Advanced Telco Analytics
To achieve a panoramic view of customer experience and build a sustainable, competitive advantage, Telecom companies must gain a deep understanding of customer attitudes, behaviors and actions. Simply, Telcos need to monetize these customer data through actionable insights to improve customer acquisition and manage/reduce retention and even optimize operational efficiency.
Telcos need to leverage and integrate huge amounts of customer data from a range of sources -like Call Data Records (CDR), customer care, product/service portfolios, cost and billing, and network service quality— in a holistic way, to understand the entire customer life cycle minimizing poor association between siloed departments.
Optimize Networks for Maximum Revenue
CUBE integrates heterogeneous, multi-source data and prepares it for easy analysis with advanced analytics tools for Telcos to leverage on their customer data. CUBE is capable to analyze the entire subscriber base defining unlimited business parameters and segmenting rules. Utilizing latest machine learning and sampling techniques in its Prediction model, CUBE allows Telcos to forecast prospective tendencies to take immediate actions.
CUBE’s descriptive analytics help Telcos to understand the relationship between subscribers and products and helping them to gain an proper understanding of what approach to take in the future for better improvement. Our analytical engine identifies and examines historical patterns in subscribers’ product and service consumption behavior helping Telcos to learn from past behavior to influence future outcomes.
CUBE offers predictive analytics providing Telcos with actionable insights based on existing data. It provides an estimation regarding the possibility of a future action. CUBE utilizes various machine learning, clustering and data mining techniques to perform precise and accurate analysis. CUBE’s prediction model allows Telcos to forecast prospective tendencies to take immediate actions in three ways.
Propensity to Churn – Predict probability to churn within the next 3 months with 75% of accuracy based on past customer behavioral patterns helping Telcos to identify subscribers who have the potential to churn & reward them via personalized campaigns
Propensity to Collect – Predict the probability of payment receivable potential from the charging requests sent out at the end of the payment cycle with accuracy of 85% helping Telcos to identify subscribers with the potential to pay for subscriptions to optimally send the charging requests saving up on costs
Propensity to Buy – Predict the probability of each subscriber making a purchase within the next 3 months with accuracy of 85% helping Telcos to identify subscribers’ buying behaviours or who have the potential to activate applications & service plans
Diagnostic analytics of CUBE helps Telcos with root-cause analysis, data discovery and exploration. It starts during the descriptive analytics phase and extends into predictive analytics. It uncovers patterns and correlations from millions of customer data providing insights that drive predictive models offering a deeper understanding for Telcos about current proceedings. Further, CUBE has the full competence to provide right information required to develop accurate predictive models. Telcos can utilizes our diagnostic analytics to get an understanding on cross-functional data required for a root-cause analysis, negative performance variations for internal optimization, strategy development for churn reduction etc.