Big Data Meets Customer Profitability Analytics

Big Data Meets
Customer Profitability Analytics

Jaime Fitzgerald, Founder and President,
Fitzgerald Analytics
April 17, 2012

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

Nice to Meet You!

Data to Dollars™ specialist.
To do this, created a structured methodology
and toolkit to accomplish this.
Will share at EDW12!

• Key Mission is to
Find & unlock opportunities
via data, technology, people, + processes.

Principles:
Jaime Fitzgerald
@jfitzgerald “Begin with the End in Mind” (Covey)

“Quality is Free” (McGregor)


From to Data to Dollars
It’s a journey…

1 2
Small Data

Big Data
Product of Alberta

3

Really Big Data

Product of everywhere


My Perspective Towards “Big Data”

Skeptical (of the hype)…
….yet
Cautiously Optimistic!

Big Data
Product of Alberta


Big Data Hype – Does is Cause a Problem?

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


The Potential is Real…It’s Just Not Easy to Get


And Something Old, Essential, & Profitable

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

(The Practice of Management, ‘54).


Table of Contents

Introduction






Will Big Data Unlock Big Results?

It depends…

...on the
principles you
work by.

Stephen Covey


The Word’s Most Successful Data Professionals…

#B W T E I M!

What is Covey was a
data professional
today?

Stephen Covey


Beginning with the End in Mind

1. Your Goal

2. Insight You Need

3. Analytic Methods

4. Data You Need

5. Tools, Platforms, Technology,
People, 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


Data Management: Especially Important in the Big Data Era


Table of Contents

Introduction






Definition

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 of Customer Profitability Analysis:

1. Around since at least the early 1980s.

2. Banks were early adopters

3. Massive results unlocked over the years

4. Some notable mishaps along the way…

5. Still considered “obscure” by many…

The Concept Illustrated

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


Customer Profitability Metrics Can Seem Simple…

Revenue

Direct
Profit
Expense

Expenses +
Allocated
Expenses


Yet in a complex organization
Example: A “Universal Bank”

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


And with the Impact of Mergers
Here come the data silos…

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.


Data Management = Precondition of Customer Analytics


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: Better Pricing of Risk vs. Reward

Using CLV metrics to predict profits over the customer lifetime, lenders make
better decisions about lending to “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 27 27

“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 28 28

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

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 are the optimal methods to accomplish your goal?

Traditional Methods? Big-Data Methods?
or
• Centralized data storage • Distributed data storage
• Centralized processing/analysis • Distributed processing/analysis
• Relational databases (tables) • Non-relational databases
• SQL queries to access data • Map-reduce (et al) to access data
• Standardized basic analytics • Customized basic analytics
• Typical tools: • Typical tools:
• MS SQL Server • Hadoop
• Oracle • BigTable
• Tableau • Riak
• Excel pivot tables • Amazon S3


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

Table of Contents

Introduction






Profitability Management Improves through iteration, mainly
because new info and insights are gained…

Build/Maintain Customer Take Smarter Actions w/ Customers
Profitability Models:  Target: Who?
• Create consistent message
 • Message or action: What?
Target action to individuals
 Identify costs & revenues
• Optimize product / service
 Build profiles Data  Offering: Product design
portfolio
Warehouse  Service: How delivered?
 Integrate data from
“new” sources (how experienced by customer?)

External New Customer Knowledge
Data  Results of our actions
Sources
 Assess accuracy of our predictive models
 Refine segmentation schema
 Define new goals, questions, data “wish
lists” (big data? Or small…)


Impact of Speed…

Type of data and Our understanding
technology tools: Of customers:

Daily / weekly /
Small Data monthly
(+ related tech)

Big Data Instantly
(+ related tech


…as well as “resolution”

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

Instantly Father just
started at
Big Data Instantly Bank of
America
(+ related tech
Instantly
Instantly

Helping us Take Smarter Actions w/ Customers
 Target: Is he one?
 Message or action: What?
 Offering: Product design
 Service: How delivered?
(how experienced by customer?)


So how does Big Data + Related Tools Help With…

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


Q&A
…


Big Data Meets Customer Profitability Analytics

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