Presentation on theme: "Operational Systems vs."— Presentation transcript:
1 Operational Systems vs. Analytical Systems1. IT Today: The Missed OpportunityEvolution of Computer UsageBusiness Intelligence: Old Wine in a New BottleHard Data vs Soft Data5. The “Satisficing Concept”Requirements of Actionable InformationDifferent Methodology for Analytical SystemsAnalytical Tools: OLAP, Data Mining & Text MiningThe New Power of BI: Competing on AnalyticsCapital One: The Quintessential Analytic CompetitorDr. Lakshmi Mohan
2 YET . . An Opportunity To Be Seized . . . . - Computers used in Business for Nearly 50 Years- Dazzling Progress in Technology- Significant Investments in IT InfrastructureHardware, Software and PeoplewareYET . .Focus on OPERATIONAL SYSTEMS has blurredthe potential of using IT forMANAGING the BusinessDr. Lakshmi Mohan
3 The Information Age Paradox Lots of DATAbut no INFORMATIONto manage the businessThe 1980s launched the Information Age. Still, most managers are less than satisfied with their information systems.Dr. Lakshmi Mohan
4 Computer Usage Evolution ERA I: Accounting (1950s to early 1960s)e.g. Payroll, InvoicingBenefit: “Manumation”ERA II: Operations (mid 1960s to early 1970s)e.g. Airline Reservations, Inventory ControlBenefit: Improved Customer ServiceBetter Utilization of CapitalERA III: Information Support (early 1970s - )e.g. Market Analysis, Succession PlanningBenefit: Better DecisionsIncreased “People Productivity”Dr. Lakshmi Mohan
5 Evolution of Analytical Systems Decision Support Systems (DSS)Support, not replace, mangers in making decisionsExecutive Information Systems (EIS)Support top managementGroup Decision Support systems (GDSS)Support a group of managersExpert SystemsTo Manage by WireTo extract news from the dataDr. Lakshmi Mohan
6 One Idea, So Many Names!“Business Intelligence”: a term coined by Gartner in – Simply defined as using information effectively to make betterdecisionsGartner’s Emphasis Today: Corporate Performance Management – CPM means getting a better finger on the pulse of an organization to make a better, more accurate, and more timely assessment of how an organization is doing. Enterprises need to move away from asking, “How did we do last month or last quarter” to “How are we doing right now” as well as “How will we do next week”Meta Group: Business Performance Management - Companies realize they have six different tools, but they do not have a consistent approach to results reporting, management reporting, planning and budgeting, etc.
7 The Computer Made It to the Executive Suite In Late 80s! The computer has little to offer executives since their work is unstructured.- Fortune, Nov 1983Executives are finally getting fast, clear information about what's happening in the bowels of their business. The new systems can change the way top managers work.- Fortune, Mar. 1989Dr. Lakshmi Mohan
8 But Not Quite ! "Every business manager I know shares one frustration: the difficulty of obtaining fast, accurateand comprehensive market information."President, Frito-LayWall St. Journal, June 11, 1990Dr. Lakshmi Mohan
9 What Happened in the Mid-1980s? 1. INCREASING GLOBAL COMPETITION"In today's environment, a businessman without accessto good information is playing with one hand behind hisback“.2. TIMELY "INFORMATION " BECAME CRITICAL"We need enough advance warning to steer around theiceberg. What we have had so far is the world's bestdamage report“.3. CORPORATE DOWNSIZING"Fewer staff analysts available to sift through themountain of ‘data’ and cull out relevant ‘information’ ”.4. TECHNOLOGICAL ADVANCESMade Executive Information Systems systems a reality.Dr. Lakshmi Mohan
10 Major Growth Drivers for BI in 2005 Need for organizations to make a sense of the “data tsunami” that is hitting them from their enterprise applicationsFocus on performance management and the need to develop and measure the associated key performance indicatorsEnsuring accuracy, timeliness and consistency of data for regulatory reportingSource: Gartner Management Update, Nov. 2004Market for BI tools is estimated to grow from $ 3.7B in 2002 to $4.5B in 2007, according to industry analyst, IDC.Source: DM Review, April 2003
11 Case Example: 7-Eleven Japan - BI for Implementing a Customer-Centric Strategy 1974: Ito-Yokado acquired franchise rights to 7-Eleven in JapanBy 1998, expanded to over 5,000 storesCompany’s profitability reached 40% of salesIn contrast, 7-Eleven USA filed for bankruptcySold 70% of its stores to Ito-YokadoIT systems captured information about customers and their needsEvery clerk recorded customer features (gender, approx age, etc) at the time of purchaseAlso, products requested that were not available in the store’s inventoryInventory management systems based on customer informationWhich products to stock in each store?How much shelf space for each product?Which are the most sellable items at different hours of the day and hence should be displayed?
12 The Urgency for BIGartner’s March 2005 BI Summit in Chicago and London drew over 750 attendees at each event!Mountains of data; growing at 30% to 50% a yearPressure of regulations such as the US Sarbanes-Oxley Act and in financial services, the Basel II capital adequacy rules forcing companies to “fix-up” their practicesCompetition and customer demands requiring timely – and often real-time – information, and in plenty of detail - to “drill down” from high-level summarized reports, with the intervals between updates coming down, especially in financial services (“from 4 hours to minutes”)
13 One Version of the Truth? Data resides in disparate systems cobbled together over the yearsDuplication of data in different systems, frequently conflicting – Which data is accurate? MIS reports causing confusion – Which report has the correct data?Spreadsheet Hell! - Multiple spreadsheet databases created by users - Slow process; Prone to Error - Multiple Versions of the TruthThe goal of BI systems is to pull data from all internal systems AND external sources to present a SINGLE version of the Truth.
14 BI for the Masses - Not just for Statisticians and Corporate Analysts Instead of a small number of analysts spending 100% of their time analyzing data, all managers and professionals should spend 10% of their time using BI softwareSmart companies are democratizing data access with dashboards and other BI tools to empower everyone in the organization, at all levels, with analytics, alerts and feedback mechanisms - Transforms every employee into an “organization of one” who can make the right decisions at the right time in step with company objectives. - Everyone can work smarter!Smarter Companies will ensure the payoff of investments in BI systems by making the masses accountable for data-driven action and results. - Accountability could be in the form of rewards, penalties Or simply, a mandated workflow
15 GE’s Concept of “Span”- Measures the operational reliability for meeting a customer request … the time window around the Customer Requested Delivery Datein which the delivery will happen- High Span Poor capability to meet customer needObjective Zero span- Squeeze the two sides of the delivery span - days early and days late - ever closer to the center - the exact day the customer desiredRESULTS :Plastics : 50 days span to 5Aircraft Engines : 80 days span to 5Mortgage Insurance : 54 days span to 1Dr. Lakshmi Mohan
16 - In the CEO’s Annual Letter (Feb 2001) The GE Process- In the CEO’s Annual Letter (Feb 2001)When the order is taken, that date becomes known to everyone, from the first person in the process receiving the castings, circuit boards or any other components from the supplier, all the way through to the service reps who stand next to the customer as the process is started up for the first time.Every single delivery to every single customer is measured and in the line of sight of everyone; and, everyone in the process knows he or she is affecting the business-wide measurement of span with every action taken.WHAT GETS MEASUREDAND REWARDEDGETS DONE !Dr. Lakshmi Mohan
17 A Manufacturing Analogy Raw Data = Raw MaterialConversion ProcessDSS/EIS/BI SystemsExpert SystemsData Mining, etc.Actionable Information = Finished ProductQuality of the Conversion ProcessAs Important as the Quality of the DataDr. Lakshmi Mohan
18 Mere Access to Quality Data - Not Enough ! . . . Will create a data overload that can affect managerial productivity.Investments on market research, telecommunications, etc. to deliver better quality data should be complemented by investments in systems to convert the data into useful information.Quality of the data conversion process is equally important.
19 Managers Ask for Analysis, NOT just Retrieval of Data Sometimes retrieval questions come up of course, but most often the answers to important questions require non-trivial manipulation of stored data. Knowing this tells us much about the kind of software required. For example, a database management system is not enough.- John Little (1979)“Data” has to be converted into “Information” that triggers managerial action.The conversion process is critical to get value from the data warehouse.Dr. Lakshmi Mohan
20 Managerial Data Encompasses Hard and Soft Data Subjective judgments are an important source for quantities that are difficult to measure, or which cannot be measured in the time available before a decision is madeSoft data is more essential for certain functions Marketing: Competitive Intelligence ... Human Resources: Succession Planning ... Corporate Planning: Forecasts And, for upper levels of management ... External data about the environment
21 HARD vs. SOFT DATA HARD DATA ... Fairly Accurate, Easy To Get Historical Datae.g., Revenue, Direct CostsMeasured Datae.g., Bill of Materials for a ProductSOFT DATA ... Fairly Inaccurate, Difficult to GetFuture Datae.g., Sales ForecastsJudgemental Datae.g., Allocation of Overhead CostsQualitative Datae.g., High Potential of an EmployeeDr. Lakshmi Mohan
22 The Data Isn’t Where We Need It ! Senior Managers-Strategic PlanningMiddle Managers- Management ControlExternal, Soft DataFront-Lines- Operational ControlInternal, Hard DataCorporate Data WarehouseThe greatest challenge of the computer industry is to learn how to build information bases, not databases. The really important information cannot be easily quantified and exists outside the organization.- Peter Drucker (1993)Dr. Lakshmi Mohan
23 The Problem : How to Get Soft Data Develop Standard Format for Data CollectionMinimize Text Data because:time-consuminginconsistentnot easy to analyzeUse Categories, Statements, Rating ScalesExplicate Mental Model of Data ProviderDecompose the entity being estimated in one judgemental swoop into a set of smaller elements that are less difficult to estimate.Dr. Lakshmi Mohan
24 Success Dimensions for High-Potential Evaluation Adaptability – maintaining effectiveness in varying environments and with varying tasks, responsibilities, or people.Decision Making – utilizing appropriate problem solving skills to develop alternative courses of action and to subsequently direct implementation of the most advantageous method of resolution.Judgement – developing alternative courses of action and making decisions which are based on logical assumptions and which reflect factual information.Planning – establishing a course of action for self and/or others to accomplish a specific goal; planning proper assignments of personnel and appropriate allocation of resources.Dr. Lakshmi Mohan
25 Success Dimensions for High-Potential Evaluation 5. Persuasiveness – utilizing appropriate interpersonal styles and methods of communication to gain agreement or acceptance of an idea, plan or activity.6. Communications – combining necessary elements of listening, oral and written communication skills resulting in effective understanding and expression of information.7. Initiative – active attempts to influence events to achieve goals; self-starting rather than passively accepting. Taking action to achieve goals beyond what is necessarily called for; originating action.Dr. Lakshmi Mohan
26 Success Dimensions for High-Potential Evaluation 8. Leadership – utilization of appropriate interpersonal styles and methods in guiding individuals (subordinates, peers, superiors) or groups toward task accomplishments.9. Problem Solving – ability to gather relevant data, recognize and assess potential areas of concern, evaluate alternative courses of action, anticipate problem situations and develop contingent plans to resolve situations.10. Teamwork – skill in coordinating activities of own personnel with those of others to achieve complex, interrelated goals.Dr. Lakshmi Mohan
27 Succession Planning System: All the Required Data is Soft ! How to identify “high potential” employees and match them to positionsMeasure employees among 10 “success dimensions” relative to everyone elseUse a 4-point rating scaleMeasure positions across the same dimensionsMatch employees to positionsDr. Lakshmi Mohan
28 Information Must be Tailored to Management Level LowerMiddleTopOperationalControlNarrowInternalHistoricalMicroManagementControlStrategic PlanningWideExternalFutureMacro ?1. Function2. Scope ofResponsibility3. Scope ofInformation4. Sources ofData5. Time Horizon6. Level of DetailDr. Lakshmi Mohan
29 Type of Data in the BI System Not just Hard, Internal DataNot limited to Financial DataMust include Soft, External DataKey Areas to be Considered:Measurement of Customer ServiceMarket Information on Customers & CompetitionHigh-Potential Evaluation, Succession Planning & Career Development of EmployeesDr. Lakshmi Mohan
30 A Different Perspective on Data Quality ... Depending on Use Operational Systems(e.g., Invoicing, Airline Reservations, Electronic Commerce, etc.)Emphasis on complete, accurate and timely dataBut limited to internal, hard dataCost of data quality justifiable because systems will be usedAnalytical Systems(e.g., Performance Evaluation, Market Analysis, etc.)Scope of Data is Wider - External and Soft dataBut ... Is “Better” Data Worthwhile?Value is zero if system is not usedDr. Lakshmi Mohan
31 COST versus VALUE OF DATA - “Satisficing” Concept Better data Higher cost Value Impact on the decisionAim: Get a Satisficing Solution for Decision-Making- Select a satisfactory decision with limited information in a limited time instead of searching for the best solution entailing more time and information"We are subjecting every activity, every function to the most rigorous review, distinguishing between those things which we absolutely need to do and know versus those which would be merely nice to do and know."GE CEODr. Lakshmi Mohan
32 Actionable Information … Information that becomes the basis for actionMust be Timely“Satisficing” Accuracy is EnoughMust Help in ...Problem-Finding and Problem-SolvingDr. Lakshmi Mohan
33 Attributes of “Actionable” Information TimelinessIf it is late, managers will make decisions without itComplete and Accurate? How much?– Just good enough for decision-makingWhat is absolutely needed in relation to What is at stakeReason: $$$$$$ 100% Complete and Accurate takes time and is expensiveThe key concept in information accuracy and completeness is “Satisficing.”Dr. Lakshmi Mohan
34 Timeliness vs. Accuracy Problem Precise Financial Data Has a Price: TimeAccruals, adjustment entries and allocations lengthen Monthly Closing CyclesIs the Precision worth the Time Lag in the Data?“Just-In-Time” Monthly ClosingTimely Data with Satisficing AccuracyFrees up time of financial staff for value-added analysisDr. Lakshmi Mohan
35 The DURACELL EIS: How It Provides “Information” The CEO, Robert Kidder, manipulated a mouse attached to his workstation. To compare the performance of work forces in the U.S. and overseas. Computer displayed a crisp table in colors showing higher sales per employee in the U.S.He asked the computer to drill down for more data to explain the difference. At the end of the data-browsing session, the real problem was found:.... TOO MANY SALESPEOPLE IN GERMANY WERE WASTING TIME CALLING ON SMALL CUSTOMERS.Dr. Lakshmi Mohan
36 Micro-Level Data in the Duracell BI - To Trace Problems to Root Causes Customer Sales DataAble to Segment Customers by Size… Small, Medium, LargeSalespeople DataWhich Salesperson Calls on Which CustomerMost Important: Time Spent With Each CustomerFrom the Salesperson’s Call ReportsBIG Problem: Fear of “Policing”… Is the Time Data Usable?Data Feeders Must Benefit from Data!
37 Why Analytical Systems are a Different Breed Operational Systems will be used because they run the “bread-and-butter” business processes of the organization - they are mission-criticalAnalytical Systems depend on managers’ desire and ability to use them in their decision-making processes to manage the businessPrerequisite:The management process must be driven by the information provided by the system. Only then will the Analytical system be used.Dr. Lakshmi Mohan
38 Payoff from the Analytical System Depends on the Management Process If a magic fairy instantly gave you all the information...the company would ever need, do you think people would instantly know what to do with it and use it well.Peter Keen (1998)Easier to upgrade quality of the data than the management process for utilizing the high quality information.Improving the quality of data will be all costs and no benefits if the data is not used.Need to upgrade the management process to effectively use better quality data.Dr. Lakshmi Mohan
39 Efficiency vs Effectiveness There’s nothing so useless as doing efficiently that which should not be done at all. Companies wrench their guts to downsize a business they shouldn’t be in at all.…… Peter Drucker says it wellEffectiveness: Doing the Right ThingEfficiency: Doing IT RightWhich is more important in an Analytical system ?
40 A System That Is Not Used Is a Waste A Home Truth:A System That Is Not Used Is a WasteOperational Systems Will be UsedBecause they are mission-critical for runningthe organizationDSS / EIS / BI Systems ???Will not be used unless the management process is driven by these systemsDr. Lakshmi Mohan
41 To Get Payoff From Analytical Systems … Raw DataAnalytical SystemActionable InformationACT !!
42 Two “Big” Factors Affect Use of Analytical System 1. Organization Culture“Business as Usual” - Complacent Cultureversus“ How Can We Improve”2. Management Style“Left Brain” - Analytical“Right Brain” - Intuitive
43 Methodology for System Development Is Different! DATAPROCESSINGOUTPUT(1)(2)(3)TPSHENCE , BOX (1)EASY TO SPECIFYMOST EMPHASISON BOX (2)BOX (3) ISWELL-DEFINEDPRECISEDSS/EIS/BI?LEASTIMPORTANTILL-DEFINEDFUZZY
44 Standard Method for System Development Sequential ApproachAnalysis Design Development ImplementationProjectStartProjectEndSuitable for structured systemsBecause outputs of the system are easy to specifyNot Effective For DSS / EIS / BIBecause information needs cannot be completelydefined at the outset during the Analysis Phase
45 The Systems Development Life Cycle (SDLC) Project Identification& SelectionProjectInitiation& PlanningAnalysisLogicalDesignPhysicalDesignImplementationMaintenanceDr. Lakshmi Mohan
46 Things Which Management Those Which Would be Merely An Axiom For BI SystemsDistinguish BetweenThings Which ManagementMUST Do AND KnowversusThose Which Would be MerelyNice to Do and KnowDr. Lakshmi Mohan
47 Design of the BI System A Common Approach: System Has Everything. Too Many Options will Overwhelm the UserHigh “Intellectual Cost” to Use the SystemWhat is Needed:- Not Over-Designed!- Must Enable News in the Data to beQuickly GleanedDr. Lakshmi Mohan
48 A User-friendly System No Training, No Manual"Bomb-proof"Invites UsageIntuitive Paths to Navigate
49 Problems with the Standard Approach for Systems Development Interview users to define requirementsDanger: Long "Wish-list"Build system to specificationsWhat users say they wantis notWhat they actually need
50 Interviewing Executives Is Difficult Because ... No time or patience to think throughUnable to articulate requirements "Use a lot of soft information Hard to know what to tell you"Vague about their needs"I want instant access to all relevant data"
51 Prototyping - "A Must" WHY. . . A live system with real data Users can "test-drive" itConstructive feedback on system designA cost-effective means of ensuring valueof system before making the investmenton its development and implementation.
52 The “Q & D” Prototype - A "Quick and Dirty" System - For the “GO” or “NO GO” decision- To determine user needs- To ensure value of system- Low-cost System to Reduce Project Risk- Yet should spark user interest- Must use Real Data- To stimulate users- To check out potential data problem- Modify on basis of User Feedback“Quick and Dirty” Systems does not mean sloppy.Dr. Lakshmi Mohan
53 Only Means of Ensuring that System Design Meets User Needs Value of PrototypingOnly Means of Ensuring that System Design Meets User NeedsProduces a "live" system rather than a voluminous "paper“ system usually written from a technical viewpointAllows users to test-drive the system and see how it works rather than imagine its operationFacilitates constructive feedback from users about features they like in the system and modifications to make it more usefulEnables system to evolve nearer and nearer to users' needs after three to five iterationsBY paper system, this is typically the proposal document that precedes the actual “go” or “no go” decision for the project.Dr. Lakshmi Mohan
54 Evolutionary System Development Methodology USER REACTION?“No Go”PROTOTYPEINITIAL SYSTEMEND“Go”USER FEEDBACKFOR EXPANDINGSYSTEMCONVERT INTO WORKING SYSTEM<VERSION 1.0>PILOT TEST <VERSION 1.0>IMPLEMENT <VERSION 1.0>Dr. Lakshmi Mohan
55 The Iterative Approach for DSS/EIS/BI Compresses all the four phases intoa short cycleSystem evolves through a series of iterationsEnables users to specify information needsin concrete termsBecause they see actual outputs with live data from the initial versions of the system
56 Benefits of the Evolutionary Approach The system evolves through a series of iterations in short cycles, each of which results in usable versions of the system.New features, new data and new users are added from user feedback.The best way to build a big system is not to build one.Start Small and Let System Evolve
57 Features of Analytical Systems Access & ReportingStandard Reports: What Happened?Query / Drill-Down: Where Exactly is the Problem?Analysis CapabilitiesStatistical Analysis: Why is This Happening?Predictive Modeling: What Will Happen Next?Optimization: What is the Best We Can Do?Analytics integrate…… Data, Statistical Tools & Models… With Supporting Hardware & Software… To Drive Problem-Finding, Problem Solving… AND Decision Making
58 Tools to Get Value from Data Warehouses Business Intelligence ToolsTo enable users without programming skills to analyze the raw data in the data warehouse.Ad Hoc Query / ReportingOLAP Tools to “slice” and “dice” data.Data Mining ToolsAutomate the detection of patterns in the data warehouseBuild models to predict behavior through statistical and machine-learning techniques.
59 Drilldown Example Level 1 Level 2 Level 3 Washington Metro 510 Virginia160D.C.170Maryland1703-Region Total510Level 2Circles10Wheat20Sugared90Spheres40Circles60Wheat40Sugared10Spheres80Circles90Wheat100Sugared150Spheres170Circles20Wheat40Sugared50SpheresLevel 3
60 Slicing & Dicing a Data Cube - Sales by Location New York$440Chicago$380Dallas$325San Francisco$245
61 Slicing & Dicing a Data Cube - Analysis by Type of Socks Black SocksSalesNew York$170Chicago$110Dallas$125San Francisco$70Brown SocksSalesNew York$100Chicago$130Dallas$15San Francisco$40Blue SocksSalesNew York$120Chicago$40Dallas$160San Francisco$95Sweat SocksSalesNew York$50Chicago$100Dallas$25San Francisco$40
62 Slicing & Dicing a Data Cube - Analysis by Location New YorkSalesBlack$170Brown$100Blue$120Sweat$50ChicagoSalesBlack$110Brown$130Blue$40Sweat$100DallasSalesBlack$125Brown$15Blue$160Sweat$25San FranciscoSalesBlack$70Brown$40Blue$95Sweat
63 Why Data Mining ?“Now that we have gathered so much data, what do we do with it?”“The datasets are of little direct value themselves. What is of value is the knowledge that can be inferred from the data and put to use.”Data volumes are TOO BIG for traditional DSS Query/ Reporting and OLAP tools.Organizations have to get value from the huge investments of time and money made in building data warehouses.Dr. Lakshmi Mohan
64 Why is Data Mining a “Hot” Topic Today? 1. Implementation of ERP, CRM & SCM systems have resulted invast stores of operational data.2. Emergence of global competition has put the pressure oncompanies to be “data- driven” – i.e., make informed decisionsbased on facts and not hunches.3. The speed of change in the marketplace demands that the pearlsof actionable information have to be found faster in the ocean ofdata, for companies to be one step ahead of competition.4. The hardware needed to store and process a “ton of data” wasprohibitively expensive until recently – “You would have hadto have NASA at your disposal” Today, the technology makes it feasible to apply complex models to ferret out patterns left to rot in “data jails”.
65 The Payoff from Data Mining - An Example: Farmer’s Insurance Based on traditional data analysis, drivers of sports cars were determined to be at higher risk for collisions than drivers of “safe” cars such as Volvos.Hence charged them more for car insurance.Data mining discovered a pattern that changed the pricing policy…… As long as the sports car was not the only car in the household, the driver fit the profile of the “safe” family car driver, not the risky sports car driver
66 Data Mining Techniques - Decision Trees Derives rules from patterns in data to create a hierarchy of IF-THEN statements, called a Decision Tree, to classify the data.Segments the original data set:Each segment is one of the leaves of the treeRecords in each segment are similar with regard to the variable of interestExample: Classification of Credit Risks
67 Overall : 7% of Customers Responded Decision Tree for Segmenting Customers - Who Responded to a Marketing CampaignOverall : 7% of Customers RespondedSegment of Customers Who Rent with High Family Income and No Savings A/c : 45% responseTarget this Segment for Future Direct Marketing Campaign
68 Text Mining: An Imperative Today “We are drowning in information,but are starving for knowledge”Unstructured data, most of it in the form of text files, typically accounts for 85% of an organization's knowledge stores, but it’s not always easy to find, access, analyze or use.
69 Case Example: Honda Instituted An Early Warning Program To Identify Major Potential Quality Issues …From Warranty Service RecordsRecords Sent to Honda by DealersIncluded Categorized Quality Problems AND Free TextTranscripts of Calls by Mechanics to Experts in Various Domain at HeadquartersTranscripts of Customer Calls to Call CentersMined the Text Data from the Different Sourcese.g., Words appearing for the first time, particularly those suggesting major problems, such as fireFlagged for human analysts to look atSource: Davenport & Harris, Competing an Analytics, page 70
70 Another Example: HP - Adopted SAS Text Miner Software Textual Analysis of Comments by Customers in Call Center Records“Customers who were really loyal were talking to the Call Center about different things than Customers who weren’t so loyal, or Customers who did not buy as frequently or in as high a volume”Lead Classification Based on Textual Notes Collected from an Initial Call Center ContactDivide New Leads into Cold, Medium and Hot rankings80% success rate i.e., the leads performed as predicted, when the leads were passed to the sales staffSource: “7 Strategies for Profiting from Customer Data”, Destination crm.com, July 1, 2004
71 Who is An Analytic Competitor ? Companies who have built their businesses on their ability to … Collect Data,… Analyze It, AND… ACT On It.Sign on Desk of CEO“In God we trust; all others bring data”Analytic Competitors:Consumer Products: Frito-Lay, P&GFinancial Services: Capital One, Royal Bank of CanadaRetail: Wal-Mart, Tesco, AmazonTransport: FedEx, UPS, Schneider NationalIndustrial Products: CEMEX, John DeereHospitality & Entertainment: Marriott, Harrah’s Entertainment
72 Why Compete on Analytics? Geographical Advantage… Does not matter in global competitionProtective Regulation: Largely GoneProprietary Technologies: Rapidly CopiedHigh-Performance Business Processes… Last remaining points of differentiation… Execute your business with maximum efficiencies… Make the smartest business decisions possible
73 The Battle for Credit Card Customers … Capital One Case Example Winning Big in the Cut-throat World of Credit CardsIPO in 1994Revenue: Exploded from $95 M in 1995 to $4.97 B in 20002001 Year End:Posted its 18th consecutive quarter of record earningsAnnual earnings rose by over 20%; Yearly ROE: Over 20%43.8 M customers worldwide, Over 20,000 employeesConsistently Outperformed First USA, twice its sizeEarning 40% more interest incomeEnjoying double the profit marginSecret To Its Success: “Information-Based Strategy”
74 “Credit Cards Are Not Banking - They are Information” It’s all about collecting information on millions of people that you’ve never met, and, on the basis of that information, making a series of critical decisions about lending money to them, hoping that they pay you back.Each customer carries a specific and unique credit risk and potential revenue profile, based mainly on their previous credit history (or lack thereof). The better the company can understand and assess a customer’s specific risk, the better it can manage it.AND, the better it understands the customer, the more it can tailor its products to his or her needs.
75 Business Model of Capital One MISSION: Deliver the Right Product, At the Right Price, To the Right Customer, At the Right TimeUNIQUE INFORMATION-BASED-STRATEGY:When we started this company, we saw two revolutionary opportunities: We could use scientific methodology to help us make decisions, and IT to help us provide mass customization.Foundation of Capital One: TEST AND LEARNWe test every product offering, every procedural change, every job applicant. We record every customer interaction, every card purchase; and then, with the patience of a good scientist, we run experiment after experiment. For every action taken, we know what the reaction has been. If we have sent you a blue envelope or a pink one, we know which one you received, and how you reacted to that.- Ran 45,000 Tests in 2001, Average of 120 per day
76 “Information-Based Strategy” - A Three-Step Approach Create an idea for a new product offeringFind a target population and a business caseTest the idea with this group to see how they reactStep 2:Gather data on the test and analyze resultsStep 3:Use test results to identify which people are most receptive to the product offeringConduct the marketing campaign based on micro-segmentationExample: Used IBS to track visitor’s activities and offer customized promotions on its Web site – studied which online visitors it has successfully converted into customers – used that information to buy banner ads on other web sites whose visitor demographics match those of its ideal customers…Doubled its goal of opening 1 million new accounts online
77 Mass Customization of Credit Cards - Cap One’s “Invention Machine” U.S. Credit Card Market in the 1980s - “One size fits all”!Capital One Changed the Rules!“Tailor the product to meet the customer’s needs.”2001: More than 6,000 ProductsVariations of Credit Cards… Annual Percentage Rate, Credit Limits, Fees, Designs, etcExamples:No-fee Mercedes-Benz affinity card with a credit line of $20K$29 a year fee for a card with just $200 worth of creditA credit card with a Canadian moose on it. Or, a card with a map of Japan and an image of Mt. Fuji on itOther Related Products… e.g., Card Protection Plans, Payment Protection InsuranceOther Financial Services … e.g., Travel Insurance
78 Intelligent Call Routing - Implemented in 1998 # of Calls from Customers: Over 200,000 a dayThe Moment the Last Digit is Punched:Caller is Identified; About two dozen items of data AnalysedPredict the Reason for the CallALSO, What the Caller Might Want to Buy … even though he or she isn’t calling to buy anythingSelect Best of 50 Call Routing Options for This CallerDisplay the Relevant Info, including the Script for the Cross-Sell Recommendation, on Rep’s ScreenALL BEFORE the Call Arrives in the Head-SetJust 100 milliseconds, one-tenth of a second … one-eights of the time between human heart-beatsHow Good Is It?Right 40% of the time initially; 1999: 60% to 70%And, System just keeps getting SMARTER!
79 How It Works – An Example When a customer calls, system channels poor prospects to a voice-response unit and even allows them to close their accounts…Others are routed to a service rep along with information about the card holder and the likely reason for the call with a script to deal with it.If customer wants to close the account, the system will display three interest rate counter-offers.Service rep has the freedom to negotiate, and gets a bonus if customer is persuaded to stay on at the highest of the new rates
80 The Routing Software - Cisco’s Global Service Logistics System One of the few shrunk-wrapped application used by Cap One… Most software custom-built in-houseEveryone will say they use GSL the same way we do, but I think we use it more intelligently than they do.We use many more attributes in judging where the call goes.And we gather more data about that call than anyone else doesAND, we use that data as a basis for creating decision rules in our applications. - VP of Customer RelationsExamples:Do you routinely call from your boyfriend’s phone – the number for which is not on file at Capital One? Eventually, the computer will figure out that his number should be in your CIF.What language do you prefer to do business in? System will learn that and route calls accordingly.
81 Impetus for the Project - The High Phone Bill We pay for in-coming Customer Calls.Calls were taking too long to handle.Analysis showed that Customers were not to blame.Calls simply were NOT getting to the right place soon.Caller with a lost card or fraud problem ended up reaching an ordinary Rep. People who just wanted to know their balance stayed on hold to talk to a live Rep.People unhappy with their interest rate called the “lost card” number on the back of their card and had to be transferred to customer service.All that time – to take a call, to bridge the call to the right person – that annoys the customer, we are paying for the call. You wait for an agent, you wait for a transfer, you wait again for an agent.”Even one extra second per call adds up to real money with over a million calls a week.
82 How to Lower the Phone Bill ? Tried lots of options - But nothing worked?Example:Some people called much more often than the average of 5 times a year …We sent out a letter at one point that said, in effect: “Please don’t call so much” …But it did not work !If you want people to call you, send them a letter telling them not to!Ultimate Solution Suggested by ITWhy not predict the reason for each callANDthen send that call to the agent who is best able to handle it.
83 Implementation of the Intelligent Call Routing Infrastructure Analysis of Why People Call:90% of all calls fell into one of 10 categoriesRaise your customer’s interest rates, and they callSend out a new card that has to be activated; they call.Same people call once a month to find out their credit balances; some others call three times a month2. Decision-tree Software had to be written3. Computers, phone switches and telecom networks had to be taught to talk to one another
84 Examples of Automated Voice Response Example 1: Customers who call each month to check their balance are routed to an automated system that answers the phone this way: “The amount now due on your account is $ If you have a billing question, press 1 …”Example 2: Customers who call to check if their payment has arrived could be identified and the phone message would then be: “Your last payment was received on February 9. If you need to speak with a service Rep, press 1 …
85 Everyone Wins ! “We can answer your question BEFORE you ask it!” “A phone call that might have taken 20 or 30 seconds, or even a minute, now lasts 10 seconds.”Customers get where they are going immediately And, they get the information they need quickly.Customer Service Reps handle those calls that need to be handled by people, and they don’t waste any time passing calls to colleagues.Customers are automatically routed to the RIGHT Reps – best skilled to not only deal with the problem about which the customer is calling but also to cross-sell the product that the system predicts the customer might want to buy.
86 The Pay-Off for Capital One - Calling System Has Become A Competitive Advantage Lower Costs AND Better ServiceCall Centers: NOT A COST CENTREGenerate Revenues from Cross-SellingExceeds Cost of OperationsActually MAKE MONEY!“In 1998, for the first time, half of all new Cap One customers bought another product from the company within 12 months of signing up for their credit card. That’s amazing penetration and it leads to high profitability.”A simple, routine problem in search of a quick solution led to a whole new way of doing business. It enabled us to go back to the business side with a solution that went beyond that problem. What makes our “T” work has nothing to do with “T” – it has to do with our culture.”
87 Every Interaction Is A Selling Opportunity ! Most credit-card companies, including Capital One, have long tried to “Cross-Sell” their customers – often by using inserts in monthly statements to tout everything from calculators to cruisesData analysis of outbound telemarketing calls (made usually at dinner time) showed it was NOT working.New Idea: Sell things to customers when they call“It seemed like a natural. If you call me and I’m trying to sell you something, then I’m going to treat you very nicely. That will promote better service.”
88 Implementation Issues Service people are not sales people.Systems cannot service people and sell them at the same time.Even with systems in place, sales will not be enough to make longer phone calls worthwhile.“It can’t be done.”That’s all the motivation we needed.
89 Made the New Idea Work ! First Test Product: Easy & Sweet “When new customers call an automated line to activate their card, we thought: That’s the perfect time to sell them somethingThe first thing we sold was a balance transfer: “Now that you’ve got our card, is there any debt that you want to transfer to us.”Customers just bought it!Brainstorming Session: Manager of Cross-Sell Marketing, IT Head and Call-Center Manager“How to extend quickly to other products?”
90 Human HurdleService Reps Reluctant to Sell ProductsThe Solution: “Elevate this beyond the immediate transaction level. If I am a phone associate, my mission is to meet my customer’s needs. If I’ve got this great product, it might save a customer some money, or it might create convenience. If I’m committed to service, I should consider offering that product.”In 3 months, Reps started to both service and sell during field calls.
91 Management Process Ties It All Together at Capital One Organization Structure of Capital OneBy market segments based on…… Customers’ credit quality… Activity with the card, etc.Each Segment is a Profit CenterHead has the autonomy and team to run the operation for that segment like a small businessEnables opportunities to be sensed from the bottom-up and pursued quickly by motivated employeesReward System Fosters Shared Values and CollaborationLow turnover rate: 5% per year for customer-contact employees vs. industry average of 15% - 20%Improves service and helps keep costs down