Case Overview

A telco used xgboost algorithm to identify false alarms, reducing cost and better staff allocation

The telco receives a lot of service request for their broadband services across locations. A good percentage (~12%) of these requests are false alarms, and can be solved without the field staff visiting the customer location.

The telco wanted utilize predictive analytics to identify such cases so that they can reduce the cost and allocate the field team to problems that require their intervention more efficiently. This would reduce the time to service the customers.

Improving time to service with machine learning

The telco wanted a solution that would identify the false flags with a high degree of confidence – and help in reducing cost and time to service genuine requests.

  • The solution is able to predict 85% false flags accurately
  • Reduced the time to service genuine requests by 4 hours
  • The staff is able to service 2% more requests a month

The solution allowed the telco improve operational efficiencies and serve the customers more timely and at a reduced cost.

High request volume. Insufficient trained staff.

The telco receives hundreds of requests relation to network and modem issues. A noticeable percentage of such complaints are false alarms and can be solved without a visit by the field team. Segregating the genuine complaints from the false alarms could reduce the staff visits. The added benefit is that the time saved on these false alarm visits can be allocated to genuine complaints.

The primary concern for the telco was to implement a solution that predicts the false alarms with a high degree of confidence. They did not want to trade off customer satisfaction with operational costs. The requirement for the solution was primarily triggered by the addressable ‘time to service’ concerns of customers. We proposed a supervised learning approach to solve their problem and ensure the reliability of predictions.

How to identify the real ones?

Xgboost is an ensemble based and supervised learning approach to solve classification problems.  

With the solution, we have been able to achieve a prediction accuracy of 85% on false alarm cases. This has enabled the telco to reap benefits like:

a. Reduced cost on operations by reducing visits for false alarm cases

b. The time saved on unproductive visits are now utilized in addressing genuine cases, reducing ‘time to service’

c. Number of requests services per month has improved by 2%

The telco has achieved a better bottom line and saving on unwanted costs.