2.6 Million Podcasts and counting...

Challenge

Podcast advertising works best when placement matches audience, but identifying the right shows across 2.6 million active podcasts is not something a marketing team can do manually. The data that would support those decisions exists but is scattered across dozens of directories, updated at different intervals, and not aggregated anywhere useful. Episode frequency, review counts, listener growth signals, and social activity all matter, and none of it lives in one place. PodcastWise needed infrastructure to collect, update, and organize that data across every major podcast feed every day. They also needed a layer on top of the data that could turn raw numbers into placement recommendations for marketing teams who don’t have time to analyze feeds one by one.

Solution

We built a distributed scraping platform in Python that pulls updates from all 2.6 million podcast feeds daily, collecting new episodes, reviews, and social signals on a continuous cycle. RabbitMQ manages the scraping queue and the number of workers scales with current load, so the system processes quickly when the queue is heavy and backs off when it isn’t. The collected data feeds AI models that analyze audience fit and surface podcast recommendations based on a marketer’s target profile. PodcastWise customers receive recommendations built on actual data from the full podcast landscape rather than curated lists or self-reported audience claims. The data pipeline runs without manual oversight and keeps the database current across millions of feeds.

Results

PodcastWise customers make podcast advertising placement decisions with confidence because the recommendations are grounded in real data from all 2.6 million feeds. The scraping infrastructure processes at scale every day without falling behind the update cycle. Marketers using the platform find options they wouldn’t have surfaced through manual directory research. The system handles the volume it was designed for and the data stays current without anyone managing it directly. The combination of the data pipeline and the recommendation layer turned a research problem into a product that advertising teams use daily.