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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