User-driven visual content tagging is slow, cumbersome, and inconsistent. Content search and retrieval is difficult and ineffective. Product, keyword, theme, and campaign taxonomies are static with emotive, “brand personality,” and thematic dimensions poorly understood and inconsistently defined and documented. There is low-volume, manual recomposition of visual content with very limited support for dynamic automation of hyper-personalized customer engagement and segment-defined campaigns. Editorial skills for composition of advertising copy, headlines, and product descriptions are scarce, not scalable to produce an emotionally or personality-attuned one-to-one copy.
Goals and Objectives
AI-enabled characterization, tagging, and selection of textual and visual digital assets scale via supervised machine learning overseen by creative design and editorial content guidelines and strategies. AI-derived asset metadata informs management of content corpus to ensure that the curation of assets and their attributes serves marketing objectives. AI-driven content recomposition supports high-volume personalization. Editorial composition is supported by natural language generation of drafts, AI reviews for consistent application of brand, and target market segment personalities.
Cloud; machine learning; natural language processing; machine vision; visual AI analytics and generation; personality, behavioral, and conversational tone analytics; and natural language generation
Use Case Summary
Create a single content management system serving all communication channels with consistently applied well-defined content tags to ensure that portfolio meets the needs of merchandising and marketing. Scale production of authentic voice of brand content improves performance of broadcast and personalized communication channels.