SOMONITOR: Combining Explainable AI & Large Language Models for Marketing Analytics
Aleksandr Farseev, Qi Yang, Marlo Ongpin, Ilia Gossoudarev, Yu-Yi, Chu-Farseeva, Sergey Nikolenko

TL;DR
SOMONITOR is an explainable AI framework that combines large language models and predictive analytics to help marketers analyze vast online content, identify key content pillars, and generate actionable content briefs for improved campaign performance.
Contribution
The paper introduces SOMONITOR, a novel AI system integrating LLMs and predictive models to enhance marketing analytics and content strategy development.
Findings
Improved campaign effectiveness through data-driven insights.
Streamlined content creation with automated content briefs.
Enhanced understanding of competitor content and customer personas.
Abstract
Online marketing faces formidable challenges in managing and interpreting immense volumes of data necessary for competitor analysis, content research, and strategic branding. It is impossible to review hundreds to thousands of transient online content items by hand, and partial analysis often leads to suboptimal outcomes and poorly performing campaigns. We introduce an explainable AI framework SOMONITOR that aims to synergize human intuition with AI-based efficiency, helping marketers across all stages of the marketing funnel, from strategic planning to content creation and campaign execution. SOMONITOR incorporates a CTR prediction and ranking model for advertising content and uses large language models (LLMs) to process high-performing competitor content, identifying core content pillars such as target audiences, customer needs, and product features. These pillars are then organized…
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Taxonomy
TopicsTopic Modeling · Computational and Text Analysis Methods
