The document outlines a webinar by ngdata® focusing on enhancing customer engagement through personalization and the concept of 'customer DNA', a collection of metrics that predict customer behavior and preferences. It emphasizes overcoming challenges in data analysis to create a customer-centric approach by leveraging real-time analytics and machine learning. Key applications discussed include improving customer service, reducing churn, and personalizing offers across various channels such as web and mobile.

1. Increase Customer
Engagement with
Personalization
NGDATA Demo Webinar – July 16th 2014

2. 2Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
Customer Experience
Customer spamming is doing more damage than good
Company
& Customer
Activity
Customer
CRM
Systems
Customer
Web & Mobile
Customer Channel
Campaigns
Customer
Service Desk
Social Data
Website &
online apps
Mobile App Server
Mail
SMS
Print
Broadcast
Offers
direct mail
ATM
web
Agent, IVR
email
mobile
chat
Relevance?
Awareness?
Value?
Timing?
Clarity?

3. 3Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
Connecting with the Customer
Preferences
Affinities
Context
Behavior

4. 4Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
I have a customer - what are the top 3 products he is likely to buy?
Answering the Tough Questions…
Which top hundred customers are likely to buy my product X today?
What is the best channel to connect with my customer, and when?
How can I turn around my most valuable potential churners?

5. 5Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
Challenges in Becoming Customer-centric
Overcome
“Analysis
Paralysis”
Proactively
Engage with
Customers
Cope with
plethora of
data

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Becoming Customer-centric with Customer DNA

7. 7Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
What is Customer DNA?
▪ an organized collection of 1000s of predefined
descriptive metrics
▪ calculated in real time
▪ by a configuration-based calculation engine
▪ organized per identified customer
▪ based on a variety of data sources, both interaction-
and fact-based
▪ using a variety of predefined calculation, prediction
or retrieval methods
▪ providing actionable data for marketing &
customer-centric applications

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Customer DNA is based on Interaction data
and factual data (customer facts, contract info …)

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Lily Enterprise
See everything together
– comparisons with a
Set defined by you, and
evolving trend scores for
each customer
From Data to DNA –
1000s of metrics
determine individual
DNA

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Lily Enterprise
Dynamically
created Sets
defined by
your own
rules
More effective
Alerts based on
real-time
customer
metricsModels 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

11. Demo

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Lily Enterprise 3.0
Time to Value
▪ Understanding a complete picture of your customers in 6 months
− What they are about | How to engage them
Impact and Results
▪ Creating a data-aware organization
− “Transactionable” | Bringing analytics to the end-user | Delivering results
Functions
▪ Building Metrics, Affinities,
Preferences in real-time, with
continuous Machine Learning
▪ Integrates with enterprise
architecture (CRM, CMS,
BI,...), also allowing triggers
and alerts
▪ Adding data sources,
dynamically,
as available
Bringing a New, Greater Value to Customer Experience Management

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Interaction
Manager
Big Data
Repository
Catalog
Manager
Listen
Execute
Learn
Targeting
Engine
Open API
Connectors
Calculation
Engine
Machine
Learning
Listen
Connects
Customer
Preferences
Customer
DNA
Lily Enterprise
Real-Time
Processing
Architecture
Lily’s key
components &
related modules

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Learn
Execute
Listen Interaction
Manager
Big Data
Repository
Catalog
Manager
Listen
Execute
Learn
Targeting
Engine
Open API
Connectors
Calculation
Engine
Machine
Learning
Listen
Connects
Customer
Preferences
Customer
DNA
Technology Foundations
Hadoop
eco-system
components
Metastore
ZooKeeper
Flume

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Lily
Operational Systems
Lily Enterprise 3.0
Bringing a New, Greater Value to Customer Experience Management
External Data
Contract Data
Product Data
Reference Data
Reporting / Analytics
Enterprise BI and reporting
CRM and Call Center
IVR and Calls
Enterprise Analytics Applications
Marketing Data
Interaction Data
Campaign Data
CRM
Social Data
Customer Web and Mobile
Mobile Web and Apps
Customer Website & Apps
User and Brand Follow Up
CRM Data
Data Warehouse Data Outbound Marketing
Campaign Management
Real Time
Integration
Data
integration
Batch
Integration
ETL
or
Lily API
based
Lily Connector
Based
Feedback

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Key Use Cases Applied to Subscription-based Businesses
Customer Lifetime Value (Inbound, Outbound, Risk)
• Personalization
• Offer recommendations based on individual preferences
• Customer Service assistance
• Manage risk, fraud prevention, Reduce customer churn
• Personalization, Customer Services
• Better targeting, micro campaigns, social leaders
• Reduce customer churn, price sensitivity
• Media and IPTV, audience measurement
• Personalization, Cross sell/upsell
• Recommendations – ads, content, etc.
• Audience measurement
• Customer Services
Banks
Telecoms
Media and
Publishers

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Website Real Time Offer Personalization
?
?
?
Determine offer eligibility from:
• Joe’s product preferences
• Joe’s DNA (Interests, lifecycle, activity,...)
Calls to Lilly with content:
• Customer info.
• Context info.
• Session data

18. 18Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
?
?
?
Website Real Time Offer Personalization

19. 19Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission
Experience the Difference with Lily
Listen Bigger.
VOLUME
Learn Faster.
SPEED
Execute Smarter.
ANSWERS
Volumes of Data Availability Questions Answered
Start working with Lily to discover results from Day 1
Zettabytes
Exabytes
Petabytes
Terabytes
Gigabytes
Seconds
Minutes
Hours
Days
Weeks
Unknown Unknowns
Known Unknowns
Known
Knowns
DW/BIDW/BIDW/BI

20. 20Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission