Forex

Each of these three metrics measures the productivity of the
sales effort, how well a company is spending its sales dollar.
They’re important measures and easy enough to calculate,
although often hard to influence. Each of these is used when
appropriate, based on the sales model. All can be useful in a
retail environment. Some would not be useful outside of a retail
business. Let’s look at each of these briefly.
Sales per customer can be useful when a company finds its
cost to process an order is fixed or at least controllable. In that
case, it can increase profit significantly if it can increase the
average amount a customer buys, because there may be little
or no increase in the costs of making the sale (beyond the actual
cost of the merchandise, of course).
Sales per employee is most useful when the department or
company is strongly sales-driven. Retail sales organizations

often fall into this category. In some companies, the entire
organization is encouraged to think in terms of sales, while in
other companies the Sales Department is the prime mover.
However this one is used, it helps when assessing the effect on
sales of adding another employee or when comparing one
branch office with another or one division with another. When
applying this measure, CEOs need to be careful to recognize
the differences and similarities among departments or divisions.
Some business models are different enough that they cannot
efficiently be compared on a sales-per-employee basis, and to
do so would inhibit one or the other from operating most effectively
in its market.


Sales per square foot is a metric used pretty exclusively in
retail establishments, where stores must use every foot of space
productively, space is limited, and the contribution of a product
display can be measured in how much sales it produces per
foot of space it occupies. This is very commonly used by the
management of chain stores to compare the productivity of one
store’s management with another. Again, absolutes may not be
possible because of the different locations and the demographics
of their areas (higher or lower income, younger or older, blue
collar vs. white collar, and so on).