Đang chuẩn bị liên kết để tải về tài liệu:
Data For Marketing Risk And Customer Relationship Management_10

Tham khảo tài liệu 'data for marketing risk and customer relationship management_10', kinh doanh - tiếp thị, quản trị kinh doanh phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Page 253 Table 10.3 RelationshipofOddstoScaleeRiskScoree PROBABILITY GOOD LOG DERIVED OF TO OF RISK BAD BAD ODDS ODDS SCORE 50.00 1 1 0.000 480 33.33 2 1 0.693 520 26.12 2.83 1 1.040 540 20.00 4 1 1.386 560 15.02 5.66 1 1.733 580 11.11 8 1 2.079 600 8.12 11.32 1 2.426 620 5.88 16 1 2.773 640 4.23 22.64 1 3.120 660 3.03 32 1 3.466 680 2.16 45.26 1 3.813 700 1.54 64 1 4.161 720 1.09 90.74 1 4.508 740 0.78 128 1 4.846 760 0.55 180.82 1 5.197 780 0.39 256 1 5.545 800 rts 20 row float box Risk Score V run Z y Figure 10.11 depicts gnnd newt fnr Eastern Telan. Mntt nf First Reserve s customers have a relatively low risk level. If Eastern selects all names with a score of 650 nr above it will have almost 125 000 low-risk First Reserve customers tn solicit. A Different Kind of Risk Fraud The main focus of this chapter has been on predicting risk for default on a payment. And the methodology translates very well to predicting the risk of claims for insurance. There is another type of risk that also erodes profits the risk of fraud. Losses due to fraud cost companies and ultimately consumers millions of dollars a year. And the threat is increasing as more and more consumers use credit cards telecommunications and the Internet for personal and business transactions. Team-Fly Page 254 I-----------------------------------------------------1------------------------------------1------------------------------------1 1 Risk Seo re Ị I 1 Customers 1 Predieted 1 Probability j 450 500 1 42. on 1 500 550 1 25 B6 1 1550 - 6Ũ0 1 2 638 - 1 1 18.37 I 1 1 aoo - 050 20 6531 I 7.4ữt 550-700 56 860I 3 20 I 1700 - 750 I 07 5521 1.m I Total L 1 1 3.52 1 1 1 Figure 10.11 Tabulation of risk scores. Jaya Kolhatkar Director of Fraud Management for Amazon.com discusses the mechanics of developing fraud models and the importance of proper implementation Fraud in the e-tailing world has increased rapidly over the past two years. Being a virtual marketplace most of the fraud checks in the physical retail