Applying Value Drivers to Monitor Performance  561
manufacturing error rate. These are important because invested capital is fixed 
over the next several years, and labor and raw materials costs per unit are very 
high. In contrast, Exhibit 29.6 shows a value driver tree for a grocery retailer. In 
this very different example, the key value drivers for gross margin are the aver-
age basket size (the number of transactions per square foot is important but al-
ways has an upper limit) and the markdown percentage on product prices. For 
operating costs, labor productivity is key, as most other components are fixed 
in the near term. Similarly, within invested capital, inventory level is one of 
the key value drivers; again, most other components are fixed in the near term.
How do you tailor the tree to get such insights? Our experience has taught 
us that developing different initial versions of trees based on different hy-
potheses and business knowledge will stimulate the identification of uncon-
ventional sources of value. The information from these versions should then 
be integrated into one tree (or in some cases, a few trees) that best reflects the 
understanding of the business.
To illustrate this process, we apply it to a hypothetical company running 
a chain of bicycle repair shops. Exhibit 29.7 shows four different approaches 
Exhibit 29.6  Basic Value Driver Tree: Grocery Retailer
ROIC
NOPAT1
Invested 
capital
Gross margin
contribution
Operating 
costs
Taxes
Fixed
assets
Net working
capital
Transactions per 
square foot
Average basket size
Markups
Markdowns
Shrinkage
Labor
Rent
Depreciation
Other
Revenues per square foot
Square footage
Gross margin per revenues
Central costs
Store costs
Land and buildings
Fixtures and equipment
IT
Other
Inventory
Cash
Debtors
Creditors
Key value drivers
1 Net operating profit after taxes.

Exhibit 29.7  Alternative Value Driver Trees for a Bicycle Repair Company
Traditional P&L tree
Location value tree
Customer value tree
Segment value tree
Value
ROIC
Growth
Costs
Capital
Revenue
Value
Number of
shops
Economic profit 
per shop
Value
Value of
customer growth
Value of
customer base
Number of
customers
Number of
mechanics
Operating profit
per mechanic
Utilization
Capital charge 
per shop
NPV1 per 
customer
Customer
growth
Customer 
acquisition costs
Annual margin on
customer service
revenues2
Average customer
lifetime
Cost of
capital
Value
Economic profit,
traditional bikes
Market share
Economic profit,
e-bikes
Size of e-bike
repair market
Margin on e-bike
repair revenues2
1 Net present value.
2 Including capital charge.
562

Applying Value Drivers to Monitor Performance  563
to developing the short-term portion of a value driver tree for this company. 
We used these trees to develop the summary short-term value driver tree 
shown in Exhibit 29.8. Adopting the most useful insights provided by the 
original four approaches, this tree combines the location and customer value 
driver trees.
Managers often expect that the most natural and easiest-to-complete tree 
is one based on a profit-and-loss (P&L) structure. Such a tree, however, is 
unlikely to provide the insight gained by looking at the business from the 
perspective of a customer, a shop location, or some other relevant vantage 
point. For example, in most parts of the world, fuel service stations create 
much more value per customer from selling food and beverage products 
than fuel. As a result, the conversion of station visits into food and bever-
age sales is an even more important value driver than the number of station 
visits itself.
When you develop value driver trees, pay particular attention to the driv-
ers of growth, because of the lag time between investment in developing a 
growth opportunity and the eventual payoff. Lag times for opportunities will 
differ. Continuing the example of the bicycle repair company, Exhibit 29.9 il-
lustrates a value tree created for developing business in a new geographic 
market. For this opportunity, important value drivers include those associ-
ated with building the customer base (such as market share, revenues per 
customer, customer acquisition costs, and number of shops per customer) and 
improving employee productivity in the new geography (the number of me-
chanic hours per dollar of revenues), both of which take time to achieve.
Exhibit 29.8  Combined Location and Customer Value Driver Trees: Bicycle Repair 
Company
Value
Economic profit
per shop
Number of
shops
Capital charge
per shop
Number of 
customers per shop
Operating profit
per customer
Share of shops
in geography
Total number of
shops in geography
Operating margin
on customer
service revenues
Customer
acquisition costs
Other cost per
service revenues
Labor cost per
service revenues
Service revenues
per customer
Mechanic cost
per hour
Mechanic hours per
service revenues