turning big data into more effective customer experiences

Turning Big Data intoMore Effective Customer ExperiencesExperience the Difference with Lily

2Copyright 2014 NGDATA®, Inc. Confidential – Distribution prohibited without permission

New channels adopted by the Customer

Airplane Telephone Radio Television PC Internet iPod Facebook

Years until mass adoption

68 years

50 years

38 years

22 years

14 years

7 years 3 years 2 years


New channels adopted by the Customer

0

1,000,000,000

500,000,000

1,500,000,000

2,000,000,000

2,500,000,000

3,000,000,000

We are here

Tablets

Smartphones

Personal Computers

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012e 2013e 2014e 2015e 2016e

Global Internet Device Sales


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


The Value of Data

However, few companies have been able to implement

Source: Econsultancy, Digital Marketing Exchange

of those surveyed believe that "personalization is critical to our current and future success”

94%


Connecting with the Customer

Preferences

Affinities

Context

Behavior


Barriers to Customer Experience Management

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

84%


Customer Experience Customer Spamming is doing more damage than good

Customer insight is limited to a subset of available data - Limiting relevance and timeliness of offers

Company

Customer

CustomerCRM

Systems

Network Data

Call Details

Customer Data

Social Data

Usage

QoS

Mail

SMS

direct mail

shop

web

agent, IVR

email

mobile

chat

Relevance?Awareness?

Value?Timing?Clarity?


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?

Can I turn around my most valuable potential churners?


The Personalization Customer Experience“I have a customer – what does that customer need most?”

Company’s KPI’s improve: Customer Satisfaction & Advocacy /

Retention / Profitability

Excellent!Fun!

Great!Relaxed!

Safe!

shop

QoS

Network Data

Call Details


Towards a new architectureOne single customer view

www

STB

Mobile

Network

App Phone

Call Centre

CDR

Shop

POS

Personalization

Acquisition

Journey

ARPU / CLTV

Marketing

Advertising

Churn

Fraud

www

STB

Mobile

Network

App Phone

Call Centre

Shop

POS

CDR

Personalization

Acquisition

Journey

ARPU / CLTV

Marketing

Advertising

Churn

Fraud


Overcome “Analysis Paralysis”

Proactively Engage

with Customers

Cope with plethora of

data

Transactional

Engagement

Content

Offers

LocationSocio-Demographic


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


Current RealityNo Solutions – No Automation – Manual Work – Low ROI

Customer Back Office Systems

External Data

External Systems

Call Details

Network Data

Customer ERP/CRM Data

3rd Party Reference Data

3rd Party Master Data

Reporting / AnalyticsEnterprise BI and reporting

Applications

Social Data

Customer Web and Mobile

Mobile App Server

Customer WebsiteAnd Online Apps

Customer Channel Campaigns

Mail

SMS

Print

POS

Mar

ketin

g Ca

mpa

ign

Mgt

Customer Service Desk

Customer CRM systems

Company and Customer activity

Customer Interactions

3rd Party Operational Data

Enterprise Analytics Applications

Sam

ple

data

set

s –

File

ext

racti

ons

– Fi

les

?


Lily Real Time PersonalizationWithin the Current Enterprise Architecture – Improving existing BI landscape

InteractionDatabase

Single View DNA

Targeting

Lily Enterprise

Customer Back Office Systems

External Data

External Systems

3rd Party Reference Data

3rd Party Master Data

Reporting / AnalyticsEnterprise BI and reporting

Applications

Social Data

Customer Web and Mobile

Mobile App Server

Customer WebsiteAnd Online Apps

Customer Channel Campaigns

Mail

SMS

Print

POS

Mar

ketin

g Ca

mpa

ign

Mgt

Customer Service Desk

Customer CRM systems

Company and Customer activity

3rd Party Operational Data

Enterprise Analytics Applications

Lily

Ent

erpr

ise

Conn

ecto

r – E

TL T

ools

Call Details

Network Data

Customer ERP/CRM Data

Customer Interactions


Lily Listen – Learn: Know Your CustomerFrom siloed channel interactions into ONE customer “DNA”

Lily Listen Lily Learn

Lily Customer DNA

Lily Learn: Matching – VCELily Customer

Interaction DB

Lily Listen: Channel all sources of customer

interactions into one data model

CRM and Demographic

Debit & CreditCard Transactions

Online Data

Location

Network Information

CDR Data

Call Data


Lily Delivers Next Generation PersonalizationFrom Raw Data to Individual Preferences

Fully Automated Product Preference

Learning

Transactional

Engagement

Content

Offers

Location

Socio-Demographic


Turning Data into Valuable Customer DNA

Identify unique customer behaviors and preferences in real timeView thousands of metrics for each customer

Continuously monitor customers’ evolving preferences to identify opportunitiesBring Analytics to the data – Open towards DW/BI


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 DNAResults-Driven

Genius of “AND”

Maximize Architecture

Objectivity

Governance


From Data to DNA

Lily CommonCustomer DNA

data sources

Custom DNA extensions

e.g. analytical data sets

Lily Data Model for TelcoOtherCustomer Item Interaction QoS

customers, products, subscriptions, contracts, transactions, interactions, market & social data

Context

Industry DNA Extensions

variable calculation engine

map / load / transform: interactions with context & customer source records

ModelSpecific

DNA


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


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


What’s different?

“My traditional BI environmentwill give the answers tomorrow

of yesterday’s problem”CIO leading US Bank


Key Value Proposition – result in < 90 days

Single View ofthe Customer

Customer DNA Standard DataModel & 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.

RecommendationsXhurn preventionXustomer acquisitionAegment of oneTargeted marketingPersonalized service

Real-time updates Online, continuous learning Automated metrics calculation

Use Cases for Telecommunications


1-2-1 Marketing and Micro CampaignsTelecom

Individual Product and Content Preferences –

Marketing campaigns with higher Frequency (> x10), higher Response rate (x4) and reduced Budget (< ½)

Result

“ 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.”

Director of CRM and consumer Intelligence, major telco

• Improve ROI (conversion ratio) of Marketing Campaigns by targeting individual customers

Objectives

• 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

Solution


Churn PreventionTelecom

Real Time Churn Propensity – Being prescriptive on Churn and reduced attrition with more than 20%.

Result

“ 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.”

• Improve customer retention and loyalty through prescriptive churn scoring

Objectives

• 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

Solution

Director of CRM and consumer Intelligence, major telco


Experience the Difference with Lily

Listen Bigger.

VO

LU

ME

Learn Faster.

SP

EED

Execute Smarter.

QU

ES

TIO

NS

DW/BI

Volumes of Data

Availability

Questions Answered

Start working with Lily to discover Day 1 results

Zettabytes

Exaabytes

Petabytes

Terabytes

Gigabytes

DW/BI

Seconds

Minutes

Hours

Days

Weeks DW/BI

Unknown Unknowns

Known Unknowns

Known Knowns