Twc Memo 1

Memo #1 on Time Warner

Time Warner, Inc. - Background Information:

From time to time, all businesses experience challenges within their operations. The same holds true for Time Warner. Started in 1990 through the merger of Time, Inc. and Warner Communications, Time Warner has experienced its own successes and challenges. One such challenge is that of lower than anticipated STARZ Network subscriptions.

Memo #1 states that Time Warner currently has 852 of its basic service subscribers that are also STARZ members. The overall concern is that the number of subscribers is much lower than what Time Warner had planned. In order to increase the number of subscriptions to the STARZ Network, Time Warner is considering offering a summer promotion to all current and new members. The Pricing Manager (author of the memo) needs to know if decreasing the price of the service would result in higher revenues. In addition, the manager also wants to determine an estimate of the maximum monthly revenues from the STARZ Network (Baye, p. 586).

Using the raw data supplied from the Time Warner Memo, a regression analysis (run through Excel) will provide statistical verification of that data and allow an approximate demand equation to be constructed and used to predict the effects of the proposed price changes to the STARZ channel packages. The resulting data is as follows:

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.886

R Square 0.784

Adjusted R Square 0.773

Standard Error 0.179

Observations 21.0

ANOVA

dt SS MS F Significance F

Regression 1.000 2.223 2.223 69.122 0.000

Residual 19.000 0.611 0.032

Total 20.000 2.834

Coefficients Standard Error t Stat P-Value Lower 95% Upper 95%

Intercept 1.971 0.135 14.596 0.000 1.688 2.254

X Variable 1 -0.107 0.013 -8.314 0.000 -0.135 -0.080

Using the linear demand model, Q = ? + ?P + ?? and after reviewing the resulting data above, it can be determined that the law of demand holds true because the coefficient associated with the price variable is negative. This confirms that an increase in price will reduce the quantity demanded of the particular good. The resulting estimated demand curve equals: Q = 1.971 -.107P. Additionally, the overall fit of the regression is good, as the R square and adjusted R squared are both high, 78%.4 and 77.3%, repectively. This would tell us that price explains 77.3% of the variation of the demand. One other factor that measures goodness of fit would be the f-statistic. A high f-statisic, 69.122 results in a good fit. Baye states that the "greater the f-statistic the better the overall fit of the regression line through the actual data" (Baye, p. 102).

Demand Curve

Q = ? + ?P + ??

Q = 1.971 -.107P

The data stated in the memo mentioned that the current STARZ customer base was 852 subscribers each paying $10.50 for the service. The price elasticity of demand in the example would be ??= -.107 * (10.50/.852) = -1.312. Professor John Abowd defines elasticity as the measure of magnitude of an economic effect in percentages" (Abowd). An additional source describes price elasticity as "a measure of how consumers react to a change in price"