Acast, the world’s largest independent podcast company, today announces the launch of Smart Recommendations — an AI search engine that allows advertisers to find their perfect podcast audiences in seconds, simply by describing who they want to reach. Its aim is to supercharge how podcast advertising is bought and sold, making campaign planning faster, smarter and more effective.
Powered by a decade of Acast’s own data and experience, and using OpenAI’s LLM technology combined with cutting-edge retrieval-augmented generation (RAG), Smart Recommendations acts like an AI media planner — turning a simple prompt like “I want to reach women in Canada interested in investing” into a curated list of high-fit podcasts. It’s designed to surface the right shows, faster, using natural language and advanced search to deliver unmatched precision in audience targeting.
Smart Recommendations is the first release from Acast Intelligence, a new core capability from Acast dedicated to using AI to enhance its product suite for both creators and advertisers. By blending human expertise with intelligent data processing, Acast Intelligence unlocks the potential of Acast’s expansive dataset to drive smarter discovery, planning and performance in podcast advertising.
“The podcast landscape is vast, and finding the perfect audience can be time-consuming and challenging,” says Matt MacDonald, Chief Product Officer for Acast. “Smart Recommendations solves this by harnessing the power of Acast’s decade of proprietary data combined with Podchaser’s insights, and providing a ‘second brain’ for advertisers. This empowers them to discover hidden gems and connect with ideal audiences with unprecedented speed and precision. It’s about moving beyond guesswork to truly understand and reach the right audiences.”
Smart Recommendations is available to advertisers launching campaigns via Acast’s ad platform, and used by Acast’s internal sales teams around the world.
Key features include:
Natural language prompts: Advertisers can simply describe their target audience in plain language.
Advanced semantic search: The system understands the intent behind a query, including context and related concepts. It can extrapolate from a prompt and apply a deeper level of nuance.
Rich show insights: Recommendations are based on deep analysis of podcast content, audience demographics, engagement, tone, and style.
Transparent recommendations: Every suggestion is accompanied by detailed explanations of why it was chosen, helping advertisers make informed decisions.
Early testing of Smart Recommendations with Acast’s internal sales teams and select customers directly has yielded very promising results. Over 200 campaign briefs have utilized the tool in testing, leading to a significant reduction in planning time – by up to as much as 92%. Notably, 80% of ad buyers in testing discovered additional, previously unconsidered podcasts for their campaigns. Early indicators from shows with under 50,000 weekly listens show a significant uplift, including a 14% increase in median purchase rate and a 35% increase in median visit rate, according to Podscribe data.
“This is more than just a search engine; it’s a powerful tool that transforms data into campaign success, ultimately helping advertisers maximize their podcast ad ROI with AI,” MacDonald added. “Smart Recommendations is the next step in our vision to become the intelligence layer powering podcast advertising globally, offering AI-powered podcast recommendations that truly deliver.”
About Acast
Since 2014, Acast has been building the world’s most valuable podcast marketplace, creating the technology that connects podcast creators, advertisers, and listeners. Its marketplace spans more than 140,000 podcasts, 3,300 advertisers, and one billion quarterly listens. Crucially, those listens are monetized wherever they happen—across any podcast app or listening platform. The company operates worldwide, with headquarters in Stockholm, Sweden. Acast is listed on the Nasdaq First North Premier Growth Market (ACAST.ST). Certified Adviser is FNCA Sweden AB, info@FNCA.se.
