HCL Music

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HCL Music wanted an auto-suggestive search for classical songs by artists and genres.

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There was no recommendation engine in place, affecting user engagement as users couldn’t get auto suggestions based on their taste and consumption history.

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The client aimed to run a User Acquisition campaign and expand their user base.

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The hybrid app had frontend issues, leading to stability and scalability issues. Users were unable to update to the latest version of the app.

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The backend architecture design increased infra costs, which would further escalate as the user base grew.

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Implemented MoEngage to track user behavior and assist them in listening to songs of their choice.

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BigOhTech integrated the app with marketing platforms such as MoEngage, Appsflyer, and Facebook Analytics.

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Re-architected both the iOS and Android apps, CMS, and backend to scale for marketing needs.

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Redesigned the app into a native tech stack without affecting the current user base.

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Redesigned the backend infrastructure to avoid using extra managed services and created resources customarily, reducing costs.

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Implemented Elastic Search, Logstash, and Kibana tech stack to reduce user search time for server-side tags.

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Developed an end-to-end product with Elasticsearch to assist users in searching songs by artists and genres.

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Utilized AWS managed services for CDN and CloudWatch for monitoring services.

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Implemented predictive analysis via a recommendation engine to recommend songs based on user interest and listening pattern.

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Smooth search functionality allows users to easily search for songs by artists and genres.

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Personalized recommendations for latest concerts, trending, and newly released songs.

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Successful user acquisition and engagement campaigns resulting in a user base growth of approximately 2.5 times.

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Significantly reduced cost for managed services by approximately 60%.

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Effortless updating of the latest app versions and a native experience with new features.