Augmenting wi-fi failure root cause analysis with ML

98% accuracy in identifying issues

Meet our client


CUSTOMERA telco company from the US

How we did it

Customer complaints cost companies millions annually. Many complaints, however, could be avoided if customers were better aided in solving problems by themselves.

The challenge
The overwhelming scale of telecommunications operations means the companies must handle a huge volume of complaints. The amount of work this requires could be reduced by empowering end-users to fix some problems themselves.

The solution

To reduce the workload of their customer support specialists, telecoms increasingly use tools to support both end-users and internet carriers in quickly identifying issues with their wifi networks. designed a machine learning-based tool that enhanced our client’s rule-based root cause analysis process, significantly boosting the accuracy of failure cause identification.

The solution detects new patterns in hardware and software failures and adjusts to the changing environment.

The effect

Applying models designed by enabled the client to increase root cause prediction accuracy by up to 98%. Boosting the performance of first-line support improved overall customer satisfaction and reduced the workload of customer support specialists.

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    Find us
    •, Inc.
    • 2100 Geng Road, Suite 210
    • Palo Alto, CA 94303
    • United States of America
    • Sp. z o.o.
    • al. Jerozolimskie 162A
    • 02-342 Warsaw
    • Poland
    • ul. Łęczycka 59
    • 85-737 Bydgoszcz
    • Poland
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