A global hospitality enterprise required a clear and defensible method to determine which promotional pricing programs were actually driving incremental revenue. Traditional reporting couldn’t isolate true promotional lift, leaving revenue teams unsure of what was genuinely working. 

Using AI-driven causal inference algorithms, KPI Partners isolated true promotional lift, improved forecast accuracy, and enabled a repeatable, statistically reliable approach to evaluating promotions across markets.

This case study demonstrates how AI-based causal measurement techniques can replace assumption-driven analysis with reliable insights, facilitating faster decisions, more informed pricing strategies, and long-term revenue optimization.

By applying AI-driven causal inference techniques, KPI Partners replaced guesswork with statistically reliable measurement. Using algorithmic comparison models and rigorous causal analysis, KPI revealed the real impact of each promotion, highlighting what worked, what didn’t, and why.

The result was measurable revenue lift, sharper forecast accuracy, and far more confident pricing decisions. Today, the client uses this AI-based, repeatable measurement approach to evaluate promotions across markets, reduce uncertainty, and accelerate revenue strategy with data-backed precision.

Download the Extended Case Study (.pdf)