Data-driven attribution
Data-driven attribution (DDA) is an advanced way of observing how different touchpoints contribute to a conversion or intended customer action. Unlike traditional models, which attribute credit for conversions based on rules defined beforehand (e.g., first-click or last-click attribution), DDA uses real-time data and machine learning models to distribute credit among many touchpoints based on how they influence the customer journey. Through this more accurate and dynamic approach, marketers can enhance their strategies and make better resource allocations. Advantages of Data-Driven