Targeted Marketing

Targeted marketing powered by causal inference identifies not just which customers respond to campaigns, but why they respond and how different messages affect heterogeneous populations. Traditional segmentation relies on correlation; our approach uncovers causal treatment effects across customer subgroups.

We applyCausal Machine Learningto solve complex business challenges.

We apply randomized controlled trials and observational causal inference methods to isolate the true incremental impact of each marketing intervention. This means you eliminate wasted spend on customers who would have converted anyway and identify high-value segments where your marketing has the strongest causal influence. Using methods like causal forests and Bayesian additive regression trees, we estimate [heterogeneous treatment effects](/research#heterogeneous-treatment-effects) that reveal which customer characteristics predict response to specific messages and channels.

Organizations using our platform can significantly reduce customer acquisition costs while maintaining acquisition quality. E-commerce retailers improve email campaign ROI by precisely targeting customers most likely to respond to specific product recommendations. B2B companies optimize account-based marketing by identifying decision-maker segments where their messaging drives measurable behavior change.

The platform provides transparent attribution at the customer level so you understand exactly which audiences drive incremental revenue from each campaign.