Resolving kpn Customer Journey Variances through a Suitable Similarity Measure

The customer journey approach is quickly becoming the game changer within KPN  to become a customer-centric service provider and to improve the customer experience to an un-telco like level. Within this context, we have already connected various touch points of the customer, including calls, chats, store visits, online visits and engineer visits, and we are continuously adding more touch points to our customer journey dataset. In addition to these touch points also metadata, such as cost, is added to the data set. For research purposes, we use only data from customers who have given permission for this.

We are starting to harvest the benefits from a customer journey approach. For example, we use insights about follow-up touch points to direct our efforts to improve business process towards a more efficient and self-service oriented user portal.

A major obstacle however in finding opportunities to improve upon is the huge variance within the customer journey. A customer has countless of ways to reach the same goal. To improve our opportunity finding we need to bring order to this chaos. In this project, we want to achieve that by clustering journeys according to customer intend. Therefore, a similarity measure between the observed variants needs to be developed. First, available distance measures (e.g. for trace clustering) should be explored. Then, using domain knowledge and unsupervised evaluation measures, one would like to identify the best available similarity measure and optimize it.

Finally, by using this similarity measure the customer journey variants can be clustered, where each cluster should then represent a specific customer intend. This final cluster result should help KPN in opportunity finding by better sizing and prioritization of our efforts as well as creating better solutions by having a more complete picture of the average journey per customer intend.

KPN is one of the largest Dutch telecommunication companies with a market share of approximately 40% for fixed services, with a customer base of about 3 million broadband customers and 5,5 million postpaid mobile customers. We deliver our services through several channels with a myriad of touchpoints.


For more information, contact dr. ing. Marwan Hassani.

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