Chapter 11: Business Intelligence and Knowledge Management

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Chapter 11: Business Intelligence and Knowledge Management
Data Mining and Online Analysis
* Data warehouses are useless without software tools
* Process data into information
* Business intelligence (BI): information gleaned with information tools Data Mining
* Data mining: selecting, exploring, and modeling data
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* Supports decision making
* Finds relationships and ratios within data
* Finds unknown relationships
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* Queries are more complex than traditional
* Combination of data-warehouse and data-mining facilitates predictions * Data mining has four objectives
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* Sequence or path analysis
* Classification
* Clustering
* Forecasting
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* Techniques applied to various fields
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* Marketing
* Fraud detection
* Marketing to individual
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* Data mining can predict customer behavior
* Banking
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* Find profitable customers
* Find patterns of fraud
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* Mobile phones
* Customers tend to switch companies often
* Customer loyalty programs ensure steady flow of customer data Potential Applications of Data Mining
Data Mining Application| Description|
Consumer clustering | Identify the common characteristics of consumers who tend to buy the same products and services from your company.| Costumer churn | Identify the reason customers switch to competitors; predict which customers are likely to do so.| Fraud detection | Identify characteristics of transactions that are most likely to be fraudulent.| Direct marketing | Identify which prospective clients should be included in a mailing or e – mail list to obtain the highest response rate.| Interactive marketing| Predict what each individual accessing a web site is most likely to be interested in seeing.| Market basket analysis| Understand what products or services are commonly purchased together, and on what days of the week.| Trend analysis | Reveal the difference between a typical customer this month and a typical customer last month.| * Utilizing loyalty programs

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* Frequent flier
* Consumer clubs
* Amass huge amount of data about customer
* Harrah’s Entertainment Inc.
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* Uses data mining to discern big spenders
* Allows sales agents to charge big spenders less money *
* Inferring demographics
* Predict what customers likely to purchase in future
* Amazon.com
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* Age ranges estimated from purchase history
* Advertises for appropriate age group
* Anticipates holidays
Online Analytical Processing
* Online analytical processing (OLAP): application to exploit data warehouses *
* Extremely fast response
* View combinations of two dimensions
* Drilling down: start with broad info and get more specific * Can receive info in numbers or percentages
* Uses specifically tailored data or relational database *
* OLAP application composes tables immediately
* Dimensional database: data organized into tables
* Tables show information in summaries
* Companies sell multidimensional database packages
* OLAP applications are powerful tools for executives
* Ruby Tuesday restaurant chain case
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* One location was performing below average
* Customers were waiting longer than normal
* Appropriate changes were made
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* OLAP applications installed on special server
* OLAP faster than relational applications
* OLAP increasingly used by corporations
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* Office Depot used OLAP on data warehouse
* CVS let 2,000 employees run analyses
* Ben & Jerry’s track ice cream popularity
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* BI software becoming easier to use
* Intelligent interfaces
More Customer Intelligence
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