This case study explores how a major US digital TV streaming company overcame challenges related to the optimization of their advertisement Campaigns. Our client needed a solution to identify optimal bids for serving ad impressions purchased through real-time auctions, occurring at a rate of 3 million requests per second.

The solution needed to address several different of challenges:

• Understanding which model was accountable for each decision.
• A/B testing and experimentation involved custom code changes, increasing the complexity of the code.
• Model scoring occasionally experienced delays, leading to reliance on subpar decision-making.
• Expensive cloud costs running custom Hadoop-based algorithms.
• Hard to evaluate the business impact of decisions made by various models.
• One monolithic process launched for training models,  
• Unexpected changes in underlying data produced sub optimal bid decisions.

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The implementation of these solutions brought about remarkable benefits for the client. There was an impressive 80% reduction in cloud costs. Additionally, bid decisioning now occurs in less than 1 millisecond, ensuring swift and efficient ad serving. The ability to concurrently operate thousands of models through controlled A/B tests has enhanced the company's agility and decision-making capabilities. And lastly, there has been a notable 30% reduction in CPA, signifying improved cost-effectiveness and performance in advertising campaigns.

• +80% Cost Reduction in cloud costs  
• 30% Reduction in CPA

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