Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital Marketing

In digital marketing, precise audience targeting is crucial for campaign efficiency. However, digital marketing agencies often struggle with incomplete user profiles and interaction details from Advertising Identifier (ADID) data in user behavior modeling. To address this, we introduce the Deep Journey Hierarchical Attention Networks (DJHAN). This novel method enhances conversion predictions by leveraging heterogeneous action sequences associated with ADIDs and encapsulating these interactions into structured journeys. These journeys are hierarchically aggregated to effectively represent ADID's behavioral attributes. Moreover, DJHAN incorporates three specialized attention mechanisms: temporal attention for time-sensitive contexts, action attention for emphasizing key behaviors, and journety attention for highlighting influential journeys in the purchase conversion process. Emprically, DJHAN surpasses state-of-the-art (SOTA) models across three diverse datasets, including real-world data from NasMedia, a leading media representative in Asia. In backtesting simulations with three advertisers, DJHAN outperforms existing baselines, achieving the highest improvements in Conversion Rate (CVR) and Return on Ad Spend (ROAS) across three advertisers, demonstrating its practical potential in digital marketing.