RSS.com Podcasting provides industry-leading analytics to podcasters. Our analytics services follow state-of-the-art guidelines in the industry to measure podcast metrics. An overview of the metrics we provide to our podcasters is available in our Knowledge Base.
Our analytics service is based on the data we collect each time we receive a download request for your show. Because we are the hosting platform, when a listener downloads one of your episodes via Spotify, Apple Podcasts, Google Podcasts, Amazon Music etc... they leave a footprint in our systems through a, so called, “log”.
These logs consist of data that includes IP address (from which we can infer geolocation of the download), device and operating system used, and which specific files were downloaded.
At RSS.com Podcasting, we have automatic processes in place that continuously ingest, curate and analyze the data collected in our logs to extract relevant metrics and insights that we are offered to our users via the Analytics tab in our Dashboard.
Some of these metrics are mathematical calculations (e.g. total downloads), while others (e.g. followers) are statistical inferences based on certain assumptions.
The most important metric of all is the number of Downloads. To calculate the number of downloads, we take a conservative approach and make sure that we filter our logs to exclude false positives which include methods to identify duplicates. This avoids inflated metrics and it is part of the technical guidelines for podcast measurement by the IAB Tech Lab.
Because we host your episode in our system, we are able to measure downloads across all the podcasting platforms. For instance, if you get 50 downloads from Google Podcasts and 50 from Apple Podcasts, our analytics will show a total of 100 downloads.
This information allows you to assess the overall performance of your podcast, to get an estimate of your number of Followers, to understand their segmentation and geolocation, as well as to identify patterns that can help you maximize the reach of your show (e.g. peak downloads by weekdays and hours).