1. You should develop the simulation model just described. Only one @RISK output is required, the total retained impressions. Based on a run of 10 simulations (one for each number of ad agencies solicited) of 1000 iterations each, you should recommend to Joel whether his company should indeed start soliciting competing ads from several ad agencies.

2. Joel believes something has been omitted from the simulation model, although he has no idea how important it is. As it stands, on each iteration the simulation chooses the ad with the highest simulated performance measure. In reality, however, when Joel and his group view the draft ads and try to choose the most effective, there is an element of unreliability in their choice. The one they choose as apparently best might not be the one that is actually best. One way to model this is to have two correlated performance measures, each with the same lognormal distribution, for each ad agency. One will be the apparent performance measure and the other will be the actual. On each iteration, the simulation should identify the ad agency with the highest apparent performance measure, but then this agency’s actual performance measure should be used in the objective. You should use a correlation of 0.8, although you might want to experiment with other correlations. The real question is whether this more complex model makes a difference.

The Crescent Soap Company is rethinking the advertising strategy for one of its main products, Fresh Spring bath soap. Joel Spencer, the manager in charge of advertising for Fresh Spring, suspects that the company’s traditional reliance on a single ad agency to create ads might not be the most effective strategy. He wonders whether it might be more effective to solicit draft ads from a number of ad agencies and then choose the best of them. Joel has a fixed $10 million budget for advertising Fresh Soap in the current planning period. Traditionally, only a small part of this budget has been used to create the ads, and the rest has been spent on placing the ads in the media. Joel realizes that if he acts on his new strategy, a larger percentage of the budget will go toward creating the ads and less will be available for the media. However, his real goal is to maximize the number of “retained impressions,” a measure of the effectiveness of ads. By having several ad agencies compete for the most effective ad, a larger retained impressions per media dollar might outweigh the fact that fewer media dollars are being spent. Joel is confident about the dollar costs involved. Once a draft ad from an agency has been chosen, the cost of producing the ad is $750,000. There is also a marginal cost of each $250,000 for each draft ad solicited. For example, if Crescent Soap solicits draft ads from five ad agencies, this marginal cost will be multiplied by 5. Finally, there is an extra fixed cost of $100,000 if more than one ad adjacency is solicited.

This fixed cost covers the process of reviewing the ads and choosing the best. Joel is not nearly as confident about his ability to measure the key performance measure of any ad, the retained impressions per media dollar spent on the ad. He has seen studies where this performance measure varies widely, with a right-skewed distribution. Therefore, he decides to model this performance measure with a right-skewed lognormal distribution. After experimenting with the parameters of this distribution, using the @RISK Define Distributions tool as a guide, he has chosen a mean of 30 and a standard deviation of 25. The mean appears to be consistent with his past experience with advertising effectiveness, and the standard deviation provides the type of skewness he wants. Joel decides to use this distribution in an @RISK simulation, where he will vary the number of ad agencies solicited from 1 to 10 with a RISKSIMTABlE function. Each ad agency will generate an independent performance measure from this lognormal distribution and, on each iteration, the ad agency with the highest performance measure will be chosen. Then this highest performance measure will be multiplied by the dollars left for media spending to calculate the real objective, total retained impressions. Joel has asked you to develop, run, and interpret the @RISK simulation. Specifically, you should do the following.