big data meets customer profitability analytics

Architects of Fact-Based Decisions™

Big Data Meets Customer Profitability Analytics

April 10, 2012

Brought to you by the team at Fitzgerald Analytics

Introduction

1. Big Data… Big Results?

2. Customer Profitability Analysis

3. Implications of Big Data

4. Conclusion and Questions

Table of Contents

Tonight’s Event

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

Really Big Data

Product of everywhere

Big DataProduct of Alberta

Small Data

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)

Jaime Fitzgerald@jfitzgerald

Craig Williston@craig_williston

NikhilMahen@nikhilmahen

Konrad Kopczynski@konradFA

Gniewko Lubecki

Introduction

Table of Contents

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?

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!

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. Results2. Analytics1. Data

Introduction

Table of Contents

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 Statement

Deconstructed into a P&L for each of your customers

erCustomer Profitability Output: Classic 1st Step

Top(MostProfitable10%)

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

Profitable10%)

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

Average

Best Customers

Mid-ValueLosing Money

Customer Segmentation and Lifetime Value (CLV)

Customer Retention

Cross-sell, Up-sell

Marketing Optimization & ROI

What do Customer Profitability Metrics Enable?

New Financial Product Design & Innovation5

A Top 5 List…

Integration: Connecting The Dots

New Product Design5

Customer Lifetime Value + Segmentation

Customer Retention Cross-Sales / Up-Sales

Marketing ROI4

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

Example: Taking Profitable Risks…

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

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

More Risk Less RiskCredit 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….

“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

$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

Quartile1

Quartile2

Quartile3

Quartile4

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

Example: Tata Nano

Initial target: “Cheap” car for middle class

What actually happened:1) Cost 20-50% greater than initially proposed; lost “Cheap” tag2) “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… …

Challenges to Creating Customer Profit Metrics

Calculating profit seems pretty simple!

Revenue

Expenses

Profit Direct Expense

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.

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

Impact of Mergers

Mergers add to the complexity…

• 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 2Bank 1

Europe AsiaAmericas

EquityTrading

“Slicing” Customer Profitability

Firms often seek to view customer profitability by:

Client

Client Segments

Product

Region

What about other metrics that may help with profit analytics:

Trade Volumes

Trade Fails

Client Service Center Issues

Assets Under Management

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

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 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:

Build Customer Profitability Models Identify costs & revenues Build profiles Feed data from

internal and external sources

Maintain data warehouses

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

• Create consistent message • Target action to individuals• Optimize product / service

portfolio

Data Warehouse

New Customer Knowledge Feed campaign results into data

warehouses

Test predictive accuracy of model

Break down segment into individual customer analyses

Drive Action Into Frontline Systems Create consistent message

Target action to individuals

Optimize product/service portfolio

Face-to- Face

InternetExternal Data

Sources

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-datamethods make more sense

For smaller datasets,conventional methods aremore cost-effective

Dataset size

For a given desired speed of analysis…

Traditional methods

Big-datamethods

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

Analysis speed

For a given dataset size…

Conventionalmethods

Big-datamethods

SLOW FAST

Data on its own is useless

?Big Data

Related Technologies

Methods

Add Customer Profitability

Instantly

Daily / weekly / monthlySmall Data

Big Data

Add new business rules

Instantly

Big Data

Father just started at Bank of America

His son’sfavorite color is

All his friends have

InstantlyBig Data

Introduction

Table of Contents

big data meets customer profitability analytics

big data big results

customer profitability

questionsbig data

customer profitability

implications of big

processesbig data

nikhilmahenbig data

manybig data

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