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Discover Hidden Relationships in Your Data

Understand What Drives Behavior and Decision-Making

CAUSALITY PROVIDES STRATEGIC, TACTICAL ACTIONS


The most important business question starts with the word “why.”

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.

OUR APPROACH

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. 

 

RECENT CUSTOMER ENGAGEMENTS

 
IMPROVE CREDIT CARD COLLECTIONS

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. 

  • This client is expected to save $7 million per year by optimizing agent actions in their Will Pay segment.
  • We identified the 10-20% of account holders who are most likely to pay their bills if given a bit more time. 
  • Our solution generated data segmentation, drivers and recommendations automatically.
IDENTIFY CAUSAL BREAST CANCER GENES

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.   

  • The solution increased predictive accuracy by 22%—from 62% to 81%.
  • It analyzed 25K genes and identified causal gene sequences on 3-5 genes, rather than 70. 
  • Most importantly, it provided optimal breast cancer treatment plans for patients.  
REDUCE CHURN AND BETTER UNDERSTAND YOUR CUSTOMERS

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.

  • The client now understands the top customers to watch in particular geographical areas.
  • They’ve increased their total number of customers by improving retention.
SAVE TIME, MONEY AND DECREASE PORTFOLIO RISK

An insurance company needed to reduce time and costs and improve portfolio risk. 

  • They identified the 9 variables with the best stability and relevance to predict outcomes. 
  • The solution significantly improved the speed and cost of policy issuance and reduced the costs of further tests.
  • It also maximized the number of “preferred” cases they accepted for their agents and customers.
IMPROVE RESOLUTION TIME AND REDUCE COSTS

A telecommunications company needed to improve resolution time and reduce costs associated with their Network Operations Center.

  • They identified causal patterns and recommended solutions for each type of incident.
  • The solution suggested the top 3 results for any give problem. 
  • The result is improved productivity and a reduction in both redundancy and knowledge disparity.

Spotlight

Influence AI Outcomes


Causality allows businesses to uncover the why and influence future outcome

WATCH VIDEO
Emotion in Decision Making


The key to creating the greatest impact is understanding which data is causal.

WATCH VIDEO

TAKE THE FIRST STEP

Serving customers by looking forward as well as back is a big promise, but the power of today's new digital capabilities is vast and growing.

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