User-driven content tagging is slow, cumbersome, and inconsistent. Product, keyword, theme, and campaign taxonomies are static, with emotive “brand personality” and thematic dimensions poorly understood and inconsistently defined and documented.
Goals and Objectives
AI-enabled characterization, tagging, and selection of textual and visual digital assets via supervised machine learning overseen by creative design and editorial content guidelines. AI-derived asset metadata informs management of content corpus to ensure that the curation of assets and their attributes serves marketing objectives.
Cloud, machine learning, natural language processing, machine vision, visual AI analytics and generation, personality, behavioral, and conversational tone analytics, natural language generation
Use Case Summary
Create single content management system serving all communication channels with consistently applied well-defined content tags to ensure portfolio meets the needs of merchandising and marketing. Scale production of authentic voice of brand content to improve the performance of broadcast and personalized communication channels.