1. Retailing
MKTG 6211
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Professor Edward Fox
Cox School of Business/SMU

2. Retail Site Selection
Openings
Expansions
Closings
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3. Retail Site Selection
WWhhyy Does It Matter?
Access to consumers
Number
Characteristics
Growth
Locations of other stores
Cannibalization – own stores
Agglomeration
Competition
Complementarity
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4. Retail Site Selection
HHooww 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

5. Retail Site Selection
Agglomeration
AAgggglloommeerraattiioonn 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-stop-shopping,
and offers a wider variety of goods to
choose from
Examples: shopping centers and shopping malls
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6. 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

7. 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?

8. Retail Agglomeration
Trip Chains
TTrriipp cchhaaiinnss reflect the routing problem faced by shoppers
Consumers minimize shopping costs by reducing
travel, subject to fulfilling diverse product/service needs
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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

9. Retail Site Selection
Agglomeration
How does retail location affect multi-store shopping?
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Retail
Retail
Competition
Competition
Destination
Effect
Destination
Effect
Specifically, how are retailer revenues affected by nearby
supermarkets, drug stores, mass merchandisers and
supercenters, dollar stores and warehouse clubs?

10. Retail Agglomeration
Preliminary Model - Data Description
Retailer N Spending Penetration
Store
Visits
Travel
Time (min)
BiLo 1790 $79 0.472 2.6 10.4
Food Lion 1790 $184 0.785 7.1 4.9
Harris Teeter 1790 $145 0.570 3.7 8.7
Winn Dixie 1790 $56 0.478 2.4 8.8
Wal-Mart Supercenter 1790 $122 0.617 4.0 21.2
Wal-Mart Discount 1790 $30 0.343 1.7 16.8
Demographic N Average Std Dev
Income (x $1,000) 358 55.1 30.3
Family Size 358 2.65 1.15
Head of Household Age 358 51.4 11.4
College Education 358 0.38 0.49
Working Woman 358 0.50 0.46

11. Retail Agglomeration
Preliminary Model Results – Travel Times
D
Distance to
BiLo
Resulting Revenues at
BiLo Food Lion Harris Teeter Winn Dixie WM Super WM Discount
-0.095 0.250
0.373
0.246
0.162
-0.921
( -1.289 , -0.509 ) ( -0.075 , 0.405 ) ( -0.015 , 0.502 ) ( 0.009 , 0.737 ) ( -0.360 , 0.171 ) ( -0.025 , 0.521 )
Food Lion
0.133
-0.139
0.135
0.377
-0.400
0.184
( -0.184 , 0.568 ) ( -0.613 , -0.182 ) ( 0.116 , 0.645 ) ( -0.204 , 0.484 ) ( -0.370 , 0.094 ) ( -0.074 , 0.360 )
Harris Teeter
0.333
-0.008
-0.733
0.280
0.390
0.323
( -0.106 , 0.745 ) ( 0.099 , 0.678 ) ( -0.960 , -0.501 ) ( -0.146 , 0.751 ) ( -0.282 , 0.266 ) ( 0.076 , 0.591 )
Winn Dixie
-0.416
0.291
-0.934
0.223
0.393
0.134
( 0.012 , 0.806 ) ( 0.019 , 0.429 ) ( -0.101 , 0.385 ) ( -1.222 , -0.607 ) ( 0.025 , 0.556 ) ( -0.618 , -0.218 )
WM Super
-0.252 0.200
-0.460
0.396
-0.340
0.096
( -0.818 , 0.143 ) ( -0.280 , 0.467 ) ( -0.584 , 0.105 ) ( -0.435 , 0.827 ) ( -0.778 , -0.139 ) ( -0.112 , 0.920 )
WM Discount
1.015 -1.005
0.036
0.216
0.037
0.081
( -0.416 , 0.504 ) ( -0.246 , 0.411 ) ( -0.334 , 0.410 ) ( -0.329 , 0.789 ) ( 0.604 , 1.432 ) ( -1.304 , -0.703 )
 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

12. Retail Agglomeration
Preliminary Model Results - Agglomeration
D
Agglom of
Club
Resulting Revenues at
BiLo Food Lion Harris Teeter Winn Dixie WM Super WM Discount
-0.027 -0.062
-0.018
0.016
-0.019
-0.021
( -0.091 , 0.067 ) ( -0.045 , 0.013 ) ( -0.074 , 0.112 ) ( -0.092 , 0.078 ) ( -0.097 , 0.053 ) ( -0.190 , 0.092
Dollar
0.588
-0.197
-0.139
-0.405
-0.154
-0.070
( -0.407 , 0.257 ) ( -0.438 , 0.129 ) ( -0.616 , -0.182 ) ( -0.532 , 0.272 ) ( -1.246 , 0.846 ) ( 0.214 , 0.963
0.619
-1.191
0.255
0.012
0.349
0.056
0.038
0.156
0.179 -0.056
0.044
-0.053 .
-0.016
0.053
-0.096
-0.629
0.086
0.057
-0.201
-0.114
0.117
0.075
0.068
 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
Drug
( -1.226 , -0.008 ) ( -0.480 , 0.240 ) ( -0.159 , 0.866 ) ( -0.789 , 0.938 ) ( -0.170 , 0.675 ) ( 0.030 , 1.241
Grocery
( -0.783 , 0.995 ) ( -0.281 , 0.495 ) ( -0.326 , 0.621 ) ( -0.703 , 0.804 ) ( -0.775 , 0.807 ) ( -2.210 , -0.173
Discount
( -0.177 , 0.306 ) ( -0.086 , 0.241 ) ( 0.017 , 0.352 ) ( -0.139 , 0.275 ) ( -0.376 , 0.273 ) ( -0.086 , 0.185
Supercenter
( -0.327 , -0.053 ) ( -0.051 , 0.193 ) ( -0.080 , 0.057 ) ( -0.277 , 0.105 ) ( -0.221 , 0.113 ) ( . , .

13. Multi-Channel Retailing
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14. 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

15. 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

16. 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

17. Multi-Channel Retailing
What do shoppers buy on the Internet?
Category Total Spend Rank
Airline Tickets $6,665,374 1
Computer hardware $3,907,186 2
Other $3,544,600 3
Hotel Reservations $3,262,206 4
Apparel $2,580,352 5
Toys/Video Games $2,346,174 6
Consumer Electronics $2,262,047 7
Books $2,201,026 8
Car Rental $1,660,432 9
Food/Beverages $1,654,286 10
Software $1,624,707 11
Music $1,526,183 12
Health and Beauty $1,334,326 13
Office supplies $1,271,997 14
Videos $1,085,490 15
Jewelry $824,178 16
Sporting Goods $807,614 17
Linens/Home Decor $761,820 18
Footwear $600,100 19
Small appliances $596,605 20
Flowers $590,454 21
Tools and Hardware $509,188 22
Furniture $443,254 23
Appliances $283,579 24
Garden Supplies $188,857 25
Source: PCDataonline Jan 00-Jan 01

18. 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