To address the challenges of complex multi-touchpoint interaction chains, ambiguous channel contribution boundaries, fragmented revenue measurement dimensions, and insufficient utilization of marketing semantic information in marketing technology platforms, this paper proposes a unified research framework for attribution modeling, revenue assessment, and fine-tuning of a large language model. This method first constructs a joint representation mechanism based on user interaction sequences, channel contact signals, and time-related features, characterizing the contextual associations and temporal dependencies of marketing behavior in a unified representation space. Then, it models the role of different marketing touchpoints through a context-aware attention mechanism and contribution scoring strategy, and achieves a stable allocation of multi-touchpoint contributions through attribution normalization. Based on this, the attribution results are further mapped to revenue-related objectives, forming a comprehensive analytical path from behavioral understanding to value quantification. Simultaneously, this paper incorporates fine-tuning of a large language model into the overall framework to enhance the model's understanding of marketing texts, business contexts, and platform rules, thereby improving the semantic support level in the attribution analysis and revenue characterization processes. Related research results show that the method presented in this paper can more effectively complete key touchpoint identification, value association modeling, and unified decision representation learning in marketing technology platform scenarios, providing a new methodological foundation for digital marketing analysis, platform intelligent evaluation, and refined resource allocation.
pdf
