wed-04_mwa11_customer_experience_analytics_v0.6 (1).pdf
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7/27/2019 Wed-04_MWA11_Customer_Experience_Analytics_v0.6 (1).pdf
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© 2011 TeleManagement Forum | 1 www.tmforum.orgv2011.1
v2011.1
Customer Experience Analytics
MWD12
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KPI Alphabet Soup
KAI – Key Attitude indicators
KBI – Key Business indicators
KCI – Key Customer indicators
KDI – Key Driver index
KEI – Key Experience indicators
KFI – Key Financial indicators
KGI – Key Goal indicators
KHI – Key Health indicators
KII – Key Improvement indicator
KJI - Key Jurisdiction indices
KLI - Key Lifestyle indicatorsKMI – Knowledge Management index
KNI - Key Navigation indicator
KOI – a Fish or Key Operations Indicator
KPI - Key Performance Indicator
KQI – Key Quality indicator
KRI – Key Revenue Indicator
KSI – Key Success Indicator
KTI - Key Trending indicator
KUI – Key User indicator
KVI – Key Volume indicator
KWI – Key Wealth Indicator
KXI – any key metric for measuring status
KZI – Key Zoning indicator
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Analytics-What If
A system could identify that a high ARPUcustomer had just terminated their service
for no apparent reason…and
The system could identify the next twenty
customers with twin Customer
Profiles…and A system that could look outside the firewall
to capture what your customers are really
saying about your products and services
Do you think your Marketing Departmentmay be interested in this data?
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Business Problem
CSP Annual reports all list customer experience (NPS®/CDI Indices) as
central to business success
The factors influencing the customer’s
experience are many & diverse, ranging
from point of sale service to tariff plans
to data throughput
With the explosion in mobile data usage
the factors influencing Customer’s
Experience are growing
CSP’s that measure & manage the
Customer’s experience continuously will
be the most successful
NPS a registered name by Satmetrix
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Solution
Managing the customer’s experience requires Accurate measures of customer
experience for each interaction with the
CSP
Collation of these measures into a
Customer Experience Management Index(CEMI)
An ability to drill-down from the CEMI to
the factors influencing the experience
Volumes & complexity of data generated in
networks made analyzing customer
experience a challenge but advances in data
integration, storage & analytics now make it
possible
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Solution
This catalyst program proposes a Customer Experience
(CEM) model & Customer Experience Index (CEI) tosurface & collate the Customer’s Experience
It proposes a logical multi-tiered
decomposition of the CEI into it’s
multiple components which alignswith Frameworx
This enables the surfacing of
customer issues into an operational
executive dashboard, with drill-down supported to identify the root
cause of problems
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Service Operations Center
Service & Network
Surveillance
(Real time
measurements)
Trend Analysis
&
Service Quality Management
Reporting
Business Strategic Areas :
Service & Network
Surveillance
(Real time
measurements)
Service & Network
Surveillance
(Real time
measurements)
Service & Network
Surveillance
&Troubleshooting
•Real Time Measurements
•KPI surfacing •Post Processing
•KPI KQI
Phase 1 Phase 2
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•Deep visibility for real time IT systems performing services related to the provided network quality
•Define and activate Improvement actions for the most critical systems
•Monitoring customer experience for provided services
•Tuning che customer profile for each service
Expected benefits
•Deep visibility for real time IT systems performing services related to the provided network quality
•Define and activate Improvement actions for the most critical systems
•Monitoring and improving Customer Experience for provided services
•Tuning the customer profile for each service
Benefits
•Executive level (e.g. Customer Experience Index)
•Operational level
•Department / engineering level (e.g. Software & systems Quality )
• Customer level
KPI & KQI
Service Operations Center Concept
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Technology
Implementing this logical framework
requires that the following
challenges are addressed
Accurate measurement of QoE
Analysis of massive volumes of
complex data
Collation of data from sources of
disparate ownership
An architecture is proposed to
address these technical challenges
leveraging recent advances in OSS
& BSS technologies
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Big Data
Big data and information integration capabilities make it possible to
generate insight from vast quantities of data—fundamentallychanging the way organizations use information. It means:
Filtering petabytes of data per second from almost any
connected device
Analyzing the data while still in motion, deciding what, if any,
data must be stored,
Using analytics tools to virtually integrate the data with data
stored in traditional warehouses.
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Description of Demonstration
The demonstration focuses on thecontribution to the CEI of the Network
Quality and Social Media
It traces how a customer impacted by
a quality issue is surfaced in the CEMConsole and is subsequently
analyzed and resolved by traversing
the multi-tiered CEM Model for KXI
Surfacing Customer impacting issues arising
from the other components of the
CEM Model can be analysed in an
analogous manner
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Benefits
CSP’s have identified customer experience as being
important because happy customers Use more services
Promote the CSP to friends
Churn less
By optimising the customer’s experience the CSP will grow
revenue, reduce churn & increase market share..and find
those next ten customers!
This project proposes a model &
reference architecture to measure
& optimise the Customer
Experience in an operational
manner
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At a Glance
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Flashboard
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Customer Experience Management Index detail
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CEMI Trend
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Catalyst Team Members
Robert Segat IBM/Netezza rssegat@us.ibm.com
Prof. Paul Morrissey Ventraq pmorrissey@ventraq.com
Dan O’Donnell
Stewart Howe
Network
Critical
daniel.odonnell@networkcritical.com
Simona La Rosa Telecom Italia simona.larosa@telecomitalia.it
Jing Shang China Mobile Jing.shang@chinamobilehk.com
Ted Stuchberry Genband Ted.stuchberry@genband.com
Steve Koudelis Ventraq skoudelis@ventraq.com
C t S ti t A l ti
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Customer Sentiment Analytics
List of the KPIs that are for the sentiment analysis
Ventraq & IBM apply analytics to the social media feed.
Currently we are conducting three measurements.
Brand Sentiment Index This is a combination of the twoscores below.
Brand Sentiment Score This measures the level of positive and
negative statements
Brand Intensity Score This measures how strong a position thecustomer is taking in their positive or negative a statements
Supported languages from the social media analytic e.g.
Twitter, Facebook
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