Open Source Telecommunications: Enabling Anyone to Build a Bad Telephony Application
OTE Group in Greece Operations Overview - SAS · OTE is the leading integrated telecommunications...
Transcript of OTE Group in Greece Operations Overview - SAS · OTE is the leading integrated telecommunications...
OTE Group in Greece Value Based Rollout
October 2016
Contents
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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
OTE is the leading integrated telecommunications operator in southeast Europe,
providing Fixed and Mobile services (Telephony and Broadband) and TV
Romania
Greece
Greece
Mobile
7,4
TV
0,5
Broadband
1,4
Fixed
2,7
Customers (mil.)
Νο 1 in Fixed
Νο 1 in Mobile
Albania
Mobile
1,7
Customers (mil.)
Νο 2 in Mobile
Romania
Broadband Mobile
6,0
TV
1,5 1,2
Fixed
2,2
Customers (mil.)
Νο 1 in Fixed
Νο 3 in Mobile
All data: 31/12/2015
Revenues Distribution
Romania
25%
Albania
2% Greece
73%
ΟΤΕ Group: Leader in South East Europe…
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4
SAS
Access to Hadoop
Ded
icate
d
SA
S S
ch
em
a
RAW data Agg. data
SAS Analytics interworking with Big Data
HADOOP - Cloudera
Slave nodes
Active-Active HA solution
SAN
Data preparation & processing takes place on Hadoop and then loaded in memory for Analytics & Visualization
• Distributed environment
• Parallel processing
• Memory analytics
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Centralised Collection & Mediation
Aggregation &
Metrics calculation
TDR Filter traffic
by VLAN tagging
Time
Aggregation Layer
GTP stream
DPI Layer
CONTROL Plane USER Plane
2G/3G LTE
Distributed collection Layer
Partitioning & Storage
Customer Data Sessions
SAS VA
SAS
Access to Hadoop Real Time Broker (RTB)
Processing engine
2G & 3G
Enriched CDRs
Distributed collection Layer
Primary storage
Encryption Layer
Enrichment Layer
IP Data stream
Voice/SMS stream
SAS Schema
RAW data Agg. data
KPIs calculation
Data Ingestion Layer
SAS Analytics positioning in the Mobile CEM Landscape
In memory
Analytics
HADOOP - Cloudera
Visualization
SAS Access to Hadoop Use Cases
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1. Rollout Prioritization
2. LTE Devices Capability
3. Device Analytics
4. Location Analytics
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
Methodology - Revenue Distribution Rules 8
Calculate Value (€) per SITE based on :
o ARPU per Customer
o Distribution of Customer Traffic in the Network Cells
Customer ARPU & usage
ARPU (€) Customer
usage (MB)
Revenues
per MB
Customer A 10 € 200 0.05 €
Customer B 20 € 500 0.04 €
…
Customer X 12 € 75 0.16 €
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 siteSite 1 Site 2 … Site Y
Customer A 5 € 2 € 3 €
Customer B 2 € 2 € 16 €
…
Customer X 4 € 5 € 4 €
Revenues per
Site11 € 9 € 23 €
Traffic per site (MB)
Site 1 Site 2 … Site Y
Customer A 100 40 60
Customer B 50 50 400
…
Customer X 23 30 23
Traffic per
site MB173 120 483
Revenues per
MB0.06 € 0.07 € 0.05 €
Illustrative example
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
Comment
Regional Analytics - Overview 9
SAS Access to Hadoop Use Cases
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1. Rollout Prioritization
2. LTE Devices Capability
3. Device Analytics
4. Location Analytics
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
SAS Access to Hadoop Use Cases
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1. Rollout Prioritization
2. LTE Devices Capability
3. Device Analytics
4. Location Analytics
Devices analytics/monitoring (II) 13
Penetration of devices
Geo-map
Devices footprint monitoring
SAS Access to Hadoop Use Cases
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1. Rollout Prioritization
2. LTE Devices Capability
3. Device Analytics
4. Location Analytics
Location analytics 15
Classify services/subscribers based on Mobility
Optimize network to support enhanced mobility patterns
Combine with ARPU to define QoS if necessary
Thank You !
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