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Big Data MeetsCustomer Profitability AnalyticsApril 10, 2012                 Brought to you by the team at Fitzgerald Anal...
Table of Contents                         Introduction                         1. Big Data… Big Results?                  ...
Tonight’s Event       As usual, it’s about the journey to results.           1                                            ...
Our Perspective       Skeptical…                                                      Cautious…                           ...
What’s Wrong with a Little Hype ??Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, a...
We are Talking about Something New and Exciting:                                                              “Data is the...
And Something Old, Essential, & Profitable                                                                    “There is on...
Co-Presenters (#AnalyticsFSI)                                                         Craig Williston                     ...
Table of Contents                         Introduction                         1. Big Data… Big Results?                  ...
Will Big Data Unlock Big Results?           It depends…           ...on the           principles you           work by.Big...
The Word’s Most Successful Data Professionals…                                                                            ...
Beginning with the End in Mind                                                      1. Your Goal                          ...
“A Journey of a Thousand Miles….”                                                                                         ...
Key Steps in the Journey to Results                     1. Data                              2. Analytics                 ...
Table of Contents                         Introduction                         1. Big Data… Big Results?                  ...
Definition & History      Customer Profitability Analysis is:      1) Measuring the contribution each customer makes to ov...
The Concept Illustrated                      Your P&L                                                      Deconstructed i...
Customer Profitability Output: Classic 1st Step                           Best Customers                                  ...
What do Customer Profitability Metrics Enable?          A Top 5 List…         1                      Customer Segmentation...
Integration: Connecting The Dots                  A few examples of how inter-related these processes are…                ...
Example: Taking Profitable Risks…                                   IF well managed, card companies often get most of thei...
“Lifetime Performance Curves”: Finance + Late Fee Income      The divergence is even more striking when Late Fees are adde...
Example: Tata Nano                                                 Initial target: “Cheap” car for middle class           ...
Challenge: From Descriptive to Prescriptive.       I can’t deposit decile charts in the bank either…     And my analysts c...
Known Pitfall: Not Looking Beyond the Data…       …       …             1995             2012Big Data Meets Customer Profi...
Challenges to Creating Customer Profit Metrics       Calculating profit seems pretty simple!                              ...
Conceptually Simple       At first this seems simple enough…        Personal Banking           • Checking           • Sav...
Representative “Universal Bank” Product Suite       But today’s banks are big, complex, and poorly integrated.            ...
Impact of Mergers       Mergers add to the complexity…                                                                    ...
“Slicing” Customer Profitability       Firms often seek to view                                         What about other m...
Solution? Data Management       Data management is a precondition to customer metrics…       Good:        ETL Process fee...
Perspective on Data ManagementBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all r...
Table of Contents                         Introduction                         1. Big Data… Big Results?                  ...
Defining Big Data: “Three Vs”        "Big Data“ is seen as data with:                       greater volume…               ...
Another Way to Define “Big Data” -       What methods are required to realistically       make use of it?                 ...
Profitability Management Becomes More Refined Over Time       through an Iterative Process Driven by Customer Knowledge   ...
Big-Data Approaches and Tools Make Data Analysis        Possible, for very large data sets that cannot be handled at all ...
Big Data Allows Us To Work with Large Datasets       We can analyze datasets larger than ever before                      ...
Big Data Allows Us To Get Results Faster       We can get results faster than ever before                                 ...
Data on its own is useless                                                                   ?                            ...
Add Customer Profitability                              Small Data                    Daily / weekly / monthly            ...
Add new business rules                      Big Data                                 Instantly                            ...
Table of Contents                         Introduction                         1. Big Data… Big Results?                  ...
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Big Data Meets Customer Profitability Analytics

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For more content from the same event, including a discussion of Customer Profitability Analysis and Big Data tools, please see:
meetup.com/Analytics-and-Data-in-Financial-Services/pages/Big_Data_meet_Customer_Profitability_Analytics/

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  • 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
  • Jaime:
  • Transcript of "Big Data Meets Customer Profitability Analytics"

    1. 1. Big Data MeetsCustomer Profitability AnalyticsApril 10, 2012 Brought to you by the team at Fitzgerald Analytics Architects of Fact-Based Decisions™
    2. 2. Table of Contents Introduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and QuestionsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 2
    3. 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 everywhereBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 3
    4. 4. Our Perspective Skeptical… Cautious… Optimism….Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 4
    5. 5. What’s Wrong with a Little Hype ??Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 5
    6. 6. We are Talking about Something New and Exciting: “Data is the New Oil” – World Economic Forum ReportBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 6
    7. 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. 8. Co-Presenters (#AnalyticsFSI) Craig Williston Gniewko Lubecki @craig_williston Jaime Fitzgerald @jfitzgerald Konrad Kopczynski NikhilMahen @konradFA @nikhilmahenBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 8
    9. 9. Table of Contents Introduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and QuestionsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 9
    10. 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. 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. 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 ProcessesBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 12
    13. 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. 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 AcquisitionBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 14
    15. 15. Table of Contents Introduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and QuestionsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 15
    16. 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. 17. The Concept Illustrated Your P&L Deconstructed into a P&L Statement for each of your customersBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 17
    18. 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. 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 & InnovationBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 19
    20. 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 DesignBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 20
    21. 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 RiskBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 21 21
    22. 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 PurchaseBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 22 22
    23. 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. 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. 25. Known Pitfall: Not Looking Beyond the Data… … … 1995 2012Big Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 25
    26. 26. Challenges to Creating Customer Profit Metrics Calculating profit seems pretty simple! Revenue Direct Profit Expense Expenses + Allocated ExpensesBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 26
    27. 27. Conceptually Simple At first this seems simple enough…  Personal Banking • Checking • Savings  Brokerage Account with Checking • Investments/Trading • Checking • SavingsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 27
    28. 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 ExchangeBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 28
    29. 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. 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. 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. 32. Perspective on Data ManagementBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 32
    33. 33. Table of Contents Introduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and QuestionsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 33
    34. 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. 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. 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 analysesBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 36
    37. 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. 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 methodsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 38
    39. 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 methodsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 39
    40. 40. Data on its own is useless ? Related Technologies Big Data MethodsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 40
    41. 41. Add Customer Profitability Small Data Daily / weekly / monthly Big Data InstantlyBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 41
    42. 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 InstantlyBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 42
    43. 43. Table of Contents Introduction 1. Big Data… Big Results? 2. Customer Profitability Analysis 3. Implications of Big Data 4. Conclusion and QuestionsBig Data Meets Customer Profitability Analytics | Copyright Fitzgerald Analytics 2012, all rights reserved 43
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