Abstract

A podcast growth startup sought to empower creators with actionable audience insights. We developed a real-time analytics service to track listener engagement and an update service to maintain up-to-date podcast metadata. By leveraging a scalable, event-driven architecture, we enabled seamless data capture, processing, and delivery—driving data-driven decision-making for over 12000 podcasters.

About Our Client

The client focuses on helping podcasters grow their audience through a platform where they can connect and collaborate. We partnered with them to develop:

• An analytics service providing podcast and attribution insights to the podcasters using the platform.
• An internal service used to maintain up-to-date metadata for over a million podcasts.

Business Challenges

Business Needs

Seamlessly track podcast listens

Enable podcasters to set up trackable URL prefixes on hosting providers to capture listener visits and redirect them to the RSS feed.

Streaming ETL and Analytics

Preprocess, transform and perform analytics on listener visit data.

Attribution Algorithm

Leverage listener's digital fingerprint in an attribution algorithm to identify collaboration-driven audience growth.

Report analytics via REST APIs.

Analytics and Attribution insights are delivered through REST APIs

Challenges

Speed

For a good user experience, the listener should be routed to the actual episode with minimal latency (in milliseconds).

Scale

The real-time streaming ETL and analytics pipeline should be able to handle millions of visits per day. During peak traffic, we need to expect a huge burst of concurrent visits.

Data Authenticity

The metrics delivered by our service must be accurate for podcasters to interpret the insights.

Privacy Compliance

Anonymize personally identifiable information while collecting analytics data.

Solution Details

1. Capturing listener visits

Our service is seamlessly integrated with podcast feeds by setting up a tracking prefix on any hosting platform. Once set up, listener visits to the podcast are captured.

2. Real-time streaming data ETL pipeline

Our ETL pipeline extracts, pre-processes, transforms and loads listener visit data into a relational database. Given the scale of millions of visits per day, the data is pre-aggregated ahead of time to optimize processing. It is then ready to be queried downstream.

3. Delivering analytics and attribution insights through APIs

Podcast and episode-level metrics, along with attribution insights, are delivered via our Analytics Reporting APIs.

4. Service Monitoring and Alerts

Automated monitoring triggers email alerts in case of infrastructure issues.

5. Audience Attribution

Based on our understanding of how podcasters grow their audience through collaborations, we developed a custom attribution algorithm.

Key Features of Solution

Listener Redirect and Data Capture

Integrated tracking prefix that captures listener visits and redirects them to the audio file

Real-Time ETL Pipeline

A scalable streaming pipeline to process, transform, and store real-time listener visit data.

Data Cleaning and Enrichment

Clean, deduplicate, standardize and enrich listener visit data, ensuring accuracy and consistency for downstream analytics.

Data Aggregation

Utilize PGSQL stored procedures for pre-aggregating data, ensuring fast and scalable API performance for podcast metrics.

Attribution Algorithm

A custom attribution logic to measure audience growth impact as a result of podcast collaborations.

Technology Used

Results and Impact

Actionable Podcast Metrics

Powered platform with podcast and episode-level metrics to optimize growth and advertising.

Measure collaboration effectiveness

Attribution algorithm measures audience growth from collaborations.

Scalable and Reliable Analytics Solution

Delivered a low-latency, event-driven and scalable system to give podcasters up-to-date metrics

Conclusion

Managing real-time podcast analytics at scale presents unique challenges, but with the right architecture and strategies, it becomes a seamless process. By leveraging an event-driven architecture and robust monitoring, our solution ensures minimal latency, scalability and high availability. This solution empowers podcasters with actionable insights, drives audience growth, and strengthens the platform’s competitive edge in the podcasting landscape.