AI-Powered Personalization Boosts Podcast Engagement 7×

Podcast Platform Boosts Engagement 7× Using AI Recommendations

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.

Media

Data Science

Data Insights & Forecasting

higher user engagement

0.5

secs

average recommendation delivery speed

recommendations/sec throughput

The client is a leading web and mobile podcast platform in Saudi Arabia, recognized across the MENA region for its high-quality, authentic journalism in text, visual, and audio formats.

Databricks

TensorFlow

Spark

PostgreSQL

Databricks vector search

THE CHALLENGE

The podcast platform faced very limited personalization due to its manual recommendation process

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.

No Adaptability to User Behavior

Recommendations weren’t responsive to user preferences, resulting in low engagement and poor discovery of new content.

Missing Real-Time Data Infrastructure

The system lacked pipelines to process data instantly, preventing real-time insights and timely content delivery.

Cold Start Problem for New Users

With no recommendation system for users without history, new listeners had a poor first experience.

Scalability Gaps

The legacy approach couldn’t support rapid growth in users and content, limiting future expansion.

Boost Engagement 7× with AI-Powered Personalization

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Boost Engagement 7× with AI-Powered Personalization

Validate your strategy with a 1-Week PoC

THE SOLUTION

AI-Powered Recommendation Engine with 7x Engagement Boost

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.

Automated Data Pipelines

Developed real-time ETL pipelines to collect and process behavioral data, ensuring accuracy and enabling consistent recommendations.

Contextual Cold Start Solution

Used contextual data such as time, location, and language to deliver relevant suggestions for new users without history.

Modular, Scalable Architecture

Created a system with multiple recommendation modules and a ranking model that scales with users and content growth.

Cross-Signal Personalization

Integrated listening history, comments, and metadata to generate recommendations across all content types, boosting relevance.

THE RESULT

AI-Powered Personalization: 7x More Podcast Engagement and 30% Higher User Interaction

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.

<0.5 sec

average recommendation delivery speed

~20

personalized recommendations processed per second

7×

higher user engagement compared to the manual system (A/B tested)

Podcast Platform Boosts Engagement 7× Using AI Recommendations

Steps of providing data scraping services

Step 1 of 5

Free consultation

It's a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.

Step 2 of 5

Discovering and feasibility analysis

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs. At this stage, we need to ensure that we understand the full scope of the project. Receive from you or perform a set of interviews and prepare the following documents: list of features with detailed description and acceptance criteria; list of fields that need to be scraped, solution architecture. Ultimately we make a project plan which we strictly follow. We are a result-oriented company, and that is one of our core values as well.

Step 3 of 5

Solution development

At this stage, we develop the scraping engine core logic. We run multiple tests to ensure that the solution is working properly. We map the fields and run the scraping. While scraping, we keep the full raw data so the final model can be enlarged easily. Ultimately we store data in any database and run quality assurance tests.

Step 4 of 5

Data delivery

After quality assurance tests are completed, we deliver data and solutions to the client. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project. While developing and crawling data, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value.

Step 5 of 5

Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.