OTE Group in Greece Value Based Rollout. October 2016

Transcription

1 OTE Group in Greece Value Based Rollout October 2016

2 Contents 2 1. OTE Group overview 2. Customer Network Experience Landscape in OTE 3. SAS VA Access to Hadoop Setup 4. Problem Definition - Targets 5. Enabling Sound Decisions based on Value Insights Drill down

3 ΟΤΕ Group: Leader in South East Europe 3 OTE is the leading integrated telecommunications operator in southeast Europe, providing Fixed and Mobile services (Telephony and Broadband) and TV Greece Romania Romania Customers (mil.) Νο 1 in Fixed Νο 1 in Mobile 7,4 Customers (mil.) Νο 1 in Fixed Νο 3 in Mobile 6,0 2,7 1,4 0,5 2,2 1,2 1,5 Fixed Broadband TV Mobile Fixed Broadband TV Mobile Greece Albania Revenues Distribution Νο 2 in Mobile Customers (mil.) 1,7 Romania 25% Albania 2% Greece 73% Mobile All data: 31/12/2015

4 Dedicated SAS Schema SAS Analytics interworking with Big Data 4 HADOOP - Cloudera SAS Access to Hadoop Active-Active HA solution SAN RAW data Agg. data Slave nodes Distributed environment Parallel processing Memory analytics Data preparation & processing takes place on Hadoop and then loaded in memory for Analytics & Visualization

5 SAS Analytics positioning in the Mobile CEM Landscape 5 Customer Data Sessions IP Data stream HADOOP - Cloudera Data Ingestion Layer Encryption Layer Voice/SMS stream Aggregation & Metrics calculation CONTROL Plane USER Plane Centralised Collection & Mediation Enrichment Layer KPIs calculation Partitioning & Storage RAW data Agg. data Primary storage Filter traffic by VLAN tagging TDR DPI Layer SAS Schema Enriched CDRs Time Aggregation Layer SAS Access to Hadoop SAS VA In memory Analytics Real Time Broker (RTB) Processing engine Distributed collection Layer Distributed collection Layer GTP stream Visualization 2G/3G LTE 2G & 3G

6 SAS Access to Hadoop Use Cases 6 1. Rollout Prioritization 2. LTE Devices Capability 3. Device Analytics 4. Location Analytics

7 Problem Definition Rollout Prioritization 7 Traditionally the Rollout Activities are based on a number of inputs: High Traffic demand (Voice and Data) in geographical areas Coverage targets Commercial priorities and hot spots Strategic locations (airports, ports, etc) Nowadays operators have to deal with new challenges, which makes the Rollout Planning activities even more complex: Flat Rates (Voice and Data) New Technologies/Services Smartphones penetration Micro-spots Budget restrictions Value Based Rollout provides an alternative way to prioritize Network Rollout based on two key elements: Faster Return On Investment Targeted Rollout based on Devices capabilities

8 Methodology - Revenue Distribution Rules 8 Calculate Value ( ) per SITE based on : o ARPU per Customer o Distribution of Customer Traffic in the Network Cells Illustrative example Customer ARPU & usage ARPU ( ) Customer usage (MB) Revenues per MB Customer A Customer B Customer X Customer traffic distribution Site 1 Site 2 Site Y Customer A 50% 20% 30% Customer B 10% 10% 80% Customer X 30% 40% 30% Customer revenue distribution per site Site 1 Site 2 Site Y Customer A Customer B Customer X Revenues per Site Traffic per site (MB) Site 1 Site 2 Site Y Customer A Customer B Customer X Traffic per site MB Comment The ARPU of each individual customer is distributed amongst the sites Key for the distribution algorithm is the traffic of each individual customer per site As each customer pays a different price per Mbyte (e.g flatrate but different usage), each site has a different value per MByte Revenues per MB

9 Regional Analytics - Overview 9

10 SAS Access to Hadoop Use Cases Rollout Prioritization 2. LTE Devices Capability 3. Device Analytics 4. Location Analytics

11 Prioritize Rollout/Commercial actions based on Value & LTE Share 11 LTE Devices capability share per SITE NO LTE Traffic Prioritize LTE Sites Rollout based on existing LTE Devices Share and Value

12 SAS Access to Hadoop Use Cases Rollout Prioritization 2. LTE Devices Capability 3. Device Analytics 4. Location Analytics

13 Devices analytics/monitoring (II) 13 Penetration of devices Geo-map Devices footprint monitoring

14 SAS Access to Hadoop Use Cases Rollout Prioritization 2. LTE Devices Capability 3. Device Analytics 4. Location Analytics

15 Location analytics 15 Classify services/subscribers based on Mobility Optimize network to support enhanced mobility patterns Combine with ARPU to define QoS if necessary

16 Thank You! 16