1. Operational Systems vs.
Analytical Systems
1. IT Today: The Missed Opportunity
Evolution of Computer Usage
Business Intelligence: Old Wine in a New Bottle
Hard Data vs Soft Data
5. The “Satisficing Concept”
Requirements of Actionable Information
Different Methodology for Analytical Systems
Analytical Tools: OLAP, Data Mining & Text Mining
The New Power of BI: Competing on Analytics
Capital One: The Quintessential Analytic Competitor
Dr. 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 Infrastructure
Hardware, Software and Peopleware
YET . .
Focus on OPERATIONAL SYSTEMS has blurred
the potential of using IT for
MANAGING the Business
Dr. Lakshmi Mohan

3. The Information Age Paradox
Lots of DATA
but no INFORMATION
to manage the business
The 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, Invoicing
Benefit: “Manumation”
ERA II: Operations (mid 1960s to early 1970s)
e.g. Airline Reservations, Inventory Control
Benefit: Improved Customer Service
Better Utilization of Capital
ERA III: Information Support (early 1970s - )
e.g. Market Analysis, Succession Planning
Benefit: Better Decisions
Increased “People Productivity”
Dr. Lakshmi Mohan

5. Evolution of Analytical Systems
Decision Support Systems (DSS)
Support, not replace, mangers in making decisions
Executive Information Systems (EIS)
Support top management
Group Decision Support systems (GDSS)
Support a group of managers
Expert Systems
To Manage by Wire
To extract news from the data
Dr. Lakshmi Mohan

6. One Idea, So Many Names!
“Business Intelligence”: a term coined by Gartner in – Simply defined as using information effectively to make better
decisions
Gartner’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 1983
Executives 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. 1989
Dr. Lakshmi Mohan

8. But Not Quite ! "Every business manager I know shares one frustration:
the difficulty of obtaining fast, accurate
and comprehensive market information."
President, Frito-Lay
Wall St. Journal, June 11, 1990
Dr. Lakshmi Mohan

9. What Happened in the Mid-1980s?
1. INCREASING GLOBAL COMPETITION
"In today's environment, a businessman without access
to good information is playing with one hand behind his
back“.
2. TIMELY "INFORMATION " BECAME CRITICAL
"We need enough advance warning to steer around the
iceberg. What we have had so far is the world's best
damage report“.
3. CORPORATE DOWNSIZING
"Fewer staff analysts available to sift through the
mountain of ‘data’ and cull out relevant ‘information’ ”.
4. TECHNOLOGICAL ADVANCES
Made 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 applications
Focus on performance management and the need to develop and measure the associated key performance indicators
Ensuring accuracy, timeliness and consistency of data for regulatory reporting
Source: Gartner Management Update, Nov. 2004
Market 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 Japan
By 1998, expanded to over 5,000 stores
Company’s profitability reached 40% of sales
In contrast, 7-Eleven USA filed for bankruptcy
Sold 70% of its stores to Ito-Yokado
IT systems captured information about customers and their needs
Every clerk recorded customer features (gender, approx age, etc) at the time of purchase
Also, products requested that were not available in the store’s inventory
Inventory management systems based on customer information
Which 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 BI
Gartner’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 year
Pressure 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 practices
Competition 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 years
Duplication 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 Truth
The 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 software
Smart 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 Date
in which the delivery will happen
- High Span  Poor capability to meet customer need
Objective  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 desired
RESULTS :
Plastics : 50 days span to 5
Aircraft Engines : 80 days span to 5
Mortgage Insurance : 54 days span to 1
Dr. 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 MEASURED
AND REWARDED
GETS DONE !
Dr. Lakshmi Mohan

17. A Manufacturing Analogy
Raw Data = Raw Material
Conversion Process
DSS/EIS/BI Systems
Expert Systems
Data Mining, etc.
Actionable Information = Finished Product
Quality of the Conversion Process
As Important as the Quality of the Data
Dr. 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 made
Soft 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 Data
e.g., Revenue, Direct Costs
Measured Data
e.g., Bill of Materials for a Product
SOFT DATA ... Fairly Inaccurate, Difficult to Get
Future Data
e.g., Sales Forecasts
Judgemental Data
e.g., Allocation of Overhead Costs
Qualitative Data
e.g., High Potential of an Employee
Dr. Lakshmi Mohan

22. The Data Isn’t Where We Need It !
Senior Managers
-Strategic Planning
Middle Managers
- Management Control
External, Soft Data
Front-Lines
- Operational Control
Internal, Hard Data
Corporate Data Warehouse
The 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 Collection
Minimize Text Data because:
time-consuming
inconsistent
not easy to analyze
Use Categories, Statements, Rating Scales
Explicate Mental Model of Data Provider
Decompose 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 positions
Measure employees among 10 “success dimensions” relative to everyone else
Use a 4-point rating scale
Measure positions across the same dimensions
Match employees to positions
Dr. Lakshmi Mohan

28. Information Must be Tailored to Management Level
Lower
Middle
Top
Operational
Control
Narrow
Internal
Historical
Micro
Management
Control
Strategic Planning
Wide
External
Future
Macro ?
1. Function
2. Scope of
Responsibility
3. Scope of
Information
4. Sources of
Data
5. Time Horizon
6. Level of Detail
Dr. Lakshmi Mohan

29. Type of Data in the BI System
Not just Hard, Internal Data
Not limited to Financial Data
Must include Soft, External Data
Key Areas to be Considered:
Measurement of Customer Service
Market Information on Customers & Competition
High-Potential Evaluation, Succession Planning & Career Development of Employees
Dr. 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 data
But limited to internal, hard data
Cost of data quality justifiable because systems will be used
Analytical Systems
(e.g., Performance Evaluation, Market Analysis, etc.)
Scope of Data is Wider - External and Soft data
But ... Is “Better” Data Worthwhile?
Value is zero if system is not used
Dr. Lakshmi Mohan

31. COST versus VALUE OF DATA - “Satisficing” Concept
Better data Higher cost Value Impact on the decision
Aim: 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 CEO
Dr. Lakshmi Mohan

32. Actionable Information
… Information that becomes the basis for action
Must be Timely
“Satisficing” Accuracy is Enough
Must Help in ...
Problem-Finding and Problem-Solving
Dr. Lakshmi Mohan

33. Attributes of “Actionable” Information
Timeliness
If it is late, managers will make decisions without it
Complete and Accurate? How much?
– Just good enough for decision-making
What is absolutely needed in relation to What is at stake
Reason: $$$$$$ 100% Complete and Accurate takes time and is expensive
The key concept in information accuracy and completeness is “Satisficing.”
Dr. Lakshmi Mohan

34. Timeliness vs. Accuracy Problem
Precise Financial Data Has a Price: Time
Accruals, adjustment entries and allocations lengthen Monthly Closing Cycles
Is the Precision worth the Time Lag in the Data?
“Just-In-Time” Monthly Closing
Timely Data with Satisficing Accuracy
Frees up time of financial staff for value-added analysis
Dr. 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 Data
Able to Segment Customers by Size
… Small, Medium, Large
Salespeople Data
Which Salesperson Calls on Which Customer
Most Important: Time Spent With Each Customer
From the Salesperson’s Call Reports
BIG 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-critical
Analytical Systems depend on managers’ desire and ability to use them in their decision-making processes to manage the business
Prerequisite:
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 well
Effectiveness: Doing the Right Thing
Efficiency: Doing IT Right
Which 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 Waste
Operational Systems Will be Used
Because they are mission-critical for running
the organization
DSS / EIS / BI Systems ???
Will not be used unless the management process is driven by these systems
Dr. Lakshmi Mohan

41. To Get Payoff From Analytical Systems …
Raw Data
Analytical System
Actionable Information
ACT !!

42. Two “Big” Factors Affect Use of Analytical System
1. Organization Culture
“Business as Usual” - Complacent Culture
versus
“ How Can We Improve”
2. Management Style
“Left Brain” - Analytical
“Right Brain” - Intuitive

43. Methodology for System Development Is Different!
DATA
PROCESSING
OUTPUT
(1)
(2)
(3)
TPS
HENCE , BOX (1)
EASY TO SPECIFY
MOST EMPHASIS
ON BOX (2)
BOX (3) IS
WELL-DEFINED
PRECISE
DSS/EIS/BI ?
LEAST
IMPORTANT
ILL-DEFINED
FUZZY

44. Standard Method for System Development
Sequential Approach
Analysis Design Development Implementation
Project
Start
Project
End
Suitable for structured systems
Because outputs of the system are easy to specify
Not Effective For DSS / EIS / BI
Because information needs cannot be completely
defined at the outset during the Analysis Phase

45. The Systems Development Life Cycle (SDLC)
Project Identification
& Selection
Project
Initiation
& Planning
Analysis
Logical
Design
Physical
Design
Implementation
Maintenance
Dr. Lakshmi Mohan

46. Things Which Management Those Which Would be Merely
An Axiom For BI Systems
Distinguish Between
Things Which Management
MUST Do AND Know
versus
Those Which Would be Merely
Nice to Do and Know
Dr. Lakshmi Mohan

47. Design of the BI System A Common Approach: System Has Everything.
Too Many Options will Overwhelm the User
High “Intellectual Cost” to Use the System
What is Needed:
- Not Over-Designed!
- Must Enable News in the Data to be
Quickly Gleaned
Dr. Lakshmi Mohan

48. A User-friendly System
No Training, No Manual
"Bomb-proof"
Invites Usage
Intuitive Paths to Navigate

49. Problems with the Standard Approach for Systems Development
Interview users to define requirements
Danger: Long "Wish-list"
Build system to specifications
What users say they want
is not
What they actually need

50. Interviewing Executives Is Difficult Because ...
No time or patience to think through
Unable 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" it
Constructive feedback on system design
A cost-effective means of ensuring value
of system before making the investment
on 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 Prototyping
Only Means of Ensuring that System Design Meets User Needs
Produces a "live" system rather than a voluminous "paper“ system usually written from a technical viewpoint
Allows users to test-drive the system and see how it works rather than imagine its operation
Facilitates constructive feedback from users about features they like in the system and modifications to make it more useful
Enables system to evolve nearer and nearer to users' needs after three to five iterations
BY 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”
PROTOTYPE
INITIAL SYSTEM
END
“Go”
USER FEEDBACK
FOR EXPANDING
SYSTEM
CONVERT 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 into
a short cycle
System evolves through a series of iterations
Enables users to specify information needs
in concrete terms
Because 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 & Reporting
Standard Reports: What Happened?
Query / Drill-Down: Where Exactly is the Problem?
Analysis Capabilities
Statistical 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 Tools
To enable users without programming skills to analyze the raw data in the data warehouse.
Ad Hoc Query / Reporting
OLAP Tools to “slice” and “dice” data.
Data Mining Tools
Automate the detection of patterns in the data warehouse
Build models to predict behavior through statistical and machine-learning techniques.

59. Drilldown Example Level 1 Level 2 Level 3 Washington Metro 510
Virginia
160
D.C.
170
Maryland
170
3-Region Total
510
Level 2
Circles 10
Wheat 20
Sugared 90
Spheres 40
Circles 60
Wheat 40
Sugared 10
Spheres 80
Circles 90
Wheat
100
Sugared
150
Spheres
170
Circles 20
Wheat 40
Sugared 50
Spheres
Level 3

60. Slicing & Dicing a Data Cube - Sales by Location
New York
$440
Chicago
$380
Dallas
$325
San Francisco
$245

61. Slicing & Dicing a Data Cube - Analysis by Type of Socks
Black Socks
Sales
New York
$170
Chicago
$110
Dallas
$125
San Francisco
$70
Brown Socks
Sales
New York
$100
Chicago
$130
Dallas
$15
San Francisco
$40
Blue Socks
Sales
New York
$120
Chicago
$40
Dallas
$160
San Francisco
$95
Sweat Socks
Sales
New York
$50
Chicago
$100
Dallas
$25
San Francisco
$40

62. Slicing & Dicing a Data Cube - Analysis by Location
New York
Sales
Black
$170
Brown
$100
Blue
$120
Sweat
$50
Chicago
Sales
Black
$110
Brown
$130
Blue
$40
Sweat
$100
Dallas
Sales
Black
$125
Brown
$15
Blue
$160
Sweat
$25
San Francisco
Sales
Black
$70
Brown
$40
Blue
$95
Sweat

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 in
vast stores of operational data.
2. Emergence of global competition has put the pressure on
companies to be “data- driven” – i.e., make informed decisions
based on facts and not hunches.
3. The speed of change in the marketplace demands that the pearls
of actionable information have to be found faster in the ocean of
data, for companies to be one step ahead of competition.
4. The hardware needed to store and process a “ton of data” was
prohibitively expensive until recently – “You would have had
to 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 tree
Records in each segment are similar with regard to the variable of interest
Example: Classification of Credit Risks

67. Overall : 7% of Customers Responded
Decision Tree for Segmenting Customers - Who Responded to a Marketing Campaign
Overall : 7% of Customers Responded
Segment of Customers Who Rent with High Family Income and No Savings A/c : 45% response
Target 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 Records
Records Sent to Honda by Dealers
Included Categorized Quality Problems AND Free Text
Transcripts of Calls by Mechanics to Experts in Various Domain at Headquarters
Transcripts of Customer Calls to Call Centers
Mined the Text Data from the Different Sources
e.g., Words appearing for the first time, particularly those suggesting major problems, such as fire
Flagged for human analysts to look at
Source: 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 Contact
Divide New Leads into Cold, Medium and Hot rankings
80% success rate i.e., the leads performed as predicted, when the leads were passed to the sales staff
Source: “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&G
Financial Services: Capital One, Royal Bank of Canada
Retail: Wal-Mart, Tesco, Amazon
Transport: FedEx, UPS, Schneider National
Industrial Products: CEMEX, John Deere
Hospitality & Entertainment: Marriott, Harrah’s Entertainment

72. Why Compete on Analytics?
Geographical Advantage
… Does not matter in global competition
Protective Regulation: Largely Gone
Proprietary Technologies: Rapidly Copied
High-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 Cards
IPO in 1994
Revenue: Exploded from $95 M in 1995 to $4.97 B in 2000
2001 Year End:
Posted its 18th consecutive quarter of record earnings
Annual earnings rose by over 20%; Yearly ROE: Over 20%
43.8 M customers worldwide, Over 20,000 employees
Consistently Outperformed First USA, twice its size
Earning 40% more interest income
Enjoying double the profit margin
Secret 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 Time
UNIQUE 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 LEARN
We 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 offering
Find a target population and a business case
Test the idea with this group to see how they react
Step 2:
Gather data on the test and analyze results
Step 3:
Use test results to identify which people are most receptive to the product offering
Conduct the marketing campaign based on micro-segmentation
Example: 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 Products
Variations of Credit Cards
… Annual Percentage Rate, Credit Limits, Fees, Designs, etc
Examples:
No-fee Mercedes-Benz affinity card with a credit line of $20K
$29 a year fee for a card with just $200 worth of credit
A credit card with a Canadian moose on it. Or, a card with a map of Japan and an image of Mt. Fuji on it
Other Related Products
… e.g., Card Protection Plans, Payment Protection Insurance
Other Financial Services … e.g., Travel Insurance

78. Intelligent Call Routing - Implemented in 1998
# of Calls from Customers: Over 200,000 a day
The Moment the Last Digit is Punched:
Caller is Identified; About two dozen items of data Analysed
Predict the Reason for the Call
ALSO, What the Caller Might Want to Buy … even though he or she isn’t calling to buy anything
Select Best of 50 Call Routing Options for This Caller
Display the Relevant Info, including the Script for the Cross-Sell Recommendation, on Rep’s Screen
ALL BEFORE the Call Arrives in the Head-Set
Just 100 milliseconds, one-tenth of a second … one-eights of the time between human heart-beats
How 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-house
Everyone 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 does
AND, we use that data as a basis for creating decision rules in our applications. - VP of Customer Relations
Examples:
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 IT
Why not predict the reason for each call
AND
then 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 categories
Raise your customer’s interest rates, and they call
Send 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 month
2. Decision-tree Software had to be written
3. 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 Service
Call Centers: NOT A COST CENTRE
Generate Revenues from Cross-Selling
Exceeds Cost of Operations
Actually 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 cruises
Data 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 something
The 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 Hurdle
Service Reps Reluctant to Sell Products
The 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 One
By market segments based on…
… Customers’ credit quality
… Activity with the card, etc.
Each Segment is a Profit Center
Head has the autonomy and team to run the operation for that segment like a small business
Enables opportunities to be sensed from the bottom-up and pursued quickly by motivated employees
Reward System Fosters Shared Values and Collaboration
Low turnover rate: 5% per year for customer-contact employees vs. industry average of 15% - 20%
Improves service and helps keep costs down