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Business Impact

  • Optimized

    Ad campaigns

  • 5x

    Increase in Conversion Rate

Customer Key Facts

  • Location : Mountain View, California
  • Size : 201-500 employees
  • Industry : Media & Entertainment

Problem Context

The client, a TV data company, wanted to determine the switching pattern of viewers and identify users who have lapsed a particular show. They also wanted to assign a propensity score to each user having a higher tendency of watching a new show on TV to segment the group and target them efficiently.

Challenges

 

  • Inconsistent attribute details in the metadata
  • Multiple sources of data resulting in disparity in granularity

Technologies

Amazon S3
Amazon SageMaker
Amazon Lambda
Amazon Athena

Leveraging Statistical Analysis and ML-Driven Clustering for Switching Behavior Analysis and Propensity Scoring

Solution

Quantiphi leveraged Statistical Analysis and ML-driven clustering to generate insightful viewership patterns, understand switching behavior and viewership preferences. It helped the firm target the right set of audiences to optimize and improve the Ad campaign.

Result

Generation of Insightful viewership preferences in real-time leading to better campaign optimization

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