Chapter–by–chapter aids: Appendix B
Instructor’s Manual to Accompany Essentials of Marketing IV-B-1
APPENDIX B: MARKETING ARITHMETIC
APPENDIX B – COMMENTS ON QUESTIONS AND PROBLEMS
B- 1. a. Net sales plus returns and allowances equal gross sales.
b. Purchases at billed cost, plus transportation, less discounts, equal purchases at net cost.
B- 2. Gross margin and gross profit are the same. Net profit occurs after subtraction of all expenses.
B- 3. Markups usually exceed gross margin because some markdowns must be expected. Markdowns
do not appear on operating statements.
B- 4.
Gross sales
$1,300,000
Less: Sales returns and allowances
250,000
Net sales
$1,050,000
Cost of sales:
Beginning inventory at cost
$ 150,000
Purchases at billed cost
$ 330,000
Plus: Freight–in
80,000
Net cost of delivered purchases
410,000
Cost of goods available for sale
560,000
Less: Ending inventory at cost
250,000
Cost of sales
310,000
Gross margin
$ 740,000
Expenses:
Rent
$ 60,000
Salaries
400,000
Heat and light
180,000
640,000
Net Profit
$ 100,000
B- 5.
Gross sales
$6,150,000
102.5%
Returns and allowances
150,000
2.5
Net sales
$6,000,000
100.0
Cost of sales:
Beginning inventory
$4,070,000
Purchases
1,000,000
Transportation
30,000
Cost of goods available for sale
$5,100,000
Closing inventory
600,000
Cost of sales
4,500,000
75.0
Gross margin
$1,500,000
25.0
Expenses
1,200,000
20.0
Net Profit
$ 300,000
5.0
Part IV
Chapter–by–chapter aids: Appendix B
B-13. The factor method of forecasting tries to forecast sales by finding a relationship between the
company’s sales and some other factor (or factors). For example, sales lumber, brick and other
materials used in residential construction are often related to the interest rate for home
mortgages. Also, see the discussion of the Sales & Marketing Management Survey of Buying
Power and related discussion in section “Forecasting Company and Product Sales by
Extending Past Behavior.”
The population of the city of Boulder (97.1 thousand) is only 65.6% of the population of
Lakewood (148.0 thousand). Therefore, the effective buying power for Boulder
($2,480,204,000) compared to that for Lakewood ($3,451,207,000) is smaller as would be
expected because of the population difference. But let’s take a closer look. If we go a step
further and consider the effective buying power per capita in each location, we discover that
(on a per capita basis) the effective buying power for a Boulder resident is actually higher than
It becomes clear that this sort of difference can be meaningful when we look at retail sales. In
contrast to the case with EBI, the total retail sales for Boulder ($2,147,663,000) are higher than
for Lakewood ($2,065,827,000), even though Boulder has the smaller population. When we
consider retail sales per capita (by dividing these figures for retail spending by the respective
populations of each city), we see that residents of Boulder spend significantly more
(a) Prepared cereals: If we look at food sales for the two cities, we can see that Boulder’s
figure of $439,133,000 is much higher than Lakewood’s figure of $297,529,000 (even though
there are fewer people in Boulder doing the spending). This tells us that residents of Boulder
spend significantly more for food than residents of Lakewood. If we go a step further (as
above) and calculate per capita figures, we can see that Boulder residents are spending 125%
Part IV
(c) Furniture: If we look at furniture sales for the two cities, we can see that Boulder’s figure of
$117,520,000 is less than Lakewood’s figure of $124,879,000. But (as in our auto sales
discussion above) this nevertheless indicates that Boulder residents are spending more per
capita on furniture than are Lakewood residents. Doing the calculations, we see that Boulder
residents are spending 43% more than Lakewood residents for furniture ($1,210.30 for Boulder
versus $843.78 for Lakewood).
As we surmised earlier, these differences can be explained by the income advantage in
Boulder. This reinforces the idea that secondary data can be very useful, but it also shows that
one should be cautious in interpreting such data. For example, one might want to look at
demographic characteristics in the context of the whole competitive environment. For this
reason, it might be desirable to ask students to look at data for communities nearer to their
residences–to obtain a better appreciation of the kind of data that is available and the