Advanced A/B Testing

Standard A/B testing answers "does this variant win?" Our advanced experimentation platform answers "for whom does it win, when, and why" while designing experiments that finish faster and with stronger statistical rigour. We combine sequential analysis, heterogeneous treatment effects, and adaptive designs to extract maximum learning from your experiments.

We applyCausal Machine Learningto solve complex business challenges.

Our methodology includes sequential hypothesis testing that reduces experiment duration while maintaining statistical validity, [causal forest methods](/research#heterogeneous-treatment-effects) to identify which customer segments benefit most from treatments, and network effect detection for cases where users influence each other. For marketplaces and social platforms, we identify and account for interference where randomization of one user affects other users' outcomes, avoiding biased estimates that traditional A/B tests produce.

E-commerce companies running our platform increase experiment velocity while maintaining rigour. Teams identify nuanced insights like "checkout optimization benefits new users but hurts repeat customers" that simple A/B test summaries miss. Companies avoid overweighting results from segments that would have converted anyway, instead focusing iterations on high-leverage populations.

Multi-armed bandit capabilities let you balance exploration and exploitation, dynamically allocating traffic to better-performing variants while experiments run, maximizing cumulative impact.