Big Data Meets Customer Profitability Analytics

Big Data Meets
Customer Profitability Analytics
April 10, 2012

Brought to you by the team at Fitzgerald Analytics

Architects of Fact-Based Decisions™

Table of Contents

Introduction

1. Big Data… Big Results?

2. Customer Profitability Analysis

3. Implications of Big Data

4. Conclusion and Questions

Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 2

Tonight’s Event
As usual, it’s about the journey to results.

1 2
Small Data

Big Data
Product of Alberta

3

Really Big Data

Product of everywhere


Our Perspective

Skeptical…
Cautious…
Optimism….


What’s Wrong with a Little Hype ??


We are Talking about Something New and Exciting:

“Data is the New Oil”
– World Economic Forum Report


And Something Old, Essential, & Profitable

“There is only one valid definition
of a business purpose:
to create a customer.”

(The Practice of Management, ‘54).


Co-Presenters (#AnalyticsFSI)

Craig Williston Gniewko Lubecki
@craig_williston

Jaime Fitzgerald
@jfitzgerald

Konrad Kopczynski NikhilMahen
@konradFA @nikhilmahen

Table of Contents

Introduction






Will Big Data Unlock Big Results?

It depends…

...on the
principles you
work by.


The Word’s Most Successful Data Professionals…

#B W T E I M!

What is Covey was a
Big Data Gal in 2012?


Beginning with the End in Mind

1. Your Goal

2. Insight You Need

3. Analytic Methods

4. Data You Need
5.
Tools, Platforms, Technology, Peo
ple, and Processes

“A Journey of a Thousand Miles….”

2

1
Fitzgerald Analytics: Converting Data to Dollars™

Better Data Better Analysis Better Results

3

Worth The Trip!

Key Steps in the Journey to Results

1. Data 2. Analytics 3. Results

 Data Governance  Better Decisions
Analysis Insight
 Data Management  Better Processes

 Data Quality  More Customers

 New Data Source  Happier Customers
Acquisition


Table of Contents

Introduction






Definition & History

Customer Profitability Analysis is:
1) Measuring the contribution each customer makes to overall profits, and to
the key drivers of those profits. In other words, a “customer-level version” of
your corporations P&L statement.
2) Analysis that USES these customer-level metrics to improve results
(there are a large number of applications)

History:
Around since at least the early 1980s.
Banks were early adopters
First Manhattan Consulting Group a pioneer
Massive results unlocked over the years and ongoing
Some notable mishaps along the way…
Still considered “obscure” by many…


The Concept Illustrated

Your P&L Deconstructed into a P&L
Statement for each of your customers


Customer Profitability Output: Classic 1st Step

Best Customers

Losing Money
Profit per Customer

Mid-Value
Loss per Customer

Top 2nd 3rd 4th 5th 6th 7th 8th 9th Bottom Average
(Most (Least
Profitable Profitable
10%) 10%)

Profitability Deciles
(each bar = 10% of customers, ranked by profitability)


What do Customer Profitability Metrics Enable?

A Top 5 List…
1
Customer Segmentation and Lifetime Value (CLV)

2
Customer Retention

3
Cross-sell, Up-sell

4
Marketing Optimization & ROI

5
New Financial Product Design & Innovation


Integration: Connecting The Dots

A few examples of how inter-related these processes are…

1
Customer Lifetime Value + Segmentation
New Information and Insights

2 3 Cross-Sales /
Customer Retention
Up-Sales

4
Marketing ROI

5
New Product Design


Example: Taking Profitable Risks…

IF well managed, card companies often get most of their “riskier” customers

$0.10
Lifetime Profit per Dollar of Sales

The Riskier Half of The Card Company Customers
Generate 6 to 9 Cents per Dollar of Sales….
$0.08

$0.06 …while the “Safer Half” of The Card
Company Customers Produce only
1 to 3 Cents per Dollar of Sales….
$0.04

$0.02

$-

1st Quartile 2nd Quartile 3rd Quartile 4th Quartile

More Risk Credit Score Band Less Risk

Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 21 21

“Lifetime Performance Curves”: Finance + Late Fee Income
The divergence is even more striking when Late Fees are added to Finance Income.

Performance Curves by Credit Quartile:
Income from Finance and Late Fees

$175.00
Quartile1 1st Quartile
$150.00 Quartile2 Accounts
generate more
Finance Fees + Late Fees

Quartile3
$125.00 than 6 times as
Quartile4
$100.00
much revenue
from these
$75.00 sources as
accounts from
$50.00
the 4th
$25.00 Quartile….

$0.00
1 4 7 10 13 16 19 22 25 28 31

Months after 1st Purchase

Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 22 22

Example: Tata Nano

Initial target: “Cheap” car for middle class

What actually happened:
1) Cost 20-50% greater than initially proposed; lost
“Cheap” tag
2) “Middle Class” less willing to accept the technical
glitches the Nano faced..

RESULT: Customer Expectations not met

Customer Analysis: Bought heavily by people who already own
one car

New target: “Utility” car for city dwellers, often a 2nd car.


Challenge: From Descriptive to Prescriptive.

I can’t deposit decile charts in the bank either…

And my analysts can only think up so many customer
segments, A|B Tests, Etc….

Known Pitfall: Not Looking Beyond the Data…
…

…

1995

2012

Challenges to Creating Customer Profit Metrics
Calculating profit seems pretty simple!

Revenue

Direct
Profit
Expense

Expenses +
Allocated
Expenses


Conceptually Simple
At first this seems simple enough…

 Personal Banking
• Checking

• Savings

 Brokerage Account with Checking
• Investments/Trading

• Checking

• Savings


Representative “Universal Bank” Product Suite
But today’s banks are big, complex, and poorly integrated.

Sales & Trading Investment Banking Transaction Banking
 Equities  Capital Markets (IPO)  Cash Management
 Stocks  Mergers & Acquisitions  Trade Finance
 Derivatives
 Project Financing  Corporate Trust
 Program Trading
 Structured Financing  Custody
 Fixed Income
 Corporate Bonds
 Municipal Bonds
 Derivatives
 Interest Rate
 Credit Asset Management Private Wealth Mgmt

 Commodities  Mutual Funds  Wealth Management
 Futures  Separately Managed Consulting
 Forwards  Trust Services
 Foreign Exchange


Impact of Mergers
Mergers add to the complexity…

Equity
Single Product Area
Trading

By Region Americas Europe Asia

By Company Bank 1 Bank 2 Bank 1 Bank 2 Bank 1 Bank 2

• One product, if booked into regional systems and sold by both companies, in a
merger can feed from 6 separate systems.

• At the very least, numbering schemes from the two companies will be different.

• At worst, every system will have a unique number or name for a single client.


“Slicing” Customer Profitability
Firms often seek to view What about other metrics that
customer profitability by: may help with profit analytics:

 Client  Trade Volumes

 Trade Fails
 Client Segments
 Client Service Center Issues
 Product
 Assets Under Management
 Region
(AUM)

If you can’t even get the revenue by client how will you tie in other information?


Solution? Data Management
Data management is a precondition to customer metrics…
Good:
 ETL Process feeding a superimposed external client structure
(and for each dimension such as product, etc)
Better:
 Single client identifier 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 analytic
advantage.

Perspective on Data Management


Table of Contents

Introduction






Defining Big Data: “Three Vs”

"Big Data“ is seen as data with:

greater volume…

greater variety…

and/or

greater velocity….


Another Way to Define “Big Data” -

What methods are required to realistically
make use of it?

Traditional Method? Big-Data Method?

Note that this definition hinges on methods applied, not on dataset sizes:


Profitability Management Becomes More Refined Over Time
through an Iterative Process Driven by Customer Knowledge

Build Customer Profitability Models
 Identify costs & revenues Drive Action Into Frontline Systems Face-to-
• Create consistent message Face
 Build profiles  • Create consistent individuals
Target action to message
 Feed data from Data  • Target action to individuals
Optimize product / service
internal and external Warehouse portfolio Mail
sources  Optimize product/service portfolio
 Maintain data warehouses

Phone

External New Customer Knowledge Internet
Data  Feed campaign results into data
Sources warehouses
 Test predictive accuracy of model
 Break down segment into individual
customer analyses


Big-Data Approaches and Tools Make Data Analysis

 Possible, for very large data sets that cannot be handled at all with typical
relational databases.

 Faster, for large data sets that can be handled with typical relational
databases, but doing so would take a long time. This is the situation in the
example above.

 Cheaper, for large data sets that can be handled with typical relational
databases, but doing so would be very expensive.


Big Data Allows Us To Work with Large Datasets
We can analyze datasets larger than ever before

For a given desired speed of analysis…

Beyond a certain point, conventional
methods just aren’t feasible –
Google couldn’t run on a relational DB
IT Costs

For larger datasets, big-data
methods make more sense

Dataset size
For smaller datasets,
conventional methods are
more cost-effective Traditional Big-data
methods methods

Big Data Allows Us To Get Results Faster
We can get results faster than ever before

For a given dataset size…
IT Costs

SLOW FAST Analysis speed

Conventional Big-data
methods methods

Data on its own is useless

?
Related
Technologies

Big Data

Methods


Add Customer Profitability

Small Data Daily / weekly / monthly

Big Data Instantly


Add new business rules

Big Data Instantly

His son’s
favorite
All his
color is
friends have
blue
Chase

Instantly Father just
started at
Instantly Bank of
America
Big Data
Instantly
Instantly


Table of Contents

Introduction






Big Data Meets Customer Profitability Analytics

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