Session11-INFS1000-S2-2016-preview [Handouts Format].pdf

Question for discussion is a high lift the only

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Question for discussion Is a high lift the only relevant information we need to know to make a decision about which items to advertise/promote together? What about confidence? What about support? MORE EXAMPLES: Data Mining in Action Customer churn: Mobilcom GmBH, Germany 4.5 million customers & 1100 employees Uses IBM’s DB Intelligent Miner to identify customers that may switch to another mobile carrier The software periodically looks for patterns of customer churn and assigns a score representing the likelihood of cancelling the contract Variables used: complaint history, number of days to contract expiry, type of contract, phone model. Inferring demographics: Amazon Prediction of what customers are likely to buy in the future – software developed by Amazon “Customers who purchased this item also bought … “ Determines the age of the recipient of an item purchased by a customer -> if you bought an item for a baby girl, in a few years Amazon will suggest you buy items for a young girl If you purchased perfume a week before Valentine’s Day, it will infer that you bought the item as VD gift for a woman and offer certain colours for the wrapping
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9/10/2016 14 MORE EXAMPLES: Data Mining in Action (cont’d) Fraud detection: Return Exchange, California Retailers who accept returns joined “Return Exchange” program You can return merchandise to various retailers without receipts, just need to present the driver’s licence or some ID Uses sophisticated statistical models to determine if certain customers engage in fraud -> return stolen items for cash Did not disclose the criteria used to make the decision Using loyalty programs: Harrah’s Entertainment Inc., USA Casino and hotel chain Tailors accomodation, dining, and gambling packages that are attractive to their customers Determines small and big spenders -> decides how to price their services according to the individual spending patterns Did not disclose the technique Agenda for today 3. Decision Making & Business Intelligence 4. Activity: Market Basket Analysis (MBA) 1. Repetition W9: Internet Business & Web 2.0 2. Activity: Homework & MCQ Activity W10(2): Market Basket Analysis
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9/10/2016 15 Key “take-away”: Key concepts you need to know Business Data Architecture BI Methods Reporting OLAP Data Mining
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