Data Tech 2026

Most campaign measurement frameworks rely on redemption-based metrics, which fail to distinguish true incremental impact from temporal demand shifting. We introduce the Longitudinal Measurement Framework (LMF), a causal inference–driven approach to quantify the long-term effects of campaigns on guest behavior.

LMF estimates changes in Guest Lifetime Value and churn using a difference-in-differences design over pre- and post-campaign periods. The framework produces treatment effects that account for partial incrementality, separating short-term transactional lift from sustained behavioral change. By incorporating counterfactual baselines and longitudinal tracking, LMF mitigates biases inherent in redemption-based approaches, including selection effects and pull-forward behavior.

We present the methodological foundations of LMF along with key considerations for large-scale implementation in real-world settings, emphasizing the need to move beyond point-in-time metrics toward longitudinal, causally grounded evaluation of marketing effectiveness.