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$200,000 for a complex model. So why does anyone do modeling Because, for large projects, it may pay off and give you results that are better than those you could get without the model. Summit Marketing Group in St. Louis did marketing for a major property and casualty insurance company. The P&C insurance market is very competitive. Hundreds of different companies offer automobile insurance, and customers continually shop for the best deal. The pressure to squeeze pro ts from every marketing dollar is intense. To help solve the problem, Summit developed an optimization model that was designed to maximize pro ts for each marketing campaign. To acquire new automobile insurance customers, the P&C company sent mailings to approximately 1.2 million households in a 10-state area each month. The mailing list was drawn from a database of 9 million households that included response and sales history, demographics, and credit scores. To maximize pro ts, Summit focused on return on promotion (ROP). ROP is a discounted cash ow technique that is used to measure the net present value of a marketing investment. It is theoretically similar to ROI. ROP considers response probabilities, conversion rates, risk probabilities, acquisition costs, and other marketing decision factors. The goal of the model is to determine which factors are most

The following code defines and uses a delegate with no parameters and no return value. Note the following about the code: Class Test defines two print functions. Method Main creates an instance of the delegate and then adds three more methods. The program then invokes the delegate, which calls its methods. Before invoking the delegate, however, it checks to make sure it s not null.

// Define a delegate type with no return value and no parameters. delegate void PrintFunction(); class Test { public void Print1() { Console.WriteLine("Print1 -- instance"); } public static void Print2() { Console.WriteLine("Print2 -- static"); } } class Program { static void Main() { Test t = new Test(); PrintFunction pf; pf = t.Print1; // pf pf pf //

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important in creating the largest return on promotion, and to use these factors in selecting the 1.2 million names to receive the mailing each month. Since the company mailed every month, Summit had the previous months history to use as a base. The objective was to use the model to get better and better at selecting the names to be mailed to, in order to continually improve pro ts. The modeling process produced some surprising results: The best candidates were not necessarily the best responders. The best candidates probably already had insurance somewhere else and were unwilling to shift. The best responders were often the least creditworthy, since they have trouble getting insurance. Since the model mixes lifetime value, creditworthiness, and probability of response, the top response deciles are not necessarily the most pro table. The goal was pro t, not response. Table 14-1 shows the results of one month s mailing, comparing the previous month s selection methods with those used in the current month. The control group is what they were previously doing each month. The optimized group is the new way of doing exactly the same thing with better results. Notice that the control group had higher revenue per sale ($1630) than the optimized group did ($1360). The optimized group compensated for this by having a higher response rate and a higher sales rate. Table 14-1 Return on Promotion

// Create a test class instance. // Create a null delegate. // Instantiate and initialize the delegate.

of these vertices. However, for each pixel put on the screen, XNA keeps track of its distance to the camera in the depth buffer, and that is what this third coordinate is used for. By specifying 0 as distance, you indicate the image is drawn as close to the camera as possible (to be more exact, you indicate it is drawn on the near clipping plane).

Add three more methods to the delegate. += Test.Print2; += t.Print1; += Test.Print2; The delegate now contains four methods.

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