Marketing Exam Notes

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University of California, Riverside *

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MGT

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Economics

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Apr 3, 2024

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In the last quarter, Tommy Timms sold 1077 units of colored water bottles. Each of the colored water bottles were priced at $66, on average. The CMO is considering a price change to $60 each and has asked the marketing analysts to compute the change in sales revenue. Traditionally, the products of Tommy Timms have exhibited an elasticity of 1.5. Compute the % change in sales revenue of Tommy Timms. -3.16% , Not Selected Incorrect answer: -9.87% Correct Answer: -21.5% -21.5% , Not Selected -6% , Not Selected -3.48% , Not Selected Feedback Based on answering incorrectly The solution is -21.5%. Step 1: Compute change in sales based on given elasticity and changes in price. Elasticity = [(New Sales - Old Sales)/Old Sales] / [(New Price - Old Price)/Old Price] 1.5 = [(New Sales - 1077)/1077] / [(60 - 66)/66] => New sales = 930 units Step 2: Compute change in revenue Change in revenue = (New Sales X New Price) - (Old Sales X

Old Price) Change in revenue = [(930 X 60) - (1077 X 66)]/(1077 X 66) = -21.5% In the last quarter, TommyTimms sold 1867 units of hard drives. Each of the hard drives were priced at $60, on average. The CMO is considering a price change to $52 each and has asked the marketing analysts to compute the change in sales revenue. Traditionally, the products of TommyTimms have exhibited an elasticity of -0.44. Compute the % change in sales revenue of TommyTimms. -5.36% , Not Selected Correct answer: -8.23% -4.2% , Not Selected -4.49% , Not Selected -4% , Not Selected Feedback Based on answering correctly The solution is -8.23%. Step 1: Compute change in sales based on given elasticity and changes in price. Elasticity = [(New Sales - Old Sales)/Old Sales] / [(New Price - Old Price)/Old Price] -0.44 = [(New Sales - 1867)/1867] / [(52 - 60)/60] => New sales = 1977 units Step 2: Compute change in revenue Change in revenue = (New Sales X New Price) - (Old Sales X

Old Price) Change in revenue = [(1977 X 52) - (1867 X 60)]/(1867 X 60) = -8.23% Silver Choco normally sells 20000 million digital ads at $7/ad. It changes the price to $6.75/ad and sees the number of ad calls change to 26000 million ads. Compute the price elasticity. Round to 3 decimals. -7.476 , Not Selected Incorrect answer: -8.904 Correct Answer: -8.4 -8.4 , Not Selected -15.12 , Not Selected -7.644 , Not Selected Feedback Based on answering incorrectly The solution is -8.4. Elasticity = % change in demand / % change in price. -8.4 = ((26000-20000)/20000) / [(6.75-7) /7]

Salim, the marketing manager at Cloudy has been tasked with determining the optimal advertising budget for each brand, as well as the most effective media mix to achieve their advertising goals. Over the last 6 months, Salim has collected data on the weekly ad spends for each brand and has used this data to compute advertising elasticity for each brand. Using the regression results, Salim has identified the key variables that have the most impact on each brand’s advertising effectiveness. These controls include brand prices and customer satisfaction. The results of the estimated model are as follows: Call: lm(formula = Sales ~ Adv + price + Cust_satis + Num_stores, data = q) Residuals: Min 1Q Median 3Q Max -1.41005 -0.59317 0.03186 0.58464 1.52270 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.02667 0.08512 0.313 0.754867 Adv 0.54228 0.08656 6.265 2.14e-08 *** price -0.31208 0.08614 -3.623 0.000528 *** Cust_satis 0.24023 0.10171 2.362 0.020781 * Num_stores 0.47434 0.09562 4.960 4.26e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.7501 on 75 degrees of freedom Multiple R-squared: 0.4659, Adjusted R-squared: 0.4374 F-statistic: 16.35 on 4 and 75 DF, p-value: 1.13e-09 Where Adv Marketing spend on advertising per period price Brand’s price Cust_satis Customer satisfaction with the brand Num_stores Number of stores in the city Based on the regression results, what is the advertising elasticity of LivLong? Round your answer to 3 decimals.

0.542 , Not Selected Incorrect answer: 0.437 Correct Answer: 0.542 -0.312 , Not Selected None of the above , Not Selected 6.265 , Not Selected Feedback Based on answering incorrectly The solution is 0.542. Note that the results suggest present the estimates of a linear model. In such a model, total impact of advertising on sales is the coefficient of advertising. -0.312 False. -0.312 is the coefficient of price 0.542 True. Good job! 0.437 False. 0.437 is the Adj. R_squared, a measure of explanatory power of the full model None of the above False. Hope the above explanations help to understand how to read the results :). 6.265 False. 6.265 is the t-value associated with the variable measuring advertising spend Results for question 2. 2 0 / 0.25 points

Sanjay, the marketing manager at Rieny has been tasked with determining the optimal advertising budget for each brand, as well as the most effective media mix to achieve their advertising goals. Over the last 6 months, Sanjay has collected data on the weekly ad spends for each brand and has used this data to compute advertising elasticity for each brand. Using the regression results, Sanjay has identified the key variables that have the most impact on each brand’s advertising effectiveness. These controls include brand prices and customer satisfaction. The results of the estimated model are as follows: Call: lm(formula = Sales ~ Adv + price + Cust_satis + Num_stores, data = q) Residuals: Min 1Q Median 3Q Max -1.4296 -0.7446 -0.0189 0.6061 1.6080 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.02512 0.09793 -0.257 0.798225 Adv 0.29927 0.10506 2.849 0.005663 ** price -0.20856 0.11060 -1.886 0.063203 . Cust_satis 0.20393 0.10443 1.953 0.054560 . Num_stores 0.39344 0.11404 3.450 0.000924 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.8583 on 75 degrees of freedom Multiple R-squared: 0.3005, Adjusted R-squared: 0.2632 F-statistic: 8.057 on 4 and 75 DF, p-value: 1.851e-05 Where Adv Marketing spend on advertising per period price Brand’s price Cust_satis Customer satisfaction with the brand Num_stores Number of stores in the city Based on the regression results, what is the advertising elasticity of Natural_Liqd? Round your answer to 3 decimals. 0.263 , Not Selected Incorrect answer:

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