Calculating customer profitability seems simple enough. Subtract expenses from revenue, by client. But the complexity of today's financial institutions and the messy data that has resulted complicates gaining a good picture of individual clients and their value to the organization.

1. Big Data Meets
Customer Profitability Analytics
April 10, 2012
Brought to you by the team at Fitzgerald Analytics
Architects of Fact-Based Decisions™

2. Challenges to Creating Customer Profit Metrics
Calculating profit seems pretty simple!
Revenue
Direct
Profit
Expense
Expenses +
Allocated
Expenses
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3. Conceptually Simple
At first this seems simple enough…
 Personal Banking
• Checking
• Savings
 Brokerage Account with Checking
• Investments/Trading
• Checking
• Savings
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4. 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
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5. 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.
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6. “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?
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7. 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.
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