Business System Analysis Decision Making

Slide 1:Business System Analysis & Decision Making- Lecture 14

Zhangxi Lin ISQS 5340 Summer II 2006

Slide 2:Chapter 12: Improving Decision Making

Outline of the chapter Strategy 1: Acquiring Experience and Expertise Strategy 2: Debiasing Judgment Strategy 3: Analogical Reasoning Strategy 4: Taking an Outsider�s View Strategy 5: Using Linear Models and Other Statistical Techniques Strategy 6: Understanding Biases in Others

Slide 3:Decision Making in Sports

Statistics has outperformed experts in predicting the outcomes of sport games The Future of NBA Statistics: Part 1, Part 2 Houston Rocket Performance in 2006 Yao Ming�s statistics Questions Why did it take so long for rationality to enter into decision making in sports (baseball)? To what extent are managers in other industries still replying on false expertise when better strategies exist?

Slide 4:Experience vs. Expertise

�Experience is a dear teacher� (Dawes 1988) �Learning from an experience of failure � is indeed �dear�, �� Need to realize the value of gaining a conceptual understanding of how to make a rational decision, rather than simply depending on the relatively mindless, passive learning obtained via experience. The final benefit of developing a strategic conceptualization of decision-making concerns transferability � the ability to pass on the knowledge to future generations. Key element is to avoid the many biases in individual and group context.

Slide 5:Debiasing Judgment

Unfreezing Change Refreezing

Slide 6:Business Intelligence and Data Analysis

Slide 7:Adopting Business Intelligence

Collecting data � database and data warehousing Using linear models - regression Using other statistical techniques � ANOVA, correlation analysis, time series analysis, etc. Applying data mining techniques Classification Clustering Association analysis Link analysis Text mining Adopting new business intelligence ideas Web mining 6 sigmas Realtime advertising/marketing Accurate marketing Narrowcasting

Slide 8:A model of course contents

IT Business Intelligence Behavioral Biases Models Tools Methods Data Decision Problems

Slide 9:Business Intelligence (restate)

Wikipedia.org�s definition: A broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all of the factors that affect your business. It is imperative that you have an in depth knowledge about factors such as your customers, competitors, business partners, economic environment, and internal operations to make effective and good quality business decisions. Business intelligence enables you to make these kinds of decisions. Reference: http://zlin.ba.ttu.edu/6347/ISQS6347.htm

Slide 10:Business Intelligence (restate)

The Data Warehousing Institute�s definition: The processes, technologies, and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business action. Business intelligence encompasses data warehousing, business analytic tools, and content/knowledge management.

Slide 11:Benefits for MBA Students in Business Intelligence

Understand the growing trend of demand in data mining from industry Know the general concepts and ideas in data analysis Be able to manage data mining projects for businesses Understand what technical people are doing Understand the outcomes from data mining projects Catch the advanced business concepts, business processes and new working patterns

Slide 12:Sending Advertising Materials

100,000 customer Only 10% of them may be interested in life insurance Mailing an insurance advertising package costs $1 (material printing, stamp, processing, etc.) If someone purchases the insurance, the company will make $4 net profit. So, if a letter results no purchase of the insurance package, the loss is $1. Questions What is the total profit if sending the ad to all customers? How to improve the efficiency of advertising and make positive profits?

Slide 13:Data

What like of data we have now? Historical dataset. It shows previous life insurance purchase history Customers� profile dataset. It contains customers� properties and other information, except the information whether they will purchase the life insurance.

Slide 14:Case: Life Insurance Promotion

Slide 15:Customer Profiles Dataset

Slide 16:Performance Analysis

Originally, 40% customers purchased life insurance, i.e. P(�Life Ins�) = 0.4 We notice 3 out of 5 females purchase life insurance, i.e. P(�Life Ins�|Female) = 3 / 5 = 0.6 3 out of 4 customers who purchase credit card insurance also purchase life insurance, i.e. P(�Life Ins�| �Credit Ins�) = 3 / 4 = 0.75 there is strong correlation between �Life ins� and �Credit ins�, or �Life Ins� and �Female�. So, we may send promotion packages to female customers or to those who purchase credit card insurance. This will improve the acceptance rate.

Slide 17:Definitions

If we send the life insurance promotion package to female customers, the acceptance rate is 0.6, which is called accuracy rate. As the strategy will likely improve the acceptance rate from original 0.4 (based on all customers) to 0.6. The ratio of them, 0.6 / 0.4 = 1.5, is called Lift. A lift value greater than 1 indicates the improvement. However, we can see that one of the customers who also purchases life insurance is a male. He will be excluded from the promotion mailing list. Therefore, using the rule �female� only covers 3 out of 4 customers who purchase life insurance. The ratio �# of included targets� / �# of all target�, i.e. 3 / 4 = 0.75 in this case, is called Coverage rate. A coverage rate less than 1 implies some valuable customers are lost. To improve the accuracy of decision-making, we may apply more than one criterion, e.g. �Female� plus �Credit Ins�.

Slide 18:Performance Evaluation (Rule: �Female�) Using a Confusion Matrix

Actual Accept Actual Reject Computed Accept Computed Reject True or 1 3 True or 1 4 False or 0 2 False or 0 1 5 Accuracy = 3 / (2+3) =0.6 5 Coverage = 3 / (3 + 1) = 0.75

Slide 19:Performance Evaluation (Rule: �Female�)

Actual Accept Actual Reject Computed Accept Computed Reject P(Actl A|Comp A) = 60% (3) P(Actl R|Comp R) = 80% (4) P(Actl R|Comp A) = 40% (2) P(Actl A|Comp R) = 20% (1) 5 Accuracy = 3 / (2+3) =0.6 5 Coverage = 3 / (3 + 1) = 0.75

Slide 20:Decision Tree (1)

Total: 10 Accept: 4 Reject: 6 Accuracy: 40% Coverage: 100% Gender Female Male Total: 5 Accept: 3 Reject: 2 Accuracy: 60% Coverage: 75% Total: 5 Accept: 1 Reject: 4 Accuracy: 20% Coverage: 25% Credit Card Insurance Yes No Total: 2 Accept: 2 Reject: 0 Accuracy: 100% Coverage: 50% Total: 3 Accept: 1 Reject: 2 Accuracy: 33.3% Coverage: 25%

Slide 21:Decision Tree (2)

Total: 10 Accept: 4 Reject: 6 Accuracy: 40% Coverage: 100% Gender Female Male Total: 4 Accept: 3 Reject: 1 Accuracy: 75% Coverage: 75% Total: 6 Accept: 1 Reject: 5 Accuracy: 16.7% Coverage: 25% Credit Card Insurance Yes No Total: 2 Accept: 2 Reject: 0 Accuracy: 100% Coverage: 50% Total: 2 Accept: 1 Reject: 1 Accuracy: 50% Coverage: 25% What are the differences of this decision tree from the last one?

Slide 22:Rules from the analysis

1. IF Sex = Female Then Life Insurance Promotion = Yes Rule accuracy: 60% Rule Coverage: 75% 2. IF Credit card Insurance = Yes Then Life Insurance Promotion = Yes Rule accuracy: 75% Rule Coverage: 75% 3. IF Sex = Female & Credit card Insurance = Yes Then Life Insurance Promotion = Yes Rule accuracy: 100% Rule Coverage: 50%

Slide 23:Total Benefit

Rule 1 Gain: $4 * 3 = $12; Loss: $1 * 2 = $2; Net = $12 - $2 = $10 Rule 2 Gain: $4 * 3 = $12; Loss: $1 * 1 = $1; Net = $12 - $1 = $11 Rule 3 Gain: $4 * 2 = $8; Loss: $1 * 0 = $0; Net = $8 No Rule Gain: $4 * 4 = $16; Loss: $1 * 6 = $6; Net = $16 - $6 = $10 Conclusions Choosing the best rule maximizes the profit Sometime �No Rule� could be better than some rule, which depends on the number of instances being included by the rule. So, we need a greater coverage rate from a rule.

Slide 24:Exercise 4

100,000 customer Only 10% of them may be interested in life insurance Mailing an insurance advertising package costs $1 (material printing, stamp, processing, etc.) If someone purchases the insurance, the company will make $4 net profit. So, if a letter results no purchase of the insurance package, the loss is $1. If there are three rules available to improve the accuracy of marketing, which one is the best? Calculate the total benefits based on each rule and provide your argument. Rule 1: picking out 20,000, 30% accuracy rate (6,000 / 10,000 = 60% coverage) Rule 2: picking out 30,000, lift = 2 (accuracy rate = 2 * 10% = 20%, 30,000 * 20% = 6,000, 6,000 / 10, 10,000 = 60% coverage rate) Rule 3: picking out 10,000, 60% accuracy rate Rule 1: 30% accuracy rate, 60% coverage rate Rule 2: lift = 2, 65% coverage Rule 3: 60% accuracy rate, 50% coverage rate

Slide 25:What is Data Mining?

Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns 

Slide 26:Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional Techniquesmay be unsuitable due to Enormity of data High dimensionality of data Heterogeneous, distributed nature of data

Origins of Data Mining Machine Learning/ Pattern  Recognition Statistics/AI Data Mining Database systems

Slide 27:Lots of data is being collected and warehoused Web data, e-commerce purchases at department/grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Provide better, customized services for an edge (e.g. in Customer Relationship Management)

Why Mine Data? Commercial Viewpoint

Slide 28:Why Mine Data? Scientific Viewpoint

Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarray s generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists in classifying and segmenting data in Hypothesis Formation

Slide 29:Data Mining Tasks

Prediction Methods Use some variables to predict unknown or future values of other variables. Description Methods Find human-interpretable patterns that describe the data. From [Fayyad, et.al.] Advances in Knowledge Discovery and Data Mining, 1996

Slide 30:Data Mining Tasks...

Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Regression [Predictive] Deviation Detection [Predictive]

Slide 31:Using Data Mining Tools

Statistics Analysis System (http://www.sas.org) �SAS�9 is the most recent release of SAS. It delivers analytical, data manipulation and reporting capabilities within a completely new framework. � SPSS (http://www.spss.com) �SPSS customers include telecommunications, banking, finance, insurance, healthcare, manufacturing, retail, consumer packaged goods, higher education, government, and market research. � Weka, an open source software product (http://www.cs.waikato.ac.nz/ml/weka/ ) Microsoft SQL Server comes with major data mining utilities There are more.

Slide 32:SAS Data Mining Examples

Credit Promotion Dataset CreditProm German Credit Data Online SAS materials (View PDF (2.24MB)) P70, dataset description P71, decision matrix

Slide 33:Life Insurance Promotion Data (more detailed)