Analysys Mason Now Factory Big Data Dec2012
Content
1
Big Data:
Turning Insights into Profit
A webinar brought to you by:
Patrick Kelly
Research Director
David Andrews
Director of Strategy
2
1.
Introduction
Agenda:
2.
3. 4. 5. 6. 7.
What is Big Data
Drivers behind Big Data Biggest Opportunities for Big Data The Role of Analytics and Insights Use Cases Q+A
BIG DATA: TURNING INSIGHT INTO PROFIT
3
CSPs have vast amounts of diverse data, but it is not fully exploited in making strategic business decisions
The average customer from a telecoms operator generates data entries on a daily basis: Tier 1 and 2 CSPs collect billions of data records per day. The quantity of data is forecast to increase as broadband data services proliferate. Telecoms operators’ data includes different data dimensions including telecoms patterns, location, devices used, content accessed, online transactions, and demographics. Growing services such as mobile payments, M2M, and other services related to near field communication (NFC) are projected to increase further the diversity of data available. CSPs know more about customer usage, patterns of behaviour, and financial status than most OTT companies: Telefónica Digital recently announced an offer to monetise location based data for O2 customers known as Smart Steps
Are my customers delighted? What impact do new devices have on my network? What OTT apps are crippling other services? Which customers are at risk of churning to other providers? How do I target new offers to the right set of customers?
Figure 1: Harvesting real-time network data to act now and predict future scenario [Source: Analysys Mason, 2012]
Millions of customers
Billions of transactions per day
Location based service
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
4
What are the sources of data to understand customer behaviour and usage patterns?
Customer data
Customer usage Customer location Customer device Customer demographics
Market intelligence
Analytics
Real-time network data
Market dimension Market demographics Market segmentation
Service quality Call center efficiency Revenue optimisation Benchmarking
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
5
Business benefits achieved in less than 6 months
Driver
Decrease churn
Description
Emerging markets have significant churn rates (+50%) and most customers are prepaid. Even relatively small changes in reducing subscriber churn can have a dramatic effect on profit margins. Sell more to the same customer. Music, gaming, social media, M-commerce. It’s the Amazon model – Customers who bought this item also purchased these items. The need for operators to expand their networks, while at the same time keeping costs down.
Action
Identify high probability users about to churn using KPI metrics. Understand their roles in social networks and the ability to influence other users.
Timeframe
3 to 6 months
Cross/up-sell products
For data services customer profiling enhances the take up of certain products based on usage patterns and demographic profiles.
3 to 6 months
Optimize network capex
Optimisation of roll-out, using geo-marketing analysis, prioritising locations based on customer value and availability of spectrum and tower space.
3 to 6 months
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
6
Business benefits achieved in less than 12 months
Driver
Faster mean time to resolution Improve financial performance and profit margins
Description
Data abstraction from network operations is put in the context of call center first line support.
Action
Fewer call escalations to 2nd and 3rd line support, faster problem resolution, and lower operational support cost. Analytics can be used to assess credit risk, identify optimal routes for inter-connect, and defer unnecessary capital investments.
Timeframe
6 to 12 months
Operators facing tightening margins as pricing continues to fall and major investments in infrastructure is required to remain competitive.
6 to 12 months
Improve customer experience
The customer experience occurs during the evaluation, purchasing, delivery, billing, consumption, and support touch points.
Customer satisfaction can be increased through a more complete understanding of the customer.
6 to 12 months
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
7
What are the fundamental building blocks of a big data strategy?
Figure 2: Analytics system components [Source: Analysys Mason, 2012]
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
8
Who are the suppliers and who are the users of big data systems?
Figure 2: Analytics system components [Source: Analysys Mason, 2012]
IT toolkits used by Data Scientist and Business Analyst
Enterprise Data Warehouse Suppliers used by DB Admins
NEMS and Telecom ISV Suppliers used by Network Operations
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
9
What is the playbook to get started?
1) Define the business problem 2) Keep it small in scope 3) Assess your capabilities internally 4) Identify the systems already deployed (data sources and data store) 5) Identify gaps and weaknesses in current operating environment 6) Select key suppliers/partners (that have demonstrated expertise in solving # 1) 7) Plan project 8) Execute!
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
10
Poll Question: What is driving the business case for big data analytics in your company (choose only one)?
A) Increase revenue and/or profits B) Improve the customer experience C) Make more intelligent CAPEX investments D) We don’t have a strategy for big data analytics
© Analysys Mason Limited 2012
11
Insights & Analytics The Key to Unlocking Value
Extract value from Big Data
Results need to meet different requirements across the organization – real-time, near real-time and postprocessing
Multi-dimensional insights that intelligently combine data from multiple sources deliver the best results
12
Narrow Focus on Big Data Focus on Specific Use Cases
Focus on the key challenges facing the business today The “Question” is just as important as the “Answer” Prioritize use cases that offer the quickest return balanced with the maximum impact
13
14
Use Case - Optimise LTE Investment The Challenge
Operators need to build out LTE networks to meet the upsurge in mobile data services Greater competition from OTT Players There is a need to prioritize where in the network to make LTE investments so as to maximize profitability Customers expect seamless Quality of Experience (QoE) with the promise of higher speeds and bandwidth
Use Case - Optimise LTE Investment
15
16
Use Case - Optimise LTE Investment The Role of Analytics
Identify the usage patterns of high-value customers – what applications they are using, typical throughputs they receive, etc. Pinpoint what locations in the network have higher concentrations of usage among high-value customers Enables prioritization of LTE investments based on specific usage patterns
17
Use Case - Improve First Call Resolution for Mobile Data The Challenge
Huge surge in the use of smart devices and applications causing more complex support issues for customers Volume of mobile data related support calls rising and handling times becoming longer The operator is becoming the first point of call for all support issues, including handsets and applications
Use Case - Improve First Call Resolution
7 % of calls are escalated from 1st Line to 2nd Line Support
18
3.5 % of calls are escalated from 2nd Line to 3rd Line Support
CSR
CST
12 minutes: Average call time
BEFORE
Use Case - Improve First Call Resolution
Number of calls escalated from 1st Line to 2nd Line Support reduced to 3.5% (was 7%)
19
Now only 2.5 % (was 3.5%) of total calls are escalated to 3rd line Support
CSR
CST
33% saving by cutting call times by up to 4 minutes
AFTER
20
Use Case – Improve First Call Resolution for Mobile Data The Role of Analytics
Understand customers’ usage patterns in real time across different devices, applications and network locations Empower support teams with more detailed customer experience metrics in real time – throughput performance, network alerts, handset issues, etc. Identify typical usage patterns across different customer segments and arrange support resources appropriately
21
Use Case - Deliver More Targeted Marketing Campaigns The Challenge
Consumers have more choice than ever when it comes to mobile and voice services
Brand Equity among handset manufacturers and app providers increasing at the expense of the operator With falling margins and a greater pressure to invest in new technologies, operators need to monetize their networks
Use Case - Deliver More Targeted Marketing Campaigns
22
=
23
Use Case - Deliver More Targeted Marketing Campaigns The role of Analytics
Understand typical usage patterns among different customer groups especially highvalue customers, e.g. what devices and applications they use
Offer more targeted campaigns and promotions based on actual usage patterns
Share information with handset manufacturers and 3rd parties on the performance and usage of their respective products and services and open up new revenue channels and business models
24
25
Thank you
& If you have any questions, please feel free to ask
26
PATRICK KELLY Ph: +1 603 969 2125 Mail: [email protected]
DAVID ANDREWS Ph: +353 87 797 4149 Mail: [email protected]
Big Data:
Turning Insights into Profit
A webinar brought to you by:
Patrick Kelly
Research Director
David Andrews
Director of Strategy
2
1.
Introduction
Agenda:
2.
3. 4. 5. 6. 7.
What is Big Data
Drivers behind Big Data Biggest Opportunities for Big Data The Role of Analytics and Insights Use Cases Q+A
BIG DATA: TURNING INSIGHT INTO PROFIT
3
CSPs have vast amounts of diverse data, but it is not fully exploited in making strategic business decisions
The average customer from a telecoms operator generates data entries on a daily basis: Tier 1 and 2 CSPs collect billions of data records per day. The quantity of data is forecast to increase as broadband data services proliferate. Telecoms operators’ data includes different data dimensions including telecoms patterns, location, devices used, content accessed, online transactions, and demographics. Growing services such as mobile payments, M2M, and other services related to near field communication (NFC) are projected to increase further the diversity of data available. CSPs know more about customer usage, patterns of behaviour, and financial status than most OTT companies: Telefónica Digital recently announced an offer to monetise location based data for O2 customers known as Smart Steps
Are my customers delighted? What impact do new devices have on my network? What OTT apps are crippling other services? Which customers are at risk of churning to other providers? How do I target new offers to the right set of customers?
Figure 1: Harvesting real-time network data to act now and predict future scenario [Source: Analysys Mason, 2012]
Millions of customers
Billions of transactions per day
Location based service
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
4
What are the sources of data to understand customer behaviour and usage patterns?
Customer data
Customer usage Customer location Customer device Customer demographics
Market intelligence
Analytics
Real-time network data
Market dimension Market demographics Market segmentation
Service quality Call center efficiency Revenue optimisation Benchmarking
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
5
Business benefits achieved in less than 6 months
Driver
Decrease churn
Description
Emerging markets have significant churn rates (+50%) and most customers are prepaid. Even relatively small changes in reducing subscriber churn can have a dramatic effect on profit margins. Sell more to the same customer. Music, gaming, social media, M-commerce. It’s the Amazon model – Customers who bought this item also purchased these items. The need for operators to expand their networks, while at the same time keeping costs down.
Action
Identify high probability users about to churn using KPI metrics. Understand their roles in social networks and the ability to influence other users.
Timeframe
3 to 6 months
Cross/up-sell products
For data services customer profiling enhances the take up of certain products based on usage patterns and demographic profiles.
3 to 6 months
Optimize network capex
Optimisation of roll-out, using geo-marketing analysis, prioritising locations based on customer value and availability of spectrum and tower space.
3 to 6 months
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
6
Business benefits achieved in less than 12 months
Driver
Faster mean time to resolution Improve financial performance and profit margins
Description
Data abstraction from network operations is put in the context of call center first line support.
Action
Fewer call escalations to 2nd and 3rd line support, faster problem resolution, and lower operational support cost. Analytics can be used to assess credit risk, identify optimal routes for inter-connect, and defer unnecessary capital investments.
Timeframe
6 to 12 months
Operators facing tightening margins as pricing continues to fall and major investments in infrastructure is required to remain competitive.
6 to 12 months
Improve customer experience
The customer experience occurs during the evaluation, purchasing, delivery, billing, consumption, and support touch points.
Customer satisfaction can be increased through a more complete understanding of the customer.
6 to 12 months
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
7
What are the fundamental building blocks of a big data strategy?
Figure 2: Analytics system components [Source: Analysys Mason, 2012]
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
8
Who are the suppliers and who are the users of big data systems?
Figure 2: Analytics system components [Source: Analysys Mason, 2012]
IT toolkits used by Data Scientist and Business Analyst
Enterprise Data Warehouse Suppliers used by DB Admins
NEMS and Telecom ISV Suppliers used by Network Operations
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
9
What is the playbook to get started?
1) Define the business problem 2) Keep it small in scope 3) Assess your capabilities internally 4) Identify the systems already deployed (data sources and data store) 5) Identify gaps and weaknesses in current operating environment 6) Select key suppliers/partners (that have demonstrated expertise in solving # 1) 7) Plan project 8) Execute!
© Analysys Mason Limited 2012
BIG DATA: TURNING INSIGHT INTO PROFIT
10
Poll Question: What is driving the business case for big data analytics in your company (choose only one)?
A) Increase revenue and/or profits B) Improve the customer experience C) Make more intelligent CAPEX investments D) We don’t have a strategy for big data analytics
© Analysys Mason Limited 2012
11
Insights & Analytics The Key to Unlocking Value
Extract value from Big Data
Results need to meet different requirements across the organization – real-time, near real-time and postprocessing
Multi-dimensional insights that intelligently combine data from multiple sources deliver the best results
12
Narrow Focus on Big Data Focus on Specific Use Cases
Focus on the key challenges facing the business today The “Question” is just as important as the “Answer” Prioritize use cases that offer the quickest return balanced with the maximum impact
13
14
Use Case - Optimise LTE Investment The Challenge
Operators need to build out LTE networks to meet the upsurge in mobile data services Greater competition from OTT Players There is a need to prioritize where in the network to make LTE investments so as to maximize profitability Customers expect seamless Quality of Experience (QoE) with the promise of higher speeds and bandwidth
Use Case - Optimise LTE Investment
15
16
Use Case - Optimise LTE Investment The Role of Analytics
Identify the usage patterns of high-value customers – what applications they are using, typical throughputs they receive, etc. Pinpoint what locations in the network have higher concentrations of usage among high-value customers Enables prioritization of LTE investments based on specific usage patterns
17
Use Case - Improve First Call Resolution for Mobile Data The Challenge
Huge surge in the use of smart devices and applications causing more complex support issues for customers Volume of mobile data related support calls rising and handling times becoming longer The operator is becoming the first point of call for all support issues, including handsets and applications
Use Case - Improve First Call Resolution
7 % of calls are escalated from 1st Line to 2nd Line Support
18
3.5 % of calls are escalated from 2nd Line to 3rd Line Support
CSR
CST
12 minutes: Average call time
BEFORE
Use Case - Improve First Call Resolution
Number of calls escalated from 1st Line to 2nd Line Support reduced to 3.5% (was 7%)
19
Now only 2.5 % (was 3.5%) of total calls are escalated to 3rd line Support
CSR
CST
33% saving by cutting call times by up to 4 minutes
AFTER
20
Use Case – Improve First Call Resolution for Mobile Data The Role of Analytics
Understand customers’ usage patterns in real time across different devices, applications and network locations Empower support teams with more detailed customer experience metrics in real time – throughput performance, network alerts, handset issues, etc. Identify typical usage patterns across different customer segments and arrange support resources appropriately
21
Use Case - Deliver More Targeted Marketing Campaigns The Challenge
Consumers have more choice than ever when it comes to mobile and voice services
Brand Equity among handset manufacturers and app providers increasing at the expense of the operator With falling margins and a greater pressure to invest in new technologies, operators need to monetize their networks
Use Case - Deliver More Targeted Marketing Campaigns
22
=
23
Use Case - Deliver More Targeted Marketing Campaigns The role of Analytics
Understand typical usage patterns among different customer groups especially highvalue customers, e.g. what devices and applications they use
Offer more targeted campaigns and promotions based on actual usage patterns
Share information with handset manufacturers and 3rd parties on the performance and usage of their respective products and services and open up new revenue channels and business models
24
25
Thank you
& If you have any questions, please feel free to ask
26
PATRICK KELLY Ph: +1 603 969 2125 Mail: [email protected]
DAVID ANDREWS Ph: +353 87 797 4149 Mail: [email protected]
