Why are customers buying our products or why aren’t they? Why are employees leaving the company? What’s affecting this? Without understand the ‘why’ behind the issue at hand, businesses can never truly resolve their most critical issues, and they may even be wasting time on the wrong ones. Most automated machine learning (ML) 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 the 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. Cognizant’s assumption-free Causality 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.
A leading provider of co-branded credit cards needed help better understanding the factors involved in debt collection to reduce their high default risk and provide insights on how to improve their collections and reduce their $900 million annual write-offs.
A genetic diagnostic company wanted to improve their diagnostic testing and apply the most effective treatment regimen. Genetic information and historical data that followed 300 subjects over 10 years was analyzed.
Large U.S.-based food and beverage company wanted help understanding and reducing churn worldwide. This was accomplished by identifying the drivers of churn and the data patterns they could alter to affect change.
An insurance company needed to reduce time and costs and improve portfolio risk.
A telecommunications company needed to improve resolution time and reduce costs associated with their Network Operations Center.
Causality allows businesses to uncover the why and influence future outcome