Media & Entertainment Applications

Content Recommendation

Content recommendation models analyze user preferences and behaviors to personalize content for an individual user. Since consumer behavior changes with time and context, production models often struggle to keep up with evolving trends, map non-linear relationships, and navigate highly subjective data.

  • Deploy automated monitors at scale to continuously identify prediction groups impacted by evolving trends for quick resolution
  • Calculate custom metrics, such as popularity bias and uniqueness, tailored to areas of concern informed by model performance
  • Share notable insights with dynamic dashboards for stakeholders to revisit the weights of a feature or rebuild a feature that impacts prediction relevancy
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Targeted Advertising

Engagement models customize ads based on user behaviors or traits. These models inform a significant amount of media spend and use large amounts of data that are often limited by historical trends and data privacy.

  • Use cohort filtering and native A/B comparison support to understand how metadata (e.g. age, gender, location) impacts other features to inform retraining or rebuilding efforts
  • Gain a historical view of model data to improve user engagement based on time sensitive information such as seasonal trends or release timing
  • Track custom metrics to attribute business outcomes, such as conversion rate, with model performance values to quantify ROI
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Churn Forecasting

Churn forecasting models are used to predict the likelihood of a customer discontinuing their subscription or switching to a different service. These models are typically built using historical data that include features such as viewing habits, usage frequency, and content preferences. These features drive prediction outcomes and can become stale or irrelevant quickly, leading to performance degradation.

  • Account for all new data in real-time to continuously evolve marketing strategies as preferences and trends change
  • Gain better visibility into the model features and dimensions that actually influence customer churn, with the ability to monitor custom metrics
  • Automatically surface the most relevant features impacting model performance and drift to inform future model versions
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