data feed management
The advertising industry is increasingly reliant on machine learning algorithms to optimise bidding strategies, target audiences and allocate resources. As a result, there is a growing need to improve the efficiency and cost-effectiveness of these algorithms.
This project proposes a comprehensive three-phase approach to influence machine learning systems in advertising platforms and improve their learning capabilities.
Biggest Challenges
- Bringing the Business Model the the crowd platforms;
- Build a robust Data Architecture for analysis & smart activation;
- Dynamically feed the platforms with enriched data;
- Understand the evolution of Machine Learning and AI and cohort;
- Scale with Performance;
Our Approach
Aggregating data from a brand’s entire online ecosystem.
Incorporating product demand data and market benchmarking to further enhance algorithm efficiency.
Implementing insights from the previous phases to improve aspects of the advertising process – product labeling, campaign structures, and business relations with products.
Aggregating data from a brand’s entire online ecosystem.
Incorporating product demand data and market benchmarking to further enhance algorithm efficiency.
Implementing insights from the previous phases to improve aspects of the advertising process – product labeling, campaign structures, and business relations with products.
How we did it
The project is being developed by Wise Pirates’ internal team, specialized on business intelligence and advanced feed management in straight collaboration with key players on e-commerce and retail ecosystem and using google advanced features to deploy the best quality and information on the feed ecosystem.
The changing path
- Correlate bidding incrementality with business goals
- Dynamic Budget Allocation based on performance
- Campaign Dynamic Product allocation based on performance cutpoints
- Category product performance based on business intelligence data deploy
Our Framework
System benefits
- Automatic dynamic product allocation and optimization between campaigns based on external algorithm;
- Dynamic budget allocation in product/performance ratio;
- Use of business intelligence in feed;
- Responsiveness to product performance across all channels;
- Average market price and integrated demand;
- Increased routes and transactional potential;
- Capture Missed query opportunities.
