252  Analyzing Performance
Using this formula, determine whether prices or quantities are driving 
growth. Do not, however, confuse revenue per unit with price; they can be 
different. If revenue per unit is rising, the change could be due to rising prices, 
or the company could be shifting its product mix from low-price to high-price 
items.
The operating statistics that companies choose to report (if any) depend 
on the industry’s norms and competitors’ practices. For instance, most retail-
ers provide information on the number of stores they operate, the number of 
square feet in those stores, and the number of transactions they conduct an-
nually. By relating different operating statistics to total revenues, it is possible 
to build a deeper understanding of the business.
Consider this retailing standard:
Revenues
Revenues
Stores
Stores
=
×
Exhibit 12.9 reports disguised operating statistics for two big-box retail-
ers we’ll call Delta and Gamma. Using the operating statistics reported in 
Exhibit 12.9, we discover that Delta has more stores than Gamma and 
­generates more revenue per store ($47 million per store for Delta in 2018 
versus $37 million for Gamma). Using the three operating statistics, it is 
possible to build ratios on revenues per store, transactions per store, square 
feet per store, dollars per transaction, and number of transactions per 
square foot.
Although operating ratios are powerful in their own right, what can really 
change one’s thinking about performance is how the ratios change over time. 
Exhibit 12.10 organizes each ratio based on Exhibit 12.9 into a tree. Rather than 
report a calculated ratio, such as revenues per store, however, we report the 
growth in the ratio over the period analyzed and relate this back to the growth 
in revenue.
EXHIBIT 12.9  Hypothetical Retailers: Operating Statistics
Delta
Gamma
Reported
2016
2017
2018
2016
2017
2018
Revenues, $ million
51,081
54,488
58,430
35,109
37,054
38,507
Average number of stores
1,229
1,232
1,234
1,076
1,070
1,050
Number of transactions, millions
834
853
875
510
515
511
Average square footage, millions
90
90
90
85
86
85
Derived
Revenues per store, $ million
42
44
47
33
35
37
Transactions per store, thousands
678
692
709
474
481
487
Revenues per transaction, $
61
64
67
69
72
75

Credit Health and Capital Structure  253
As the exhibit demonstrates, Delta grew faster than Gamma, because 
Gamma closed stores while Delta slightly increased the number of stores, and 
Delta grew revenues per store faster than Gamma. Growth in revenues per 
store is the key driver for these two companies, because the category is near 
full penetration. This growth in same-store sales is extremely important, to 
the point that financial analysts have a special name for growth in revenue 
per store: comps, shorthand for comparables, or year-to-year same-store sales.7 
Why is this revenue growth important? First, the number of stores to open is 
an investment choice, whereas same-store sales growth reflects each store’s 
ability to compete effectively in its local market. Second, new stores require 
large capital investments, whereas growth in comps requires little incremental 
capital. Hence, same-store sales growth comes with higher capital turnover, 
higher ROIC, and greater value creation.
Moving farther right in the tree, we gain additional insight into what 
has been driving same-store sales for each company. Delta also generated 
more foot traffic, increasing transactions per store at 2.5 percent versus 
Gamma’s 1.2 percent. Revenues per transaction grew at the same rate for 
the two stores.
Credit Health and Capital Structure
To this point, we have focused on the operating performance of the com-
pany and its ability to create value. We have examined the primary drivers of ­ 
7 In Exhibit 12.10, we present the change in revenues per store. This value differs from comparable-
store sales reported by each company, which includes only stores that were open for at least 13 months.
EXHIBIT 12.10  Hypothetical Retailers: Organic Revenue Growth Analysis, 2018
Growth rates, %
Delta 
7.2
Gamma 
3.9
Revenue
growth
Delta 
7.0
Gamma 
5.9
Revenues/store
Delta 
0.2
Gamma 
–1.9
Number of stores
Revenues/
transaction
Delta 
2.5
Gamma 
1.2
Transactions/store
Delta 
4.5
Gamma 
4.7