analysys mason now factory big data dec2012

Big Data: Turning Insights into Profit

A webinar brought to you by:

David Andrews Director of Strategy

Patrick Kelly Research Director

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1. Introduction

2. What is Big Data

3. Drivers behind Big Data

4. Biggest Opportunities for Big Data

5. The Role of Analytics and Insights

6. Use Cases

7. Q+A

Agenda:

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Analysys Mason Limited 2012

BIG DATA: TURNING INSIGHT INTO PROFIT

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:

Telefnica Digital recently announced an offer to monetise

location based data for O2 customers known as Smart Steps

Figure 1: Harvesting real-time network data to act now and predict future

scenario [Source: Analysys Mason, 2012]

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?

Location based service

Millions of customers

Billions of transactions

per day

3



What are the sources of data to understand customer behaviour

and usage patterns?

Customer data

Market

intelligence

Real-time network

data Analytics

Customer usage Customer location Customer device Customer demographics

Service quality Call center efficiency Revenue optimisation Benchmarking

Market dimension Market demographics Market segmentation

4



Business benefits achieved in less than 6 months

Driver Description Action Timeframe

Decrease

churn

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.

Identify high probability users about to churn

using KPI metrics. Understand their roles in

social networks and the ability to influence other

users.

3 to 6 months

Cross/up-sell

products

Sell more to the same customer. Music,

gaming, social media, M-commerce. Its the Amazon model Customers who bought this item also purchased these

items.

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

The need for operators to expand their

networks, while at the same time

keeping costs down.

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

5



Business benefits achieved in less than 12 months

Driver Description Action Timeframe

Faster mean

time to

resolution

Data abstraction from network operations is put

in the context of call center first line support.

Fewer call escalations to 2nd and 3rd line

support, faster problem resolution, and lower

operational support cost.

6 to 12 months

Improve

financial

performance

and profit

margins

Operators facing tightening margins as pricing

continues to fall and major investments in

infrastructure is required to remain competitive.

Analytics can be used to assess credit risk,

identify optimal routes for inter-connect, and

defer unnecessary capital investments.

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

6



What are the fundamental building blocks of a big data

strategy?

Figure 2: Analytics system components [Source: Analysys Mason, 2012]

7



Who are the suppliers and who are the users of big data

systems?

Figure 2: Analytics system components [Source: Analysys Mason, 2012]

NEMS and

Telecom

ISV

Suppliers

used by

Network

Operations

Enterprise

Data

Warehouse

Suppliers

used by DB

Admins

IT toolkits

used by

Data

Scientist

and

Business

Analyst

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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!

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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 dont have a strategy for big data analytics

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Extract value from Big Data

Results need to meet different requirements across the organization real-time, near real-time and post-processing

Multi-dimensional insights that intelligently combine data from multiple sources deliver the best results

Insights & Analytics The Key to Unlocking Value

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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

Narrow Focus on Big Data Focus on Specific Use Cases

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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 The Challenge

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Use Case - Optimise LTE Investment 15

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

Use Case - Optimise LTE Investment The Role of Analytics

16

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 for Mobile Data The Challenge

17

Use Case - Improve First Call Resolution

CSR CST

3.5 % of calls are escalated from 2nd Line to 3rd Line Support

7 % of calls are escalated from 1st Line to 2nd Line Support

12 minutes: Average call time BEFORE

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CSR CST

Now only 2.5 % (was 3.5%) of total calls are escalated to 3rd line Support

Number of calls escalated from 1st Line to 2nd Line Support reduced to 3.5% (was 7%)

33% saving by cutting call times by up to

4 minutes

AFTER

Use Case - Improve First Call Resolution 19

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

Use Case Improve First Call Resolution for Mobile Data The Role of Analytics

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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 The Challenge

21

Use Case - Deliver More Targeted Marketing Campaigns

=

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Understand typical usage patterns among different customer groups especially high-value 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

Use Case - Deliver More Targeted Marketing Campaigns The role of Analytics

23

Thank you &

If you have any questions, please feel free to ask

25

PATRICK KELLY

Ph: +1 603 969 2125

Mail: [email protected]

DAVID ANDREWS

Ph: +353 87 797 4149

Mail: [email protected]

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analysys mason now factory big data dec2012

Documents

big data

data entries

sources of data

quantity of data

broadband data services

telecoms operators data

billions of data records

different data dimensions