  Telecommunications operators (telcos) traditional sources of income, voice
and SMS, are shrinking due to customers using over-the-top (OTT) applications
such as WhatsApp or Viber. In this challenging environment it is critical for
telcos to maintain or grow their market share, by providing users with as good
an experience as possible on their network.
  But the task of extracting customer insights from the vast amounts of data
collected by telcos is growing in complexity and scale everey day. How can we
measure and predict the quality of a user's experience on a telco network in
real-time? That is the problem that we address in this paper.
  We present an approach to capture, in (near) real-time, the mobile customer
experience in order to assess which conditions lead the user to place a call to
a telco's customer care center. To this end, we follow a supervised learning
approach for prediction and train our 'Restricted Random Forest' model using,
as a proxy for bad experience, the observed customer transactions in the telco
data feed before the user places a call to a customer care center.
  We evaluate our approach using a rich dataset provided by a major African
telecommunication's company and a novel big data architecture for both the
training and scoring of predictive models. Our empirical study shows our
solution to be effective at predicting user experience by inferring if a
customer will place a call based on his current context.
  These promising results open new possibilities for improved customer service,
which will help telcos to reduce churn rates and improve customer experience,
both factors that directly impact their revenue growth.
