Uncover the Why Behind Your Data

The challenge

A mid-market e-commerce retailer was losing customers at an accelerating rate. Their existing ML model flagged at-risk customers but couldn't explain why they were churning — making interventions generic and ineffective.

Our approach

Cogxta deployed Cogxta DataLens to analyse 18 months of behavioural, transactional, and support data. The causal model identified that delayed delivery notifications — not product quality — was the primary driver of churn, mediated through a drop in app engagement.

The result

Targeted interventions on the delivery notification flow reduced churn by 23% within two quarters. The team could now act on root causes, not symptoms.

The challenge

A fintech lender's credit scoring model was producing high false-positive rates, leading to rejected applications from creditworthy customers and regulatory scrutiny around model explainability.

Our approach

The Cogxta Inference Engine was integrated into the existing scoring pipeline to perform causal feature selection — removing spurious correlates and identifying the true causal predictors of default risk.

The result

Model accuracy improved by 40%, false positives dropped by 31%, and the team produced a fully auditable causal explanation for every credit decision — satisfying both internal risk teams and regulators.

The challenge

A healthcare operations company found that their AI diagnostic tool performed significantly worse for certain demographic groups — a bias problem that traditional fairness metrics couldn't fully resolve.

Our approach

Cogxta applied causal debiasing techniques to identify and block the confounding pathways through which demographic proxies were influencing predictions. The causal model separated legitimate clinical signals from spurious demographic correlations.

The result

Diagnostic bias across demographic groups was reduced by 61%, and overall model performance improved. The organisation could now deploy the tool with confidence across their full patient population.