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Foundations of Incrementality
Sophie MacIntyre – Ads Research Lead, Marketing Science, Meta
Randomized Control Trials (RCTs) are the gold standard for unbiased measurement of incrementality according to Sophie MacIntyre (Meta). However, there are situations where RCTs are not available so Meta explored other methods to improve the measurement of incrementality. Meta’s researchers wanted to know how close they could get to the experimental result by using non-experimental methods. The researchers were unable to accurately measure an ad campaign’s effect with sophisticated observational methods. Additionally, traditional non-experimental models like propensity score matching and double machine learning were difficult to use and resulted in large errors. Sophie presented incrementality as a ladder of options that get closer to measuring true business value as the ladder is ascended. The different rungs of the ladder are based on how well a particular measurement approach can isolate the effect of a campaign from any other factors. This research was undertaken in collaboration with the MMA and analyzed non-incremental models, quasi-experiments with incrementality models and randomized experiments. Meta revealed that incrementality could be achieved with modeling if the research included some RCTs. Using PIE (predictive incrementality by experimentation) estimates for decision making led to results similar to experiment-based decisions. Sophie stated that academic collaborations provide quantitative evidence of the value of incremental methods. Key takeaways:- Incrementality matters because it is the foundation of good business decisions and should be the “North Star.”
- Randomized Control Trial (RCTs) are the gold standard for determining incrementality.
- Using a significant amount of data and complex models can improve the performance of observational methods but does not accurately measure an ad campaign’s effect.
- Using Predictive Incrementality by Experimentation (PIE) estimates for decision making leads to results similar to experiment-based decisions.