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AI use case · Network & operations

Quality of Experience (QoE) Prediction

Network quality issues directly impacting subscriber experience — high latency, packet loss, slow data speeds — are detected after complaints have already been filed rather than predicted in advance.

Regression MLCorrelation Analysis
MediumBusiness priority
Network & operationsDomain
4Main data sources

The problem

Network quality issues directly impacting subscriber experience — high latency, packet loss, slow data speeds — are detected after complaints have already been filed rather than predicted in advance.

The AI approach

ML models correlate network performance metrics with subscriber experience signals to predict QoE degradation per subscriber segment and geography. Proactive network optimisation actions triggered before QoE falls below SLA thresholds.

How it works

  1. 1
    Collect

    Combine network measurements, device data and usage.

  2. 2
    Predict

    Estimate each customer's experience score.

  3. 3
    Detect

    Find customers and areas with poor experience.

  4. 4
    Act

    Prioritise network fixes and proactive care.

Data it uses

Network Performance DataCDR DataSubscriber Location DataDevice Type Data

How to measure value

Practical tips

Standards & references

Data governanceUse governed, consented data only
Responsible AIExplainable, monitored, human-in-the-loop

Related pages

This page describes generic industry practice and public standards. It is not based on, and does not describe, any particular vendor's product or operator's systems.