Discover how you can improve customer experiences and increase profitability for Telecoms. To learn more about NGDATA or Lily Enterprise 3.0, please visit ngdata.com

1. Turning Big Data into
More Effective Customer Experiences
Experience the Difference with Lily

2. New channels adopted by the Customer
Years until mass adoption
68 years
50 years
38 years
22 years
14 years
7 years 3 years 2 years
Airplane Telephone Radio Television PC Internet iPod Facebook
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3. New channels adopted by the Customer
3,000,000,000
2,500,000,000
2,000,000,000
1,500,000,000
1,000,000,000
500,000,000
0
We are here
Tablets
Smartphones
Personal Computers
Global Internet Device Sales
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012e 2013e 2014e 2015e 2016e
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4. And it’s getting worse…
How to deal with all this data?
What value is in there?
Internet of THINGS provides more
information than ever before
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5. The Value of Data
94%
of those surveyed believe that "personalization is
critical to our current and future success”
Source: Econsultancy, Digital Marketing Exchange
However, few companies have been able to implement
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6. Connecting with the Customer
Preferences
Context
Affinities
Behavior
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7. Barriers to Customer Experience Management
84%
regard IT roadblocks and lack of technology as
barriers to adopting or improving personalization
Source: Econsultancy, Digital Marketing Exchange
No Solutions – No Automation – Manual Work – Low ROI
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8. Customer Experience
Customer Spamming is doing more damage than good
Relevance?
Awareness?
Value?
Timing?
Clarity?
Customer insight is limited to a
subset of available data - Limiting
relevance and timeliness of offers
Social Data
Company
Customer
Network Data
Usage
QoS
Call Details
Mail
SMS
Customer Data
Customer
CRM
Systems
direct mail
shop
web
agent, IVR
email
mobile
chat
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9. Answering the Tough Questions…
Which top hundred customers are likely to buy my product X today?
I have a customer - what are the top 3 products he is likely to buy?
What is the best channel to connect with my customer, and when?
Can I turn around my most valuable potential churners?
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10. The Personalization Customer Experience
“I have a customer – what does that customer need most?”
Excellent!
Fun!
Great!
Relaxed!
Safe!
Company’s KPI’s improve:
Customer Satisfaction & Advocacy /
Retention / Profitability
shop
QoS
Network
Data
Call Details
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11. Towards a new architecture
One single customer view
App Phone
Mobile
www
STB
Network
Shop
Call Centre
CDR
POS
Personalization
Acquisition
Journey
ARPU / CLTV
Marketing
Advertising
Churn
Fraud
App Phone
Mobile
www
STB
Network
Shop
Call Centre
POS
CDR
Personalization
Acquisition
Journey
ARPU / CLTV
Marketing
Advertising
Churn
Fraud
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12. Overcome
“Analysis
Paralysis”
Proactively
Engage
with
Customers
Transactional
Cope with
plethora of
data
Engagement
Content
Offers
Location
Socio-Demographic
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13. DNA: Understand Lifestyle and predict behaviour
Many think that ‘Life stage’ is sufficient to capture and understand underlying ‘Needs’
• Born in 1948
• Grew up in
Great Britain
• Married, w/
children
• Successful,
wealthy, celebrity
• Loves dogs and
the alps
• Born in 1948
• Grew up in
Great Britain
• Married, w/
children
• Successful,
wealthy, celebrity
• Loves dogs and
the alps
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14. Current Reality
No Solutions – No Automation – Manual Work – Low ROI
Customer
Back Office Systems
Call Details
Network Data
Customer ERP/CRM Data
Customer Interactions
External Data
3rd Party Reference Data
External Systems
3rd Party Master Data
Reporting / Analytics
Enterprise BI and reporting
Applications
Social Data
Company and
Customer activity
Customer Web and Mobile
Customer Website
And Online Apps
Mobile App Server
Customer Channel Campaigns
Mail
SMS
Print
POS
Marketing
Campaign Mgt
Customer Service Desk
Customer CRM systems
3rd Party Operational Data
Enterprise Analytics Applications
Sample data sets – File extractions – Files
?
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15. Lily Real Time Personalization
Within the Current Enterprise Architecture – Improving existing BI landscape
Reporting / Analytics
Enterprise BI and reporting
Applications
Enterprise Analytics Applications
Lily Enterprise
Interaction
Database
Single View
DNA
Targeting
Customer
Back Office Systems
Call Details
Network Data
Customer ERP/CRM Data
Customer Interactions
External Data
3rd Party Reference Data
External Systems
3rd Party Master Data
Social Data
Company and
Customer activity
Customer Web and Mobile
Customer Website
And Online Apps
Mobile App Server
Customer Channel Campaigns
Mail
SMS
Print
POS
Marketing
Campaign Mgt
Customer Service Desk
Customer CRM systems
3rd Party Operational Data
Lily Enterprise Connector – ETL Tools
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16. Lily Listen – Learn: Know Your Customer
From siloed channel interactions into ONE customer “DNA”
Lily Learn:
Lily Listen: Channel all
sources of customer
interactions into one data
model
Lily Customer Matching – VCE
Interaction DB
Lily Listen Lily Learn
Lily Customer
DNA
Debit & Credit
Card Transactions
CDR Data
Call Data
Online Data
Network
Information
CRM and Demographic
Location
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17. Lily Delivers Next Generation Personalization
From Raw Data to Individual Preferences
Fully Automated
Product Preference
Learning
Transactional
Engagement
Content
Offers
Location
Socio-Demographic
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18. Turning Data into Valuable Customer DNA
Identify unique customer behaviors and preferences in real time
View thousands of metrics for each customer
Continuously monitor customers’ evolving preferences to identify opportunities
Bring Analytics to the data – Open towards DW/BI
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19. Lily Customer DNA - Value
• Bigger: Scale trumps Smarter and Better
• Results Driven: Actionable DNA
• “AND” not “OR”: Co-existing with DW/BI
• Prescriptive: Trends better than Values
• Big Data Governance
• Continuous learning
• Objective: Facts on everyone’s desk
• Architecture: One Single View
Discover the Unknown Unknowns with a single
view of your customers always available…
Analytics Transactions Strategic
Machine Learning
Bigger is Better
Current BI
Solutions
Prescriptive
Customer DNA
Results-Driven
Genius of “AND”
Maximize
Architecture
Objectivity
Governance
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20. From Data to DNA
Lily Common
Customer DNA
Industry DNA
Extensions
variable calculation engine
Lily Data Model for Telco
Model
Specific
DNA
Customer Item Interaction QoS Other
map / load / transform: interactions with context & customer source records
data sources
Custom DNA
extensions
e.g. analytical data sets
Context
customers, products, subscriptions, contracts, transactions, interactions, market & social data
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22. Lily Enterprise
From Data to DNA –
1000s of metrics
determine individual
DNA
See everything together –
comparisons with a Set
defined by you, and
evolving trend scores for
each customer
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23. Lily Enterprise
Dynamically
created Sets
defined by
your own rules
More effective
Alerts based on
real-time customer
metrics
Models available, or
easily and dynamically
add new models from
all available metrics
Manage Big Data -
Breaking down data
silos to gain insights
on all customer
interactions in one
place
With Lily’s Customer DNA
and Machine Learning
Engine, individual product
Preferences are available
each moment
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24. What’s different?
“My traditional BI environment
will give the answers tomorrow
of yesterday’s problem”
CIO leading US Bank
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25. Key Value Proposition – result in < 90 days
Single View of
the Customer
Customer DNA
Standard Data
Model & DNA
Machine Learning
• Ingest from any
data source: batch,
streaming, trickle-feed
and ODI
• Expose through
REST/Java/SQL for
marketing
execution, BI and
Analytics
• Customer-centric
data organization
• 1000s of built-in
customer metrics
• Automatically calculated
based on factual &
interaction data
• Tracks metrics over time
for trend analysis &
alerts
• Standard customer-centric
data model for
finance, retail, telco and
media, with support for
behavior data
• Default DNA categories
e.g.
Socio-demo, Life Time Events,
Mobility, Affluence, Social,
Affinity, Lifestyle, Competitor,
Segment, Communication
Preferences, Communication
History, ....
• Extensible/customizable
• Broad range of built-in
techniques e.g.
Collaborative filtering,
clustering, classification,
RF, logit, rule-based
• Support business use
cases e.g.
Recommendations
Xhurn prevention
Xustomer acquisition
Aegment of one
Targeted marketing
Personalized service
Real-time updates Online, continuous learning Automated metrics calculation
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26. Use Cases for Telecommunications

27. 1-2-1 Marketing and Micro Campaigns
Telecom
“ With the international expertise of NGDATA, our CRM
department now exists at the center of all our inbound and
outbound customer interactions, sharing real, actionable
business intelligence and insights, executing hundreds of
targeted campaigns on a yearly basis.”
Result
Director of CRM and consumer Intelligence, major telco
Individual Product and
Content Preferences –
Marketing campaigns with
higher Frequency (> x10),
higher Response rate (x4) and
reduced Budget (< ½)
Objectives
• Improve ROI (conversion ratio) of Marketing
Campaigns by targeting individual customers
Solution
• Real-time ingest of CDRs
• DNA with focus on usage, network, context based
interactions and product preferences
• Learned preferences to feed event-based marketing
actions & send SMS offer to stimulate subscription
renewal
• Personalized video
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28. Churn Prevention
Telecom
“ To gain maximum profit from retaining customers, companies
should consider not only the churn probability of customers, but
also how to mitigate that risk, the likelihood that they will
respond to the right retention offer, and the cost of the offer
itself.”
Result
Director of CRM and consumer Intelligence, major telco
Real Time Churn Propensity –
Being prescriptive on Churn
and reduced attrition with
more than 20%.
Objectives
• Improve customer retention and loyalty through
prescriptive churn scoring
Solution
• Real-time ingest of CDRs
• Customer DNA with focus on usage, payment status,
claims, helpdesk calls,...
• Detect trends and trigger alerts to inform call center
agents in real time and to feed marketing actions to
stimulate subscription renewal and upselling
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29. Experience the Difference with Lily
Listen Bigger.
VOLUME
Learn Faster.
Volumes of Data Availability Questions Answered
SPEED
Execute Smarter.
QUESTIONS
Unknown Unknowns
Known Unknowns
DW/BI
Known
Knowns
Start working with Lily to discover Day 1 results
Zettabytes
Exaabytes
Petabytes
Terabytes
Gigabytes
DW/BI
Seconds
Minutes
Hours
Days
Weeks DW/BI
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