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A leading podcast platform partnered with Dataforest to replace manual recommendations with an AI-powered personalization engine. The new system analyzes user behavior and context in real time to deliver tailored suggestions in under 0.5 seconds, handling up to 20 recommendations per second. This resulted in 7× higher user engagement, enhancing listener experience and significantly increasing the client’s revenue.
7
×
higher user engagement
<
0.5
secs
average recommendation delivery speed
~
20
recommendations/sec throughput
Databricks
TensorFlow
Spark
PostgreSQL
Databricks vector search
THE CHALLENGE
The podcast platform relied on static, manually curated recommendations that couldn’t adapt to user behavior. This restricted engagement, slowed revenue growth, and left the platform behind competitors who leveraged dynamic personalization.
Recommendations weren’t responsive to user preferences, resulting in low engagement and poor discovery of new content.
The system lacked pipelines to process data instantly, preventing real-time insights and timely content delivery.
With no recommendation system for users without history, new listeners had a poor first experience.
The legacy approach couldn’t support rapid growth in users and content, limiting future expansion.
THE SOLUTION
We built a flexible recommendation model that processes diverse user signals in real time. It delivers highly relevant podcast suggestions, improving user engagement by 7x and enabling scalable growth.
Developed real-time ETL pipelines to collect and process behavioral data, ensuring accuracy and enabling consistent recommendations.
Used contextual data such as time, location, and language to deliver relevant suggestions for new users without history.
Created a system with multiple recommendation modules and a ranking model that scales with users and content growth.
Integrated listening history, comments, and metadata to generate recommendations across all content types, boosting relevance.
THE RESULT
A leading podcast platform in Saudi Arabia and the MENA region needed to replace its static, manually curated recommendations to drive growth and user satisfaction. DATAFOREST delivered a scalable AI-powered recommendation engine that personalizes podcast suggestions in real time based on user behavior, preferences, and context.
We developed a modular system with a learning-based ranking model and a real-time data pipeline to process user activity efficiently. Key challenges included scaling architecture, integrating diverse interaction signals, and solving the cold start problem for new users. It was addressed by combining behavioral data with contextual metadata (e.g., time, language, location). Recommendations now auto-update every 48 hours, ensuring ongoing relevance and eliminating manual work.
This transformation enabled the podcast to personalize content at scale, increase user satisfaction, boost revenue, and future-proof its platform with a flexible, data-driven solution.
average recommendation delivery speed
personalized recommendations processed per second
higher user engagement compared to the manual system (A/B tested)
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