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Cognizant telecom clients

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The analysts needed to preprocess and analyze this large data set. They wanted to generate plots and other visualizations that they could use to evaluate algorithm output and communicate the results to their customer. Rao and his team imported the raw data and cleaned it by developing preprocessing algorithms that used built-in MATLAB functions to remove records with null, negative, and missing values.

They then explored the data. They generated scatter plots in MATLAB to identify outliers in the data and visualize the average time taken to detect and resolve service issues, a key customer metric for churn. The team used Deep Learning Toolbox to create, train, and simulate a neural network for churn prediction. Using Statistics and Machine Learning Toolbox, the team developed one churn prediction algorithm based on decision trees and another based on multivariate logistic regression.

Their analysis identified three key drivers of churn: delayed responses, delayed delivery of services, and problems with quality of service. After comparing results from the neural network, the decision tree, and logistic regression, the team found that logistic regression produced most accurate churn predictions.

Algorithm development time cut from weeks to days. Project completed in less than half the allotted time. Hundreds of gigabytes of data analyzed. Learn more about Cognizant. Select a Web Site. Choose a web site to get translated content where available and see local events and offers.

Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance. The analysts needed to preprocess and analyze this large data set. They wanted to generate plots and other visualizations that they could use to evaluate algorithm output and communicate the results to their customer. Rao and his team imported the raw data and cleaned it by developing preprocessing algorithms that used built-in MATLAB functions to remove records with null, negative, and missing values.

They then explored the data. They generated scatter plots in MATLAB to identify outliers in the data and visualize the average time taken to detect and resolve service issues, a key customer metric for churn. The team used Deep Learning Toolbox to create, train, and simulate a neural network for churn prediction.

Using Statistics and Machine Learning Toolbox, the team developed one churn prediction algorithm based on decision trees and another based on multivariate logistic regression. Their analysis identified three key drivers of churn: delayed responses, delayed delivery of services, and problems with quality of service. After comparing results from the neural network, the decision tree, and logistic regression, the team found that logistic regression produced most accurate churn predictions.

Algorithm development time cut from weeks to days. Project completed in less than half the allotted time. Hundreds of gigabytes of data analyzed. Learn more about Cognizant. Select a Web Site. Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance.

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The Three Types of Customers Connected Products Can Help - Cognizant

WebNov 5, †∑ Cognizantís new location in Riga Ė a one-stop-shop for strong telco and IT/technology expertise Ė opened in May The center, with its + strong team, . WebThere are many prestigious client Cognizant have, some of them are listed below. JPMC; Wells Fargo; BMW; Sanofi; GSK; Pfizer; Merck; Etihad Airways; Johnson and Johnson; . WebIn the telecom industry, retaining existing customers costs less than attracting new customers. As a result, telecom companies focus on reducing the customer churn .