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

Direct marketing identify which prospective clients

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Direct marketing Identify which prospective clients should be included in mailing lists to obtain the highest response rate Interactive marketing Predict what each individual accessing a website 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
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9/10/2016 11 What drives Data Mining? Example methodology: Market-Basket Analysis (MBA) Market-basket analysis is a data-mining technique for determining sales patterns Uses statistical methods to identify sales patterns in large volumes of data Shows which products customers tend to buy together Helps identify cross-selling opportunities , e.g. ‘Customers who bought book X also bought book Y’ Used to estimate probability of customer purchase, e.g., in 20% of transactions where product ‘A’ was purchased, products ‘B’ and ‘C’ were purchased Example: Data Mining – Ethical Implications Uses 25 variables to calculate pregnancy prediction scores (based on previous purchasing patterns) Forecasts which women are pregnant and due date Coupons are sent Backlash from teen pregnancy story Changed its approach Legal. But ethical..? How do you do go about doing it?
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9/10/2016 12 Market Basket Analysis example Sales of 1000 items in a dive shop 1000 items Mask Tank Fins Weights Dive computer Sales per items 270 200 280 130 120 Mask 20 20 150 20 50 Tank 20 80 40 30 30 Fins 150 40 10 60 20 Weights 20 30 60 10 10 Dive computer 50 30 20 10 5 No additional product 10 - - - 5 Out of 1000 items sold, 280 were Fins Market Basket Analysis Example Probability that a customer will buy item A P(Mask) = 270/1000 = 0.27 (read from Table) P(Fins) = … ? Support = probability that a customer will buy two items (A & B) together P(Fins & Mask) = 150/1000 = 0.15 (read from Table) P(Fins & Weights) = … ? P(Fins & Fins) = … ? Confidence = probability that customer buys B, given that they already bought A P(Fins | Mask) = P(Fins & Mask) / P(Mask) = 0.15/0.27 = 0.5556 (Support/Probability) P(Mask | Fins) = … ? Lift = ratio of confidence to the base probability of just buying items P(Fins | Mask)/ P(Fins) = 0.556/ 0.28 = 1.98 (Confidence/Base Probability) P(Mask |Fins)/ P(Mask) = … ? Market Basket Analysis Example Sales of 1000 items in a dive shop 1000 items Mask Tank Fins Weights Dive computer Sales per items 270 200 280 130 120 Mask 20 20 150 20 50 Tank 20 80 40 30 30 Fins 150 40 10 60 20 Weights 20 30 60 10 10 Dive computer 50 30 20 20 5 No additional product 10 - - - 5 Out of 1000 items sold, 280 were Fins
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9/10/2016 13 Market Basket Analysis Example Interpreting the lift results: P(Fins | Mask)/ P(Fins) = 0.556/ 0.28 = 1.98 If someone buys a mask, the likelihood he/ she will buy fins almost doubles from 0.28 to 0.5556! The likelihood of walking in and just buying fins is 0.28 The likelihood of someone buying fins, given he/she buys a mask is 0.5556 Ratio between 0.28 : 0.5556 is… (= 0.556/ 0.28) = 1.98 Conclusion : Sales people should be trained to try to sell fins to anyone buying masks (2 times more likely to buy a fin)!
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