Industries — Media & Entertainment

Media Innovation

Enhancing media and entertainment with AI and data consulting

At Aishore, we specialize in data and AI consulting for media and entertainment:

Audience Understanding: AI analyzes user data and social media to personalize content recommendations and predict trends.
Optimized Distribution: AI insights identify best channels for targeted content distribution, maximizing audience engagement. Enhanced User Experience: Personalized search and interactive experiences enhance engagement.
Monetization Strategies: AI optimizes ad placement and prevents piracy, safeguarding intellectual property. Aishore enables media companies to leverage AI for business growth in a competitive market.

Use Case

Data mining to convert free users to paid subscribers

An freemium online music platform had challenges in converting free users to paid subscribers in a freemium model

User Retention Without Conversion

Many users remain on the free version indefinitely, creating a challenge in converting them into paying subscribers.

Balancing User Growth and Revenue

Companies must find a way to maintain a large free user base while generating enough revenue to stay viable.

Low Predictive Accuracy in Conversion

Despite using multiple classification models, predicting which users will convert to paid subscribers remains difficult with an overall misclassification rate of 21%.

Identifying High-Value Subscribers

Differentiating between profitable and less profitable users requires complex analysis, as premium subscribers are 24 times more valuable than free users.

Solution

We developed an AI-driven credit assessment system to streamline and enhance evaluations with three core components:

The solution involved building several classification models, including logistic regression, neural nets, and classification trees, to predict which free users were most likely to convert to paying subscribers. The classification tree model was recommended for maximizing profit, while logistic regression minimized misclassification errors. Additionally, unsupervised clustering methods identified patterns among users likely to upgrade, enabling better targeting and monetization strategies.

FMCG