Transcript PPT 8-1

5th Edition
PPT 8-1
Chapter 8
Site Location
McGraw-Hill/Irwin
PPT
8-2
Levy/Weitz:
Retailing Management, 5/e
Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved.
Retailing Strategy
Human Resource
Management
Chapter 9
Retail Market and
Financial Strategy
Chapter 5, 6
Retail Locations
Chapter 7
Site Locations
Chapter 8
Information and
Distribution
Systems
Chapter 10
PPT 8-3
Customer
Relationship
Management
Chapter 11
Location Chapters
• Chapter 7
– General Description of the Location Types
– Advantages and Disadvantages of Different Location
– Appendix – Terms and Condition Involved in Leasing
Sites
• Chapter 8
– Considerations in Selecting Area for Locating Store
– Issues in Evaluating Specific Sites
PPT 8-4
Three Levels of Analysis
PPT 8-5
Trade Area Issues
• Which Trade Areas Are Most Attractive for
Locating 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 Cannibalization
and Less Sales per Store
PPT 8-6
Factors Affecting Demand
for a Region or Trade Area
PPT 8-7
Factors Affecting the
Attractiveness of a Site
• How Attractive Is the Site to the Retailer’s
Target Market?
– Match Between Trade Area Demographics and
Retailer’s Target Market
– Likelihood of Customers Coming to Location
• Convenience
• Other Attractive Retailers At Location
Principle of cumulative attraction - a cluster of similar
and complementary retailing activities will have greater
drawing power.
PPT 8-8
Convenience of Going to Site
Accessibility
• Road pattern and condition
• Natural and artificial barriers
• Visibility
• Traffic flow
• Parking
• Congestion
• Ingress/egress
PPT 8-9
Location Within a Center
• In High Traffic Areas
•Near Anchor
•Center of Shopping Area
• Near Stores Selling Complementary
Merchandise
•Clustering Specialty Stores Appealing to
Teenagers
• Better locations cost more
PPT 8-10
Map of Dallas’ North Park Center
PPT 8-11
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
PPT 8-12
Trade Area
Primary zone - 60 to 65 percent of its customers
Secondary zone - 20 percent of a store’s sales
Tertiary zone - customers who occasionally shop
at the store or shopping center
PPT 8-13
Factors Defining Trade Areas
•Accessibility
•Natural & Physical Barriers
•Type of Shopping Area
•Type of Store
•Competition
•Parasite Stores
PPT 8-14
Oblong Trade Area Caused by
Major Highways and Natural Boundaries
PPT 8-15
Sources of Information
• Customer Spotting
• Census Data
• Geodemographic Information
Systems
– ACORN
• Information on Competition
– Yellow Pages
PPT 8-16
Customer Spotting
Purpose: to spot, or locate, the residences of
customers 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
PPT 8-17
Census Data of the U.S.
Only once in 10 years.
Each household in the country is
counted to determine the number
of persons per household,
household relationships, sex, race
age and marital status.
.
PPT 8-18
Geodemographic Information Systems
Demographic data vendors specialize in
repackaging and updating census-type data.
Geographic Information System (GIS) is a
computer system that enables analysts to
visualize information about their customers’
demographics, buying behavior, and other data in
a map format.
• GIS is a spatial database that stores the location and
shape of information.
• Analysts can identify the boundaries of a trade area
and isolate target customer groups
PPT 8-19
Indices for Assessing Sales Potential
• Market Potential Index (MPI)
– Number of Households Purchasing a Product or
Service in a Trade Area
• Spending Potential Index (SPI)
– Average Amount Spent on a Product or Service by a
Household in a Trade Area
PPT 8-20
Sources for Measuring Competition
• The Internet - lists current locations and future
sites.
• Yellow Pages
• Other Sources: Directories published by trade
associations, chambers of commerce, Chain
Store Guide, International Council of Shopping
Centers, Urban Land Institute, local newspaper
advertising departments, municipal and county
governments, specialized trade magazines, list
brokers
PPT 8-21
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
PPT 8-22
Competitive Analysis for
Edward Breiner
PPT 8-23
Methods for Estimating Demand
Analog Approach
Multiple Regression Analysis
Huff’s Model
PPT 8-24
The Analog Approach
3 Steps:
1. Current trade area is determined by using the
customer spotting technique.
2. Based on the density of customers from the store, the
primary, secondary and tertiary trade area zones are
defined.
3. Match the characteristics of our current store with the
potential new stores’ locations to determine the best
site.
PPT 8-25
Income Distribution of Three-Mile
Ring Surrounding Edward Breiner Optical
PPT 8-26
Demographic Trends for Three-Mile
Ring Surrounding Edward Breiner Optical
PPT 8-27
Breiner Optical
ACORN Neighborhood Lifestyle Clusters
for Three-Mile Ring
PPT 8-28
Descriptions of Largest PRIZM
Clusters Surrounding Edward Breiner Optical
PPT 8-29
Description of Largest PRIZM
Clusters Surrounding Edward Breiner Optical
PPT 8-30
Description of Largest PRIZM
Clusters Surrounding Edward Breiner Optical
PPT 8-31
Description of Largest PRIZM
Clusters Surrounding Edward Breiner Optical
PPT 8-32
Descriptions of Edward Breiner Optical
and Four Potential Locations’ Trade Areas
PPT 8-33
Multiple Regression Analysis
• Need to define the retail trade area potential
for retail chains with greater than 20 stores.
• Similar to the analog approach, it uses
statistics rather than judgement to predict
sales for a new store.
PPT 8-34
Multiple Regression Steps
• Current trade areas are determined by using
the customer spotting technique
• Primary, secondary, and tertiary zones are
determined by plotting customers on a map
• Select appropriate measures of
performance, such as per capita sales or
market share.
• Select a set of variables that may be useful
in predicting performance.
• Solve the regression equation and use it to
project performance for future sites.
PPT 8-35
Yearly Sales, Population, and
Income for 10 Home Improvement Centers
PPT 8-36
Regression of Population on Sales
PPT 8-37
Illustration of Regression Approach
1. Specify Regression Model – Identify Critical Predictors
of Store Sales
Sales = B0 + B1 x X1 + B2 x X2
X1 = population in trade area
X2 = average household income in trade
area
2. Estimate Weights - B0,B1, B2
3. Use Estimated Weights to Forecast sales
Sales = -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
PPT 8-38
Huff’s Gravity Model
Based on the premise that the probability that a
given customer will shop in a particular store
or shopping center becomes larger as the size
of store or center grows and distance or
travel time from customer shrinks
PPT 8-39
Huff’s Model Formula
S j  Tij b
Pij  n
 S j  Tij b
j 1
Where
Pij  Probabilit y of a customer at a given point of origin i traveling to a
particular shopping center j
S j  Size of shopping center j
Tij  Travel time or distance from customer's starting point to shopping
center
b  An exponent to Tij that reflects the effect of travel time on different
kinds of shopping trips
PPT 8-40
University and Shopping Centers:
Gravity Model Illustration
PPT 8-41
Huff’s Model: The Solution
Pij =
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 areas
and then sum them.
PPT 8-42