AI Case Study

Condé Nast improves average response time to issues during new content and capability rollout to their websites by 70% using machine learning

Condé Nast ensures consumers of digital and video content always get a smooth experience using AI. Rollouts of new content or features are constantly monitored and application performance is analysed using machine learning to detect anomalies or issues enabling them to react promptly.

Industry

Consumer Goods And Services

Media And Publishing

Project Overview

Condé Nast manages resources to ensure seamless rollout of constant content and features by using New Relic's DevOps platform. The platform constantly monitors network and traffic to identify any glitches and generates alerts. By identifying key hotspots Conde Nast was able to reduce average response time by 70%.

Reported Results

The company claims:

* Reduced mean time to resolution by 60% across the video application ecosystem
* Improved average response time by 70% for aggregate video application
* Gives operations and development a common language to work together to manage performance

Technology

A baseline version of the existing IT ecosystem including code, GUI, applications, micro services etc is mapped and any changes or anomalies during or post rollout are automatically identified with machine learning.

Function

Information Technology

Quality Assurance

Background

"Condé Nast is a premier media company attracting 95 million consumers across its industry-leading print, digital and video brands.
To support its U.S. dotcom properties, Condé Nast has more than 600 servers running Java, PHP, and Node.js. The U.S. websites garner approximately 800 million page views per month. "

Benefits

Data