big data meets customer profitability analytics

Architects of Fact-Based Decisions™

Big Data Meets Customer Profitability Analytics

Jaime Fitzgerald, Founder and President,

Fitzgerald Analytics

April 17, 2012

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

Introduction:

1. Big Data… Big Results?

2. Customer Profitability Analysis

3. Implications of Big Data

4. Conclusion and Questions

Table of Contents


Nice to Meet You!

Jaime Fitzgerald @jfitzgerald

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

Principles:

“Begin with the End in Mind” (Covey) “Quality is Free” (McGregor)

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


From to Data to Dollars

It’s a journey…

Really Big Data

Product of everywhere

Big Data Product of Alberta

Small Data

1

3

2


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).


Introduction





Table of Contents


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


2. Insight You Need

3. Analytic Methods

4. Data You Need

5. Tools, Platforms, Technology, People, and Processes

1. Your Goal

Beginning with the End in Mind


Fitzgerald Analytics: Converting Data to Dollars™

Better Data Better Analysis Better Results

“A Journey of a Thousand Miles….”

Worth The Trip!

1

3

2


Key Steps in the Journey to Results

Data Governance

Data Management

Data Quality

New Data Source Acquisition

Analysis

Insight

Better Decisions

Better Processes

More Customers

Happier Customers

3. Results 2. Analytics 1. Data


Data Management: Especially Important in the Big Data Era


Introduction





Table of Contents


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 Statement

Deconstructed into a P&L for each of your customers


Customer Profitability Metrics Can Seem Simple…

Revenue

Expenses

Profit Direct

Expense

Allocated Expenses

+


Yet in a complex organization

Example: A “Universal Bank”

Equities Stocks Derivatives Program Trading

Fixed Income

Corporate Bonds Municipal Bonds Derivatives

Interest Rate Credit

Commodities Futures Forwards

Foreign Exchange

Sales & Trading

Capital Markets (IPO)

Mergers & Acquisitions

Project Financing

Structured Financing

Investment Banking

Cash Management

Trade Finance

Corporate Trust

Custody

Transaction Banking

Mutual Funds

Separately Managed

Asset Management

Wealth Management

Consulting

Trust Services

Private Wealth Mgmt


Single Product Area

By Region

By Company

And with the Impact of Mergers

Here come the data silos…

• 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.

Bank 2 Bank 1 Bank 2 Bank 1 Bank 2 Bank 1

Europe Asia Americas

Equity Trading


Data Management = Precondition of Customer Analytics


Loss

pe

r C

ust

om

er

Customer Profitability Output: Classic 1st Step

Top (Most Profitable 10%)

2nd 3rd 4th 5th 6th 7th 8th 9th Bottom (Least

Profitable 10%)

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

Average

Best Customers

Mid-Value

Losing Money

Pro

fit

pe

r C

ust

om

er


Customer Segmentation and Lifetime Value (CLV)

Customer Retention

Cross-sell, Up-sell

Marketing Optimization & ROI

What do Customer Profitability Metrics Enable?

2

3

1

4

New Financial Product Design & Innovation 5

A Top 5 List…


Integration: Connecting The Dots

New Product Design 5

Customer Lifetime Value + Segmentation

Customer Retention Cross-Sales /

Up-Sales

1

2 3

Marketing ROI 4

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

New

Info

rmat

ion

an

d In

sigh

ts

27 Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 27

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

$-

$0.02

$0.04

$0.06

$0.08

$0.10

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

Lif

eti

me P

rofi

t p

er

Do

llar

of

Sale

s

More Risk Less Risk Credit Score Band

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….

28 Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 28

“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

$0.00

$25.00

$50.00

$75.00

$100.00

$125.00

$150.00

$175.00

1 4 7 10 13 16 19 22 25 28 31

Months after 1st Purchase

Fin

an

ce F

ees +

Late

Fees

Quartile1

Quartile2

Quartile3

Quartile4

1st Quartile Accounts generate more than 6 times as much revenue from these sources as accounts from the 4th Quartile….


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


Introduction





Table of Contents


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 •Centralized processing/analysis •Relational databases (tables) •SQL queries to access data •Standardized basic analytics •Typical tools:

•MS SQL Server •Oracle •Tableau •Excel pivot tables

•Distributed data storage •Distributed processing/analysis •Non-relational databases •Map-reduce (et al) to access data •Customized basic analytics •Typical tools:

•Hadoop •BigTable •Riak •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

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

For larger datasets, big-data methods make more sense

For smaller datasets, conventional methods are more cost-effective

Dataset size

IT C

ost

s

For a given desired speed of analysis…

Traditional methods

Big-data methods


Big Data Allows Us To Get Results Faster

We can get results faster than ever before

Analysis speed

IT C

ost

s

For a given dataset size…

Conventional methods

Big-data methods

SLOW FAST


Introduction





Table of Contents


Build/Maintain Customer Profitability Models:

Identify costs & revenues

Build profiles

Integrate data from “new” sources

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

• Create consistent message

• Target action to individuals

• Optimize product / service

portfolio

Data Warehouse

New Customer Knowledge

Results of our actions

Assess accuracy of our predictive models

Refine segmentation schema

Define new goals, questions, data “wish lists” (big data? Or small…)

Take Smarter Actions w/ Customers

Target: Who?

Message or action: What?

Offering: Product design

Service: How delivered? (how experienced by customer?)

External Data

Sources


Impact of Speed…

Instantly

Daily / weekly / monthly Small Data

(+ related tech)

Big Data (+ related tech

Our understanding Of customers:

Type of data and technology tools:


…as well as “resolution”

Instantly

Instantly

Instantly

Instantly

Father just started at Bank of America

His son’s favorite color is

blue

All his friends have

Chase

Big Data (+ related tech

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?)


Customer Segmentation and Lifetime Value (CLV)

Customer Retention

Cross-sell, Up-sell

Marketing Optimization & ROI

So how does Big Data + Related Tools Help With…

2

3

1

4

New Financial Product Design & Innovation 5


Q&A

…

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