PPT th Edition

PPT 8-2 McGraw-Hill/Irwin Levy/Weitz: Retailing Management, 5/e Copyright © 2004 by The McGraw-Hill Companies, Inc. All rights reserved. Site Location Chapter 8

PPT 8-3 Retailing Strategy Retail Locations Chapter 7 Site Locations Chapter 8 Human Resource Management Chapter 9 Information and Distribution Systems Chapter 10 Customer Relationship Management Chapter 11 Retail Market and Financial Strategy Chapter 5, 6

PPT 8-4 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 8-5 Three Levels of Analysis

PPT 8-6 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 8-7 Factors Affecting Demand for a Region or Trade Area

PPT 8-8 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 8-9 Convenience of Going to Site Accessibility Road pattern and condition Natural and artificial barriers Visibility Traffic flow Parking Congestion Ingress/egress

PPT 8-10 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 Location Within a Center

PPT 8-11 Map of Dallas’ North Park Center

PPT 8-12 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 8-13 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 8-14 Factors Defining Trade Areas Accessibility Natural & Physical Barriers Type of Shopping Area Type of Store Competition Parasite Stores

PPT 8-15 Oblong Trade Area Caused by Major Highways and Natural Boundaries

PPT 8-16 Sources of Information Customer Spotting Census Data Geodemographic Information Systems –ACORN Information on Competition –Yellow Pages

PPT 8-17 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 8-18 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 8-19 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 8-20 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 8-21 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 8-22 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

PPT 8-23 Competitive Analysis for Edward Breiner

PPT 8-24 Methods for Estimating Demand Analog Approach Multiple Regression Analysis Huff’s Model

PPT 8-25 The Analog Approach 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. 3 Steps:

PPT 8-26 Income Distribution of Three-Mile Ring Surrounding Edward Breiner Optical

PPT 8-27 Demographic Trends for Three-Mile Ring Surrounding Edward Breiner Optical

PPT 8-28 ACORN Neighborhood Lifestyle Clusters for Three-Mile Ring Breiner Optical

PPT 8-29 Descriptions 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 Description of Largest PRIZM Clusters Surrounding Edward Breiner Optical

PPT 8-33 Descriptions of Edward Breiner Optical and Four Potential Locations’ Trade Areas

PPT 8-34 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 8-35 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 8-36 Yearly Sales, Population, and Income for 10 Home Improvement Centers

PPT 8-37 Regression of Population on Sales

PPT 8-38 Illustration of Regression Approach 1. Specify Regression Model – Identify Critical Predictors of Store Sales Sales = B 0 + B 1 x X 1 + B 2 x X 2 X 1 = population in trade area X2 = average household income in trade area 2. Estimate Weights - B 0,B 1, B 2 3. Use Estimated Weights to Forecast sales Sales = -144, ,937 x X ,132 x X 2 Sales = -144, ,937 x 55, ,132 x 28,000 = $521,085

PPT 8-39 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 8-40 Huff’s Model Formula

PPT 8-41 University and Shopping Centers: Gravity Model Illustration

PPT 8-42 Huff’s Model: The Solution P ij = 1000  3 2 (1000  3 2 ) + (500  5 2 ) + (100  1 2 ) Probability = 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.