What is Attribution Modeling?

When a customer interacts with multiple marketing channels before making a purchase, attribution modeling determines which channels receive credit for the sale. The simplest model, last-click attribution, gives 100% of the credit to the final touchpoint before conversion. If a customer saw a Facebook ad, clicked a Google search result, and then purchased after clicking an email link, last-click attribution credits the email entirely.

Other common models include first-click (credits the initial touchpoint), linear (distributes credit equally across all touchpoints), time-decay (gives more credit to touchpoints closer to the conversion), and position-based (gives 40% to the first and last touchpoints, splitting the remaining 20% across middle interactions). Each model tells a different story about which channels are "working," and no single model is universally correct.

The model you choose directly impacts budget allocation decisions. Last-click attribution tends to overvalue bottom-of-funnel channels like branded search and email, while undervaluing awareness channels like social media and display ads. This can lead to a feedback loop where you cut spend on the very channels that feed your conversion funnel, leading to gradually declining results. Conversely, first-click attribution can lead to over-investing in top-of-funnel channels that generate clicks but never convert.

Data-driven or algorithmic attribution models, now available in platforms like Google Analytics 4, use machine learning to analyze your specific conversion data and assign credit based on the statistical contribution of each touchpoint. These models are more sophisticated but require sufficient conversion volume to produce reliable results. For smaller Shopify stores, simpler models combined with common sense and incrementality testing often provide more actionable insights than algorithmically complex approaches.