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8/11/2019 8. Site Location
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PPT 8-1
5thEdition
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PPT 8-2
McGraw-Hill/IrwinLevy/Weitz: Retailing Management, 5/e Copyright 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Site Location
Chapter 8
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PPT 8-3
Retailing Strategy
Retail LocationsChapter 7
Site LocationsChapter 8
Human ResourceManagement
Chapter 9
Information andDistribution
SystemsChapter 10
CustomerRelationshipManagement
Chapter 11
Retail Market andFinancial Strategy
Chapter 5, 6
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PPT 8-4
Location Chapters
Chapter 7
General Description of the Location Types
Advantages and Disadvantages of Different Location
AppendixTerms and Condition Involved in LeasingSites
Chapter 8
Considerations in Selecting Area for Locating Store
Issues in Evaluating Specific Sites
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PPT 8-5
Three Levels of Analysis
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PPT 8-6
Trade Area Issues
Which Trade Areas Are Most Attractive forLocating Retail Outlets?
How Many Outlets to Locate in a Trade Area? More Stores Increases Economies of Scale and
Reduces Costs
More Stores also Results in More Cannibalizationand Less Sales per Store
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Factors Affecting Demandfor a Region or Trade Area
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Factors Affecting theAttractiveness of a Site
How Attractive Is the Site to the RetailersTarget Market?
Match Between Trade Area Demographics and
Retailers Target Market Likelihood of Customers Coming to Location
Convenience
Other Attractive Retailers At LocationPrinciple of cumulative attraction- a cluster of similar
and complementary retailing activities will have greaterdrawing power.
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Convenience of Going to Site
Accessibility
Road pattern and condition
Natural and artificial barriers
Visibility
Traffic flow
Parking
Congestion
Ingress/egress
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In High Traffic Areas
Near Anchor
Center of Shopping Area Near Stores Selling Complementary
Merchandise
Clustering Specialty Stores Appealing toTeenagers
Better locations cost more
Location Within a Center
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Map of Dallas North Park Center
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Estimating Demand for a New Location
Definition of the Trade Area
Primary, Secondary, Tertiary Zones
Approaches for Estimating Demand
Analog Approach
Regression Approach
Huff Gravity Model
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PPT 8-13
Trade Area
Primary zone- 60 to 65 percent of its customers
Secondary zone- 20 percent of a stores sales
Tertiary zone- customers who occasionally shopat the store or shopping center
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PPT 8-14
Factors Defining Trade Areas
Accessibility
Natural & Physical Barriers
Type of Shopping Area
Type of Store
Competition
Parasite Stores
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PPT 8-15
Oblong Trade Area Caused byMajor Highways and Natural Boundaries
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PPT 8-16
Sources of Information
Customer Spotting
Census Data
Geodemographic InformationSystems
ACORN
Information on Competition
Yellow Pages
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PPT 8-17
Customer Spotting
Purpose: to spot, or locate, the residences ofcustomers for a store or shopping center.
How to obtain data:credit card or checks
customer loyalty programs
manually as part of the checkout process
automobile license plates
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PPT 8-18
Census Data of the U.S.
.
Only once in 10 years.
Each household in the country iscounted to determine the numberof persons per household,household relationships, sex, raceage and marital status.
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PPT 8-19
Geodemographic Information Systems
Demographic data vendorsspecialize inrepackaging and updating census-type data.
Geographic Information System(GIS) is a
computer system that enables analysts tovisualize information about their customersdemographics, buying behavior, and other data ina map format.
GIS is a spatial database that stores the location andshape of information.
Analysts can identify the boundaries of a trade areaand isolate target customer groups
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PPT 8-20
Indices for Assessing Sales Potential
Market Potential Index (MPI)
Number of Households Purchasing a Product orService in a Trade Area
Spending Potential Index (SPI)
Average Amount Spent on a Product or Service by aHousehold in a Trade Area
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PPT 8-21
Sources for Measuring Competition
The Internet- lists current locations and futuresites.
Yellow Pages
Other Sources: Directories published by tradeassociations, chambers of commerce, ChainStore Guide, International Council of Shopping
Centers, Urban Land Institute, local newspaperadvertising departments, municipal and countygovernments, specialized trade magazines, listbrokers
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PPT 8-22
Measuring Competition
Calculate total square footage of retail space
devoted to a type of store per household
Higher ratios will indicate higher levels of
competition
C titi A l i f
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PPT 8-23
Competitive Analysis forEdward Breiner
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PPT 8-24
Methods for Estimating Demand
Analog Approach
Multiple Regression Analysis
Huffs Model
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PPT 8-25
The Analog Approach
1. Current trade area is determined by using thecustomer spotting technique.
2. Based on the density of customers from the store, theprimary, secondary and tertiary trade area zones aredefined.
3. Match the characteristics of our current store with thepotential new stores locations to determine the best
site.
3 Steps:
I Di ib i f Th Mil
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PPT 8-26
Income Distribution of Three-MileRing Surrounding Edward Breiner Optical
D hi T d f Th Mil
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PPT 8-27
Demographic Trends for Three-MileRing Surrounding Edward Breiner Optical
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PPT 8-28
ACORN Neighborhood Lifestyle Clustersfor Three-Mile Ring
Breiner Optical
D i ti f L t PRIZM
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PPT 8-29
Descriptions of Largest PRIZMClusters Surrounding Edward Breiner Optical
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PPT 8-30
Description of Largest PRIZMClusters Surrounding Edward Breiner Optical
D i ti f L t PRIZM
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PPT 8-31
Description of Largest PRIZMClusters Surrounding Edward Breiner Optical
D i ti f L t PRIZM
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PPT 8-32
Description of Largest PRIZMClusters Surrounding Edward Breiner Optical
D i ti f Ed d B i O ti l
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PPT 8-33
Descriptions of Edward Breiner Opticaland Four Potential Locations Trade Areas
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PPT 8-34
Multiple Regression Analysis
Need to define the retail trade area potentialfor retail chains with greater than 20 stores.
Similar to the analog approach, it usesstatistics rather than judgement to predictsales for a new store.
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PPT 8-35
Multiple Regression Steps
Current trade areas are determined by usingthe customer spotting technique
Primary, secondary, and tertiary zones aredetermined by plotting customers on a map
Select appropriate measures ofperformance, such as per capita sales ormarket share.
Select a set of variables that may be usefulin predicting performance.
Solve the regression equation and use it toproject performance for future sites.
Yearly Sales Population and
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PPT 8-36
Yearly Sales, Population, andIncome for 10 Home Improvement Centers
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Regression of Population on Sales
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PPT 8-38
Illustration of Regression Approach
1. Specify Regression ModelIdentify Critical Predictorsof Store Sales
Sales = B0+ B1x X1+ B2x X2
X1 = population in trade area
X2 = average household income in tradearea
2. Estimate Weights - B0,B1, B2
3. Use Estimated Weights to Forecast salesSales = -144,146 + 6,937 x X1+ 10,132 x X2
Sales = -144,146 + 6,937 x 55,000 + 10,132 x 28,000 = $521,085
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PPT 8-39
Huffs Gravity Model
Based on the premise that the probability that agiven customer will shop in a particular store
or shopping center becomes larger as the size
of store or center grows and distance ortravel time from customer shrinks
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PPT 8-40
Huffs Model Formula
tripsshoppingofkinds
differentontimetravelofeffectthereflectsthatoexponent tAn
center
shoppingpoint tostartingscustomer'fromdistanceortimeTravel
centershoppingofSize
centershoppingparticular
atotravelingoriginofpointgivenaatcustomeraofyProbabilit
Where
ijTb
ijT
jj S
j
iijP
n
1j
bijTjS
bijTjSijP
University and Shopping Centers:
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PPT 8-41
University and Shopping Centers:Gravity Model Illustration
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Huffs Model: The Solution
Pi j = 1000 32
(1000 32) + (500 52) + (100 12)
Probability = .48
.48 x 12,000 students = 5,760 customers
5,760 customers x $150 = $864,000
Repeat steps 1 to 3 for the remaining areasand then sum them.