1. Creating Business Value from Big Data A Big Data Catalyst Project

2. Business Opportunity: Benefit realization Learning from experience to calculate best margin growth Plan scarce network costs based on value in response to weather, news or social events Remove uncertainty in marketing & network investment plans By Atomic modelling that predicts the impact on network patterns from events in weather, festivals, sport or news Present and visualize evidence based investment options by modelling different scenarios Predicting and forecasting many complex investment options in acceptable time frames Create the most value by moving customers through value based segments By Integrating Big Data from across the enterprise and learning the predictive factors

3. Using the TMForum Frameworx Starting with eTOM Focus on Customer Interaction Management Track, notify, report and engage with customer interaction

4. Functions Embedded in the solution under development

5. Platform Influenced by TM Forum’s Big DATA Reference MODEL Catalyst project is founded on the BIG DATA REFERENCE MODEL Catalyst project is founded on the BIG DATA REFERENCE MODEL Margin Forecasting Location estimation Network Value segmentation Customer value segmentation Network Event Prediction Supporting embedded functions in Investment Management forecasting application Location estimation Customer value segmentation Catalyst demonstrates Reference Model Data Analysis functions with Location Estimation and Customer Segmentation.

6. CV1Customer Value Scoring CV2Customer Lifetime Value Scoring and Prediction CL2Customer Location Detection CL3Customer Location Prediction CL4Key Location Profiling Increase 1 - Profitability Increase 2 - Average Revenue per User (ARPU) Manage 5 - % Revenue, by Bearer Service and Application Type Manage 6 - % Revenue, by Voice Services Manage 7 - % Revenue, by Data Services Manage 8 - % Customers Acquired Manage 9 - % Customers Lost Manage 44 - % Cost of Sales, of Revenue Manage46 - % Revenue, by Channel Type Increase 62 - Service Availability Manage 71 - % Problem Reports, by Cause Type Increase 107 - Net Promoter Score, Relational (NPS-R) Manage 108 - Net Promoter Score, Transactional (NPS-T) Reduce 123 - # SLA Violations Manage 154 - $ cost of sales Increase 181 - # Customers Acquired Reduce 182 - # Customers Lost Increase 183 - $ Operating Income Increase 184 – Revenue (6) Manage 186 - $ Opex Manage 187 - $ Revenue (5) Increase 188 - $ Revenue, by Channel Type (2) Manage 189 - $ Revenue, by Data Services Increase 199 - # Customers Manage 204 -$ Cost of Customer Management Influenced By TM FORUM USE CASES, Metrics & BUILDING BLOCKS Use CaseMetricsBuilding Blocks Forecast and monitor impact of investment in network upgrades and marketing programs Catalyst demonstration focuses on a subset Using TM Forum Use Cases, Metrics & Building blocks to direct the business value

7. Cells used in traffic events Call Logs Network Data Cells used in traffic events Call Logs Network Data CRM Data Purchase History Subscriber Margin Subscriber Services Usage & billing info CRM Data Purchase History Subscriber Margin Subscriber Services Usage & billing info DATA SOURCES: DATAAVAILABILITY Sources used for the Catalyst project

8. INDUSTRY OPPORTUNITY: DATAHARMONIZATION Competit or Data Network Health Data Subscribe r Data Trouble Ticket Data Regulator y Complian ce Data ERP Financial Data Customer Contact Data Social Media Data

9. SIMULATION ENVIRONMENT SIMULATION ENVIRONMENT Insight Engine Insight Engine Our Input Data Your Input Data Per customer revenue breakdown: Automatically calculate cost and revenue for each service component Investments Network Modification 1 1 3 3 2 2 4 4 Margin Map Segmentation Action List Impact Forecast REVELATIONS Automatic customer segmentation Automatically identify different customer groupings within our network 5 5 Competito r Data Network Health Data Subscriber Data Trouble Ticket Data Regulatory Complianc e Data ERP Financial Data Customer Contact Data Social Media Data TRAILS EMERGING OPPORTUNITY: DATAOPTIMIZATION

10. A Visualisation of Expected Value

11. Created by Combining technology & Modelling functions Data Ingestion Raw Financial Raw Subscriber Raw Network Topology Raw Usage (CDR/Web) Raw Usage (CDR/Web) Data Transformation RATED DATA RATED DATA Simulation World Simulation Consumer Analytics Network Analytics Management Engines for user config. Management Engines for user config. Predictive model output Geographical Analytics Geographical Analytics User Interface Aggregated user data External Data User Segments User Models Aggregated Network data External Data Network Segments Network Models Network Models Analytics Engines Events Data World Data External Event data News Feeds Geodata Movement Calculation Event and world data aggregator Simulators Known modelling techniques, integrated with high performance technology able to mange Big Data Reflecting the Big Data Reference Model Machine Learning, Segmentation, Prediction & modelling

12. Demonstrated Components of the solution Items in Red are components developed for demonstration in the Catalyst Data Ingestion Raw Financial Raw Subscriber Raw Network Topology Raw Usage (CDR/Web) Raw Usage (CDR/Web) Data Transformation RATED DATA RATED DATA Simulation World Simulation Consumer Analytics Network Analytics Management Engines for user config. Management Engines for user config. Predictive model output Geographical Analytics Geographical Analytics User Interface Aggregated user data External Data User Segments User Models Aggregated Network data External Data Network Segments Network Models Network Models Analytics Engines Events Data World Data External Event data News Feeds Geodata Geographic Calculation Event and world data aggregator Simulators Reflecting the Big Data Reference Model Machine Learning, Segmentation, Prediction & modelling Segment Location

13. Data for the demonstration Network Cell Tower Traffic Events Network Cell Tower Traffic Events Customer Aggregate Record (sample) Public Maps

14. Demonstration of Location Simulation

15. Demonstration of Segmentation function