X3M Drives Average Revenue Per User With Real-Time Waterfall Optimization
Case Study: X3M
Posted by Mateo De Monasterio
on June 13, 2023 · 3 mins read
About The Company
X3M emerges as a fresh and dynamic AdTech enterprise with strong industry support. Their primary goal is to establish a publisher-centric approach in an industry predominantly focused on advertisers. Originally receiving support from Etermax, X3M has since evolved into an autonomous entity committed to transparency and impartiality. Their comprehensive range of services encompasses a fully managed AdOps service, a mediator, an exchange, and various other offerings.
X3M's mediator encompasses a key aspect of their services: optimizing waterfalls. This presents a unique challenge as publishers possess less data compared to advertisers and must navigate multiple advertising platforms with varying behaviors, all while contending with the ever-shifting landscape of buying mechanisms.
Essentially, it becomes a strategic game where one party aims for higher CPMs while the other strives for impressions at lower CPMs. Consequently, X3M must continuously fine-tune their logic to gain a competitive advantage, considering the intricate understanding of each network's behavior and developing a scalable solution that accommodates diverse demographics and game scenarios.
While X3M had been refining this process for a considerable time, they wanted to consider a fresh approach leveraging AI and Machine Learning to achieve the next significant breakthrough. With this in mind, X3M determined that Mutt was the ideal partner for tackling this ambitious endeavor.
Our team of expert #DataNerds worked with the X3M team to successfully develop an innovative real-time waterfall optimization algorithm driven by state-of-the-art AI systems. By utilizing predictive models for fill rate and latency, the task force created real-time waterfalls that optimize revenue potential for each advertising opportunity, considering the available placements.
To validate our logic, an offline system simulator for any waterfall policy was developed, enabling the team to evaluate the behavior of the new implementation and further iterations. This tool proved instrumental in assessing the impact of upcoming changes and became an integral part of the daily toolkit for data scientists, AI specialists, and machine learning engineers working on waterfalls. Through these simulations, X3M efficiently iterated on new machine learning models, business rules, heuristic adjustments, and other design decisions.
Throughout our AI journey, we elevated the AB testing process by automating analyses, conducting significance testing, and establishing key performance indicators (KPIs). To achieve this, we harnessed the power of Vowpal Wabbit, Airflow, MLFlow, Databricks, and good old-fashioned maths. We're AWS Advanced Partners, and this project utilized AWS tools and technology.
The implementation of X3Ms and Mutt Data's AI solution at X3M's client company, Etermax, yielded a significant impact.
- 20% increase in CPM
- 5% increase in ARPDAU (average revenue per active daily user)
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