As third-party cookies phase out, measuring marketing performance is becoming more complex.
Advertisers rely on various attribution methods, each with strengths and limitations. Choosing the right one requires understanding their differences.
For instance, Google Analytics doesn’t capture LinkedIn lead gen forms, while multi-touch attribution (MTA) does.
MTA, however, misses YouTube views and other upper-funnel initiatives MMM accounts for.
This article breaks down the pros and cons of:
Common attribution models: The pros and cons
1. Google Analytics (session-based attribution)
Google Analytics focuses on user sessions and uses different attribution models (e.g., last-click, first-click, or data-driven) to assign credit within a session.
The pros
Granular data: Provides detailed insights into user behavior at a session level.
Customizable models: Allows marketers to choose or customize attribution models to fit their business needs.
Real-time tracking: Captures real-time user interactions, offering immediate feedback on performance.
Cross-channel insights: Integrates data from multiple channels (organic, paid, referral, etc.), enabling better cross-channel analysis.
The cons
Limited to owned data: Relies on first-party data, making it less effective in environments with poor tracking (e.g., cookie restrictions, blocked JavaScript).
Bias toward measurable interactions: Doesn’t account for offline or untrackable influences (e.g., word of mouth).
Session-centric focus: May overlook the broader customer journey, especially for longer purchase cycles.
Dig deeper: Your guide to Google Analytics 4 attribution
2. Advertising platforms (click and impression-based attribution)
PPC platforms like Google Ads and Facebook Ads attribute conversions to clicks or impressions tied to their specific ads.
The pros
Channel-specific insights: Provide detailed performance metrics for individual ad platforms.
Immediate ROI tracking: Excellent for tracking direct-response campaigns and performance-based advertising.
Impression data: Includes visibility data even if the user doesn’t click, allowing for broader analysis of brand awareness.
The cons
Walled gardens: Each platform operates within its ecosystem, often overstating its role in conversions because of a lack of cross-platform visibility.
Overlapping attribution: Different platforms may claim credit for the same conversion, leading to double-counting.
Short-term focus: Often overemphasizes direct clicks and conversions, neglecting long-term brand effects or multi-touch journeys.
3. Multi-touch attribution
MTA assigns credit to multiple touchpoints leading to a conversion rather than just the first or last interaction.
It’s typically based on clicks (sometimes impressions) but does not account for branding initiatives.
The pros
Comprehensive view: Captures the contribution of each touchpoint in the customer journey.
Optimizes campaigns: Enables better budget allocation by highlighting impactful channels.
Customizable models: Supports various methods like linear, time decay, or algorithmic models.
The cons
Complex implementation: Requires advanced tracking and integration across channels.
Tracking limitations: Cookie restrictions and data silos can hinder accuracy.
Data overload: Processing and interpreting the vast amount of data can be challenging for smaller teams.
Branding blindness: As noted above, branding campaigns without measurable clicks or impressions (think: anything analog, out-of-home, etc.) aren’t included in the analysis.
Dig deeper: How to evolve your PPC measurement strategy for a privacy-first future
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4. Salesforce (CRM-based attribution)
Salesforce uses CRM data to track the entire customer lifecycle, from lead generation to sales and retention, offering attribution for both online and offline interactions.
The pros
Full-funnel view: Tracks interactions across sales, marketing, and customer service.
Offline and online integration: Combines offline (e.g., in-person sales) and online data.
Custom reporting: Highly customizable to align with specific business goals.
Retention and LTV insights: Tracks post-conversion metrics like customer lifetime value (LTV).
The cons
Data dependency: Relies heavily on accurate and comprehensive data entry and segmentation across departments.
Complexity: Requires integration with other systems and significant setup effort.
Delayed feedback: May not be as real-time as tools focused on web analytics.
Dig deeper: Advanced analytics techniques to measure PPC
5. Shopify (ecommerce attribution)
Shopify tracks customer interactions and sales on its platform, offering insights into purchase behavior and campaign performance.
The pros
Ecommerce-specific: Tailored for tracking online purchases, abandoned carts, and revenue.
Seamless integration: Works natively with Shopify stores, requiring minimal setup.
Real-Time metrics: Provides immediate insights into sales and campaign performance.
Built-in tools: Integrates with marketing apps and channels like email, Facebook, and Google.
The cons
Limited cross-channel insights: Focuses mostly on Shopify-driven interactions.
Dependent on Shopify ecosystem: Not ideal for businesses with significant offline or non-Shopify sales.
Cookie reliance: Affected by privacy restrictions and tracking limitations in browsers.
6. Media mix modeling (spend-based attribution)
This approach uses statistical models to analyze the relationship between marketing spend and business outcomes (e.g., sales).
The pros
Holistic view: Captures the impact of all marketing efforts, including offline channels (TV, radio, print).
Long-term impact analysis: Accounts for brand-building activities and delayed conversion effects.
No cookie dependency: Not affected by digital tracking restrictions, as it’s based on aggregated data.
The cons
Delayed insights: Results are retrospective and require substantial historical data, making it less suitable for real-time decision-making.
Complexity: Requires expertise in data science and advanced modeling techniques.
Aggregation loss: Lacks granularity, as it focuses on high-level trends rather than individual user behavior.
Dig deeper: Why marketing mix modeling is crucial in 2025 and beyond
Which should you choose?
No single attribution model is perfect.
The best approach is to understand what each model captures (and what it doesn’t) so you can combine them strategically.
Here’s a quick breakdown of when each model works best:
Google Analytics is great for overall session-based behavior insights.
Ad platforms are ideal for optimizing campaigns within their ecosystems – all the way to the ad level.
MTA provides a nuanced view of the digital customer journey, and helps mitigate overlapping attribution across channels.
Salesforce is powerful for tracking the customer journey, including offline interactions and evaluating lead quality.
Shopify excels in ecommerce-specific insights for merchants within its platform, such as distinguishing one-time purchases and subscriptions.
Media mix modeling is suited for strategic, omnichannel decision-making and accounts for the entire customer journey, from branding to down-funnel activities.
The best attribution strategy: A balanced approach
At my agency, we love to run MMM regularly to give branding initiatives the credit they deserve, helping to fine-tune marketing strategies for long-term success.
However, no single model is sufficient on its own.
The best approach is integrating multiple attribution tools for a more complete view of marketing performance across platforms and touchpoints.
Attribution is an inexact science. It requires ongoing testing and adjustments.
Start by aligning on the KPIs that matter most to your marketing team, then choose the models that best assess your campaign success.
Dig deeper: 5 PPC measurement initiatives to set yourself up for 2025 success
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