Measuring and managing customer profitability in the big-data era. How to capitalize on the opportunity. In today's era of Big Data and related technology, the benefits of "customer-centricity" are within our reach. Analysis of Big Data sources helps to better understand customer needs, preferences, attitudes, expectations, sentiments, and buying behavior. Yet to achieve this potential, organizations need to understand and apply the classic but essential concepts of customer profitability, customer lifetime value (CLV), and customer value management analytics. Join us for an event on how to approach this challenge. When linked with customer profitability metrics, these insights enable more profitable decisions in product design, sales, marketing, customer care, loyalty management, and risk management. This session will help attendees capitalize on this opportunity. We will cover the classic high-impact basics of measuring and managing customer profitability, customer lifetime value (CLV), as well as how to use new Big Data insights to get more value from these efforts. This tutorial which cover the topic in 5 practical steps: 1. Introduction to Customer Profitability Analytics: What is customer profitability analysis, why is it so valuable, and what are the key concepts and methodologies used to measure customer profitability, customer lifetime value (CLV), and related metrics? 2. High-Impact Use-Cases of Customer Profitability Analytics: What are the key ways customer profitability analytics is used enhance results? We will describe the highest-value ways to use customer profitability metrics to improve business results, with concrete examples in each of the following categories: o Customer Lifetime Value optimization ("CLV") o Customer loyalty and retention o Share of wallet maximization o Marketing ROI o Impact of Customer Service, Customer Experience, and Customer Satisfaction on Profit o Product design, pricing, promotion, and positioning o Allocation of resources (capital, budget, HR, etc) o Risk management 3. How to Calculate Profitability at the Customer Level : We will walk through the algorithms you need to use to turn raw data into customer profitability metrics, and share tips on how to customize them depending on your business. Related applications will also be covered, such as how to use the same algorithms to measure profit per household, salesperson, distributor, or other entity relevant to how your business makes money. 4. Data & Tech Requirements 5. Using Big Data to Maximize ROI on Customer Analytics: What are the top 5 opportunities to use Big Data to increase the benefits achieved through customer profitability analytics and related initiatives? Speakers: Jaime Fitzgerald, Founder and Managing Partner, Fitzgerald Analytics, and Konrad Kopczynscki, Director at Fitzgerald Analytics. Konrad and Jaime have applied customer profitability methodologies to dozens of clients.

1. Architects
of
Fact-­‐Based
Decisions™
Profi%ng
from
Customer
Analy%cs
in
the
era
of
Big
Data
March
25th,
2014

2. 2
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Introduc%on:
Jaime
and
Konrad
17+ years advising clients in Financial
Services, Retail, and Public Sector.
Created the Data to Dollars Value Chain™
framework & methodology, used by to serve
our clients at Fitzgerald Analytics.
Now “open-sourcing” the methodology via:
• The Book
• Online learning resources
• Training seminars on data-monetization
• Customized training + consulting
Specialists
in
the
process
of
turning
Data
into
Results.

3. 3
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
The
Data
to
Dollars™
Stack
Insights
Analysis
Data
Tools,
PlaCorms,
Technology,
People,
and
Processes
Decisions,
Ac%ons,
and
Results
Made
be'er
by
the
right
Created
by
the
right
Which
depends
on
access
to
the
right
And
selec7on
of
the
right
Plan:
Act:

4. 4
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
The
Stack
is
Also
a
Value
Chain…
Insights
Analysis
Data
Tools,
PlaCorms,
Technology,
People,
and
Processes
Decisions,
Ac%ons,
and
Results
Plan:
Act:
Dollars
To
Data
Made
be'er
by
the
right
Created
by
the
right
Which
depends
on
access
to
the
right
And
selec7on
of
the
right

5. 5
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
§ New
Data
Source
Acquisi5on
§ Data
Discovery
§ Data
Quality
§ Data
Governance
Analysis
Insight
§ Decisions
§ Ac5ons
§ Financial
Impact
§ New
Data
§ New
Opportuni5es
The
Data
to
Dollars
Value
Chain™
3.
Dollars
2.
Analysis
1.
Data
Naviga%on
Tips:
1. Set
Clear
Goals
and
translate
into
concrete
plans
2. Stay
Agile
(loop
back
oQen)
3. Keep
Oriented
(“line
of
sight”
/
“why
am
I
doing
this?)

6. 6
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Set
Your
Ul%mate
Goal
“Yes,
that
math
works…”
“Yep,
those
are
the
two
types
sources
of
gross
profit”
“Yep…math
works
here
too…”
Causal
Models
and
Causal
Clarity™
Causal
Clarity™
is
star@ng
with
our
goal
and
then
figuring
out
what
we
needs
to
be
done
in
order
to
deliberately
cause
the
goal
to
happen.
Source:
CFNA
/
Bridgestone-­‐Firestone
Presenta@on
Service
Marke7ng
Compensa7on
Gross
Profit
Store
Expenses
Retail
Store
Profits
Sales
Gross
Margin
on
Sales
Gross
Margin
on
Sales
Sales
Tires
Overhead
Illustra%ve
Example

7. 7
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Table
of
Contents
1. Customer
Profitability
Analy%cs
(CPA)
2. High
Impact
Use
Cases
3. Calcula5ng
CPA
at
the
Customer
Level
4. Data
and
Tech
Requirements
5. Using
Big
Data
to
Maximize
ROI
on
CPA

8. 8
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Seeking
the
Origins
of
Profitability…

9. 9
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Customer
Rela%onships
are
the
Source
of
Results
“There
is
only
one
valid
defini5on
of
a
business
purpose:
to
create
a
customer”
-­‐
Peter
Drucker,
The
Prac@ce
of
Management,
1954

10. 10
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Customer
Profitability
Defined
(aka
“CPA”)
Your
P&L
Statement
Deconstructed
into
a
P&L
for
each
of
your
customers
The
contribu7on
each
customer
makes
to
your
total
profit
or
loss.
In
other
words,
a
“customer-­‐level
P&L
statement”

11. 11
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
History
of
Customer
Profitability
Analysis
§ Prac5ced
since
the
early
1980s.
Banks
were
early
adopters
§ First
Manha_an
Consul5ng
Group
one
of
several
firms
to
pioneer
the
method
for
clients
§ Massive
results
unlocked
over
the
years
and
ongoing
§ Some
notable
mishaps
along
the
way…
§ S5ll
considered
by
many
to
be
“obscure”
or
“not
possible
here”
…which
is
unfortunate!

12. 12
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Customer
Profitability
is
The
Ul%mate
KPI
“There
is
only
one
valid
defini5on
of
a
business
purpose:
to
create
a
customer”
(The
Prac5ce
of
Management,
‘54)

13. 13
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Loss
per
Customer
Example
CPA
Output:
“Decile
Chart”
Top
(Most
Profitable
10%)
2nd
3rd
4th
5th
6th
7th
8th
9th
Bo_om
(Least
Profitable
10%)
Profitability
Deciles
(each
bar
=
10%
of
customers,
ranked
by
profitability)
Average
Best
Customers
Mid-­‐Value
Losing
Money
Profit
per
Customer

14. 14
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
The
“reality
behind
the
averages”
enables
beaer
decisions
Loss
per
Customer
Top
(Most
Profitable
10%)
2nd
3rd
4th
5th
6th
7th
8th
9th
Bo_om
(Least
Profitable
10%)
Profitability
Deciles
(each
bar
=
10%
of
customers,
ranked
by
profitability)
Average
Priori%ze
for
reten%on,
target
to
acquire
more….
Grow
share
of
wallet
Revisit
costs
to
serve,
pricing,
and
root
causes
of
unprofitability
Profit
per
Customer

15. 15
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example
of
an
Individual
P&Ls:
Bank
P&L
Item
(Yearly)
High
Profit
Customer
Low
Profit
Customer
Revenue
Checking
Account
$300
$36
Savings
Account
$100
N/A
Credit
Card
$600
$15
Mortgage
$1,000
N/A
Cost
Of
Goods
Sold
(Interest
Expense)
$800
$5
Opera%onal
Costs
Pro-­‐Rated
Customer
Acquisi5on
(Sales
+
Marke5ng
Expense)
$80
$40
Other
Marke5ng
$5
$5
Customer
Service
Offline
/
Online
/
Phone
$5
/
$2
/
$5
$20
/
$2
/
$5
Statements
Offline
/
Online
$0
/
$1
$30
/
$1
Other
Opera5ons
$5
$5
Net
Profit
$1,097
($62)
Large
Varia7ons
Illustra%ve

16. 16
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Classic
CPA
Output:
“Waterfall
Chart”
Product
A,
$50
Product
B,
$40
Services,
$25
Cost
to
Aquire,
$30
Cost
to
Serve,
$30
Overhead,
$20
Profit,
$35
$0
$50
$100
Product
A
Product
B
Services
Cost
to
Aquire
Cost
to
Serve
Overhead
Profit
Key
components
of
profit
and
loss
per
customer
$
per
Customer
16

17. 17
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Maximizing
profitability
of
the
full
customer
rela%onship
Customer
Life%me
Value
(aka
CLV)
=
the
accumulated
profit
or
loss
from
each
customer
over
the
course
of
that
customer’s
rela5onship
with
you.
Including:
1. Cost
of
acquiring
the
customer
(genera%ng
first
purchase)
2. Revenue
from
all
products
over
%me
3. Costs
of
goods
and
services
sold
(COGS)
4. Customer
service
costs
5. Opera%ng
costs
6. Cost
of
capital

18. 18
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Table
of
Contents
1. Customer
Profitability
Analy5cs
(CPA)
2. High
Impact
Use
Cases
3. Calcula5ng
CPA
at
the
Customer
Level
4. Data
and
Tech
Requirements
5. Using
Big
Data
to
Maximize
ROI
on
CPA

19. 19
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Managing
Customer
Life%me
Value
Customer
Behavior
Offers
Service
Customer
Experience
Messaging
Our
Offerings
+
Ac%ons
Business
Impact
Advocacy
Recep5vity
(to
new
info,
offers,
etc.)
Revenue
$
Now
$
Future
Intangibles
Word
of
Mouth
Advocacy
Referral
Nega5ve
Word
of
Mouth
Costs
Loyalty
Demographics
Customer
Interac%ons
Aaributes
Wants
+
Needs
Customer
Knowledge
Psychographics
Profitability
/
History
Affini5es
Rela5onships
Etc.
Situa5onal
needs
Situa5onal
Aspira5ons
Price
Sensi5vity
Service
Sensi5vity
Channel
Preferences
Etc.

20. 20
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Elements
of
Maximizing
Customer
Life%me
Value
Symbol
Elements
Customer
Acquisi5on
/
Marke5ng
ROI
Share
of
Wallet
Maximiza5on
Customer
Loyalty
and
Reten5on
Product
Design,
Pricing,
Promo5on,
and
Posi5oning.
Alloca5on
of
Resources
(Capital,
Budget,
HR,
etc..)
Impact
of
Customer
Service,
Customer
Experience,
and
Customer
Sa5sfac5on
on
Profit
Risk
Management
In
this
sec%on
we
share
a
set
of
case
studies,
each
of
which
involves
the
use
of
customer
profitability
analysis
to
improve
one
or
more
of
the
elements
below

21. 21
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example:
Credit
Cards
–
Taking
Profitable
Risks
Life%me
profit
per
dollar
of
credit
card
sales
$-
$0.02
$0.04
$0.06
$0.08
$0.10
1st Quartile 2nd Quartile 3rd Quartile 4th Quartile
LifetimeProfitperDollarofSales
More Risk Less RiskQuartiles by Risk Level
The Riskier Half of The Card Company Customers
Generate 6 to 9 Cents per Dollar of Sales….
…while the “Safer Half” of The Card
Company Customers Produce only
1 to 3 Cents per Dollar of Sales….
CLV
Elements
Customer
Acquisi5on
Product
Design
Risk
Management

22. 22
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example:
High-­‐Value
Customers
of
Apple
“Apple
Evangelists”
-­‐-­‐
Buy
Mul@ple
Products…and
Upgrade
ORen
-­‐-­‐
Self-­‐sufficient
/
expert
users
–
the
need
less
support
CLV
Elements
Customer
Acquisi5on
Share
of
Wallet
Customer
Loyalty

23. 23
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example:
Mid-­‐Value
Customers
of
Apple
“Limited
Rela7onship”
-­‐-­‐
Buy
only
1
or
2
Apple
Products…and
rarely
upgrade
-­‐-­‐
Not
self-­‐sufficient,
need
more
help
from
support
CLV
Elements
Share
of
Wallet
Customer
Service
Customer
Loyalty

24. 24
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example:
Nega%ve-­‐Profit
“Customers”
of
Apple
“Resource
Hogs”
-­‐-­‐
Rarely
buy,
if
ever,
and
buy
lowest
margin
products
-­‐-­‐
Consume
dispropor@onate
sales,
service,
and
support
resources.
-­‐-­‐
Frequent
warrantee
or
insurance
replacement
claims
CLV
Elements
Resource
Alloca5on
Customer
Service
Product
Design
Risk
Management

25. 25
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
CLV
Elements
Loyalty
Product
Design
Resource
Alloca5on
Risk
Management
Customer
loyalty:
Delta’s
Frequent
Flier
Program
Decision
Implemented:
Tie
Tier
Status
to
Revenue
per
Mile
instead
of
solely
miles
traveled.
Key
insight:
Customer’s
were
gaming
the
system
to
gain
lucra5ve
5er
status
Behavior
Observed:
A
surprising
%
of
not
profitable
customers
were
earning
elite
status.

26. 26
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Delta’s
Loyalty
Program:
Causal
Model
Revenue
Revenue
/
Mile
=
Miles
Flown
X
Before
the
change,
Delta
was
incen7vizing
miles
flown
The
new
program
is
incen7vizing
revenue
1
2
CLV
Elements
Loyalty
Product
Design
Resource
Alloca5on
Risk
Management

27. 27
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
What
Delta
Must
have
Realized…
Decile:
1
2
3
4
5
6
7
8
9
10
%
of
All
Elite
Members
30%
20%
10%
10%
8%
8%
8%
3%
2%
1%
Rev
/
Mile
$10
$8
$5
$4
$4
$4
$2
$1
$1
$1
Illustra%ve
CLV
Elements
Loyalty
Product
Design
Resource
Alloca5on
Risk
Management

28. 28
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Risk
Management:
American
Express
Forgets
to
Bill
Decision
Implemented:
discover
and
fix
an
opera5onal
error
that
led
to
some
customers
not
being
charged
their
annual
fee.
Key
insight:
Certain
customers
had
not
been
billed
a
yearly
fee
in
YEARS
Behavior
Observed:
A
sub-­‐sec5on
of
loyal
customers
appeared
to
be
genera5ng
no
revenue
from
Annual
Fees
CLV
Elements
Product
Design
Risk
Management

29. 29
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
American
Express
Pla%num:
Illustra%ve
Customer
P&L
1-­‐year
Elements
of
P&L
Customer
#1
Customer
#2
Revenue
Annual
Fees
$500
$0
Late
Fees
$20
$20
Interest
Expense
$30
$30
Other
Fees
$60
$60
Cost
Of
Goods
Sold
(Interest
Expense)
$50
$50
Opera%onal
Costs
$150
$250
This
difference
should
not
exist
for
the
same
product
CLV
Elements
Product
Design
Risk
Management

30. 30
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Guide
to
Capitalizing
on
CLV
(use
this
to
recap
from
examples)
If
you
Know
This
About
Your
Customers
You
Can
Benefit
in
These
Ways:
The
right
risky
customers
end
up
crea5ng
a
huge
amount
of
value
over
their
life5me.
ID
the
most
important
customers
and
retain
more
value
from
customers
that
on
first
glance
seem
risky.
Customers
who
only
buy
one
or
two
items
end
up
cos5ng
us
the
most
in
in-­‐person
customer
support
Create
customer
service
alterna5ves
that
will
migreate
these
customers
to
less
costly
customer
support
channels.
Frequent
travelers
make
up
the
majority
of
your
best
customers,
but
a
sizable
minority
of
frequent
travels
are
below
average,
in
large
part
because
they
use
other
carriers
most
of
the
5me.
Poach
travellers
from
other
carriers
If
certain
customer
of
the
same
product
are
not
genera5ng
fee
revenue.
You
can
iden5fy
where
there
may
be
an
opera5onal
lapse
where
you
are
leaving
money
on
the
table.

31. 31
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Table
of
Contents
1. Customer
Profitability
Analy5cs
(CPA)
2. High
Impact
Use
Cases
3. Calcula%ng
CPA
at
the
Customer
Level
4. Data
and
Tech
Requirements
5. Using
Big
Data
to
Maximize
ROI
on
CPA

32. 32
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Gesng
the
Math
Right
Key
Drivers
of
Profit
–
Simple
Map
Gross
margin
Expenses
Customer
Profit
Non-­‐Capital
Expenses
Gross
Sales
COGS
Cost
of
Capital

33. 33
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Gesng
the
Math
Right:
Rela%ve
Difficulty
The
challenge
increases
as
you
proceed
downward…
Gross
margin
Expenses
Customer
Profit
Non-­‐Capital
Expenses
Gross
Sales
COGS
Cost
of
Capital
HarderMath/
TougherChoices

34. 34
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
The
Math:
Gross
Margin
Gross
Sales
=
The
Sum
of
the
Number
of
Sales
of
Each
Product
x
the
Selling
Price
of
Each
Product
Less
The
Sum
of
the
Number
of
Sales
of
Each
Product
x
the
Cost
of
Each
Product
(to
the
company)

35. 35
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Gross
Sales:
Product
Examples
from
Financial
Services
§ Personal
Banking
• Checking
• Savings
• Credit
Card
• Mortgage
§ Brokerage
Account
with
Checking
• Investments/Trading
• Checking
• Savings

36. 36
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Expenses:
Variable
vs.
Fixed
Variable
Expenses
Fixed
Expenses
§ Expenses
which
vary
from
period
to
period
based
on
the
volume
of
a
unit
§ Examples:
ACH
Transac5ons,
Statements
Printed,
Receipts
§ Expenses
which
remain
fixed
despite
fluctua5ng
volumes
§ Example:
Cost
of
DEVELOPING
a
Web-­‐Based
Banking
Applica5on
(although
the
cost
of
hos5ng
+
support
is
variable)
Expenses
Non-­‐
Capital
Expenses
Cost
of
Capital
Fixed
Expenses
Variable
Expenses

37. 37
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
The
Math:
Alloca%ng
Variable
Expenses
For
each
expense
line
item,
Customer
Expense
equals
Expense
per
Unit
x
Number
of
Units
Example:
3
Bank
Teller
TXNS
x
$10
per
Teller
Transac%on
Expenses
Non-­‐
Capital
Expenses
Cost
of
Capital
Fixed
Expenses
Variable
Expenses

38. 38
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
The
Math:
Alloca%ng
Fixed
Expenses
For
each
category
of
fixed
costs,
allocate
based
on
the
factor
that
makes
the
most
sense
given
your
analy%c
purpose.
Common
op%ons:
1) Per
customer
2) Per
transac%on
3) Per
ac%vity
4) Per
dollar
of
sales
or
Gross
Profit
Expenses
Non-­‐
Capital
Expenses
Cost
of
Capital
Fixed
Expenses
Variable
Expenses

39. 39
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
What
Affects
“Cost
to
Serve”?
Low
Cost-­‐to-­‐Serve
Customers
High
Cost-­‐to-­‐Serve
Customers
Order
standard
products
Order
custom
products
High
order
quan55es
Small
order
quan55es
Predictable
order
arrivals
Unpredictable
order
arrivals
Standard
delivery
Customized
delivery
No
changes
in
delivery
requirements
Change
delivery
requirements
Electronic
processing
(EDI)
(zero
defects)
Manual
processing
Li_le
to
no
pre-­‐sales
support
(standard
pricing
and
ordering)
Large
amounts
of
pre-­‐sales
support
(marke5ng,
technical,
and
sales
resources)
No
post-­‐sales
support
Large
amounts
of
post-­‐sales
support
(installa5on,
training,
warranty,
field
service)
Replenish
as
produced
Require
company
to
hold
inventory
Pay
on
5me
Pay
slowly
(high
accounts
receivable)
Source:
Kaplan
&
Narayanan
with
revisions
by
Fitzgerald
Analy5cs

40. 40
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Week
of: 31-­‐ Oct 7-­‐ Nov 14-­‐ Nov 21-­‐ Nov 28-­‐ Nov 5-­‐ Dec 12-­‐ Dec 19-­‐ Dec 26-­‐ Dec 2-­‐ J an 9-­‐ J an 16-­‐ J an 23-­‐ J an
Phase
1.4 Define methodological
approach (methods, concepts,
technology options)
1.2 Determine
potential
segmentation criteria
3.4 Troubleshoot data
Key
Tasks
2.3 Develop revenue
and costing
algorithms
2.4 Account for cross-
unit effects
4.4 Document recommendations
for ongoing maintenance and
enhancement
1.1 Gather input via interviews
1.3 Determine data availability
1.5 Plan development
of prototype
2.5 Document methodology and
data sources
1.
Strategy
&
Planning
2.
Design
Methodology
and
Algorithms
3.
Build
Prototypes 4.
Segment
Analysis
2.1 Understand data sources in
detail
2.2 Request and test data
extracts
4.3 Identify key insights to drive
additional segmentation analysis
4.1 Rank customers
by decile
4.2 Initial
segmentation analysis
3.1 Program customer profitability
algorithms
3.2 Validate and modify where
necessary to ensure accuracy
3.3 Finalize documentation of
data definitions and profitability
algorithms
Example
Project
Timeline
(Aggressive
Ini%al
Prototype)

41. 41
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Table
of
Contents
1. Customer
Profitability
Analy5cs
(CPA)
2. High
Impact
Use
Cases
3. Calcula5ng
CPA
at
the
Customer
Level
4. Data
and
Tech
Requirements
5. Using
Big
Data
to
Maximize
ROI
on
CPA

42. 42
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Data
Requirements:
Input
Data
Data
Type
Purpose
in
CPA
Crucial
Considera%ons
Customer
List
+
Aaributes
Basis
of
Analysis.
Unique
ID
Defini5on
of
Customer
(!)
or
relevant
en55es
(Household?
B2B
Account?
Etc.)
Sales
Transac%on
Data
Gross
Revenue
Transac5ons
need
to
be
product
specific
Product
Cost
Data
Gross
Margin
How
variable
is
cost
for
a
given
product?
What
product
sourcing
decisions
might
we
make?
Expenses
by
Line
Item
Alloca5ng
Costs
How
to
categorize
costs
Ac%vity
and
transac%on
volume
data
To
allocate
costs
of
ac5vi5es
Where
possible,
ac5vity
data
that
is
customer
specific
is
best
Where
ac5vity
data
is
not
tracked
by
customer
served,
other
categoriza5on
is
useful
(example:
product,
geography,
etc.)

43. 43
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Data
You
Must
Create
to
Implement
CPA
Data
Type
Decisions
Cost
Alloca%on
Factors
Granularity
of
ABC
cos%ng
“Anomaly
Management”
Best
way
to
allocate
fixed
costs
“Proxy
Benchmarks”
What
missing
data
needs
to
be
es%mated
with
a
proxy,
and
under
what
circumstances?
What
proxy
best
suits
the
purpose

44. 44
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example:
Credit
Card
CPA
Model
Revenue Side
• The Customer Profitability process takes all customer
transaction activity * (revenue-generating and charge -offs) and
organizes them by customer , by year , and by month
• Key assumption : calculated factor to assess direct mail revenue
Dimensions
Customer
Month
Year
Measures
Customer
Statement
Balance
Risk Management Data
Dimensions
Customer
Month
Year
Measures
Sales
Fees/Charges
Direct Mail
Bad Debt
TXN Data
Input Process Output
Dimensions
Customer
Month
Year
Measures
Customer Profitability
Model
1. Revenue line
items*
2, Expense
generating line
items**
3. Profit
Expense Side
Expense line item assumptions
• The model breaks down all expense line items and
attributes them at the customer level
• The model attributes them at the customer level by applying cost
factors (to various customer activities that imply costs
Interest expense assumptions
• Cost to private label card companyof its accounts receivables (i.e.
cost of borrowing money customer statement balances)
• Dependent on various interest rate indices
Expense Data

45. 45
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Data
Management
Good:
§ ETL
Process
feeding
a
superimposed
external
client
structure
(and
for
each
dimension
such
as
product,
etc)
Beaer:
§ Single
client
iden5fier
inside
all
systems
for
straight-­‐through
processing.
Other
standard
reference
tables.
Best:
§ An
ability
to
adapt
to
changes
in
business
structure
with
changes
to
data
management
and
data
quality.
In
short,
companies
who
manage
data
well
have
an
analy5c
advantage.

46. 46
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Example:
Data
Flow
Data
Used
in
CPA
Analysis
POS Sale
ECSDS
HEMS
Host ECSDS
Management System
ICD JDA
NEW marketing
Automation System
CustomerLevelMetrics
CustomerProfitability Data
Prophix
Accounting System
ReportWeb
Accounting:
P&L
CostAdjustment
Cost Master Book
Labor cost
Parts cost
Generic product cost
Nat’l Customer Database
HR database
future
Archer
OLD Marketing
Information System

47. 47
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Table
of
Contents
1. Customer
Profitability
Analy5cs
(CPA)
2. High
Impact
Use
Cases
3. Calcula5ng
CPA
at
the
Customer
Level
4. Data
and
Tech
Requirements
5. Using
Big
Data
to
Maximize
ROI
on
CPA

48. 48
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Big
Data
+
CLV
Management:
3
Key
Spots
Customer
Behavior
Offers
Service
Customer
Experience
Messaging
Our
Offerings
+
Ac%ons
Business
Impact
Advocacy
Recep5vity
(to
new
info,
offers,
etc.)
Revenue
$
Now
$
Future
Intangibles
Word
of
Mouth
Advocacy
Referral
Nega5ve
Word
of
Mouth
Costs
Loyalty
Demographics
Customer
Interac%ons
Aaributes
Wants
+
Needs
Customer
Knowledge
Psychographics
Profitability
/
History
Affini5es
Rela5onships
Etc.
Situa5onal
needs
Situa5onal
Aspira5ons
Price
Sensi5vity
Service
Sensi5vity
Channel
Preferences
Etc.
1
2
3
Richer
Customer
Knowledge
Beaer
predic%ons
Ac%ons

49. 49
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Big
Data
+
Customer
Knowledge
Demographics
Attributes Wants
+
Needs
Customer
Knowledge
Psychographics
Profitability
/
History
Affinities
Relationships
Etc.
Situational
needs
Situational
Aspirations
Price
Sensitivity
Service
Sensitivity
Channel
Preferences
Etc.
1
Text
Analy%cs:
1)
Call
center
transcripts
2)
Social
Media
(Listening
+
Service)
Social
Media
1)“Graph
Analysis”
2)
Affinity
signals
Loca%on
data
High-­‐performance
processing!
Clickstream
Analy%cs
-­‐-­‐
Interests
-­‐-­‐
Response
to
UI
Examples:

50. 50
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Big
Data
+
Customer
Behavior
Advocacy
Receptivity
(to
new
info,
offers,
etc.)
2
Text
Analy%cs:
1)
Call
center
transcripts
2)
Social
Media
(Listening
+
Service)
Social
Media
1)“Graph
Analysis”
2)
Affinity
signals
Loca%on
data
High-­‐performance
processing!
Clickstream
Analy%cs
-­‐-­‐
Interests
-­‐-­‐
Response
to
UI
Examples:

51. 51
How
to
profit
from
Customer
Analy5cs
in
the
era
of
Big
Data
|
Copyright
Fitzgerald
Analy5cs
2014,
all
rights
reserved
Big
Data
+
Our
Offerings
and
Ac%ons
Customer
Behavior
Offers
Service
Customer
Experience
Messaging
Our
Offerings
+
Actions
Advocacy
Receptivity
(to
new
info,
offers,
etc.)
Loyalty
Customer
Interactions
2
3
Text
Analy%cs:
1)
Call
center
transcripts
2)
Social
Media
(Listening
+
Service)
Social
Media
1)“Graph
Analysis”
2)
Affinity
signals
Loca%on
data
High-­‐performance
processing!
Clickstream
Analy%cs
-­‐-­‐
Interests
-­‐-­‐
Response
to
UI
Examples: