Your Attribution Authority:  A Guide For Taking Back Control

Where Are We Now 

Marketing accountability is at an all-time high, yet many marketers are more in the dark than ever. With increasing pressure to prove impact, 35% of marketers say that using data to inform your marketing efforts has become even more important in 2025 (source: HubSpot). 

Attribution was supposed to be the solution.

Instead, it has become one of the most misunderstood, manipulated, and misused concepts in digital marketing. Many teams struggle to connect-the-dots between efforts & activities with results & outcomes – and it’s not getting any easier, thanks to: 

• Dark / AI Agent traffic that obscures referral sources as people share links privately
• New media channels fragmenting the customer journey
• Privacy regulations and third party-cookie deprecation limiting tracking capabilities
• AI reshaping how data is collected and analyzed
• Users constantly switching between devices and platforms

Despite these challenges, we believe attribution as a concept (and solution) is worth examining under the microscope in 2025. Attribution can help you determine where and how you can deploy strategies to enable your team to tell the most practical and accurate performance narratives possible. 

But first, let’s take a step back and revisit why attribution exists in the first place…

Why Attribution Exists

Attribution was created because businesses need a way to measure which marketing efforts actually contribute to success, so they can invest smarter, scale what works, and cut what doesn’t.

Without it, marketing decisions become guesswork, and growth becomes unpredictable. By measuring how different touchpoints contribute to conversions, attribution in a nutshell helps businesses:

• Optimize budgets by identifying the highest-value channels.

• Refine strategies by identifying what’s working (and what’s not, eliminating wasted spend).

• Improve ROI by eliminating wasted spend and inefficiencies.

But to achieve these benefits and get the most of your attribution efforts, you have to confront its challenges head-on. To best address those challenges, it requires recognizing the limitations as well as the control and authority you have in shaping how attribution works for your team. 

The Hard Realities About Attribution 

Perhaps the most common myth about attribution is the belief there is a model that yields absolute truth. This is, unfortunately, unequivocally false. There is no absolute truth–only the system you choose to define and/or use for your team.

In fact, let’s just go ahead and set the record straight here out of the gates:

• There is no single "one-size-fits-all", "universal" or "perfect" attribution model.

• There is no "best" model for any industry or business type.

• Attribution modeling is not static – it is a framework & set of rules YOU control.

You see, attribution models are like setting grading scales in a classroom. Some teachers will grade heavily on test scores, while others weigh participation, homework, and effort more. Just like there’s no universal way to define a student’s final grade—there’s no single way to assign marketing credit. The way you and your team decide to assign credit shapes how executives and teammates perceive performance that guide budget decisions, strategy, and priorities based on the system you control. 

Start To Take Back Control 

The degree of control of attribution is strongly predicated by the presence and deployment of standard vs custom models. 

Please note when we say “standard” models, we really just mean “widely accepted” ways to measure performance. Contrarily, custom models allow you to define how exactly credit is assigned, quantified and valued based on unique business goals and/or customer journey behavior patterns. 

It’s like you and your team are The Fed Reserve controlling the value of interest rates. 

So if you were to ask me if any business could benefit from custom attribution, the answer is almost always yes—but that doesn’t mean it’s the right choice for every business at every stage. But before we share some methods to help you evaluate whether custom attribution is right for you, let’s align on some consequences of doing nothing. 

The Cost of Doing Nothing

If your team chooses to do nothing and simply rely on default attribution settings within your platforms, it’s likely you have been using single-touch attribution. Let’s break this down by looking at GA4. 

Google Analytics 4

Out of the box, GA4 defaults to Data-Driven Attribution (DDA), which uses machine learning to distribute credit based on conversion likelihood. This setting affects all key Event reports and Explorations that use event-scoped traffic dimensions. In Explore, you can view a full list of dimensions that are compatible with attribution. 

Despite this, DDA is often not utilized for a number of reasons. One of the primary reasons  being because the Traffic and User Acquisition reports in GA4 by default use traffic dimensions that strictly and enforce Last Click (with dimensions prefixed “Session”) and First Click attribution (with dimensions prefixed “User-”) and are unaffected by setting changes. 

🚨Thus, if you are using those reports in GA4 today be aware: 

• Your marketing channels are credited based on the first (User Acquisition) or last (Traffic Acquisition) touchpoint and does not include holistic credits 

• When using Last Click, lower funnel efforts are likely to receive skewed credit and demonstrate positive performance

• When using First Click, upper funnel efforts are likely to receive skewed credit and demonstrate positive performance

• As a result, your budget allocation and investment analysis will suggest different insights

To effectively leverage DDA modeling in GA4, we recommend using the Attribution paths report (previously known as "Conversion paths"). This report is specifically built to analyze the journey customers take before triggering key events and how various attribution models allocate credit along those paths.

Within the Attribution paths report, you'll see updates to several metrics when paired with event-scoped traffic dimensions, including Key events, Total revenue, Purchase revenue, Touchpoints to key event, and Total ad revenue.

Additionally, when selecting an attribution model other than last-click, you’ll notice the introduction of decimal values or "fractional credit." This occurs because credit for a given key event is distributed across multiple contributing ad interactions based on the chosen attribution model.

While DDA is certainly a huge step and value-add in the right direction by Google in providing greater transparency to the full user journey to its customers, just remember some of those current attribution pain points we shared at the top of this post. This is why we recommend at least giving some time-of-day to evaluate whether custom attribution is right for you.   

The Custom Attribution Dilemma: How To Decide If It’s Worth The Investment

As much as we would love to recommend custom attribution to everyone, the reality is if you don’t have the right data, resources, or internal alignment, custom attribution can turn into an expensive, overcomplicated mess that creates more confusion than clarity. 

For these reasons, it’s our recommendation to early on take the initial steps to evaluate whether custom attribution even makes sense for your business.

Taking the time to assess your readiness ensures that you’re not building a model for the sake of it—but instead creating a strategy that actually improves decision-making and drives results.

Ask yourself the below questions regarding your team to help determine if custom attribution is right for you: 

• Do You Have The Right Data Infrastructure?

If your business doesn’t have clean, connected, and complete data across platforms, no attribution model—custom or otherwise—will give you meaningful insights.

A data warehouse like BigQuery is highly recommended for housing raw, unsampled data from sources like GA4, CRM systems, paid media platforms, and offline conversions and can be critical in allowing the flexibility necessary for modeling and analysis. 

• Do You Have The Internal Resources To Implement And Maintain A Custom Model?

For the initial implementation of a custom attribution model, your team needs technical expertise in data collection, integration, and modeling—this typically includes skills in Google Analytics 4, BigQuery, Paid Media, SQL, and others for data analysis.

From there, attribution isn’t a one-and-done setup—it requires ongoing analysis, testing, and adjustments. If you don’t have a team to manage it, you may be better off for the time being continuing to use out-of-box attribution capabilities. 

• Are Your Marketing Channels Complex Enough To Justify A Custom Approach?

If your business relies heavily on just a few channels, custom attribution might be unnecessary. But if you have many diverse channels with a multi-channel strategy (search, social, email, affiliates, offline, etc.), custom modeling can unlock valuable insights.

If Custom Attribution Isn’t Right For You, What Should You Do? 

If those questions have you feeling like your team is not ready for custom attribution, you’re probably asking yourself “Okay then what should I be doing now?” and “What can we do to work towards custom attribution…?” 

This section answers those two questions with a strategic framework of common solutions to have your attributable data be as accurate and trustworthy as possible while also providing practical steps to improve your digital maturity to reach custom attribution. 

Establish Your Measurement ABCs

Key Insight: Most companies measure what’s easy, not what’s important.

They drown in dashboards, tracking vanity metrics that don’t lead to action. It doesn’t matter whether you’re able to do custom attribution or not, your performance narrative will only be as valuable as the funnel KPIs you tie to it. 

Recommendation: Adopt the ABCs of Measurement framework—Awareness, Behaviors, Conversions, and Loyalty—to ensure attribution aligns with real business goals.

Here’s a quick preview of each category: 

Want to learn more about our ABC’s framework? Check out here. 

Accept That Your Attribution Is Incomplete—And Adjust Your Expectations Accordingly

Key Insight: Single-platform attribution is inherently biased. Google Analytics prioritizes GA-tracked touchpoints, while Facebook will over-credit its own ecosystem. Neither of which tells the full story.

Recommendation: Instead of taking the numbers at face value, acknowledge their limitations and set expectations internally—GA4 will likely undervalue social campaigns, while Facebook will over-credit its own ads.

Use these numbers directionally, not as absolute truth and compare attribution models to identify different trends where possible. 

Use Consistent Lookback Windows to Reduce Discrepancies

Key Insight: Attribution windows differ by platform, which can make reporting even more inconsistent. For example, GA4’s default lookback window for conversions is up to 90 days, while Facebook’s default is 7-day click / 1-day view.

Recommendation: Standardize your attribution window if possible across all platforms. If possible, manually adjust GA4’s lookback settings to align with Facebook’s (or vice versa) to ensure your comparisons aren’t skewed by differences in time-based attribution.

Document and Align on Attribution Models Internally & With Vendors

Key Insight: Misalignment on which attribution models are being used for what can lead to major misinterpretations in performance analysis—both internally and with external teams or vendors. Make sure everyone on your team—including leadership, vendors, and external agencies—understands which models are being used and why. Misalignment can lead to false conclusions and bad budget decisions.

Recommendation: Clearly document which attribution models you are using for which platforms and reports. This should include:

• Which model is being used in GA4 (Data-Driven, Last Click, etc.).
• What attribution setting is being used in Facebook Ads (e.g., 7-day click, 1-day view).
• How attribution is being reported across channels (are you blending GA4 & Facebook data or keeping them separate?).

Compare Models Within the Platform

Key Insight: GA4 and Facebook both allow for different attribution models within their own systems. Testing them against each other can expose how each platform “thinks” about conversions.

Recommendation: Regularly compare multiple attribution models within the same platform. In GA4, test Data-Driven Attribution vs. Last Click to see how credit shifts. In Facebook, compare 7-day click vs. 1-day view attribution settings. These comparisons reveal how dependent your reported performance is on the model itself.

Leverage UTM Parameters, Offline Data, and Custom Channel Groupings in GA4

Key Insight: Enhancing data granularity to include campaign and channel level reporting is essential for accurate attribution.​

Recommendation:

• Implement Detailed UTM Tracking: Use comprehensive UTM parameters in all marketing links to track specific campaigns and traffic sources accurately.​
• Incorporate Offline Data: Integrate offline conversion data into your analytics to capture the complete customer journey.​
• Create Content Groups in GA4: Organize your website content into structured groups within GA4 to analyze performance by themes, topics, or page types, enabling deeper insights into user engagement and content effectiveness.
• Create Custom Channel Groupings in GA4: Develop custom channel groups to categorize traffic sources based on your specific marketing strategies, providing a tailored view of performance

Conduct Incrementality Testing to Measure True Channel Impact 

Key Insight: Platform-reported conversions may not reflect the actual incremental value of a channel.​

Recommendation: Perform controlled experiments, such as holdout tests or geo-lift studies, to determine the real impact of your marketing efforts. For instance, temporarily pause campaigns in select regions and compare performance against active areas to gauge true lift.

What Steps Can You Take Towards Being Able To Do Custom Attribution? 

If you’re serious about moving toward custom attribution, the process isn’t as simple as flipping a switch.

Custom attribution is only as valuable as the foundation you build for it. 

Custom attribution isn’t something you implement overnight—it requires a structured, strategic approach to ensure it’s actually useful and reliable. 

Before diving in, follow these steps to ensure you’re setting yourself up for eventual custom attribution.

Unify Your Data Sources

Key Insight: You can’t build a reliable custom attribution model if your data is fragmented across multiple platforms.

Action Steps:

• Centralize all your marketing and conversion data from key sources (e.g GA, Facebook Ads, CRM, Google Ads) in a data warehouse (BigQuery, Snowflake, Redshift, etc.).
• Eliminate data silos by ensuring consistent identifiers across platforms (e.g., user IDs, email hashes, first-party cookies).

Apply What You’ve Learned From Your Readiness Questions 

Key Insight: Your attribution model will only be as good as the foundation you build for it. Apply what you’ve learned to ensure you’re making smart, strategic moves—and not chasing complexity. Now that you’ve assessed your organization’s readiness, it’s time to put that knowledge into action.

Action Steps:

• Address gaps in your data infrastructure—If you identified missing or siloed data, start integrating sources before building a model.
• Strengthen internal resources where needed—If you lack technical expertise, invest in training, hire specialists, or partner with an experienced analytics team.
• Align leadership on realistic expectations—Ensure decision-makers understand that custom attribution refines decision-making but doesn’t create a perfect truth.
• Decide on a phased approach—If you’re not ready for full custom attribution, begin by refining standard models and testing alternative weighting structures.

Apply Learnings From Standard Attribution Models

Key Insight: If you’ve already tested and compared standard models— use those insights to inform your custom approach.

Action Steps:

• Analyze where standard models fell short. What biases or gaps did you notice in GA4’s Data-Driven Attribution? Where did Facebook’s last-touch model fail to account for the full journey?
• Determine which channels need more accurate weighting. Did your testing show that certain touchpoints were over- or under-credited?
• Document what insights you want to extract from a custom model that standard models couldn’t provide.

If You Think You’re Ready For Custom Attribution

So, you’ve evaluated your readiness, established your measurement foundation, and decided that custom attribution is the right move for your business. Now, it’s time to put your plan into action.

A successful custom attribution model is a strategic framework that influences marketing spend, optimizations, and decision-making. Below is the structured approach to build and deploy a custom attribution model that actually delivers meaningful insights.

Define the Business Logic for Your Custom Attribution Model

Key Insight: Custom attribution should align with your marketing strategy—not just distribute credit in a way that looks good on reports. Attribution should reflect reality. If your model doesn’t align with how your business makes money, it will lead to poor marketing decisions.

Strategic Actions:

• Clearly define what success looks like for your business.Ask yourself are you optimizing for lead generation, revenue, lifetime value, or another goal?
• Map out the role of each marketing channel. Ask yourself which channels serve as awareness drivers, engagement touchpoints, or direct conversion triggers?
• Determine which conversion actions matter most. Ask yourself are you tracking purchases, lead form submissions, sign-ups, or multiple conversion types?
• Decide on how much complexity your business needs. Ask yourself will a rule-based model work, or do you need machine-learning-driven attribution?

Assign Custom Weights & Test Intelligently

Key Insight: Since you’re moving beyond default models, you need a structured approach for assigning weight to each touchpoint based on real influence. If your custom model distributes credit arbitrarily, it won’t improve decision-making—it’ll just create a different version of bad data.

Strategic Actions:

Step 1: Define the Weighting Structure for Your Model

Here are some of the common methods for defining weights, this is ultimately up to you and your team to decide. 

Step 2: Use Testing & Historical Data to Validate Your Custom Weights

• Analyze past conversions to identify where users engage before making a decision.
• Run model comparisons (e.g., custom weighting vs. last-click vs. data-driven) to assess how different weight structures impact reporting.
• Validate weight assignments through incrementality testing (geo-lift, A/B holdout tests) to ensure they reflect actual business impact.
• Use controlled experiments to refine weight distribution over time.

Operationalize Your Attribution Model & Embed Insights into Decision-Making

Key Insight: Attribution is more than reporting, it should actively influence marketing spend, optimizations, and cross-team strategy.

Strategic Actions:

• Use attribution insights to adjust budgets. Scale investments into top-performing channels based on their weighted contribution to conversions.
• Optimize media mix. Identify if you need more top-funnel investment (brand awareness) or lower-funnel spend (retargeting, direct response).
• Enable cross-team adoption. Ensure marketing, finance, and leadership teams are aligned on how attribution insights should inform forecasting and spend.
• Monitor and refine weights regularly. Attribution is dynamic—what works today may not work six months from now.

Final Thoughts

Attribution will help you make better marketing decisions based on data you control, instead of chasing perfection. The reality is, no model—standard or custom—will ever provide absolute truth. However, the businesses that understand attribution as a strategic framework rather than a rigid formula will always have a competitive advantage.

If custom attribution isn’t right for you yet, don’t force it. Refine your measurement approach first, validate your existing data, and ensure your team is ready to act on insights before investing in a custom model.

If you are ready for custom attribution, remember that building the model is just the beginning. A successful custom attribution strategy and implementation takes careful planning, consideration and technical delivery. 

Attribution isn’t a one-time project—it’s a living system that grows and evolves with your business. You have the authority to own it, refine it, and make it work for you.

If you’d like help refining your custom attribution model, or even if you want to have a casual chat about whether or not you need one, get in touch with the Seer Analytics team. We’d love to talk!