TABLE OF CONTENTS

1.0 EXECUTIVE SUMMARY .....................................................................................22.0 INTRODUCTION ..............................................................................43.0 EXPLORATORY ANALYSIS ...................................................................................5

3.1 DESCRIPTIVE STATISTICS ..................................................................................53.2 INTERPRETATION OF THE GRAPHS .................................................................6

4.0 ANALYSIS OF THE REGRESSION OUTPUT ......................................................105.0 ANALYZING THE EFFECT OF A $1 REDUCTION IN AVERAGE PRICE ...........(AVEP) ON SALES VOLUME .......................................................................................176.0 ANALYZING THE EFFECT OF A $100,000 MEDIA CAMPAIGN SPLIT .............EVENLY OVER 10 WEEKS ...........................................................................................217.0 COMPARING PRICE PROMOTIONS AND ADVERTISING ..............................248.0 CONCLUSION ..........................................................................279.0 APPENDIX

A LINE GRAPHS 25B SCATTER PLOTS 28C DESCRIPTIVE STATISTICS 31D 6 VARIABLE DATA 36E 6 VARIABLE REGRESSION 39F 6 VARIABLE HISTOGRAM 43G 6 VARIABLES JARQUE BERA 44H HETEROSCEDASTICITY 49I 6 VARIABLES WHITE TEST REGRESSION 50J 6 VARIABLES DURBIN WATSON TEST 55K 3 VARIABLE DATA 58L 3 VARIABLE REGRESSION 61M 3 VARIABLE HISTOGRAM 65N 3 VARIABLES JARQUE BERA 660 3 VARIABLES WHITE TEST 69P 3 VARIABLES DURBIN WATSON TEST 74Q ANALYZING PRICE PROMOTIONS 77R ANALYZING MEDIA SPENDING 79

1.0 EXECUTIVE SUMMARY

DrinkMe, manufactured by GoodsCo, has to make a decision with regards to which

marketing activity to adopt. The report looks into the affect of Temporary Price

Reduction and Media Campaign over sales. Regression analysis has been done just to

find out that DrinkMe sales is not affected by the competitor’s price or their

advertisements and the main factors contributing to the sales of DrinkMe are DrinkMe’s

price, advertisement and the average temperature of Queensland. From the regression

output it has been noted that if average price increase by $1 volume would fall by 3.77

tonnes. For advertisements, an increase in advertisements would result in an increase in

sales volume by 0.063 tonnes and lastly an increase in temperature in Queensland would

result in a drop in sales by 0.61 tonnes.

The Jarque-Bera, White Test and Durbin-Watson test has been conducted to test for

normality, heteroscedasticity and serial correlation respectively. With a 95% confidence

level, all the tests have passed which concludes that the residuals are normally

distributed; there is no heteroscedasticity and no serial correlation.

With only three independent variables and when the assumptions of normality,

heteroscedasticity and serial correlation has been checked, the affect of price reduction on

sales has been worked out by holding all other variable constant. It came out that the

price reduction of 2.67 is the optimal point where the marginal difference is the

maximum. The marketing director is not recommended to reduce the price by 2.67 as it

would result in higher sales but an overall loss in the marketing activity. The marketing

director, depending on the outcome that is needed, needs to make a tradeoff decision

between high sales and low profit or vice versa. The media campaign on the other hand

provides the maximum marginal difference in the 10th week. The campaign is designed

only for 10 weeks but to make allowance for the residual effects of Adstock the analysis

is done over 20 weeks. The profit from this 10 week media campaign results to 13.84%.

It is recommended that the marketing director should use a combination of both price

reduction and media campaign. Both these would complement each other and the end

result would be more profitable than using only price reduction or media campaign.

2

The scope of the research is limited as outside the model competitors strategy does affect

DrinkMe’s sales and omitting them from the analysis would result in a different

conclusion. Whether the data collected represents the whole population is another

question that needs to be taken into account. Besides that, the research would assist the

marketing director in making the ultimate decisions regarding the selection between price

reductions and advertising promotions.

3

2.0 INTRODUCTION

This research is conducted to gain an insight into the effectiveness of a Temporary Price

Reduction (TPRs) activity and an Advertising Campaign (media spend) for the DrinkMe

brand in Queensland. The research will be able to prove whether a TPR or an increase in

media spent would be more effective.

Objectives

To measure the effects of a TPR of $1 on sales volume and sales value

To measure the effects of a media campaign worth $100,000 spread evenly over

10 weeks on DrinkMe’s sales volume and sales value

Data have been extracted from Electronic Point of Sale (EPOS) database dated 1st

September 2002 to 26th June 2005. The data includes DrinkMe and its competitor’s

weekly sales volume, weekly sales value, weekly distribution, weekly media spent and

the average temperature in Queensland.

For the purpose of the research, the data that has been collected is time series data as it

has been collected over a period of time. The measurement of the variables falls into the

category of ratio as the variables are conceptually quantitative measurement. The

numbers of units sold which in this case, volume, the dollar value of sales which in this

case value, are all conceptually quantified. The methodology that has been used is linear

regression. Multiple regressions have been used to find out the relationship between the

independent variables with the dependent variable which is volume in this case. The

affect of changes in 1 unit of independent variable on the dependent variable can be

determined from the regression summary output and in this case it is very important to

know this relationship as the ultimate objective is to find the effects of sales volume and

value due to the promotion of a $1 reduction in the price and the media campaign.

Moreover, as non normality of residuals, heteroscedasticity and serial correlation can

affect the explanation of the dependent variable accurately, Jarque-Bera test, White Test

and Durbin-Watson Tests are also done.

4

3.0 EXPLORATORY ANALYSIS

3.1 DESCRIPTIVE STATISTICS

A descriptive statistics was conducted and the following data was obtained. DrinkMe’s

sales volume shows an average of 14.35 tonnes while its competitor’s sales volume

shows an average of 0.36 tonnes..

DrinkMe’s sales value shows an average or mean of 131.11 ($’000) while its

competitor’s sales value shows a mean of 3.38 ($’000) DrinkMe’s sales value shows a

standard error 2.2893 and its competitor’s sales value has a standard error of 0.0817.

DrinkMe’s media spending has a mean of 2.2043 ($’000) while its competitor’s media

spending has a mean of 0.1421 ($’000). DrinkMe’s media spending has a standard error

of 0.3162 and its competitor’s media spending has a standard error of 0.0695.

DrinkMe’s average price has a mean of 9.1644 ($’000) while its competitor’s average

price has mean of 9.3959 ($’000). DrinkMe’s average price has a standard error of

0.0274 and its competitor’s average price has a mean of 0.0394.

DrinkMe’s Adstock has a mean of 4.3106 ($’000) while its competitor’s Adstock has a

mean of 0.2841 ($’000). DrinkMe’s Adstock has a standard error of 0.5173 and its

competitor’s Adstock has a standard error of 0.0958.

The average temperature in Queensland has a mean of 24.4179 degrees Celsius and has a

standard error of 0.3272. The maximum average temperature in Queensland was 30.4973

degrees Celsius while the minimum was 14.8676 degrees Celsius.

These averages will later be used in testing the effects of price reductions and media

spending.

5

3.2 INTERPRETATION OF THE GRAPHS

COMP VOL VS DM VOL

0

5

10

15

20

25

37500 37654 37808 37962 38116 38270 38424

TIME

VO

L VOLUME

COMP VOLUME

Graph 1: COMP Sales Volume vs DRINKME Sales Volume

By looking at Graph 1 it is found that DrinkMe’s sales are much higher compared to

DrinkMe’s competitor’s sales, in terms of volume. It is assumed that DrinkMe sales are

affected by price, temperature, media, competitor’s price and competitor’s media spent.

6

DM VOL VS AVE TEMP

0

5

10

15

20

25

30

35

37500 37654 37808 37962 38116 38270 38424

TIME

VO

L DM VOL

AVERAGE TEMP

Graph 2:

DRINKME Volume VS Average Temperature

Based on Graph 2, the temperature has a negative effect on sales, thus when temperature

increases, sales drop and vice versa. This relationship is also explained by the scatter plot

which is in Graph 3. The scatter plot shows that Drink Me volume has a negative

relationship with temperature.

DM VOL VS AVETEMP

0

5

10

15

20

25

0 10 20 30 40

DM VOL VS AVETEMP

Graph 3: DRINKME Sales Volume vs Queensland Average Temperature.

7

DM VOL VS DM ADVERTISING

0

5

10

15

20

25

30

9/1/

2002

12/1

/200

2

3/1/

2003

6/1/

2003

9/1/

2003

12/1

/200

3

3/1/

2004

6/1/

2004

9/1/

2004

12/1

/200

4

3/1/

2005

6/1/

2005

TIME

VO

L DM VOL

ADVERTISING

Graph 4: DRINKME Volume vs DRINKME Advertising

On the other hand, the effect of media expenditure has a positive effect on sales. Every

time the advertising has been done, there has been a delayed effect on the volume

Moreover, the effect of the media does help to increase sales and it is assumed that these

effects will have a 50% decay rate. The positive relationship is also clear in the scatter

plot in Graph 5.

DM VOL VS DM ADVERTISING

0

5

10

15

20

25

0 10 20 30

DM ADVERTISING

VO

L DM VOL VS DMADVETISING

Graph 5: DRINKME Volume vs DRINKME Advertising

8

DM - VOL VS AVEP

0

5

10

15

20

25

37500 37626 37752 37878 38004 38130 38256 38382 38508

TIME

000 DM Vol

AVEP

Graph 6: DRINKME Volume vs DRINKME Average Price

Price was supposed to be one of the key factors in determining sales, as reduction in price

would definitely increase sale and vice versa. Surprisingly from the graph in Graph 6, it

can be noted that sales keep following a zigzag pattern even when price remains almost

constant. This might explain that price has little effect on sales, rather advertising is one

of the important factors in determining sales. Same relationship has been found with

competitors pricing. The graph has been included in the appendix. Other graphs which

plays less important role in determining sales such as distribution are included in

Appendix A and B.

9

4.0 ANALYSIS OF THE REGRESSION OUTPUT

The weekly distribution for DrinkMe was not used in the regression because it has a non

linear relationship with DrinkMe’s sales volume. A constant data will not have any effect

on the regression model (if X is constant, Y does not change) or in logical terms, it does

not have significant effect on increasing or decreasing sales. This has been explained

from the scatter plot in Graph 7.

DM - DISTRIBUTION VS VOLUME

0

5

10

15

20

25

0 50 100 150

DISTRIBUTION

VO

LU

ME

DISTRIBUTION VSVOLUME

Graph 7: DRINKME Distribution vs DRINKME Volume

10

Using the assumption, a regression model has been made to test the variables and to

obtain a linear regression line.

Regression StatisticsMultiple R 0.917327265R Square 0.84148931Adjusted R Square 0.834744175Standard Error 1.307455676Observations 148

  CoefficientsStandard

Error t Stat P-valueIntercept 61.96243134 4.621776157 13.4066275 6.11421E-27DRINKME AVEP -3.866274167 0.330496193 -11.69839245 1.61554E-22ADVERTISING 0.075013926 0.021237149 3.532203206 0.000557438COMP AVEP -0.054796191 0.238855478 -0.229411492 0.81888155COMP_QLD_DIST 0.069203934 0.085094437 0.813260374 0.417439939COMP ADVERTISING 0.140780109 0.103847518 1.355642497 0.17738033AVETEMP_QLD -0.580864368 0.035845385 -16.2047182 5.23847E-34

Table 1: Regression of all independent variables

From the regression output shown above it shows the R2 of this model is 0.84148931

indicating that the postulated regression model explains 84.15% of the total variation of

the sample observations of the dependent variable. However, the competitor’s average

price, distribution and advertising have been removed as they do not satisfy the t-test rule

of thumb of a critical value of |2| proving that they are not significant variables to be

included in the model. The p-value confirms this as the p-values of the competitor’s

average price, distribution and advertising exceed the critical value of 0.05.

Using the residuals generated by the regression, certain tests have been carried out to see

whether the assumption of normality, homoscedasticity, and non-autocorrelation are met.

Firstly a histogram was created to test normality as shown in Graph 8.

11

Histogram

0

5

10

15

20

25

30

-3.2

0248

001

-2.6

8168

0004

-2.1

6087

9998

-1.6

4007

9992

-1.1

1927

9986

-0.5

9847

998

-0.0

7767

9974

0.44

31200

32

0.96

39200

38

1.48

47200

43

2.00

55200

49

2.52

63200

55M

ore

Bin

Fre

qu

en

cy

Frequency

Graph 8: Histogram

Although there are a few outliers in the histogram data, these outliers are usually omitted,

thus making the histogram normally distributed. Using the Jarque-Bera test to double

check on normality, the answer backs up the histogram as it is more than 0.05 which

means we cannot reject the null hypothesis, thus making it normally distributed.

In order to test for Heteroscedasticity and Serial Correlation, a graph was constructed

from the residuals in the regression.

12

Heteroscedasticity

-5

0

5

10

15

20

25

9/1/

2002

12/1

/200

2

3/1/

2003

6/1/

2003

9/1/

2003

12/1

/200

3

3/1/

2004

6/1/

2004

9/1/

2004

12/1

/200

4

3/1/

2005

6/1/

2005

Time

Val

ues

Volume

Predicted Volume

Residuals

Graph 9: Heteroscedasticity

From the Graph 9, homoscedasticity can be seen instead of heteroscedasticity or a serial

correlation. Thus, two further tests have been conducted to test for heteroscedasticity and

serial correlation. For testing heteroscedasticity, a white test had been used, and the result

of the test too proved that there is no heteroscedasticity. For serial correlation on the other

hand, Durbin-Watson test had been used, and the result shows that there are no serial

correlation.

After deleting the 3 variables, only 3 variables are left. Redoing the regression with the

remaining variables (temperature, DrinkMe price and DrinkMe ad stock), the adjusted R2

is 0.838633657 as shown in table 2.

Regression StatisticsMultiple R 0.915769435R Square 0.838633658Adjusted R Square 0.835271859Standard Error 1.305366564Observations 148

  CoefficientsStandard

Error t Stat P-valueIntercept 63.46456658 3.031207128 20.93706035 1.55237E-45DRINKME AVEP -3.775534315 0.323850659 -11.65825731 1.55599E-22ADVERTISING 0.063328017 0.019739416 3.208201203 0.001646425AVETEMP_QLD -0.605418237 0.03121047 -19.39792144 5.30031E-42

Table 2: Regression of final variables

13

This indicates that the postulated regression model explains 83.86% of the total variation

of the sample observations of the dependent variable. This shows that the three variables

that had been removed do not give a big impact to Drink Me sales volume. From the

regression model, price and temperature is negatively correlated, if there is a drop in price

or temperature by 1, the effect of Drink Me sales volume would increase by 0.6 tonnes

from temperature and 3.78 tonnes from price. Advertising on the other hand, has a

positive correlation between Drink Me sales volume and Ad Stock (the effect of media

spent). If there is an increase in ad stock by 1, there would be an increase in Drink Me

sales volume by 0.063 tonnes. Therefore it explains that if introducing the TPR DrinkMe

can increase their sales and by increasing advertising they can also boost their sales. As

explained earlier that by removing the 3 variables, the R square is still strong it can be

said that the competitor’s media strategy doesn’t affect DrinkMe’s sales volume that

much. Same concept can be applied for other competitor’s variables.

By removing the 3 data, a histogram has been drawn to see whether the assumptions of

normal distribution still holds. From Graph 10, it can be seen that the residuals are still

normally distributed. A Jarque-Bera test has been done again and this time, the results

also indicates that the normal distribution still prevails. The summery Jarque-Bera test

has been included in the appendix.

14

Histogram

0

5

10

15

20

25

30

-3.1

3502

957

-2.6

1842

0796

-2.1

0181

2022

-1.5

8520

3248

-1.0

6859

4474

-0.5

5198

57

-0.0

3537

6925

0.48

1231

849

0.99

7840

623

1.51

4449

397

2.03

1058

171

2.54

7666

945

Mor

e

Bin

Fre

qu

en

cy

Frequency

Graph 10: Histogram of final variables

Lastly for the three variables to see whether there is any heteroscedasticity and serial

correlation a line plot has been done as in Graph 11.

15

Heteroscedasticity

-5

0

5

10

15

20

25

9/1/

2002

12/1

/200

2

3/1/

2003

6/1/

2003

9/1/

2003

12/1

/200

3

3/1/

2004

6/1/

2004

9/1/

2004

12/1

/200

4

3/1/

2005

6/1/

2005

Time

Val

ue

Volume

Predicated Volume

Residuals

Graph 11: Heteroscedasticity of final variables

Once again the plot shows that there is no heteroscedasticity or serial correlation. To

further check for heteroscedasticity a white test has been done which agrees that there is

no heteroscedasticity. For serial correlation, Durbin-Watson test has been which also

concludes that there is no serial correlation.

16

5.0 ANALYZING THE EFFECT OF A $1 REDUCTION IN AVERAGE PRICE

(AVEP) ON SALES VOLUME

Now that the final 3 variables that have a direct impact on Drink Me Sales Volume have

been identified, GoodsCo can look at how these variables effect sales volume. The first

variable under analysis is the average price.

Y = 63.46 – (3.78*AVEP)+(0.06*Ad Stock)-(0.61*Ave Temp)

The equation above represents the relationship between the three independent variables

and the dependant variable (sales). A $1 reduction in the average price of Drink Me

would cause an increase of 3.78 tonnes in Drink Me’s Sales Volume. However to truly

examine the effects of a price reduction in drink me, the Sales Value of Drink Me has to

be calculated.

PRICE REDUCTION 0.00 1.00 AVEP 9.16 8.16 ADSTOCK 4.31 4.31 AVETEMP 24.25 24.25 VOLUME 14.45 18.23 VALUE 132.46 148.83      MARGINAL DIFFERENCE   16,371.60

Table 3: Effect of $ 1 TPR on Sale Value

Table 3 shows how a $ 1 price reduction causes an increase of $ 16,371.60 in Drink Me

Sales Value.

17

PRICE REDUCTION 0.00 1.00 2.00 2.67 3.00 5.00 6.00AVEP 9.16 8.16 7.16 6.49 6.16 4.16 3.16ADSTOCK 4.31 4.31 4.31 4.31 4.31 4.31 4.31AVETEMP 24.25 24.25 24.25 24.25 24.25 24.25 24.25VOLUME 14.45 18.23 22.00 24.53 25.78 33.33 37.11VALUE 132.46 148.83 157.65 159.34 158.92 138.81 117.42 MARGINAL DIFFERENCE 16.37 25.19 26.88 26.46 6.35 (15.04)  16371.60 25192.13 26877.44 26461.59 6347.31 (15036.44)

Table 4: Marginal Difference in Sales Value Resulting from TPR

As depicted in Table 4 the maximum marginal difference in sales value is obtained from

a price reduction of $2.67. The total increase in sales value at this point is $ 26,877.44.

This would mean that reducing Drink Me’s average price of $9.16 to $6.49 would

increase sales by $26,877.44.

AVEP

7.50

8.00

8.50

9.00

9.50

10.00

10.50

9/1/

02

11/1

/02

1/1/

03

3/1/

03

5/1/

03

7/1/

03

9/1/

03

11/1

/03

1/1/

04

3/1/

04

5/1/

04

7/1/

04

9/1/

04

11/1

/04

1/1/

05

3/1/

05

5/1/

05

Time

AV

EP

AVEP

Graph 12: Historic l Drink Me Prices

18

Unfortunately, such a large reduction may not be reasonable for Drink Me. As shown in

Graph 12 historically Drink Me’s lowest price in its TPR activity was around $ 8.00.

Profitability

(120.00)

(100.00)

(80.00)

(60.00)

(40.00)

(20.00)

0.00

20.00

40.00

0.20 0.40 0.60 0.70 0.80 1.00 1.20 1.40 1.50 1.60 2.00 2.20 2.40

TPR

PM

%

Profitability

Graph 13:Profitability of TPR Promotion

By calculating the cost of the TPR activity (Expected Sales Volume * Price Reduction)

GoodsCo can also calculate the profitability of a price reduction.

Profit Margin % = [ Marginal Difference - Cost of TPR Activity ] / Marginal Difference

Graph 13 shows how the profitability of a TPR activity has a negative relationship with

price reduction. As price is reduced the profitability of the activity also reduces. This is

because while there is extra sales value created through TPR there is also additional cost.

Unfortunately the additional cost grows at an increasing rate when compared to the

growth in sales value.

Thus the break even point of a TPR activity is at around a price reduction of $ 0.70. Thus,

a marketing director who wishes to carry a TPR promotion has to be very cautious. The

first question to be answered is the objective of carrying out a TPR promotion. If the

objective is to increase company profitability then the marketing director should have a

price reduction of about $ 0.20. However, if the objective of the TPR activity is to

reposition the company as a price competitive company then the marketing director has

19

to decide the trade off he or she is willing to make in the repositioning activity. Every

increase in price reduction will bleed the company of profits.

20

6.0 ANALYZING THE EFFECT OF A $100,000 MEDIA CAMPAIGN SPLIT

EVENLY OVER 10 WEEKS

To analyze the effects of a media campaign worth $ 100,000 allowance has to be made to

examine the residual effects on AdStock. A 10 week media campaign would require the

analysis to be made over an additional 10 weeks to account for the residuals effects of

AdStock.

Week Media Adstock Avep AveTemp Sales VolVol

Base Case MD VolMD Value

(000)MD Value

($)1 10 10 9.16 24.25 14.81 14.18 0.63 5.80 5803.662 10 15 9.16 24.25 15.13 14.18 0.95 8.71 8705.493 10 17.5 9.16 24.25 15.29 14.18 1.11 10.16 10156.404 10 18.75 9.16 24.25 15.37 14.18 1.19 10.88 10881.865 10 19.38 9.16 24.25 15.41 14.18 1.23 11.24 11244.596 10 19.69 9.16 24.25 15.43 14.18 1.25 11.43 11425.967 10 19.84 9.16 24.25 15.44 14.18 1.26 11.52 11516.648 10 19.92 9.16 24.25 15.44 14.18 1.26 11.56 11561.989 10 19.96 9.16 24.25 15.44 14.18 1.26 11.58 11584.6510 10 19.98 9.16 24.25 15.45 14.18 1.27 11.60 11595.9811 0 9.99 9.16 24.25 14.81 14.18 0.63 5.80 5797.9912 0 5.00 9.16 24.25 14.50 14.18 0.32 2.90 2899.0013 0 2.50 9.16 24.25 14.34 14.18 0.16 1.45 1449.5014 0 1.25 9.16 24.25 14.26 14.18 0.08 0.72 724.7515 0 0.62 9.16 24.25 14.22 14.18 0.04 0.36 362.3716 0 0.31 9.16 24.25 14.20 14.18 0.02 0.18 181.1917 0 0.16 9.16 24.25 14.19 14.18 0.01 0.09 90.5918 0 0.08 9.16 24.25 14.19 14.18 0.00 0.05 45.3019 0 0.04 9.16 24.25 14.18 14.18 0.00 0.02 22.6520 0 0.02 9.16 24.25 14.18 14.18 0.00 0.01 11.32

100 116.06 116061.87

Table 5: Effects of A Media Campaign

By calculating the total volume of a base case (absent media spending) the marginal

difference in sales value from a media campaign can be calculated. As the table above

shows, there is a residual effect after the media campaign of 10 weeks is over.

21

Effects of Media Spending

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Week

$

Adstock

Marginal Difference

Graph 14: Effects of Media Spending

Graph 14 shows the effects of media spending on sales value and Ad Stock. The effects

of the media also diminish after 20 weeks. More importantly, the full effect (highest

marginal difference) is at the 10th week of the media campaign where the marginal

difference is $11,595.98.

 Media Campaign

Without Media Campaign

Cost 100 0Sales Value 2715.20 2599.14Profit 2615.20 2599.14Profit Margin (%) 96.31702806 100

Table 6: Profitability of a Media Campaign

Table 6 compares the profitability of the media campaign compared to operations without

a media campaign. If operations without a media campaign is treated without cost then it

enjoys a profit margin of 100%. When media campaign is carried out the profit margins

reduce to 96.32%. This shows that the media campaign actually reduces the ratio of per

dollar profitability by around 3.68%.

22

By taking the total marginal difference in sales value and subtracting the cost of the

media campaign we can calculate the profitability the extra sales generated by the media

campaign. This would be the profitability of each extra dollar earned by the media

campaign.

Profitability % = [ Total Marginal Difference – Cost of Campaign ] / Marginal

Difference = (116061.87- 100000)/ 116061.87

= 13.84%

A media campaign spread evenly over 10 weeks results in a profit of 13.84% after taking

into account the residual effects of Ad stock.

23

7.0 COMPARING PRICE PROMOTIONS AND ADVERTISING

Activity Marginal Difference

Cost of Activity

Profit ($)

Profit Margin (%)

TPR 3895.59 3042.11 853.47 21.91Advertising 116061.87 100000.00 16061.87 13.84

Table 7: Comparing Profitability

As shown in table 7 advertising provides a more profitable outcome in total sales value.

An expenditure of $ 100,000 over 10 weeks results in additional sales of up to $

116,061.87. After taking into account the cost of advertising a profit of $16,061.87. This

represents a profit margin of 13.84% for Drink Me.

Price promotion on the other hand gives the marketer the power of discretion. Marketers

can chose the level of profit required or they can choose to bear a loss in effort of

conducting a repositioning activity or for an objective similar to it. A price reduction of

only $ 0.20 results in additional sales of $ 3,895.59. After taking into account the cost of

this price promotion Drink Me makes a profit of $853.47 representing a profit margin of

21.91%. However, a price reduction of $ 2.67 (which is the maximum marginal

difference) creates a loss of 145%.

Determining whether to use one marketing activity or both activity proves difficult.

Advertising creates high sales value while price promotion are more profitable per dollar

spent.

Activity Marginal Difference

Cost of Activity

Profit Profit Margin

TPR 3895.59 3042.11 853.47 21.91Advertising 116061.87 100000.00 16061.87 13.84

Total 119957.46 103042.11 16915.34 14.10112

Table 8: Profitability Of A Combination Of Two Marketing Activities

The above table shows that combining the two marketing activities only makes a slight

increase in profit margins for advertising. A combination of both activities proves to be

more profitable in total value than just a single marketing activity. The marketing strategy 24

should combine a suitable price reduction which is complimented by an aggressive media

campaign that promotes the price reduction conducted. Without the proper

advertisements nobody will know of the price reduction effecting in ineffectiveness.

Also the strategy could be for media campaigns to be carried out when price promotions

are not. A constant application of either one of the marketing activities will guarantee

higher sales for GoodsCo.

However, if a marketing director was forced to chose either one of the marketing

activities based on the information from the analysis conducted Goods Co is advised to

adopt advertising to increase sales. Its low profit margins are just a smoke screen to the

high sales value it creates.

One of the main reason advertising is chosen instead of price promotion is because of its

long term benefits. While price promotions allow for quick short term benefits sooner or

later competitive firms will adopt the same price reductions and Goods Co competitive

advantage through low prices will be lost once again. Advertising on the other hand can

give Goods Co an immortal competitive advantage if the advertising activity is carried

out properly. This means, clear messages to selected target markets. For instance, Coke is

a consumer product similar to Drink Me that has obtained seemingly immortality in the

soft drink industry. It achieved this through successful advertising that positioned itself as

a lifestyle drink. Its taste is not unique because it has been duplicated over the years by

various smaller firms. However, it maintains and grows its market share through

continuous advertising.

In fact advertising can potentially give the marketer the power to increase prices if done

properly. Advertising allows marketers to reposition their products. If repositioning

efforts are done properly consumers will willingly pay more for a product. For instance,

the coffee served at Starbucks is very similar to the coffee served at the usual cafes.

However, due to the correct advertising activities Starbucks has positioned its coffee as a

lifestyle product that consumer are willing to pay for. Starbucks is one of the fastest

25

growing franchises in the world today and it owes its success to the niche position it has

acquired for itself.

These are the reasons why Goods Co should adopt advertising instead of price

promotions. Focus should be on long term returns not short term profitability.

26

8.0 CONCLUSION

In conclusion it has been seen how out of all the variables in the model, only three

variables stand out. Drink Me’s average price, their advertising and the average

temperature turned out to be the major factors in determining the sales of DrinkMe.

Therefore it made sense on how to alter the price and the advertising of DrinkMe can

actually result in a boost in their sales.

However like every other model there are also limitations in this model. Firstly even

though the regression output has revealed that competitors’ activity does not affect

DrinkMe’s sale but in reality the promotion of the competitors as well as their price place

an important part in determining the sales volume of DrinkMe. Secondly it is also a

matter of question whether the data that has been used actually represents the whole

population. Such as only extracting data from EPOS of Woolworths and Coles

supermarket can only provide half of the picture. People who do not shop in these

markets will be left out. Another limitation could be that for media campaign, sales have

been forecasted for 20 weeks. In 20 weeks, competitors might come up with more

desirable drink in which case it would actually affect the sales volumes of DrinkMe.

Therefore all these should be taken into account before going ahead with TPR and media

campaign.

For future research, it would be advisable if panel data as well as qualitative data can be

collected instead of time series. Qualitative data would give more insight on the behavior

of consumers both before and after the TPR and media campaign. Based on that, further

research can be done on how DrinkMe can have an edge over their competitors.

27

28