Cognizant helps businesses detect and grasp the “why” factors, allowing them to solve the mysteries behind customer engagement, bounce rate and purchase decisions.
Most automated machine learning platforms only take existing known model structures and attempt to fit data into them. When results are based on correlation, not causation, they lack the right actionable insights and models to explain the actual predictions being made, and they don’t identify the quality of behaviors in the data that are predictive in nature.
Using predictive analytics, the Cognizant Causality Service assumption-free engine learns, understands and adapts its conclusions.This enables our clients to understand bias and to harness predictive signals in their data to quickly home in on what matters most: identifying the best actions to achieve business outcomes.
Dealing with bias and causality requires a practical, proven mathematical approach. Our causality engine simplifies the process, reduces bias, and provides strategic and tactical actions that can be taken in response to change.
It identifies relationships in the variables and builds a customized model. That model then refines, trains and corrects itself, providing true causal factors.
The engine discovers which variables are the best predictive drivers for the user-defined business objective from thousands of variables. In so doing, it powerfully discovers combination effects where factors that are weak predictors individually are strongly predictive in combination.
This system automatically provides multiple recommendations to achieve the targeted goal.