Developer team leverages machine learning (ML) to transform Podimo's search and discovery experience
STX Next, a global leader in IT consulting, successfully improved the search and discovery capabilities of Podimo, a platform for hosting and monetizing podcasts, audiobooks, and content from other audio creators. The experienced development team implemented a number of new technologies, including machine learning models, to improve the discovery experience and significantly increase engagement and retention among Podimo's user base.
Commenting on the partnership, Global Director Benjamin Vierling said: artificial intelligence Podimo said: “Our collaboration with STX Next has been very professional and fruitful. They have consistently delivered high-quality ML models, improved our search engine, and successfully prototyped our application with LLM, demonstrating their reliability and expertise.”
Founded in Copenhagen in 2019, Podimo is a platform for podcast and audiobook lovers, and audio content creators. Its content library includes over 1,000 exclusive shows and 70,000 audiobooks. Its revenue-sharing model rewards creators for making content that people want to hear, whether they have an existing fan base or a niche audience.
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Challenge
Podimo wanted to revolutionize its search capabilities to better serve its diverse user base.
With a focus on rewarding creators and providing a seamless listening experience, Podimo needed to improve matching accuracy and relevance based on user preferences.
Podimo's existing search system had limited understanding of query and content semantics, which often resulted in irrelevant results and challenged meeting user expectations. To address this, STX Next proposed incorporating advanced methodologies such as Learning to Rank (LTR), semantic search, and conversational search to improve the experience.
Additionally, STX Next was asked to implement ML models that would enable analysis of user interactions and help Podimo understand user intent to power its conversational search interface.
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solution
Given Podimo's unique needs, the dedicated team at STX Next proposed implementing new features, including dynamic functionality. ML Model Train user interactions to refine rankings of podcasts and audiobooks, tailoring search results to user preferences.
The team also implemented a semantic search model designed to understand the context of user queries, enabling content discovery beyond vocabulary matches, and integrating lexical and semantic search results. ML Model Re-rank results based on accuracy and relevance.
Finally, STX Next introduced a conversational search assistant that bridges the interaction between users and the search engine and collects feedback for further enhancements.
result
The introduction of these features has brought significant benefits to Podimo, including improved search relevance, meaning users now receive more relevant search results tailored to their specific queries and content preferences.
The team also enhanced discovery through an overhaul of semantic search capabilities, helping users find relevant content that matches their interests and intent. An intuitive and effective search experience also improved engagement and retention, with key benefits including longer session durations, reduced search time, and higher watch and follow rates.
Finally, the introduction of a conversational search assistant demonstrates Podimo’s forward-thinking approach to user interaction, differentiating the company in a competitive market and paving the way for future development.
STX Next, a global leader in IT consulting, specializes in delivering cutting-edge AI-powered software solutions. Our expertise is backed by the insights of 500+ CTOs, ensuring our services are aligned with the latest industry challenges and leadership strategies.
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