The document discusses how companies can better understand their customers by analyzing customer data from various sources to develop a "customer DNA" profile. It describes listening to customer interactions, learning from metrics and preferences to develop standardized profiles, and then executing personalized engagement and marketing based on the customer profiles. The goal is to move from siloed data views to a single customer view that empowers companies to proactively interact with customers in more meaningful ways.

1. Get
Closer
to
your
Customers
Turn your data into valuable interactions that
your customers expect!

2. 2
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Connec/ng
with
the
Customer
Preferences
Affini4es
Context
Behavior

3. 3
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Customer
Experience
Customer
spamming
is
doing
more
damage
than
good
Company
&
Customer
Ac1vity
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?

4. 4
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direct
mail
ATM
web
Agent,
IVR
email
mobile
chat
The
Personaliza/on
Customer
Experience
“I
have
a
customer
–
what
does
that
customer
value
most?”
Customer
Service
Desk
Customer
Web
&
Mobile
Customer
Channel
Campaigns
Social
Data
Company
&
Customer
Ac4vity
Customer
CRM
Systems
Mail
SMS
Broadcast
Print
Mobile
App
Server
Customer
website
&
online
apps

5. 5
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I
have
a
customer
-­‐
what
are
the
top
3
products
he
is
likely
to
buy?
Answering
the
Tough
Ques/ons…
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
poten1al
churners?

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Overcome
“Analysis
Paralysis”
Proac4vely
Engage
with
Customers
Cope
with
plethora
of
data

7. 7
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GeIng
to
Know
Your
Customer
From
siloed
channel
interac4ons
into
ONE
customer
“DNA”
Listen
Learn
Customer
DNA
Standardized,
Ac/onable
Metrics
Single
View
of
the
Customer
Channel
all
sources
of
customer
interac/ons
into
one
data
model
CRM
and
Demographic
Debit
&
Credit
Card
Transac4ons
Online
Data
Loca4on
Wallet
Informa4on
IVR
data
Call
Data

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“Listen”
is
about
Interac/ons
• Interac4ons
are
behavior
• Connect
data
silos
• Unify
&
augment
interac4ons

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Augmented

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“Learn”
is
about
Metrics
&
DNA
• Iden4fied,
standard,
comparable
Metrics
• Based
on
Interac4ons

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“Learn”
is
about
Preferences
&
Affini/es
• Interac4on-­‐based
Preference
Learning
on
Products
&
Categories
• Affinity
Scores
for
longer-­‐term
profiling

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Empower
End-­‐Users
to
work
with
Customer
DNA

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Organize
Metrics
along
ac4vity-­‐based
categories
Common,
industry
&
custom
DNA

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Beyond
metrics:
trend
&
accelera4on

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Execute
Event
marke4ng
with
ac4onable
customer-­‐level
data

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Dynamic,
metric-­‐based
segmenta4on
&
tracking
Build
Target
Groups

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Alerts
for
keeping
track
of
key
opportuni4es
(or
impediments)
Go
from
campaign-­‐based
to
event
marke4ng

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?
?
?
Website
Real
Time
Offer
Personaliza/on
Determine
offer
eligibility
from:
• Joe’s
product
preferences
• Joe’s
DNA
(Interests,
lifecycle,
ac4vity,...)
Calls
to
Lilly
with
content:
• Customer
info.
• Context
info.
• Session
data

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?
?
?
Website
Real
Time
Offer
Personaliza/on

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Lily
Enterprise
-­‐
Value
Analy/cs
Transac/ons
Strategic
Machine
Learning
Bigger
is
Beher
Current
BI
Solu4ons
Prescrip4ve
Customer
DNA
Results-­‐Driven
Genius
of
“AND”
Maximize
Architecture
Objec4vity
Governance

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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/BI
DW/BI
DW/BI

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