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.

Financial Market IT solutions increasingly depend on ultra low latency message processing and target microseconds latencies in order to provide traders with a competitive advantages over their peers. Some solutions are available on the market, ranging from general purpose systems with advanced network cards to specialized hardware solutions based on FPGA. The novel IBM PowerEN TM "Edge of Network" processor integrates network interfaces with functional accelerators and multi-threaded cores. This paper describes the design, implementation and performance evaluation of a Market Data Feed solution based on the PowerEN processor. The paper details the SoC mechanism designed to reduce latency and increase throughput and shows how these can be used to build a Market Data Feed solutions that achieves performance figures usually obtainable only by special hardware. This prototype described achieves an average latency of 6.7 µs for an OPRA v2 feed and is able to process 1M updates per second on 2 hardware thread, achieving more than 16M updates per second on a single chip solution without consuming all the resources and thus being able to also run customer software.

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