Retailing
MKTG 6211
Retailing Topics
Professor Edward Fox
Cox School of Business/SMU
Retail Site Selection
 Openings
 Expansions
 Closings
What are the effects of proposed changes in retail sites
on the revenues of new and existing stores?
Retail Site Selection
Why Does It Matter?
 Access to consumers
 Number
 Characteristics
 Growth
 Locations of other stores
 Cannibalization – own stores
 Agglomeration
Competition
Complementarity
According to Wal-Mart’s Real Estate group, the
difference between good and bad locations exceed $25
million in gross profit
Retail Site Selection
How Is It Done?
Select:
Geographic market
Site within the geographic market
If an opening or expansion, the format/size of
the store to be opened
Retail Site Selection
Agglomeration
 Agglomeration captures the countervailing effects of
complementarity and competition among retailers
 Intra-type - Stores of the same type locating near
one another
Facilitates consumer search
Examples: “motor miles” and “restaurant rows”
 Inter-type - Stores of different types locating near
one another
Facilitates multi-purpose shopping, virtual one-stopshopping, and offers a wider variety of goods to
choose from
Examples: shopping centers and shopping malls
Recognizes that consumers may use multiple
stores to meet their needs - shopping strategically!!
Retail Site Selection
Agglomeration
 “Trip chaining” – Make
unrelated purchases on the
same trip
 Price search – Search until
you find an attractive price
 “Cherry picking” – Visit
multiple stores for their bargain
prices
Retail Site Selection
Where Do Consumers Work?
 Another consideration in retail site selection is where
consumers work
 Do shopping trips begin from home?
 From work?
Retail Agglomeration
Trip Chains
 Trip chains reflect the routing problem faced by shoppers
 Consumers minimize shopping costs by reducing
travel, subject to fulfilling diverse product/service
needs
 Price search
 Our research incorporates price uncertainty, allowing
shoppers to terminate or continue a shopping trip
(unplanned)
 Data limitations require that we:
 Consider visits only to selected store formats
 Assume that shopping trips begin from the consumer’s
home
Retail Site Selection
Agglomeration
How does retail location affect multi-store shopping?
RETAIL LOCATION
Relative to customers
Relative to other stores
Retail
Competition
Destination
Effect
Specifically, how are retailer revenues affected by nearby
supermarkets, drug stores, mass merchandisers and
supercenters, dollar stores and warehouse clubs?
Retail Agglomeration
Preliminary Model - Data Description
Retailer
BiLo
Food Lion
Harris Teeter
Winn Dixie
Wal-Mart Supercenter
Wal-Mart Discount
N
1790
1790
1790
1790
1790
1790
Demographic
Income (x $1,000)
Family Size
Head of Household Age
College Education
Working Woman
N
358
358
358
358
358
Spending Penetration
$79
0.472
$184
0.785
$145
0.570
$56
0.478
$122
0.617
$30
0.343
Average
55.1
2.65
51.4
0.38
0.50
Std Dev
30.3
1.15
11.4
0.49
0.46
Store
Visits
2.6
7.1
3.7
2.4
4.0
1.7
Travel
Time (min)
10.4
4.9
8.7
8.8
21.2
16.8
Retail Agglomeration
Preliminary Model Results – Travel Times

Distance to
BiLo
BiLo
-0.921
( -1.289 , -0.509 )
Food Lion
0.162
( -0.075 , 0.405 )
Resulting Revenues at
Harris Teeter
Winn Dixie
0.246
0.373
( -0.015 , 0.502 )
( 0.009 , 0.737 )
0.184
( -0.184 , 0.568 )
-0.400
( -0.613 , -0.182 )
(
0.323
( -0.106 , 0.745 )
(
0.393
( 0.012 , 0.806 )
(
WM Super
WM Discount
Food Lion
Harris Teeter
Winn Dixie
WM Super
-0.095
( -0.360 , 0.171 )
WM Discount
0.250
( -0.025 , 0.521 )
0.377
0.116 , 0.645 )
0.135
( -0.204 , 0.484 )
-0.139
( -0.370 , 0.094 )
0.133
( -0.074 , 0.360 )
0.390
0.099 , 0.678 )
-0.733
( -0.960 , -0.501 )
0.280
( -0.146 , 0.751 )
-0.008
( -0.282 , 0.266 )
(
0.223
0.019 , 0.429 )
0.134
( -0.101 , 0.385 )
-0.934
( -1.222 , -0.607 )
(
0.291
0.025 , 0.556 )
-0.416
( -0.618 , -0.218 )
-0.340
( -0.818 , 0.143 )
0.096
( -0.280 , 0.467 )
-0.252
( -0.584 , 0.105 )
0.200
( -0.435 , 0.827 )
-0.460
( -0.778 , -0.139 )
0.396
( -0.112 , 0.920 )
0.037
( -0.416 , 0.504 )
0.081
( -0.246 , 0.411 )
0.036
( -0.334 , 0.410 )
0.216
( -0.329 , 0.789 )
1.015
0.604 , 1.432 )
-1.005
( -1.304 , -0.703 )
(
0.333
0.076 , 0.591 )
 Travel times have the expected negative effect for own-store;
cross-store travel time parameters have smaller positive effects
 We observe symmetric competition among grocery stores in terms
of location
 Revenues at EDLP stores—Food Lion and Wal-Mart Supercenter—
are least sensitive to distances that their customers have to travel
Retail Agglomeration
Preliminary Model Results - Agglomeration

Agglom of
Club
BiLo
-0.021
( -0.091 , 0.067 )
Food Lion
-0.019
( -0.045 , 0.013 )
Resulting Revenues at
Harris Teeter
Winn Dixie
0.016
-0.018
( -0.074 , 0.112 )
( -0.092 , 0.078 )
WM Super
-0.027
( -0.097 , 0.053 )
WM Discount
-0.062
( -0.190 , 0.092
-0.070
( -0.407 , 0.257 )
-0.154
( -0.438 , 0.129 )
-0.405
( -0.616 , -0.182 )
-0.139
( -0.532 , 0.272 )
-0.197
( -1.246 , 0.846 )
(
0.588
0.214 , 0.963
-0.629
( -1.226 , -0.008 )
-0.114
( -0.480 , 0.240 )
0.349
( -0.159 , 0.866 )
0.056
( -0.789 , 0.938 )
0.255
( -0.170 , 0.675 )
(
0.619
0.030 , 1.241
Grocery
0.086
( -0.783 , 0.995 )
0.117
( -0.281 , 0.495 )
0.156
( -0.326 , 0.621 )
0.038
( -0.703 , 0.804 )
0.012
( -0.775 , 0.807 )
-1.191
( -2.210 , -0.173
Discount
0.057
( -0.177 , 0.306 )
0.075
( -0.086 , 0.241 )
(
0.179
0.017 , 0.352 )
0.053
( -0.139 , 0.275 )
-0.056
( -0.376 , 0.273 )
0.044
( -0.086 , 0.185
-0.201
( -0.327 , -0.053 )
0.068
( -0.051 , 0.193 )
-0.016
( -0.080 , 0.057 )
-0.096
( -0.277 , 0.105 )
-0.053
( -0.221 , 0.113 )
Dollar
Drug
Supercenter
(
.
.
,
 Wal-Mart Discount stores are most affected by locating near other stores
 Wal-Mart Supercenters are not affected by the concentration of other
stores nearby
 Locating near club stores does not affect retailers in our sample
.
Multi-Channel Retailing
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Multi-Channel Retailing
 How “big” is the Internet -- milestones
 Mid - 1996:
online population of the
United States was 35 million
 Mid - 1998: online population became 72.6
million
 April 1999: more than 83 million users online
above age 16
 2000 Census: 42% of US households have internet
access
>50% of US households have
computers
Source: Levy & Weitz and Census Bureau
Multi-Channel Retailing
 How “big” is the Internet?
Worldwide Active Internet Home Users, July 2007
Country
Jun-07
Jul-07 Growth (%) Difference
Australia
10,818,299
10,842,782
0.23
24,483
Brazil
18,047,372
18,522,750
2.63
475,377
Switzerland
3,673,908
3,717,766
1.19
43,858
Germany
33,023,580
33,198,475
0.53
174,895
Spain
13,999,820
13,484,624
-3.68 -515,196
France
22,586,718
21,948,082
-2.83 -638,635
Italy
17,197,972
17,071,177
-0.74 -126,796
Japan
45,867,926
46,625,634
1.65
757,708
U.K.
24,651,765
24,681,279
0.12
29,514
U.S.
146,828,875
148,128,321
0.89 1,299,446
Totals
336,696,235
338,220,889
0.45 1,524,654
Source: Nielsen//NetRatings, 2007
Multi-Channel Retailing
 How “big” is Internet retail?
Estimated Quarterly U.S. Retail E-commerce Sales as a Percent of Total Quarterly Retail Sales:
4th Quarter 1999–2nd Quarter 2007
Percent of Total
Multi-Channel Retailing
 What do shoppers buy on the Internet?
Category
Airline Tickets
Computer hardware
Other
Hotel Reservations
Apparel
Toys/Video Games
Consumer Electronics
Books
Car Rental
Food/Beverages
Software
Music
Health and Beauty
Office supplies
Videos
Jewelry
Sporting Goods
Linens/Home Decor
Footwear
Small appliances
Flowers
Tools and Hardware
Furniture
Appliances
Garden Supplies
Total Spend
$6,665,374
$3,907,186
$3,544,600
$3,262,206
$2,580,352
$2,346,174
$2,262,047
$2,201,026
$1,660,432
$1,654,286
$1,624,707
$1,526,183
$1,334,326
$1,271,997
$1,085,490
$824,178
$807,614
$761,820
$600,100
$596,605
$590,454
$509,188
$443,254
$283,579
$188,857
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Source: PCDataonline Jan 00-Jan 01
Multi-Channel Retailing
 What do shoppers buy on the Internet?
Selected Product Categories' Sales Growth,
2004 and 2005 (%)
Growth
Apparel and accessories
36
Computer software (excludes PC games)
36
Home and garden
32
Toys and hobbies
32
Jewelry and watches
27
Event tickets
26
Furniture
24
Flowers, greetings, and gifts
23
Notes:
1. Sales exclude auctions and large corporate
purchases.
2. Sales are non-travel online consumer spending.
Source: comScore, 2006