Review Mining Lab: Use AI to Turn Competitor Reviews into a Conversion Upgrade

Authors obsess over covers and ads but often overlook the goldmine in public reviews. This episode teaches a repeatable, ethical workflow for using AI to analyze competitor reviews, cluster common complaints and praise, and translate those signals into concrete updates: blurb tweaks, sample rewrites, micro-edits in the opening chapters, and targeted backmatter. Dan and Aria walk through a three-part framework authors can run in a few hours: scrape and cleanse review data, use AI to extract themes and sentiment, then prioritize and implement the top fixes that move the conversion needle. Listeners get tactical prompts, low-cost tooling options, and a short A/B test blueprint so changes are measurable. Ideal for authors with 1–10 titles who want a data-driven path to better conversions without rewrites or expensive redesigns.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-22-lions-podcast-ai-self-publishing-book-empire-secrets--7016918/support.