Used regression analyses to forecast sales of new products

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Reference no: EM131437140

Scrubbing Bubbles (a brand owned by S.C Johnson) has historically used regression analyses to forecast sales of new products in the US. They have found that sales volume ( in millions of dollars) is a function of product effectiveness (PE) on a scale of 0 - 100 as rated by a test panel, advertising expenditures (AD) in millions of dollars, number of stores (supermarkets and drug stores) in which the product is available nationally in thousands of stores (S). Their regression analyses conducted based on previous product launches has produced the following regression equation (R2 = .85, regression coefficients are unstandardized): Sales ($mm) = 10 + .62*PE + 1.23*AD ($mm) + 1.34*S (000s) If Scrubbing Bubbles launches a new tile cleaner that was rated a 94 on product effectiveness, gets the product into 12,000 stores and supports it with an $8M ad campaign, how much sales revenue will they expect? What is the expected impact on sales of selling the product in one additional store? CVS ends up deciding not to stock their new product in their stores which means that the product will only be sold in approximately 11,000 stores nationwide. How many advertising dollars would Scrubbing Bubbles need to spend to maintain the initial forecasted level of sales? Imagine Scrubbing Bubbles was deciding between two new product concepts that are ready for commercialization. The first product has been rated an 87 by the consumer panel and can be stocked in 9,000 stores. The second was rated a 91 by the consumer panel but can only be stocked in approximately 7,500 stores. The advertising budget has not been finalized and approved yet but would be the same for either product launch. Which product concept would be forecasted as the bigger seller?

Reference no: EM131437140

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