The idea was to predict the customer experience, and their perception of the O2 network at both the user and area levels to drive the network and marketing investments. Here is why and how we got there. In order to measure and predict customer network experience, O2 needed a streaming big data solution which would consume billions of events coming in from the network, in real-time, to measure the performance of the network as experienced by the customer. It was important to build a platform to gather all the relevant data; to co-relate that with the customer satisfaction index (CSI) surveys to understand the relationship of metrics to score. We applied machine learning methods to predict the CSI for all users on the network. Customer insights from the network helped us to build customer segmentations which are shaping various marketing and digital propositions at O2. - The overall solution was based on a hybrid architecture, where Open Source technologies were brought together with Tableau visualization which enabled O2 to keep the maintenance cost down to a minimum. - In order to have quick ROI, the solution was built as the prototype which continued to evolve and now currently handles 30 billion transactions a day, continuously streaming into the platform, and predicting customer experience for 35m+ users. The O2 solution continued to expand every year to accommodate multi-fold growth in traffic, and to accommodate additional features. The decision to move from a community edition Hadoop to the Hortonworks-based platform enabled us to have a supported, faster, and more reliable service. The migration to Hortonworks was completed in October 2018 which has given us the reliable platform to expand the analytics use cases across the wider O2 businesses.