Transforming Customer Relationships and Experiences Through
Predictive Analytics
South Florida Interactive Marketing Association

Today’s agenda
2
De
Dawkins
NA
Sales
Leader
IBM
Predic2ve
Customer
Intelligence
Speaking to you today…
1. How
Analy2cs
can
add
value
to
six
key
use
cases
in
the
marke2ng
lifecycle
2. Iden2fy
basic
predic2ve
analy2cs
techniques
and
concepts
3. Define
an
end
to
end
data
driven,
advanced
analy2cs
powered
customer
engagement
architecture
4. Review
a
real-­‐life
case
study
This session will cover the following
areas…

Leaders leverage big data and analytics for innovation in marketing and
creating a superior customer experience
3
Source:
2014
IBM
Innova2on
Survey.
IBM
Ins2tute
for
Business
Value
in
collabora2on
with
the
Economist
Intelligence
Unit.
3

Predictive Analytics
Leveraging technology and applied mathematics to learn
from the past in order to predict the behavior of individuals
and outcomes of events in order to drive better business
decisions.

Acquire, Grow & Retain customers by improving customer interactions and
relationships by harnessing all customer data
ACQUISITION
RETENTION
PERSONALIZATION
PROFITABLE
GROWTH

To create a superior customer experience and effective marketing
campaigns, you must start with a complete view of the customer
Transac?onal
data
•
Orders
•
Transac2ons
•
Payment
history
•
Usage
history
Descrip?ve
data
•
AVributes
•
Characteris2cs
•
Self-­‐declared
info
•
(Geo)demographics
AFtudinal
data
•
Opinions
•
Preferences
•
Needs
&
Desires
Interac?on
data
•
E-­‐Mail
/
chat
transcripts
•
Call
center
notes
•
Web
Click-­‐streams
•
In
person
dialogues
WHY?
WHAT?
HOW?
WHO?
6

A Living Customer Profile
Base Customer Profile DataWhat We Know
What They’ve Told Us
How They’ve Responded
What They Are Doing
How They Feel
Living Customer Profile (360°)
Transactional Data
Explicit Preferences and Permissions
Contact & Response Data
Behavioral Data
Social Insights
What They’ve Purchased
Predictive Customer IntelligenceHow will they Act
7

Predictive Analytics enables marketers to extract deep insights from data
and better understand customers in order to send more relevant offers.
Consume greater
amounts of data
VOLUME
Make sense of
data more
quickly
VELOCITY
Amalgamate more
types of data
VARIETY
Examine and validate
uncertain data
VERACITY
Data mining:
The self-organizing use of algorithms to
interrogate data and uncover hidden
patterns, associations, and key
predictors. Great for large data sets.
“Who are the most likely consumers
of organic granola bars, and what else
do they typically buy?”
Statistical analysis:
Tests hypotheses about your data to drive
confidence in business decisions
“I think 35-year old single women in
urban metro areas are the largest
consumers of organic granola bars.”
8

Type
Classification
Identify attributes causing likelihood
of something occurring
Segmentation
Find patterns and clusters of similar
things, and outliars
Association
Discover associations, links, or
sequences in your data
Types of
models
Rule deduction, Regression, Time
Series, Decision, Trees, ANN, SVM,
KNN, ...
K-Means, Kohonen SOM,
Correspondence Analysis, Anomaly
Detection, ....
Association, Sequence,
Correspondence Analysis,......
Examples
§ What signals a customer leaving?
§ How many umbrellas will we sell in
the next three months in Chicago?
§ Who is likely to respond to a marketing
campaign?
§ Which insurance claims should we
investigate?
§ What products are purchased
together?
§ What is the series of clicks on my
web page that leads to a sale?
Use to
Build alerts for call centers to take
corrective action on customers
identified as at risk for going to a
competitor.
Increase ROMI and reduce opt-out rate
by reduce the number of people you
market to by selecting only those most
likely to respond.
Increase average sales by building
campaigns and promotions that
combine items offered or provide
recommendations for purchase
Algorithms find the relevant data among the noise
9

Example models for customer analytics
• Propensity Modeling, Campaign Response Models, Product Affinity
Models, Up Sell/Cross Sell Models – Knowing who is most likely to
respond to a campaigns, offers or product recommendation increases
campaign returns without increasing cost. It reduces customer fatigue by
not bothering customer with unnecessary messaging.
• Churn Models – Knowing who is likely to attrite, cancel contracts or buy
from competitors allows customer communication to be oriented to
retaining the customer.
• Customer Value, Life-time Value – Knowing which customer are
valuable or have the potential to be valuable changes the way markets will
communicate to them and what incentives and programs should be
aligned.
• Segmentation Models – Segmentation models cluster customers into
homogenous groups for improving marketing tests and align offers based
on common behaviors.
• Pricing Sensitivity – Insure marketing incentives are a aligned with
customers sensitivity. Protect margin by not discounting products to
customers that are not driven by price.
• Sentiment Analysis – Negative sentiment aligns with churn analysis
above. Positive sentiment helps marketers which customer may become
social advocates.§ © 2015 IBM - Internal Use
10

Customers
Contacted
Total
Sales
0
100%
100%
Rule
1:
Target
Hot
Leads
(Life
Events,
Enquirers)
Rule
2:
Affinity
Targets
Rule
3:
High
Value
Mul2-­‐Buyers
Rule
4:
Exclude
“Bad”
Prospects
50%
Coverage
=
50%
Total
Sales
100%
Coverage
=
100%
Total
Sales
Baseline
Gains
Rule
Gains
Marketing Segments and Predictive Models Working Together – Gains Chart

Customers
Contacted
Total
Sales
0
100%
100%
Some
improvement
due
to
beVer
op2miza2on
of
exis2ng
rules
Most
improvement
ader
core
rules
are
exhausted
Some
improvement
through
beVer
exclusion
of
weak
prospects
40%
70%
Rule
Gains
Baseline
Gains
Marketing Segments and Predictive Models Working
Together – Gains Chart
Predic2ve
Model

1. Customer
Intelligence
&
Insight
6.
Marke?ng
Offer
Selec?ons
Creating an analytically-powered marketing platform: six key use cases
13
5.
Real
Time
Customer
Analysis
2.
Campaign
Targe?ng
3.
Campaign
Automa?on
(in-­‐line
scoring)
4.
Marke?ng
Op?miza?on

1. Customer
Intelligence
&
Insight
14
Generate
a
more
complete
360-­‐degree
view
by
amalgama2ng
mul2ple,
disparate
data
sources
and
appending
predic2ve
insights.
Advanced
analy2cs
finds
hidden
pa]erns
and
predictors
in
large
amounts
of
structured
and
unstructured
data
that
are
most
relevant
to
customer
profiles.
Use Case #1: Know Your Customer!

2.
Campaign
Targe?ng
Advanced
analy2cs
models
help
improve
accuracy
of
targe?ng.
This
allows
markers
to
send
fewer
offers
with
higher
predicted
conversion
rates,
lowering
marke?ng
costs
and
improving
ROMI.
Use Case #2: Present Offers and Messages that Resonate
15

3.
Campaign
Automa?on
(in-­‐line
scoring)
Predic2ve
Customer
Intelligence
scores
can
be
embedded
in
Campaign
flows
and
scored
at
any
2me
during
campaign
processing,
making
analy?c
sophis?ca?on
immediately
available
to
the
marke2ng
lifecycle.
Use Case #3: Automate Campaigns
16

4.
Marke?ng
Op?miza?on
Combine
predic?ve
analy?cs
scoring
to
reveal
likelihood
of
certain
events
(e.g.
propensity
to
accept
an
offer,
risk
of
aVri2on,
etc.).
Evaluate
predic2ve
scores
alongside
business
constraints
and
within
business
rules
to
op2mize
decisions.
Use Case #4: Optimize Through Business Rules, Constraints, and Analytics
17

5.
Real
Time
Customer
Analysis
Predic2ve
Customer
Intelligence’s
real
2me
scoring
engine
allows
the
power
of
the
deep
algorithms
to
be
introduced
at
the
moment
of
impact,
including
the
inclusion
of
contextual
data
-­‐
informa2on
collected
as
the
interac2on
is
happening.
This
again
adds
depth
and
accuracy
to
the
understanding
of
the
customer
profile,
which
supports
campaign
execu2on.
Use Case #5: Interact in Real-Time and Considering Context
18

6.
Marke?ng
Offer
Selec?ons
Predic2ve
Customer
Intelligence
scores
provide
an
alternate
recommenda2on
for
marketers
to
consider
alongside
standard
naive
bayes/self
learning
algorithms
for
offer
selec2on,
grounded
in
mul?ple
algorithmic
techniques
that
examine
many
dimensions
of
data.
This
empowers
the
marketers
with
op2ons
that
may
improve
accuracy
of
offer
selec?on.
Use Case #6: Add Predictive Layers to Offer Selection
19

STEP V
Measure & Refine
Business Intelligence Engine
STEP
II
Generate
Insights
Customer Intelligence
Segmentation
Offer Propensity
Churn risk
purchase predictors
Customer profile
Etc…
STEP
I
Gather
Data
Data Integration
Customer Analytics
Platform
STEP
IV
Act
Delivery
STEP
III
Decide
Campaign
Execution
Campaign
Targets
Customer analytics produces data for targeted campaigns
Predictive INSIGHTS PROFITABLE ACTIONS
Real-­‐Time
Push
Batch
Real-­‐Time
Interac?ve
Real-­‐Time
Campaign
Cross
Channel
Offers
Event
Offer
Channel
20

Acquisition models
Campaign response models
Churn models
Customer lifetime value
Price sensitivity
Product affinity models
Segmentation models
Sentiment models
Up-sell / Cross-sell models
Etc.
Campaigns
Offers/Messaging
Customer experience design
Omni-channel campaign
management
Contact optimization
Real time marketing
Lead nurturing
Marketing event detection
Digital marketing
Customer insights drive optimized, integrated decision making
Big Data
Predictive Customer
Insight
Real time or historical Enterprise Marketing
Solutions
Chat
Voice
Email/SMS
Social
media
IVR
&
Call
Center
Web
and
Mobile
apps
Outbound,
Mail,
etc.
Omni-channel
Customer Interactions
Integrated
Decisioning
Shared
Contextual
View
of
the
Customer
HOW?
Interaction data
• Email & chat transcriptions
• Call center notes
• Web clickstreams
• In-person dialogues
WHY?
Attitudinal data
• Opinions
• Preferences
• Needs and desires
• Sentiments
WHO?
Descriptive data
• Attributes
• Characteristics
• Self-declared information
• Geographic demographics
WHAT?
Behavioral data
• Orders
• Transactions
• Payment history
• Struggles
• Interests
POS,
Kiosk
ATM
21

Communications provider C Spire Wireless uses
predictive analytics and decision models to optimize
cross-selling and prevent churn
Business Challenge ⏐ Outcompete the resource-rich wireless giants,
C Spire Wireless needed to beat them at the small things that matter most: getting
closer to customers and keeping them satisfied. Its challenge was to convert what
it knows about customers into actionable insights that help account reps craft the
optimal offers that meet their needs and head off customer dissatisfaction.
Smarter Solution ⏐ C Spire Wireless is using predictive models to examine
the complexity of its customers’ behavior and determine which service mix is
optimal for each customer’s need, as well as the indicators of imminent churn. By
embedding these insights into its customer-facing processes, C Spire Wireless has
empowered its reps to optimize their interactions with customers.
270% increase
in cross-sales of
accessory products
Increased
satisfaction
by creating a more
personalized customer
experience
50% increase
in effectiveness of customer
retention campaigns
Excellent buy-in
from front-line crew

Connecting more closely to customers
What should we offer this customer?
• Use models to predict churn risk, propensity to respond to different offers
• Use rules to enforce eligibility, policy, and regulatory compliance
“We’re not only getting a more complete picture of our customers’ needs,
we’re translating those insights into a higher-value customer experience.”
- Justin Croft, Manager of Brand Platforms and Analytics
Systems of record
PULSE database is constantly
updated with every customer
interaction – including purchases,
demographics, and prior
offers / responses
Systems of engagement
Personalize interactions across all
touch points
Connect CRM, Web and mobile
into one seamless experience
Point of Sale
Web
IVR
Email
SMS

© 2015 IBM - Internal Use 24