How PagerDuty helps customer service and IT teams improve responses

Predicting the outcome of the NCAA men’s Division I basketball tournament — an event where upsets are celebrated wildly and the outcome is notoriously difficult to foresee — is nearly as competitive as the tournament itself. For years, Warren Buffet held a contest offering a billion dollars for a perfect bracket, and nobody even came close. Speaking of unpredictability, just as fans were getting ready to make their picks for this year’s tournament, all major public sporting events were canceled. Who could have predicted that?

Even though we can’t see the future, a deep understanding of variables does enable people to make better predictions and gain an edge over the competition. Picking winners by their school mascot may work every once in a while, but an in-depth study of the best teams, coaches, and athletes is a much more effective strategy.

Likewise, customer service, devops, and IT issues are inherently unpredictable. It’s impossible for companies to know in advance when operational problems will arise, product defects will surface, or communications will go askew. Solutions driven by AI and machine learning can help teams improve their odds. These products can dramatically accelerate responses to issues, so problems are prevented or resolved before most customers encounter them. 

Companies can get thousands of alerts per minute when a problem arises within their digital app or service — a broken cart for an ecommerce website, for example — which is neither useful nor actionable for human interpreters to tackle. The overwhelming amount of noise simply leads to lost signals and many more contacts between customers and service teams before underlying problems can be addressed.

Predictive solutions for customer services are built on understanding the drivers behind the signals. Quickly identifying patterns helps companies stay ahead of the curve. Machine learning tools free up a lot of cycles for response teams by cutting through the noise, rather than distracting them over and over again with alerts and information that may not be useful.

When teams use machine learning in this way, they can boil down the signals to uncover the actual incidents that are driving the unmanageable number of alerts. Instead of scrambling to put out many small fires, they can see the big picture of where the problems actually lie and be more intelligent and informed in tackling a smaller group of larger issues.

Copyright © 2020 IDG Communications, Inc.

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