Markets move, catalogues change, and customer behaviour drifts. A decision system that is tuned once and left alone quietly gets worse. adBrain builds the learning loop that keeps decisions improving through disciplined experimentation and adaptive policies that learn in production.
Experimentation
- A/B and multivariate experiments: the classic tool for a clean, randomised comparison of alternatives.
- Geo and market-level testing: for effects that don’t split cleanly at the user level, so you can still measure causal impact on channel spend or market-wide changes.
- Incrementality testing: designed to isolate the additional impact of an intervention, so you know what genuinely worked rather than what merely correlated.
Experimentation is how adBrain turns “we think this helped” into “this moved the metric by a measured amount”: the prove in predict, decide, prove.
Adaptive optimisation with contextual bandits
Classic experiments answer one question and then end. Many commercial decisions are continuous: which variant, offer, or action to serve this request, given this context, while still learning. That is what contextual bandits are for.
- They use user or event context to personalise the choice.
- They balance exploration (trying alternatives to keep learning) against exploitation (using what already works) so the system keeps improving without freezing on an early guess.
- They are evaluated off-policy where possible, so promising policies can be assessed before they are rolled out widely.
This is a production method, not an academic one: it is how modern experimentation and decisioning platforms personalise under uncertainty, and it maps directly onto adBrain’s live decision loop.
Closing the loop
Experimentation and optimisation are where the loop closes. Real-time event processing supplies the context and the fast feedback; predictive & causal modelling supplies the estimates and the lift measurement; machine learning decisioning turns it all into the action served. Each experiment feeds the next model, and each policy update makes the next decision a little better.