SAMPLE CASES:
 

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Business Case 1 (Response & Look-alike Modeling)

Objective The 4-Clover Entertainment group owns and operates twenty different casinos throughout the United States. In an effort to increase revenue, the group is preparing a promotional mailing that will offer special incentives for spending a weekend at any of their locations in March.

Strategic Plan To minimize the cost associated with the mailings and to maximize responsiveness, it was decided to selectively mail two types of offers to three customer types. The first is the most aggressive and will be directed towards "Best Customers" most likely to respond. The second mailing is less aggressive and will be directed towards "2nd Best Customers" most likely to respond. The second mailing will also be mailed to prospective customers that resemble the current "Best Customer" group.

Data Integration The entertainment group already has its customer data located in a centralized data warehouse for analysis. This data warehouse is supplied by customer information obtained from the "Players Card" and other transactions that occur during the stay. To increase the predictive power of a response model, enhancement data was obtained from a 3rd party list vendor and appended to the customer database.

Scientific Analysis (Database Marketing) A factor analysis was performed to separate the customer database into several segments defining the consumer behavior at the casino. A Response Model was then built for the "Best" and "2nd Best" segments to predict those most likely to accept the promotional offer. Finally, a Look-alike Model was built to locate prospects from the list vendor possessing similar qualities as 4-Clover's best customers.


Business Case 2 (Business Rules & Fraud Reduction)

Objective A retailer is experiencing a high volume of fraudulent orders from its website and would like to cut the number of processed orders of this type in half.

Strategic Plan The retailer would like to implement new screening procedures during processing to flag suspicious orders. They would also like to develop some internal negative lists that will flag orders with matching demographic information. The flag will result in a terminated order in extreme cases, and a manual review process for lesser cases.

Data Integration The orders along with the necessary customer information were relayed through a SAS credit screening system that produced a flag on identified orders. This flag was then appended back to the original order data so that a final decision could be made. Reports were also generated compiling all historical information to build cases against fraudsters.

Scientific Analysis (Risk Management) A sample of subsequent online orders from a 30 day window was analyzed for fraudulent behavior patterns using various data mining techniques. Business rules were formulated as a result of the study and implemented into the credit screening system. The company's current fraudulent population was then analyzed and, as a result, various negative lists were created and implemented into the credit screening system.

     SAMPLE CASES

       STRATEGIC PLANNING »

       DATA INTEGRATION »

       DATABASE MARKETING »

       RISK MANAGEMENT »

       BUSINESS ANALYTICS »




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