Challenge

Client uses customer media data for targeted advertising and provides bleeding-edge TV solutions. It involves audience segmentation, micro-targeting and in-depth analytics. However, the audience data is big, fast moving, comes from diverse sources and is noisy.

Approach

The Machine Learning Hub curated human-like errors by de-noising TV/Cable programming metadata, performing string matching and clustering, and applying this to other domains as well.

Industry

  • Media

Tools Used

  • Data Science Experience (DSX)

Data Science Techniques

  • Unsupervised learning & control theory

Benefits:

The result was an increase in data quality, leading to better advertisement targeting. Customers now get more relevant advertisements, based upon their behavioral patterns and preferences.

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