Blue Room
We’re excited to explain our Multi-Touch Attribution (MTA) model, powered by our recurring neural network (machine learning). Unlike traditional attribution models that rely on static rules and lower funnel assumptions, our model uses deep learning to analyze real customer journeys across all touchpoints. By learning from data across channels (social media, programmatic, analytics, and search), it uncovers the hidden dynamics between platforms and how they amplify each other’s impact.
What sets our model apart is its ability to predict future behavior. It doesn’t just explain what worked, it anticipates what will work next, enabling more informed decisions in near real-time. Taking upper-funnel, brand, and predominantly impression-based media into consideration.
And because it continuously retrains itself, the model evolves alongside shifting campaigns, business goals, and consumer habits. This ensures insights remain both relevant and actionable.
This neural network doesn’t just track interactions—it interprets them, recognizing the timing, sequence, and context of each touchpoint to reveal how different moments in the customer journey contribute to a conversion.
This is attribution reimagined: intelligent, adaptive, and designed to unlock the full potential of our marketing strategy.
