User-driven visual content tagging is slow, cumbersome, and inconsistent. Content search and retrieval 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. Low-volume, manual recomposition of visual content with very limited suport for dynamic automation of hyperpersonalized customer engagement and segment-defined campaigns. Editotrial skills for composition of advertising copy, headlines, and product descriptions is scarce, not scalable to produce emotionally- or personality-attuned one-to-one copy.
Goals and Objectives
AI-enabled characterization, tagging, and selection of textual and visual digital assets scales via supervised machine learning overseen by creative design and editorial content guidelines and strategies. AI-derived asset meta-data inform 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 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, 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 needs of merchandising and marketing. Scale production of authentic voice of brand content to improve performance of broadcast and personalized communication channels.