Transcript `Big data` and analytics

Telecoms Software Strategies webinar
Webinar
Telecom software strategies with a focus on ‘BIG DATA’
January 2013
Patrick Kelly, Research Director
© Analysys Mason Limited 2013
Telecoms Software Strategies webinar
Executive summary
New programmes
‘Big data’ and analytics
Use case studies
Recommendations
© Analysys Mason Limited 2013
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Telecoms Software Strategies webinar
A look back at the past decade and business priorities
Customer
Experience
Management
.COM Crash
2000
2001
.COM Bubble
Telecom Winter
Fiber optic glut
2002
2003
Worldcom
Scandal
Other
bankruptcy
filings follow
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FTTx
IPTV
2004
2005
System
consolidation
“Concept of
One”
iPhone Launched
3G HSPA
Customer
retention
2006
2008
2007
Supplier
Consolidation
ALU formed
Dec. 2006
NSN formed Feb
2007
Source: Analysys Mason
2009
Self Care
Operational
efficiency
Cloud Computing
Self Optimizing
Networks
Software Defined
Networks
NaaS
Digital economy
2010
2012
2011
IMS
PCRF
LTE
Real time
charging
2013
Big data and
analytics
M2M
Convergence
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We have identified four primary trends driving the telecommunication
market over the next 5 years
Analytics
Customer Experience Management
 Customer insight and revenue generation
require meshing data from the network and
back-end systems to understand and predict
customer demand and buying behavior.
 Coverage includes LBS, predictive modeling,
real time network analysis
Digital Economy
 The distribution of video, data, and
content is reshaping the
telecommunication and internet.
 Coverage includes managing mobile
video, m-commerce, M2M, and
complex partner eco-systems.
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NGN
Networks
 An understanding of the customer is
paramount to lower churn and increase
net profits per subscriber.
 Coverage includes order to cash
process, e2e QoS, 360 degree view of
the customer.
Operations Transformation
 Convergence of IT and telecom
technologies are driving transformation
of the network and its operation where
software intelligence is becoming of
paramount importance.
 Coverage of SON, SDN, PCRF, SDP,
VoLTE, FMC.
Telecoms Software Strategies webinar
Executive summary
New programmes
‘Big data’ and analytics
Use case studies
Recommendations
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Telecoms Software Strategies webinar
Analytics programme
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Telecoms Software Strategies webinar
Customer Experience Management programme
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Telecoms Software Strategies webinar
Digital economy programme
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Operations Transformation programme
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Telecoms Software Strategies webinar
Executive summary
New programmes
‘Big data’ and analytics
Use case studies
Recommendations
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Telecoms Software Strategies webinar
In this webinar we will analyse the ‘big data’ and analytics segment
‘Big data’ and analytics
 Customer insight and revenue generation
require meshing data from the network and
back-end systems to understand and predict
customer demand and buying behavior.
 Coverage includes LBS, predictive
modelling, real time network analysis
 The distribution of video, data, and
content is reshaping the
telecommunication and internet.
 Coverage includes managing mobile
video, m-commerce, M2M, and
complex partner eco-systems.
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What is ‘big data’?
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Only a fraction of data that traverses the telecommunications network
needs to be captured for analysis
Customer data = account information, credit
quality, product and services
Structured data = call detail records, IP data
records, and signaling or control plane data
Semi structured data = application flow based
data acquired from deep packet monitoring
Unstructured data = tweets, personalised video
uploads, Facebook post, other social media post
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Telecoms Software Strategies webinar
‘Big data’ analytics projects that generate business benefits in the short
term
Project
Objective
Business impact
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, Mcommerce. It’s 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
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
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Telecoms Software Strategies webinar
‘Big data’ analytics projects that generate business benefits in the medium
term
Project
Objective
Business impact
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
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Market inhibitors to ‘big data’ and analytics investments
Inhibitor
Description
Effect
Timeframe
Privacy issues
CSPs’ attempt to gain richer customer insight using
advanced probing and analytics techniques could
increase the perceived threat of invasion of privacy.
Telcos are more heavily regulated than OTT companies.
Policy makers have tighter constraints on
telcos. This will limit CSPs from selling
customer data. CSPs could look at
techniques to aggregate and anonymise
customer information but this is fraught
with legal disputes and the process is not
uniform
Risk takers less than 1 year
Big data and advanced analytics projects must be aligned
to business outcomes
Cross departmental coordination can
delay investment decisions
9 to 12 months
Business alignment
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Conservative approach is
3+ years
Telecoms Software Strategies webinar
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
behavior, 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’.
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The increase in mobile internet penetration gives the possibility for
operators to expand further their data capturing capabilities
New analytical dimensions
 The increasing utilisation of mobile Internet has brings a new
set of analytical dimensions for commercial teams to explore:
 Content: A completely new customer dimension that
operators now have access to is content. Being able to
understand what types of websites and applications
customers access gives operators a more detailed
overview of their profile and provides precious information
for content strategies
 Device: Although devices were an analytical dimension
before the rise of mobile Internet, the type of device, the
screen size and the utilisation of multiple devices have
now become a more relevant and insightful analytical
variable
 Location: With mobile data permanently switched on,
capturing location data is no longer dependent on the
willingness of the customer to carry out an activity: there is
now a continuous flow of information coming to the
operator. Operators are learning how to exploit that data
both for internal decision-making, and by providing the
information to third parties.
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Deeper levels of segmentation
 Traditionally, telecoms analytical segmentation used
voice and SMS usage data and segmented customers
based on patterns in that data and the demographic
information available
 Behaviour segmentation had to be based completely
on primary research, and was necessarily done by
sampling, making it subject to sampling errors
 By offering insights into location, the content accessed
and the devices used, mobile Internet analytics allows
operators to perform a behavioral segmentation on an
individual basis, getting a much better knowledge of
individual needs
 Operators can use the available data for internal
purposes, or can package it as a product for third
parties
 The size of the opportunity is great, and it is only now
starting to be captured.
Telecoms Software Strategies webinar
What are the sources of data to understand customer behaviour and
usage patterns?
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What is driving the business case for big data analytics for CSPs in the
next year?
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‘Big data’ analytics can be leveraged to both improve internal decision
making and represent an independent source of revenues
How companies can gain from ‘big data’ analytics
Internal use
External use
•
•
•
Use ‘big data’ to support internal
management decisions as well as
improve operational efficiency and
support commercial strategy
Internal decision making can be
supported at different hierarchic
levels, with each level requiring a
different analytical frameworks and
model, different tools
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The volume of data generated in a
telecommunication company is part of
the assets of that company and should
be leveraged as such
By packaging the available data in a
structured and visual way, operators
can sell it to third parties who might
need it for their own business
As an example, handset vendors can
benefit from information about usage
trends across customers with different
handsets
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Telefónica has recently become the first large telecommunication
multinational to use ‘big data’ as a direct revenue source
Telefónica hopes 'big
data' arm will revive
fortunes
Telefónica, the Spanish mobile
phone operator and owner of
O2, is launching a new division
to sell information about its
customers.
Telefónica Digital Insights will
offer companies and public
sector bodies "analytical
insights" based on users'
anonymised location data.
It hopes that exploiting such
"big data" will help revive its
flagging fortunes after falling
sales. (…)
(Source: BBC, 9/10/2012)
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Product
Placement
 Telefónica will collect and package anonymized data
 The first product, ‘Smart Steps’, will use fully anonymised data
to understand what factors influence the number and profile of
people visiting a location at any time.
 Telefónica will collect and package anonymised data
 Currently doing direct placement
 Telefónica is now rumored to have reached a deal with
Nielsen to package and distribute its knowledge
Pricing
 Pricing is yet unknown to the public, but we would expect a
relatively low price as the product will be a showcase for the
potential of ‘big data’ analytics
Organization
 Telefónica created a distinct unit to deal with ‘big data’, within
Digital services department
 In order to overcome the initial challenges in setting up a
different type of department, Telefónica has partnered with
GFK, which are experts in selling knowledge
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Based on client’s needs and available data dimensions, the operator can
provide targeted advertising, market insights and trend analysis
Data
collected
Type of products
Demographics
•
Provide targeted advertising based on
the product corporate clients sell and
operator’s customer base
segmentation
•
Develop a market study based on data
captured from the customer for the use
of players in other industries (e.g. sell
usage patterns of customer base to
device manufacturers to support
device development)
•
Dynamic analysis of recent trends in
the market to support players in other
industries (e.g. report on shifts in
location patterns to support real estate
companies)
Targeted
advertising
Location
Internet usage
Market insights
Device
Services used
Trend analysis
Value
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Telecoms Software Strategies webinar
Spending on IT analytic platforms, data store appliances, and service
monitoring will grow from $4.1 billion in 2011 to $6.6 billion in 2016
 Business intelligence and advanced analytics investments
will grow at 11% CAGR to support more real time actionable
business decisions.
 Network, device, and service monitoring investments will
grow at 12% CAGR to support more than traditional test and
monitoring functions
 Predictive modelling using network data
 Data store and appliance market segment will grow at 6%
CAGR to support more data set storage demand but price
per Exabyte storage will fall under Moore's law.
 IT is driving innovation of network sourced data to support
better decision analysis
 IBM, Oracle, and Teradata provide highly scalable appliance
and data storage solutions that are key components in
analytics architecture for real time network analysis.
 CSPs are leveraging existing systems for data sourcing but
also deploying point solutions from niche suppliers in the
network and service monitoring segments
 Hadoop and MapReduce enable highly scalable parallelprocessing programming of big data
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Figure 10: Analytics and real time network monitoring forecast 2011 - 2016
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Real time network analytics definitions
Figure 11: Data source, storage, business intelligence, and advanced analytics definitions [Source: Analysys Mason, 2013]
Segment
Definition
Network, device and service
monitoring
Software used to monitor the network, devices, and services that span the access, aggregation, and core network. This
includes deep packet monitoring, passive probes, network element management, and event based transactional monitoring
software.
Data store and appliance
Parallel processing computing architecture distributed across many servers and often sold as dedicated hardware
appliances. The database software which can be relational, unstructured (Hadoop), and semi-structured sits on top of the
servers and distributes the workload to achieve high performance and scalability.
Advanced analytics
Software designed to apply statistical modelling, predictive algorithm models, and business insights to improve business
outcomes.
Business intelligence
Software used to measure and report on historical and current data to assess business performance metrics over a period
of time.
© Analysys Mason Limited 2013
Telecoms Software Strategies webinar
Executive summary
New programmes
‘Big data’ and analytics
Use case studies
Recommendations
© Analysys Mason Limited 2013
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Telecoms Software Strategies webinar
Use case: Improvements in first call resolution yields annual cost savings
of EUR5 million
Business drivers
Deployment
 A surge in the use of smart devices and applications is
causing more complex support issues for customers such as
increasing number of support calls and longer handling times
related to mobile data.
 Deployed network analytics solution to gain a real-time view
of consumer behaviour including applications, devices,
locations and service types.
 The CSP needed a better approach to improve first call
resolution for mobile data.
Operations approach
 For the CSP in question, 7% of calls pertaining to mobile
data received by customer care were escalated from 1st line
to 2nd line support and 50% of those calls (3.5% of total calls)
were further escalated from 2nd line to more expensive 3rd
line support team. On an average, each call took approx. 12
minutes.
Benefits
 Project executed within six months. The solution empowered
support teams with more detailed customer experience
metrics in real time such as throughput performance, network
alerts, handset issues.
 Achieved 50% reduction (3.5% instead of 7% earlier) in calls
escalated from 1st line to 2nd line support and 71% reduction
(2.5 % instead of 3.5% earlier) in calls escalated from 2nd
line to 3rd line support.
Software Strategy
 Reduced call resolution times to 8 mins, a saving of 4 mins or
33%.
 Engaged a vendor to perform a detailed analysis of what
types of issues were reported to the 1st line, what type of
information would be required to fix those issues.
 Annual cost savings of EUR5 million in reduced resource
requirements on a subscriber base of approx. 10 million
customers.
 Using analytics, the network information was sourced,
processed and presented in a way that the 1st and 2nd line
teams could better understand and use it to resolve customer
issues more efficiently.
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Telecoms Software Strategies webinar
Use case: European MVNO achieves an unified view of service usage and
profitability of its Fixed-Mobile business customers
Business drivers
 To optimise the business performance and ensure customer
profitability across the Fixed-Mobile converged solution, the
CSP needed to understand and manage the performance
and usage of its services.
 The CSP wanted to monitor service quality and proactively
avoid churn among business customers.
Operations approach
 It wanted to link usage of its different services to a single
profile of the customer. The aim was to deliver a unified view
of their business by combining product, billing, traffic and
customer data enabling the performance measurement of the
delivered business services by customer.
Software Strategy
 It commissioned a single OSS reporting system to unify
customer reporting and understanding by performing real
time network analytics. The expectation was to gain near real
time visibility and control of the business at a customer level.
Deployment
 The solution was deployed in a best of breed architecture
with the analytics platform and DPI probes provided by
different vendors.
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 The solution combined data from CRM, billing and network
management systems - covering tickets, prices, traffic data,
handsets, fault codes, locations and customer data - and
produced a holistic unified data model ready for analysis and
reporting.
 The completed solution provides regular unified reports for all
business lines to senior management, sales and marketing,
delivery, finance, legal and product management
departments. These automated reports provide detailed
information about customers’ behaviour and their product and
service usage - including top customers, revenues,
profitability, volumes, service levels and service reliability
Benefits
 Improved profitability by more efficient business management
from instant access to key business indicators. The CSP can
optimise packaging, tariffs and pricing to their customers
based on actual service consumption and revenues.
 Cost savings and improved operational efficiency with access
to information not freely available before; the usage of real
time network analytics has significantly reduced requests for
bespoke and ad-hoc reports from across the organisation.
 By delivering the reporting data via their customer portal, the
CSP is able to provide service level reporting for mobile
customers as a value added service with increased customer
satisfaction and reduced churn
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Use case: Asian CSP increases marketing effectiveness by better
customer segmentation and targeted promotions
Business drivers
Benefits
 The CSP wanted to improve customer loyalty and increase
ARPU/revenue by improving marketing effectiveness.
 The CSP’s marketing team can now discover target groups
for marketing activities as well as cross and upselling
opportunities. Post marketing campaign launch, the team are
able to monitor day to day usage and react immediately to
changes in customer behaviour and campaign performance.
Improved overall response by 30%.
Operations approach
 The CSP deployed an OSS platform to gather real time
network data and perform real-time analysis of customer
experiences and behaviour, and do effective market
segmentation as well as targeted marketing with specific
offers.
Software Strategy
 It deployed an adjunct system to achieve its operational
goals. It decided to introduce a customer experience
analytics solution to perform real time network analytics.
Deployment
 The solution combined and cross-correlated customer, billing
and network traffic records to provide a data model for
comprehensive analytics and real-time results forming the
basis for effective segmentation for a targeted and
differentiated offerings approach.
 The probes were supplied by third party vendors.
© Analysys Mason Limited 2013
 Technical problems in the network can now be discovered,
such as poor coverage, enabling network operations to
quickly identify the root causes of the issue and rapidly and
proactively fix them.
 Improved operational efficiency through quick access to
information significantly reducing the time taken to identify
target segments and improved accuracy of the lists.
Telecoms Software Strategies webinar
Executive summary
New programmes
‘Big data’ and analytics
Use case studies
Recommendations
© Analysys Mason Limited 2013
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Recommendations for CSPs
 CSPs should understand the business outcomes in specific areas of their business before investing in
big data and analytics
 Increase net profit margins 12% thru cross marketing and sales promotions
 Improve customer retention 0.2% via loyalty campaigns
 Defer capital investments in the RAN without degrading service yielding $50M in savings.
 Approach big data and real time network analytics as a broader solution and not a data warehouse silo.
 Be selective with data capture. Only a fraction of data is required and it must be made usable in minutes
not days.
 Challenge supplier claims and validate successful attributes of deployments specific to targeted
business outcomes.
 Blend organic data in-house with advanced analytics commercial software that have demonstrated
success in other industry verticals (retail / e-commerce)
 Consumer micro segmentation
 Demand driven predictive modelling.
 Tread lightly on privacy concerns. Opt-in campaigns limit regulatory scrutiny and instill goodwill towards
the brand
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About the authors
Partick Kelly (Research Director) leads Analysys Mason’s Telecoms Software research stream, which focuses on identifying the
rapidly growing segments in the telecoms software market and providing forecast and market share data on each of the 29 segments
by region and service type. He has produced research on IP next-generation service assurance, the 3G mobile software market and
customer experience management. Patrick is a frequent speaker at industry conferences. He holds a BSc from the University of
Vermont, and an MBA from Plymouth College.
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