique customer paths. This is now the default model in GA4, although users can change it in settings.

The Data-Driven model analyzes all interactions — from ads to website visits — to detect conversion-driving patterns, then dynamically distributes credit to impactful touchpoints. While highly accurate, it requires a substantial amount of data to function properly.

Google Analytics: Channel Contribution and Data Presentation

GA4 is a free tool for marketers and analysts, supporting the optimization of paid campaigns and SEO efforts. It uses advanced algorithms and AI to track user interactions across the web.

With its new attribution model, GA4 estimates the percentage and value contributed by each channel to the final conversion. However, it’s important to remember that GA4 is a Google-owned system, which means it may favor Google channels in conversion attribution, often overlooking other sources such as affiliate links and touchpoints.

GA4 is tightly integrated with Google’s ad platforms, which means data loss is minimal for Google Ads and Google Marketing Platform channels. However, data loss for non-Google systems — including affiliate networks — is significantly higher.

Customer Journey – Touchpoints with a Brand Along the User’s Purchase Path

The customer journey, also known as the sales funnel (or conversion funnel), describes the path a potential customer takes from their first encounter with a product or service to making a purchase.

Stage 1 – Capturing the Customer’s Attention

The goal at this stage is to reach as many people interested in a given topic as possible. Once potential customers become aware of the product or service, they move to stage two – Interest.

Stage 2 – Generating Interest

Provide valuable content that sparks interest and encourages further engagement.

Stage 3 – Evaluation and Consideration

Potential customers begin to assess and compare available options. At this stage, the customer is close to making a purchase. It’s crucial to offer additional incentives to support their decision.

Stage 4 – Purchase

Due to the vast number of online stores and diverse ways of presenting offers online, the average customer journey has become significantly longer. Several additional factors also influence the time needed to make a decision:

• Product price

• Product availability

• Delivery time

• Available payment options

• Availability of installment payments

Let’s analyze the purchase journey for winter boots by a woman aged 20–30. According to research by Shopalike, Poles need an average of about 7 days to complete an online purchase. During this time, they browse the internet for trend inspiration, check prices, read reviews, and analyze the most favorable options.

Here’s what a typical path might look like, with affiliate link touchpoints in bold:

• The user starts by exploring fashion trends, entering keywords related to winter boots. She clicks both paid and organic search results, visiting review sites or directly going to stores to check prices.

• On her way to work, she browses Instagram on her phone, looking for influencer inspiration.

• In the evening at home, on her laptop, she compares prices of selected models, searching specific names and clicking both paid and organic product listings on Google.

• She continues analyzing prices and product availability, narrowing her choice to 2–3 models – and looks for product reviews online.

• She then checks prices on Google Shopping, Allegro, and Ceneo.pl.

• She waits a few days until payday, frequently browsing Instagram and Pinterest for outfit ideas.

• Remarketing kicks in, and she sees ads for the shoes she viewed. She clicks through suggestions while checking prices. She’s already decided on a model in the store with the best price.

• After getting her paycheck, she visits the saved store directly, typing the store name into Google at work.

• She double-checks the price online by entering the model name. Sometimes Google displays the product at a better price in a PLA ad from a different store, prompting her to choose the cheaper option.

• With the product in her cart, she sees a promo code field. She briefly leaves the site to search for discount codes online… She remembers that an Instagrammer she follows often shares codes – so she checks Instagram (on her phone) to see if she has a code for that store.

• She returns to the store with a discount code (or without it).

• Finally, she clicks “Buy” and completes the payment.

The journey may seem long, but it’s very typical. There are up to 10 touchpoints where an affiliate link could have appeared. Depending on habits, demographic factors, income level, and online experience, every customer will have a unique path to purchase.

Regardless of the journey’s length, Google Analytics 4 will assign value to each touchpoint according to its algorithms and data models. But now let’s focus on understanding in which scenarios affiliate publishers featured during these touchpoints might not show up in GA4 reports.

Google Analytics – Reasons for the Absence of the Affiliate Channel in GA4 Reports

Why don’t affiliate publishers appear in Google Analytics 4 (GA4) attribution reports, even when they played a role at various stages of the customer’s purchase journey? There are several reasons for this.

1. Changed Session Definition in Google Analytics 4 – Default Session Lasts 30 Minutes

In GA4, if a user visits a website from different sources within a single session (which by default lasts 30 minutes), all visits will be counted as a single session entry. This means that if a user starts a session by visiting the shop from Google search results, no other source will be recorded during the next 30 minutes.

GA4 does not record the affiliate channel if it appears as an additional traffic source during an ongoing session.

If the customer finds a product via Google search, leaves the shop to look for a discount code on Instagram or a coupon site, and then returns within the same session, GA4 will still attribute the session to Google – not to the affiliate link they clicked on.

The same applies if, before completing the purchase, the customer decides to check store reviews and returns to the shop through an affiliate link. In earlier versions like Universal Analytics, these would be tracked as separate sessions from different sources.

In this case, clicking an affiliate link during the same session that began with a Google visit will not grant the affiliate channel any credit in GA4’s conversion path.

Across the entire webePartners network, over 40% of transactions are completed within 30 minutes of the last affiliate click. This means that in over 40% of cases, the user interacts with an affiliate right before purchase. However, that doesn’t mean the affiliate link appeared only at the end of the funnel. webePartners reports only the last click (due to the Last Click Wins attribution model), so earlier publisher contributions aren’t visible to advertisers in their action reports – but they might appear in GA4 only if they initiated a session.

Some users also block cookies or use script-blocking tools, which can prevent accurate tracking, leading to underreporting of affiliate traffic. This issue will grow as privacy regulations tighten across the European Union.

2. Affiliate Channels Missing When Publishers Run Paid Google Shopping Campaigns

Many affiliate publishers are certified Google Partners who run Google Shopping campaigns at their own cost, earning commissions on resulting sales.

GA4 does not include the affiliate channel when traffic comes from paid Google Shopping campaigns.

Google Shopping is a search platform for e-commerce offers that allows merchants to promote products in Google search results via Google Ads and the Google Display Network.

These ads display product images, prices, store names, and product names directly in search results. Users can click to compare or purchase.

If an affiliate publisher runs a Google Shopping ad and a user clicks on it, the sequence is: the user first hits the Google server (session starts), then the affiliate tracking server, then the advertiser’s server. GA4 then classifies all of this as Paid Google Traffic, taking full credit for the referral.

While Google indeed drove the visitor, ignoring the affiliate tracking link leads to distorted data and undervalues the affiliate’s contribution.

3. No GA4 Reports Tied to Specific Transactions

Neither GA4 nor previous versions like Universal Analytics allow advertisers to generate reports that directly link transaction IDs to specific user journeys or traffic sources. GA4 only shows the number of transactions per source – not the transaction value or ID. This creates a major challenge for validating affiliate conversions and evaluating publisher performance in detail. Without detailed, transaction-level data, GA4 is not a reliable source for validating affiliate activity, nor for building a strong affiliate network based on performance insights. Like any analytics system, GA4 has limitations in how it collects and processes data, so its reports often do not reflect the true impact of affiliate traffic on sales.

4. Cookie Consent Requirements Limit Tracking

If the user does not consent to cookies when visiting the advertiser’s website, GA4 cannot collect full tracking data. In such cases, traffic is classified as direct/(none), making it impossible to determine where the visitor or customer came from. Since March 6th, websites are required to block all marketing and analytical scripts (including GA4) for users who don’t allow cookies. This will further reduce the representativeness of GA4 data and widen the tracking gap.

5. Adblockers Can Block Google Analytics

Popular adblockers can block GA4 scripts entirely, creating another blind spot for tracking affiliate traffic.

6. GA4’s Default 30-Day Lookback Window

GA4 tracks user paths only for 30 days before conversion. So if your affiliate program uses a cookie window of 45 days, GA4 will not include publishers who influenced the purchase outside that default 30-day window.

GA4 as Analytical Support – Not a Basis for Validating Transactions in Affiliate Systems

We have listed the six most obvious reasons for discrepancies between data reported by Google Analytics 4 (GA4) and statistics in affiliate networks. Considering these factors, data from different analytical systems will always vary.

Google requires companies using GA4 to implement Consent Mode, which prevents Google Analytics script loading for users who do not consent to cookie-based tracking. This will further increase discrepancies between GA4 reporting and other marketing platforms.

Multi-Channel Attribution – Analysis as the Foundation for Correctly Assessing Campaign Contribution

Multi-channel attribution enables analysis of the effectiveness of individual channels, which is essential for optimizing marketing strategies. However, to obtain accurate attribution results, it is necessary to integrate multiple marketing and analytical tools. Integration allows for data collection and comparison from different sources. Overlaying these datasets is key to understanding how each marketing activity influences conversions.

Challenges in Marketing Attribution

1. Complexity of Customer Journeys

In the multichannel era, purchase paths are often complex and involve numerous touchpoints. This makes it difficult to accurately determine which channels have the most significant impact on conversion. Another challenge is evaluating whether removing marketing activities that have minimal or indirect contribution would significantly increase the cost of those directly affecting conversion.

2. Changing Consumer Behavior

Consumer shopping behaviors evolve constantly, requiring ongoing analysis and adjustments to attribution models to reflect campaign performance accurately.

3. Data Integration

Collecting and integrating data from various sources and platforms is challenging, but it is essential for effective attribution analysis and understanding the true value of each marketing channel.

4. Data Privacy

Data privacy regulations like GDPR and other data protection laws may limit access to certain user data, making precise attribution more difficult.

5. Attribution Modeling

There are many attribution models available, and choosing the right one can be difficult. The selected model can significantly influence the outcome of the analysis.

6. Technological Limitations

Some marketing channels have limited tracking capabilities, which makes it harder to accurately measure their impact on conversions.