Authors tired of noisy book promotion — AI-driven audience targeting & automation

Executive Summary

Authors are exhausted by noisy, manual book promotion that yields poor ROI; independent and midlist authors—roughly 4 million globally—spend about $1,500 per year on tools, ads and promo services but rarely see predictable, scalable results. Time-poor authors lack marketing expertise, creative capacity for short-form social, and data-driven audience targeting, so promotional activity is ad-hoc and inefficient. You could build an AI-driven platform that automates campaign creation and execution end-to-end: generate platform-native short videos, text hooks and ad assets from book metadata and author input, map them to algorithm-optimized audiences, run multi-platform experiments and feed sales attribution back into model-driven budget allocation. Integrations with ISBN/ASIN data, Goodreads/BookTok signals, ad APIs and author CRMs would enable low-friction onboarding and allow pricing models from subscription to performance-share. A focus on automated A/B testing, LTV-based bid optimization and templates tuned to genre conventions would make large parts of book marketing repeatable for authors who lack time or expertise. The timing is favorable: generative AI reduces creative production time and cost, short-form social is the dominant discovery channel, and more authors are treating books as recurring products—together creating an addressable market of about $6.0B (market score 95/100, revenue potential 90/100). Competition is medium and the main challenges are platform dependency, maintaining creative quality at scale, data privacy for targeting and the need to prove measurable sales uplift, but the product can stand out by training book-specific audience models, tying attribution directly to purchases, automating algorithm-optimized video assets, and pursuing publisher and retailer partnerships to solve data access and credibility gaps.