For more content from the same event, including a discussion of Customer Profitability Analysis and Big Data tools, please see:
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Big Data Meets Customer Profitability Analytics
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. 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
3. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 3
4. Our Perspective
Skeptical…
Cautious…
Optimism….
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 4
5. What’s Wrong with a Little Hype ??
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 5
6. We are Talking about Something New and Exciting:
“Data is the New Oil”
– World Economic Forum Report
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 6
7. And Something Old, Essential, & Profitable
“There is only one valid definition
of a business purpose:
to create a customer.”
(The Practice of Management, ‘54).
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 7
8. Co-Presenters (#AnalyticsFSI)
Craig Williston Gniewko Lubecki
@craig_williston
Jaime Fitzgerald
@jfitzgerald
Konrad Kopczynski NikhilMahen
@konradFA @nikhilmahen
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 8
9. 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 9
10. Will Big Data Unlock Big Results?
It depends…
...on the
principles you
work by.
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 10
11. The Word’s Most Successful Data Professionals…
#B W T E I M!
What is Covey was a
Big Data Gal in 2012?
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 11
12. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 12
13. “A Journey of a Thousand Miles….”
2
1
Fitzgerald Analytics: Converting Data to Dollars™
Better Data Better Analysis Better Results
3
Worth The Trip!
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 13
14. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 14
15. 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 15
16. 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…
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 16
17. The Concept Illustrated
Your P&L Deconstructed into a P&L
Statement for each of your customers
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 17
18. 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)
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 18
19. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 19
20. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 20
21. 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
22. “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
23. 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.
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 23
24. 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….
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 24
25. Known Pitfall: Not Looking Beyond the Data…
…
…
1995
2012
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 25
26. Challenges to Creating Customer Profit Metrics
Calculating profit seems pretty simple!
Revenue
Direct
Profit
Expense
Expenses +
Allocated
Expenses
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 26
27. Conceptually Simple
At first this seems simple enough…
Personal Banking
• Checking
• Savings
Brokerage Account with Checking
• Investments/Trading
• Checking
• Savings
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 27
28. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 28
29. 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.
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 29
30. “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?
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 30
31. 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.
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 31
32. Perspective on Data Management
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 32
33. 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 33
34. Defining Big Data: “Three Vs”
"Big Data“ is seen as data with:
greater volume…
greater variety…
and/or
greater velocity….
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 34
35. 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:
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 35
36. 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 Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 36
37. 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 Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 37
38. 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 Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 38
39. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 39
40. Data on its own is useless
?
Related
Technologies
Big Data
Methods
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 40
41. Add Customer Profitability
Small Data Daily / weekly / monthly
Big Data Instantly
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 41
42. 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
Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 42
43. 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 43
Notas del editor
Jaime:
Jaime:
Jaime:
Jaime: “Let’s Keep Two Feet on the Ground”
Best-selling books on Analytics (Competing on Analytics, Supercrunchers, etc.)New efforts (business units, teams, roles, initiatives)Success happens every day… Failure happens more than success1. Unprecedented “Buzz” about Big Data & AnalyticsOn the one hand, the potential is Buzz-Worthy!The Ugly “Open Secret”: More “Missteps” Than Success…
**Thinking of moving some contents into the speaker notes*Jaime: Speaker: Jaime Fitzgerald,@jfitzgeraldBackground: More than 15 years helping clients improve results via customer profitability analysisFocus Tonight:Implications of Big Data on the “evergreen methodology”Speaker:Craig Williston, @craig_willistonBackground:Banking veteran, including stints at Deutsch Bank, UBS, and others. Now a consultant focused on BI.Focus Tonight: Obstacles to Customer Profitability at large companies. Benefits of overcoming these obstacles.Speaker: GniewkoLubeckiBackground: Analytics and Data Professional at Fitzgerald Analytics. Specialties include Financial Services and Predictive AnalyticsFocus Tonight: Implications of Big DataSpeaker: NikhilMahen,@nikhilmahenBackground: Analyst at Deutsch Bank Focus Tonight: How customer analytics impacts customersSpeaker: KonradKopczynski,@konradFABackground: Analyst at Fitzgerald AnalyticsFocus Tonight: The longer-term potential
Jaime:
Jaime: #BWTEIM Dammit! lol
Jaime: #BWTEIM Dammit!Oh wait, although he is still with us, thank the lord, he IS already reborn as a female data scientist. His name is Hillary Mason!
Jaime:
Jaime: The argument could be made that the effectiveness and professionalism with which we manage data has gone from important to essential in the big data era.In all candor, most companies already struggle to manage their core data assets well…the additional of new data sources, bigger data sources, only adds the the importance of effective data governance, data management, and data quality capabilities.
Jaime:
Jaime:
Jaime:
Needed bc We must get as much as possible from existing resourcesAnd there is much rapid change…
Examples of customer analytics leading to better experiences for customers:Captial One Balance Transfer (debt consolidation): unheard of concept in the 90s by the credit card industry. Customers with small debts jumped at the opportunity and today Capital One is one of the largest Credit Card providers in the world Tata Nano: Initial strategy: Cheap car for middle class India. Estimated cost before release into the market: 2000 USD. Ended up being released at 2400 USD to 3000 USD. Though still cheap, major jump caused issue in perception. Small technical issues which ordinarily would have been ignored started to come into the light. Tata Studied customers and found the majority owners - > people who already had cars, people who lived in places where parking was an issue. Not necessarily the typical middle class. Tata opened exclusive showrooms in many Tier 3 and tier 4 cities to brand it as a utility vehicle instead of a “poor man’s” car. Sales jump huge. Sales last December have jumped 44% from the previous year. Today Nano is being exported and even assembled in Malaysia (similar demographic in its big cities)
Jaime: “Give me something actionable!”
Jaime:
Craig: What is profit? Seems like a silly question, but lets start with a simple example
Craig: Profitability in financial services seems simple enough. Look at these types of relationships you may be aware of. Banks largely can tie these products to the individual and produce a consolidated statement. Therefore consolidated revenue is available. The data management is built up correctly because they know you are the client and they’re trying to add on new products to you. But what does a large financial institution look like?
Craig: The typical “Universal Bank” has multiple divisions with many products and sub-products. Goldman, Morgan Stanley, UBS, Bank of America, they all look like this. Smaller banks look like parts of this. And what is behind each of these products? A trading/booking system. (Walk through example using Stocks, US vs UK, IT, ES, JP, BR)Each controller gets the right numbers. Consolidated its all correct. But nobody can tell you who the largest client was.Bank mergers add to the complexity…
Craig: Read the slide, then => System integrations might be the right time to rationalize the client list, but it gets pushed back just to get the merger done. Then its another MAPPING project.There is no golden list of clients….. Its easier to open the accounts, send them downstream. Let someone else clean it up later.Goal of merger often was to realize “Synergies” and cut costs, not invest in a new project to overhaul data management.Send the roles offshore, its cheaper that way. They can’t think about how better data unlocks the ability do do client profitability and therefore unlocks more value.
Craig: Firms like to look at profit in certain ways. Here are a few examples. (on left side)(on right side) They may track some of these in dashboards for individual areas to rate performance. But these could help with client profitability analysisGood data management is required before profitability can be reliable reported.
Craig: Read the slide and then hand off to Jaime after delivering the “Best” because he can talk about that.
Jaime:
Jaime:
Gniewko: 800GB Can Be “Traditional” 80GB Can Be “Big Data”
Gniewko: Note that this definition hinges on methods applied, not on dataset sizes:Traditional methodsCentralized data storageCentralized processing/analysisRelational databases (tables)SQL queries to access dataStandardized basic analyticsTypical tools:MS SQL ServerOracleTableauExcel pivot tablesBig-data methodsDistributed data storageDistributed processing/analysisNon-relational databasesMap-reduce (et al) to access dataCustomized basic analyticsTypical tools:HadoopBigTableRiakAmazon S3800GB can be “traditional”A brick-and-mortar retailer could use traditional methods to update customer profitability once a month, using an 800GB database of transactions80GB can be “big data”An online retailer would have to use big-data methods to update customer profitability in real-time for a web application, using an 80GB database of transactions
Gniewko:
*note: GL revised this slide*Gniewko:
*note: GL revised this slide*Gniewko:
Konrad:-Data and data tools get you nothing, if you’re using big data tools or traditional tools you still don’t get value for just data on its own.
Konrad:-You need to be able to give the data meaning, to understand what all the values are showing you, so that you can act on it.-Using “small” data, traditional data tools AND business rules from Customer Profitability analysis we can analyze the data every so often (click objects with “1” appear) and then when a customer comes to us we can know how to act, react and anticipate. In this case it seems to be a young professional male we are catering to.-With “Big Data”, “Big Data” tools and the SAME business rules we just used in Customer Profitability analysis we can analyze more data INSTANTLY (click objects with “2” appear) and thus figure out that that customer who we though was a you professional JUST found out he’s about to have kids. This is an example of a missed opportunity as with traditional data tools, it was impossible to act, react and anticipate quickly enough to take into account new information (that may have already been in the system) in our interaction with the customer.-However, even if we perform the same analysis faster, we are missing out on the best opportunities provided by new data.
-We can already do the same analysis instantly (click objects marked “1” appear) , and get the complete up to the moment analysis right when we are interacting with the customer-But we are not taking advantage of ALL of the extra data that with have. We need to add new business rules that act instantly on newly available data to give us a much more complete picture. (click objects marked “2” appear). All of these phrases tell you or I something about this customer, and give us an initial thought on their profitability. We need to be able to transfer that reaction into a concrete rule that a computer can follow, test it for validity, and then go even further to find new rules based on connections humans might never have thought of (using techniques like clustering). We can profile new groups of similar customers based on new data which allows us to make decisions and develop tactics that can optimize the customer relationship.In summary:Attach MEANING to “Big Data”Then:Act react and anticipate