Real-Time Algorithmic Digital Ad Bidding

Case Study

Real-Time Bidding Models That Power 200MM Digital Ads Every Day

Business Objective

Our client is a leading digital media company that is transforming mobile advertising through data-driven predictive solutions. To be competitive and deliver the best value to their clients, they needed reliable predictions of win probability as well as the probability of click-through of each digital ad in their inventory to determine the optimal price for real-time bidding.

Challenges

  • Tens of millions of data points to analyze every hour
  • Attributes with many levels, amounting to more than one million points
  • Clicks are very rare events – typically one for every 3,000 impressions.

Solution Methodology

  • The raw data consisted of ad impression attributes, publisher attributes, category restrictions, and user profiles
  • Performed feature engineering using feature hashing and CTR encoding to handle columns with large number of categories
  • Reduced the number of data points by ten-fold without loss of prediction accuracy through case sampling
  • Developed a predictive model to predict the win and click-through probability ofeach ad impression. Validated the models on sub-samples of various mobile platforms. One of the chief insights obtained was that the top 20% of the impressions capture 43% of the clicks
  • Built a robust solution which can accommodate constraints like category restrictions and new websites for which historical data is not available

Business Impact

  • The digital media company obtained benefits worth USD 1.2MM per quarter through the customized real-time bidding solution
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