In Part 1 of this series, we unpacked the limitations of GA4 when it comes to affiliate marketing, from inconsistent tracking to opaque channel logic. If you’ve optimized your GA4 setup but still feel like you’re flying blind, you’re not wrong. 

While GA4 is a useful tool for analyzing owned-and-operated site behavior, it wasn’t designed to reflect the nuances of today’s partnership economy.

So what comes next?

StackCommerce decided to stop patching GA4 and start building a measurement system that truly meets our business needs. We leaned into a privacy-forward, platform-agnostic attribution approach that blends media mix modeling (MMM), first-party data, and real-time performance insights and is flexible enough to evolve with our ecosystem. Here’s how we did it.

Media Mix Modeling vs. GA4 for Affiliate Performance

Our hybrid approach to measurement combines the tactical, real-time capabilities of GA4 with the strategic insights of media mix modeling. While GA4 helps us make day-to-day optimization decisions, MMM helps guide our long-term channel allocation strategy. Together, they paint a fuller picture of affiliate performance than either can provide on its own.

What is MMM?

Media mix modeling is a statistical technique that analyzes historical data to understand how different marketing channels contribute to overall business outcomes. It doesn’t rely on tracking individual users, which makes it a powerful tool in today’s privacy-first landscape. 

Unlike GA4, which attempts to follow customer journeys on a user level, MMM works with aggregated data to identify patterns and correlations between marketing activity and performance metrics like revenue, subscriptions, or customer acquisition.

At its core, MMM answers a different question than GA4. Instead of asking, “Which channel got the last click?” it asks, “Which channels are incrementally driving growth?”

Why Use MMM?

There are several advantages to using MMM alongside GA4. Because it’s not dependent on cookies or individual tracking, MMM is inherently more privacy-compliant and resilient to platform changes. It also offers a more holistic view of your marketing efforts, incorporating both digital and traditional channels in one model, from affiliates and influencers to TV, email, and native advertising.

At Stack, this helps us better capture the value of mid- and upper-funnel affiliate partners who often get under credited in last-click or default GA4 reporting—think content creators, product roundups, or newsletter partners who influence purchase decisions without closing the sale.

Another major benefit is that MMM focuses on incrementality. Rather than just measuring who got credit for a conversion, it helps us understand which investments actually moved the needle and which ones simply captured demand that already existed.

The Tradeoffs of MMM

That said, media mix modeling isn’t perfect. It’s slower, more resource-intensive, and less actionable in real time. The models require significant historical data, analytical rigor, and statistical expertise. They also often need to be updated weekly or monthly, which makes them harder to use for fast-paced campaign optimization. And during periods of volatility—like new product launches or macroeconomic swings—MMM results can become less reliable. It’s best to treat MMM as a complement. It helps us understand long-term trends across the full funnel, even when GA4 can’t connect the dots.

Word to the wise: Don’t have serious data science skills and computing power at the ready? You may need to address that gap first.

The Future of GA4 in Affiliate Marketing

GA4 isn’t going away—but for us, and increasingly for the industry, it’s no longer a standalone source of truth. It’s becoming more of an integration hub, just one of many tools in a broader system.

Affiliate marketing spans multiple properties, platforms, and browsers. GA4 struggles to track across these touchpoints and often misattributes conversions. For example, it may give paid search credit for a branded query that an upstream content partner actually triggered. These blind spots distort performance data and limit decision-making.

To move faster and get clearer insights, we needed an attribution system that could deliver real-time reporting, integrate with external publisher platforms, handle both click and view-through logic, support custom attribution windows, and prioritize privacy and data ownership.

So, we built one.

StackCommerce’s First-Party Attribution Strategy

We’ve developed a bespoke first-party data stack enabling a more responsive and accurate measurement framework. The system required significant engineering effort, but the return has been clear: faster reporting, more accurate data, and attribution that fits the realities of affiliate marketing.

At the heart of our system is Segment, which captures and unifies real-time customer events across all our properties: StackCommerce, StackSocial, Reviewed.com, and BrandCycle. These events flow into our central data warehouse, where we apply custom business logic to generate insights.

Five critical factors drove this shift away from GA4:

  • Real-time insights
  • Unified ecosystem view
  • Custom logic and flexible attribution windows
  • Data control
  • Privacy-first architecture

Segment doesn’t just fuel our attribution—it connects the dots across our ecosystem. As our customer data platform, it integrates signals from ad platforms, publishers, and internal systems, eliminating silos and powering a unified view of the customer journey.

Machine Learning to Fill the Gaps

Even with robust tracking, user journeys are rarely complete. That’s where our lightweight machine learning models come in. They estimate conversion likelihoods based on partial or anonymized data—particularly useful when cookies fall short or consent is limited.

Improving Data Sharing with Publishers

All of this has allowed us to offer a lot more. One of the most impactful things we’ve done at Stack is increase the volume and frequency of performance data we share with our publisher partners. If you want better attribution in affiliate marketing, start by making it a two-way street.

What We Share and Why It Matters

Most affiliate publishers are hungry for feedback. Whether they’re a commerce editor, email list owner, or influencer network, they can optimize their output more effectively when they know what’s working and what isn’t. 

From our experience managing programs across our owned and operated businesses, here are the most valuable data types to put in your partners’ hands:

Post-conversion metrics
Include AOV, repeat purchase rates, and subscription retention — key indicators of customer lifetime value.

Funnel insights
Share click-to-conversion lag, drop-off points, and user journey data to show how users engage before converting—and where publishers fit in.

Traffic segmentation performance
Break down conversion rates by device (mobile vs. desktop), user type (new vs. returning), and referral source to highlight what kinds of traffic perform best.

Product-level performance
Provide SKU-level insights so publishers know which products resonate with their audience and can optimize accordingly.

Product category trends
Go broader by showing which categories perform best with each partner’s audience.

Audience overlap analysis
Share anonymized data showing how publisher audiences align with your existing customer base to inform more targeted content and outreach.

A/B test results
When a publisher’s traffic is involved in a test, close the loop by sharing results. This feedback improves future collaboration and performance.

Sharing this level of insight helps publishers move beyond “spray and pray” tactics and toward more deliberate, data-backed strategies. When publishers understand what actually drives conversions, they can create more relevant, high-performing content. That smarter audience targeting means better results for everyone involved.

Transparency also signals that we’re committed to long-term growth, and clear ROI leads to deeper investment from partners who are more likely to prioritize your brand in their content calendars and promotional strategies.

When both sides are working from the same data, it’s much easier to spot—and solve—tracking issues or performance discrepancies quickly. Ultimately, better data sharing leads to stronger strategic alignment in affiliate marketing and beyond. 

Emerging Attribution Solutions

The next wave of attribution isn’t about finding the perfect platform; it’s about building adaptable, owned infrastructure that ingests multiple signals and evolves over time.

We’re also watching—and testing—several promising innovations:

  • Incrementality testing: controlled experiments that isolate the true lift from affiliate activity, helping us move beyond correlation
  • Unified ID 2.0 and alternative identifiers: industry efforts to create privacy-compliant alternatives to third-party cookies
  • Blockchain-based attribution: still early-stage but offers potential for secure, transparent, and consumer-controlled attribution

Rethinking Attribution for the Long Haul

With privacy regulations tightening, platforms fragmenting, and user journeys growing more complex, affiliate marketers need a flexible, future-ready approach to measurement. Attribution isn’t about a silver bullet—it’s about building a system that delivers actionable insights, even when data is incomplete.

At StackCommerce, that means moving to a first-party data infrastructure and layering complementary tools to form a clearer, faster picture of performance. We share signals directly with publishers and continuously test to improve accuracy.

Whether you follow a similar path or design your own mix, the most important shift is this: Stop relying on a single tool. Start building a measurement framework that fits your business and can evolve with it.

If you haven’t read Part 1 of this series, check it out for practical fixes and GA4-specific strategies to improve your tracking today.