Case Studies

Process Mining analysis on Fixedline and Wholesale lines terminations for a major Italian and International Telco Company

In summary

Background

The Global Telco Player focused its attention on two main topics:

  1. Understand why wholesale line terminations caused too many complaints

  2. Optimize retention actions in the fixedline cancellation process

Decisions and Actions

The Telco Company called us to implement two Process Mining projects, aimed at understanding the two processes and get insights on root causes of complaints on one side, and effectiveness of retention on the other side.

For both processes we carried on a full process discovery through Process Mining.

  • As per the Wholesale cancellation process, analysis aimed at identifying possible inefficiencies in the process in order to initiate corrective actions to reduce the number of complaints (due to failure to complete the request or a delay in its completion). In particular, the analysis covered some phases that made up the termination activity (such as commercialization, delivery and billing) to identify possible bottlenecks and at the same time verify the compliance with the SLAs provided for the services

  • As per the Fixedline termination process, a robust exploratory analysis on the observed cancellations was carried on in order to estimate the effectiveness of the retention action and to optimize the process. As a further development text mining techniques have been applied on complaints with the purpose of:

    • extracting key information and insights about cancellation reasons

    • complaints categorization

  • Journeys were connected to Customer clusters (journey clusterisation and match with user characteristics/metadata) to identify common patterns related to Client behavior

Results

In a few months, the Telco company got a full understanding of:

  1. internal inefficiencies in the wholesale termination process causing out-of-SLA management bringing to complaints

  2. how to recalibrate retention actions to the different clusters of Customers to get the best retention effect on fixedline terminations

Both processes were recalibrated in the next months, causing a good reduction in wholesale complaints and in fixeline drop on high value customers.

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