Problem:
In programmatic advertising, wrong decisions compound quickly – wasted resources, missed opportunities, and outcomes that are difficult to course-correct after the fact. Our client’s teams were operating reactively, evaluating campaign performance only in hindsight. Without a way to anticipate outcomes upfront, there was no clear mechanism to prioritise high-potential opportunities or allocate spend efficiently before results came in.
Approach:
-
- Define the success metric – a clearly scoped target aligned to the campaign objective, forming the foundation of the training setup.
- Construct the training dataset – past observations enriched with contextual signals to capture real-world performance patterns and serve as model predictors.
- Train and evaluate multiple ML models – several models were benchmarked against each other to identify the one that best captured the relationship between signals and outcomes.We built a predictive framework trained on historical performance data, designed to surface expected outcomes for each advertising entity before a decision is made. The solution was developed in four stages:
- Deploy into the ML pipeline – the best-performing model was operationalised to generate predictions on new, unseen cases in real time.
Impact:
The client’s teams shifted from reactive to proactive decision-making. By surfacing predicted outcomes upfront for each advertising entity, the model enables teams to focus energy on high-potential opportunities, reduce wasted spend, and improve overall campaign efficiency – at scale.
