From Multi-Touch Attribution to Marketing Mix Modeling: Leveraging Multi-Information Fusion for Advanced Advertising Analytics

In today's marketing environment, advertising is distributed across numerous platforms, compelling that advertisers assess both cross-channel and individual effects. MultiTouch Attribution (MTA) and Marketing Mix Modelling (MMM) are the primary models used for this purpose. MTA evaluates the overall marketing performance across channels but lacks detailed user insights, while MMM evaluates user-level touch points in the customer path but struggles with cross-platform complexity. Multi-Information Fusion provides a solution by incorporating various data sources, as advertising data becomes more intricate. This method enables advertisers to enhance the precision of performance evaluations and acquire a more thorough comprehension of channel interactions. This paper investigates the potential of multi-information fusion to improve advertising analytics by integrating the assets of both MMM and MTA models. We will examine the current state of fusion methodologies, evaluate their potential applications in advertising, and evaluate the extent to which they can overcome the constraints of conventional models. Advertisers can optimize their strategies for improved ROI and obtain more precise evaluations using these advanced techniques.