Transcript of Creating Business Value from Big Data A Big Data Catalyst Project.
- Slide 1
- Creating Business Value from Big Data A Big Data Catalyst
Project
- Slide 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
- Slide 3
- Using the TMForum Frameworx Starting with eTOM Focus on
Customer Interaction Management Track, notify, report and engage
with customer interaction
- Slide 4
- Functions Embedded in the solution under development
- Slide 5
- Platform Influenced by TM Forums 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.
- Slide 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
- Slide 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
- Slide 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
- Slide 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
- Slide 10
- A Visualisation of Expected Value
- Slide 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
- Slide 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
- Slide 13
- Data for the demonstration Network Cell Tower Traffic Events
Network Cell Tower Traffic Events Customer Aggregate Record
(sample) Public Maps
- Slide 14
- Demonstration of Location Simulation
- Slide 15
- Demonstration of Segmentation function