Nowadays, streaming media has been one of the most complex business scenarios. Data management ability and trend analysis are of great significance for audio streaming platform and content creators.
InetSoft's business intelligence tool makes a solution of high applicability and flexibility possible to drive business breakthroughs.
InetSoft's cloud flexible business intelligence solution allows the streaming platform to choose the most suitable data storage platform with high performance. Our data mashup technology provides end users with the foremost tool to implement ETL process on batch-updated audio feature data, user interaction data and streaming data from multiple data sources.
Furthermore, InetSoft's BI platform is well-designed for both technical and non-technical end user. All processes of data management including security control database scheme design can be easily done with point-and-click interface.
In audio streaming service, huge data volume can be another challenge for analyst to stay up-to-date with the latest trends of user preference. InetSoft utilizes caching and data blocks to increase the update efficiency of interactive analytic dashboard to achieve refined operations. Moreover, the OEM feature offers multi-tenant hosting to simplify processes and reduce costs. In streaming media product, user reaction becomes more unpredictable, thus accentuating the need for monitoring more features and key performance indicators. InetSoft's custom dashboard offers a variety of multiple charts and filter components to visualize multiple features such as popularity of songs, trend of audio features and user engagement.
For prominent audio streaming media platforms, song recommendation is a priority in refined operation. A real-time dashboard provides a vantage point to dig user preference on multiple features and identify the fusion of music elements among different genres. Targeted marketing strategies can be performed based on related insights. In a UGC (User generated content) product, whether platform can provide useful creator tools is closely associated with creator performance and churn rate. A flexible dashboard by InetSoft can be easily embedded into applications to help creators find the next hot music genre in certain area such as short-video. InetSoft's data intelligence web app is compatible with advanced machine learning model to mine hidden insights. It allows end users to have predicted information for reference to make data-driven decision.
In the example dashboard, the most important audio features impacting popularity are identified by regression model. Moreover, end user can deep dive into the music fusion by checking simulated decision tree of genre classification.
In music streaming industry, content operation of platform product is always a key part of marketing. Basically, membership and advertisement are two ways of monetization for most of the streaming media platform, which requires more engagement of users. Therefore, to better stimulate user enthusiasm, a smart BI platform is essential for business analytics, marketing, activity operation teams to track the latest trend of user preference and take corresponding actions.
The first step for executives is to get an overview of Music Population. Activity operation specialist can refer to the dashboard chart of Top 6 Genres, Top Artists and Popularity by Year to learn about music market share. By filtering the time frame and music type, merging music genre can be identified as the target of marketing and chosen for further analysis. The second step is to dismantle user's preference on genre or artists to get hidden insights. In music streaming platform, new recommendation sections popping up relies on the feature level analysis, which set a baseline for engineer team to optimize. In Example Dashboard, music feature chart shows a clear comparison between certain audio features. Besides, music is clustered by its key and mode, and different user groups tend to have different preference on those features. Multiple filters such as time signature, key and mode are preset for dashboard users to deep dive into any feature they are interested in.
The third step is to get quantitative prediction by machine learning algorithm. Popularity of Music Features Chart in example dashboard shows several nonlinear relationships between certain audio features and popularity. The most influential factors of different genres can also be checked in Popularity Factor Analysis and Genre Prediction Tree charts.
Copyright © 2024, InetSoft Technology Corp.
