Chapter 9

Report
Trading-Area
Analysis
RETAIL
MANAGEMENT:
A STRATEGIC
APPROACH
11th Edition
BERMAN
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EVANS
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Chapter Objectives
 To demonstrate the importance of store location for a
retailer and to outline the process of choosing a store
location
 To discuss the concept of a trading-area and its related
components
 To show how trading-areas may be delineated for
existing and new stores
 To examine three major factors: population
characteristics, economic base characteristics, and
competition/level of saturation
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Location, Location, Location
 Criteria to consider include
 population size and traits
 competition
 transportation access
 parking availability
 nature of nearby stores
 property costs
 length of agreement
 legal restrictions
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Figure 9-1: Location and Nine West
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Choosing a Store Location
Step 1: Evaluate alternate geographic (trading)
areas in terms of residents and existing retailers
Step 2: Determine whether to locate as an
isolated store or in a planned shopping center
Step 3: Select the location type
Step 4: Analyze alternate sites contained in the
specific retail location type
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Trading-Area Analysis
A trading-area is a geographic
area containing the customers
of a particular firm or group
of firms for specific goods or
services.
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Benefits of Trading-Area Analysis
 Discovery of consumer  Assessment of effects of
demographics and
socioeconomic
characteristics
 Opportunity to
determine focus of
promotional activities
 Opportunity to view
media coverage
patterns
trading area overlap
 Ascertain whether chain’s
competitors will open
nearby
 Discovery of ideal
number of outlets,
geographic weaknesses
 Review of other issues
(e.g. transportation)
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Figure 9-2: The Trading-Areas of Current and
Proposed Outlets
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GIS Software
 Geographic Information Systems
 Digitized mapping with key location-specific
data used to graphically depict trading-area
characteristics such as



population demographics
data on customer purchases
listings of current, proposed, and competitor
locations
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Figure 9-3(A): GIS Software in Action
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Figure 9-3(B): GIS Software in Action
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Figure 9-3(C): GIS Software in Action
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Figure 9-3(D): GIS Software in Action
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Figure 9-4: The Segments of a Trading-Area
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Figure 9-5: Delineating Trading-Area Segments
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The Size and Shape of Trading-Areas
 Primary trading-area
 50-80% of a store’s customers
 Secondary trading-area
 15-25% of a store’s customers
 Fringe trading-area
 all remaining customers
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Destination Versus Parasite Stores
 Destination stores
 Parasite stores do not
have a better
assortment,
promotion, and
image.
 They generate
trading-areas much
larger than
competitors.
 Dunkin’ Donuts: “It’s
worth the trip!”
create their own traffic
and have no real
trading-area of their
own.
 These stores depend on
people who are drawn
to area for other
reasons.
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Trading Areas and Store Types
Largest
Department stores
Supermarkets
TRADING
AREAS
Apparel stores
Gift stores
Smallest
Convenience stores
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The Trading-Area of a New Store
Different tools must be used when an area is
evaluated in terms of opportunities rather than
current patronage and traffic patterns:
 Trend analysis
 Consumer surveys
 Computerized trading-area analysis models
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Computerized Trading-Area Analysis Models
Analog Model
Regression Model
Gravity Model
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Reilly’s Law
Reilly’s law of retail gravitation—a
traditional means of trading-area
delineation—establishes a point of
indifference between two cities or
communities so that the trading-area of
each can be determined.
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Limitations of Reilly’s Law
 Distance is only measured by major
thoroughfares; some people will travel shorter
distances along cross streets.
 Travel time does not reflect distance traveled.
Many people are more concerned with time
traveled than with distance.
 Actual distance may not correspond with
perceptions of distance.
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Huff’s Law
Huff’s law of shopper attraction
delineates trading-areas on the basis of
product assortment at various shopping
locations, travel times from the
shopper’s home to alternative locations,
and the sensitivity of the kind of
shopping to travel time.
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Table 9-1a: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Population Size and Characteristics
 Total size and density
 Age distribution
 Average educational
level
 Percentage of
residents owning
homes
 Total disposable
income
 Per-capita disposable
income
 Occupation
distribution
 Trends
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Table 9-1b: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Availability of Labor
 Management
 Management trainees
 Clerical
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Table 9-1c: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Closeness to Sources of Supply
 Delivery costs
 Number of wholesalers
 Timeliness
 Availability of product
 Number of
lines
 Reliability of product
lines
manufacturers
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Table 9-1d: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Economic Base
 Dominant industry
 Freedom from
 Extent of
economic and
seasonal fluctuations
 Availability of credit
and financial facilities
diversification
 Growth projections
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Table 9-1e: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Competitive Situation
 Number and size of
 Short- and long-run
existing competition
 Evaluation of
competitor strengths
and weaknesses
outlook
 Level of saturation
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Table 9-1f: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Availability of Store Locations
 Number and type of
 Owning versus leasing
store locations
 Access to
transportation
opportunities
 Zoning restrictions
 Costs
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Table 9-1g: Chief Factors to Consider in Evaluating
Retail Trading-Areas
Regulations
 Taxes
 Minimum wages
 Licensing
 Zoning
 Operations
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Elements in Trading-Area Selection
Population
Characteristics
Economic Base
Characteristics
Nature and Saturation
of Competition
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Figure 9-9: The Census Tracts of
Long Beach, NY
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Table 9-3: Selected Population Statistics for
Trading Areas A and B
Characteristics
Area A
Area B
13,732
15,499
Population change, 1990-2000
+8.2
+2.5
College graduates, 25 +, 2000 (%)
41.4
39.2
$61,236
$61,242
45.3
45.0
Total population, 2000
Median household income, 2000
Managerial and professional
occupations (%), 2000
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