Attribution isn’t about finding one perfect model—it’s about combining insights to guide smarter decisions. For scaling SQLs and optimizing ROI, Data-Driven Attribution paired with Position-Based Attribution offers the clearest view of what’s driving success. Primary Model: Data-Driven Attribution (DDA) – If you have sufficient historical data, let machine learning do the heavy lifting. A lead discovers your product via a Google ad (First Click). Attends a webinar two weeks later (Mid-Funnel). Receives an email sequence and finally books a demo via a LinkedIn retargeting ad (Last Click). Without a robust attribution strategy, you might over-invest in the last ad and under-invest in the webinar or the initial ad that created awareness.

When I first leaned on last-click attribution, it felt like the answer—clear, immediate, and tied to results. But it didn’t take long to realize it only told part of the story. First-click helped me see where customers entered, but the real magic was in data-driven attribution. It gave me the full picture: every touchpoint, every nudge, every small moment that shaped a decision. From retargeting ads to organic posts, I started seeing the journey, not just the destination. That’s when my strategies went from reactive to intentional—and the results proved it.

When analyzing attribution data, I focus on three key areas: 1) Aligning the model with campaign goals (awareness vs. performance). 2) Accounting for the complexity of the customer journey. 3) Combining platform-specific insights with a unified strategy. No single model fits all. The key is to use attribution as a guide, testing and refining continuously. Personally, I find that Data-Driven Attribution (DDA) has delivered the most actionable insights, as it assigns credit based on the actual impact of each touchpoint and provides a holistic view of the customer journey.

The attribution model you prioritize depends on your goals: First-Click: Focuses on brand awareness by crediting the first touchpoint. Last-Click: Highlights channels driving conversions. Linear: Distributes credit equally across all touchpoints, ideal for complex journeys. Time-Decay: Weighs later touchpoints more, useful for long sales cycles. Position-Based: Emphasizes first and last touchpoints, balancing acquisition and conversion. Data-Driven: Uses algorithms to allocate credit based on performance, ideal for advanced insights with sufficient data. For beginners, use Last-Click; for advanced strategies, aim for Data-Driven Attribution.

When prioritizing attribution model data, it depends on your business goals. For a holistic view, I recommend the data-driven attribution model, which uses machine learning to assign credit across touchpoints. However, if you’re focusing on specific stages of the funnel, last-click attribution might help measure conversions, while first-click attribution is great for identifying brand awareness efforts. A hybrid approach often works best, combining multiple models to get actionable insights.