Big Data To Predict Video-On-Demand User Behavior, Driving EUR 4MM Of Revenue

Our client is a major global Telecom player, headquartered in France. They have diversified businesses spanning Mobile, Landline & Internet, Broadcasting, and Music.

Their objective was to improve Average Revenue Per User (ARPU) through targeted customer retention and upsell promotion campaigns. The high-growth VOD and gaming content divisions were identified as focus areas.

Challenges

60 GB+ dataset with over 450 million transactions
Limited existing metadata on movies/series and other VOD content
Constantly changing user base for pay-per-view format (low stickiness), with limited subscriber related demographic info (as these are household IDs).

Solution Methodology

Observed month-on-month user purchase trends, on various dimensions like purchase frequency, revenue bucket, content price, time and day of the week, etc.
Segmented users based on frequency buckets (purchase frequency per month) and their transition behavior (upward, downward, inactive, stable) month-on-month.
Augmented content metadata by extracting content attributes (genre, cast, ratings etc.) through APIs from sources such as IMDb and Google Translate for English-French translation
Developed machine learning (in this case a Parallel Random Forest model) to predict churn and upsell propensity. Used Variance Inflation Factor (VIF), Mahalanobis Distance and Correlation Matrix to guide feature selection
Fine-tuned model performance considering trade-off between accuracy and capture rate – depending on the size of user base and costs associated with intervention efforts.

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Big Data To Predict Video-On-Demand User Behavior, Driving EUR 4MM Of Revenue

Challenges

Solution Methodology

Business Impact

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