Tools For Identifying &
Recruiting Retail
Who Is Buxton?

Largest provider of customer analytics solutions

Analyzed every type of retail, restaurant, healthcare and
service concept

Over 5 billion square feet of analysis in 2007

Over 1,700 clients including retailers, restaurants,
developers and communities

Recognized as a Fast Company Fast 50 Company in 2005

Assisted more than 400 public sector clients develop
millions of square feet of retail
Some of Buxton’s Clients
What is CommunityID?
Retailer Matching
Matches the specific retail and restaurant concepts to
the buying habits and lifestyles of the consumers
living in your trade area
Unique Program
Is the only program of its kind that can factually identify
exactly which concepts should be in your community
Where is CommunityID?
CommunityID
Engagements
400+ Public Sector clients in 38 States
Buxton Engagements in Ohio
 Montgomery County (Joe Tuss – ED Director)
 Mentor (Ron Traub – Director of ED)
 Kent (Dave Ruller – City Manager)
 Miami Township (Dave Duckworth – Township
Administrator)
Benefits of a Strong Retail Sector
 Enhances residents’ lifestyles with more
shopping and dining choices
 Increases sales and property taxes
 Decreases retail leakage
 New, permanent jobs
 Expands economic opportunities by
attracting more people and businesses
Step 1: Identify The Trade Area
City Limit Trade Area
City Limit
2003 Est. Population
27,365
Population Growth,
1990-2000
10.49%
2003 Est. Households
9,947
2003 Average
Household Income
$61,537
2003 Residential
Property Value
$207,057
Ring Trade Area
8 Mile Ring
2003 Est. Population
62,596
Population Growth,
1990-2000
15.52%
2003 Est. Households
15,715
2003 Average
Household Income
$61,788
2003 Residential
Property Value
$211,085
Retailers Locate Near Customers
Customers think in terms of time and convenience…
they “think” drive time.
1 Min
3 Min
Shortest route
is calculated in
minutes for
each customer
4 Min
2 Min
Drive Time Trade Area
15-Minute
Drive Time
2003 Est. Population
71,483
Population Growth,
1990-2000
19.47%
2003 Est. Households
25,583
2003 Average
Household Income
$62,119
2003 Residential
Property Value
$218,544
Trade Areas
City
Limits
8 Mile
Ring
15 Minute
Drive Time
Households
9,947
15,715
25,583
Retail Potential
$450
million
$750
million
$1 billion
Restaurant Sales
$41
million
$64 million
$100
million
Automobile Sales
3,243
vehicles
4,522
vehicles
6,968
vehicles
Step 2: Identify The Customer
Demographic Methodology
 Traditionally, locations were selected based on:
Age
2004 PROJECTION
1999 ESTIMATE
1990 CENSUS
1980 CENSUS
GROWTH 1980 - 1990
2004 PROJECTION
1999 ESTIMATE
1990 CENSUS
1980 CENSUS
GROWTH
1980 - 1990
2004 PROJECTION
1999 ESTIMATED
POPULATION BY RACE
1999 ESTIMATE
WHITE
1990 CENSUS
BLACK
1980 CENSUS
ASIAN & PACIFIC ISLANDER
GROWTH
OTHER RACES1980 - 1990
2004 PROJECTION
1999 ESTIMATED
POPULATION
1999 ESTIMATE
HISPANIC
ORIGIN
1990 CENSUS
OCCUPIED
UNITS
2004 PROJECTION
OWNER
1980 OCCUPIED
CENSUS
1999 ESTIMATE
RENTER
OCCUPIED
GROWTH 1980 - 1990
1990
CENSUS
1991
PERSONS
PER
HH
1999
ESTIMATED
POPULATION BY RACE
1980 CENSUS
1999 EST. HOUSEHOLDS
BY INCOME
WHITE
$150,000 OR MORE
GROWTH 1980 - 1990
BLACK
$100,000 TO $149,999
2004 PROJECTION
ASIAN
PACIFIC ISLANDER
$ 75,000
TO $&1999
99,999
ESTIMATE
Sex
Race
335,270
317,227
288,000
251,960
14.30%
112,977
106,024
95,664
80,666
18.59%
335,270
317,227
317,227
38.25%
288,000
48.93%
251,960
0.90%
14.30%
11.92%
112,977
317,227
106,024
18.41%
95,66495,664
61.02%80,666
38.98%
18.59%
2.98
317,227
106,024
38.25%
4.03%
48.93%
8.57%
13.07%0.90%
Income
OTHER RACES
1990 CENSUS
1999 ESTIMATED POPULATION
1980 CENSUS
2004 PROJECTION
HISPANIC ORIGIN
GROWTH 1980 - 1990
1999 ESTIMATE
OCCUPIED UNITS
1999 ESTIMATED POPULATION BY RACE
OWNER OCCUPIED
1990 CENSUS
WHITE
RENTER OCCUPIED
1980 CENSUS
BLACK
1991 PERSONS PER
HH 1980 - 1990
GROWTH
ASIAN & PACIFIC ISLANDER
1999 EST. HOUSEHOLDS
BY INCOME
2004 PROJECTION
OTHER RACES
$150,000 OR MORE
1999 ESTIMATE
1999 ESTIMATED POPULATION
$100,000 TO $149,999
1990 CENSUS
HISPANIC ORIGIN
$ 75,000 TO $ 99,999
1980 CENSUS
OCCUPIED UNITS
$ 50,000 TO $ 74,999
GROWTH 1980 - 1990
OWNER OCCUPIED
$ 35,000 TO $ 49,999
1999 ESTIMATED POPULATION BY RACE
RENTER OCCUPIED
$ 25,000 TO $ 34,999
WHITE
1991 PERSONS PER HH
BLACK
1999 EST. HOUSEHOLDS BY INCOME
ASIAN & PACIFIC ISLANDER
$150,000 OR MORE
OTHER RACES
$100,000 TO $149,999
1999 ESTIMATED POPULATION
$ 75,000 TO $ 99,999
HISPANIC ORIGIN
$ 50,000 TO $ 74,999
OCCUPIED UNITS
$ 35,000 TO $ 49,999
OWNER OCCUPIED
$ 25,000 TO $ 34,999
RENTER OCCUPIED
1991 PERSONS PER HH
1999 EST. HOUSEHOLDS BY INCOME
$150,000 OR MORE
$100,000 TO $149,999
$ 75,000 TO $ 99,999
$ 50,000 TO $ 74,999
$ 35,000 TO $ 49,999
$ 25,000 TO $ 34,999
11.92%
317,227
18.41%
95,664
61.02%
38.98%
2.98
106,024
4.03%
8.57%
13.07%
21.87%
16.16%
11.44%
203,595
191,531
168,911
124,794
35.35%
70,933
66,197
58,156
40,242
44.52%
203,595
191,531
191,531
50.97%
168,911
41.02%
124,794
1.96%
35.35%
6.05%
191,53170,933
11.30%66,197
58,15658,156
335,270
62.99%40,242
317,227
37.01%
44.52%
288,000
2.88
191,531
251,960
66,197
50.97%
7.21%
14.30%
41.02%
11.61%
112,977
15.67%1.96%
106,024
180,704
171,169
155,053
130,920
18.43%
61,090
57,315
51,452
39,015
31.88%180,704
171,169
171,169
35.23%
155,053
58.74%
130,920
0.74%
5.29%18.43%
171,16961,090
8.96%57,315
51,45251,452
203,595
62.58%39,015
191,531
37.42%31.88%
168,911
2.98
171,169
124,794
57,315
35.23%
4.10%
35.35%
58.74%
9.41%
70,933
0.74%
13.86%
66,197
38,737
33,401
25,170
19,579
28.56%
13,189
11,341
8,688
6,626
31.12%38,737
33,40133,401
74.57%
25,170
13.50%
19,579
0.38%
11.55%28.56%
33,40113,189
17.93%11,341
8,688 8,688
180,704
38,737
63.00% 6,626
171,169
37.00%31.12% 33,401
155,053
2.8133,401 25,170
130,920
19,579
11,341
74.57%
3.78%
18.43%
28.56%
13.50%
6.71%
61,090
13,189
0.38%
10.85%
57,315
11,341
6.05%
5.29%
11.55%
95,664
58,156
51,452
8,688
191,531
171,169
33,401
80,666
40,242
39,015
6,626
335,270
203,595
180,704
38,737
11.30%
8.96%
17.93%
18.59%
44.52%
31.88%
31.12%
317,227
191,531
171,169
33,401
58,156
51,452
8,688
317,227
191,531
171,169
33,401
62.99%
62.58%
63.00%
288,000
168,911
155,053
25,170
38.25%
50.97%
35.23%
74.57%
37.01%
37.42%
37.00%
251,960
124,794
130,920
19,579
48.93%
41.02%
58.74%
13.50%
2.88
2.98
2.81
14.30%
35.35%
18.43%
28.56%
0.90%
1.96%
0.74%
0.38%
66,197
57,315
11,341
112,977
70,933
61,090
13,189
11.92%
6.05%
5.29%
11.55%
7.21%
4.10%
3.78%
106,024
66,197
57,315
11,341
317,227
191,531
171,169
33,401
11.61%
9.41%
6.71%
95,664
58,156
51,452
8,688
18.41%
11.30%
8.96%
17.93%
15.67%
13.86%
10.85%
80,666
40,242
39,015
6,626
95,664
58,156
51,452
8,688
23.51%
23.03%
21.28%
18.59%
44.52%
31.88%
31.12%
61.02%
62.99%
62.58%
63.00%
15.04%
15.81%
13.18%
317,227
191,531
171,169
33,401
38.98%
37.01%
37.42%
37.00%
9.95%
10.81%
13.82%
38.25%
50.97%
35.23%
74.57%
2.98
2.88
2.98
2.81
48.93%
41.02%
58.74%
13.50%
106,024
66,197
57,315
11,341
0.90%
1.96%
0.74%
0.38%
4.03%
7.21%
4.10%
3.78%
11.92%
6.05%
5.29%
11.55%
8.57%
11.61%
9.41%
6.71%
317,227
191,531
171,169
33,401
13.07%
15.67%
13.86%
10.85%
18.41%
11.30%
8.96%
17.93%
21.87%
23.51%
23.03%
21.28%
95,664
58,156
51,452
8,688
16.16%
15.04%
15.81%
13.18%
61.02%
62.99%
62.58%
63.00%
11.44%
9.95%
10.81%
13.82%
38.98%
37.01%
37.42%
37.00%
2.98
2.88
2.98
2.81
106,024
66,197
57,315
11,341
4.03%
7.21%
4.10%
3.78%
8.57%
11.61%
9.41%
6.71%
13.07%
15.67%
13.86%
10.85%
21.87%
23.51%
23.03%
21.28%
16.16%
15.04%
15.81%
13.18%
11.44%
9.95%
10.81%
13.82%
Problems with Demographics
 Demographic data is too general and too stale
 Identifies people not customers
 Does not explain what people like to buy
 Does not define a true trade area
 Not driving most retailers location decisions
 May do more harm than good…
Psychographics, not Demographics
It’s Customers, not People
Now, customers can be
identified based on:
Lifestyles
Purchase behavior
Media habits
Buxton Data
35 terabytes of data on over 120 million households
Managed In-House
250 In-House Data Sources
Consumer Data
Trade Potential
Business Data
Shopping Centers
Segmentation
Restaurant Data
Demographics
Automobile Data
Consumer Profiles
Telecommunications
Street Data
Segmentation
All U.S. households fall into 1
of 66 psychographic segments
4
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3
5
7
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11
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15
17
19
21
23
25
27
29
31
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61
63
65
Purchase Behavior
Psychographics focuses on
Customer Lifestyles
Media Habits
Purchasing Behavior
4
3
Segment 29 American Dreams
Item # Lifestyle Characteristics
Index
1
Eat at California Pizza Kitchen
222
2
Shop at Costco
186
3
Read Newsweek
140
4
Shop at Old Navy
135
5
Buy Women's Sweaters,$100+
124
Over 4500 individual categories available
2
1
0
1
3
5
7
9
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13
15
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31
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57
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61
63
65
Census Profile
Age:
Income:
Ethnicity:
Marital Status:
Kids:
Education:
Customer One
Customer Two
40 Year Old Male
$102,000 Income
Caucasian
Married
2 Children
Post-graduate degree
44 Year Old Male
$110,000 Income
Caucasian
Married
3 Children
College Graduate
Psychographic Profile
Owns:
Eats:
Reads:
Watches:
Drives:
Drinks:
Customer One
Customer Two
iPod
Boston Market
Barron’s
PGA Tour
BMW 5 Series
White Wine
Power Boat
Chili’s Grill & Bar
Field & Stream
Country Music TV
Dodge Ram
Bud Light
Remember: Customers, Not People
 Trade Area “A”
 Trade Area “B”
Total Households: 36,087
Total Households: 96,540
Ben & Jerry’s Customers: 14,443
Ben & Jerry’s Customers: 14,540
Total
households
Customers
Step 3: Match The Customers to Retailers
Retailer Profile
9
Ben & Jerry’s
Percent
6
3
0
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
Segment
Dominant Segments
Trade Area Profile
These segments
represent the
dominant segments
for Ben & Jerry’s
Dominant Segments
Trade Area Matches Retailer
Trade Area
Comparison
Ben & Jerry’s
Average Trade
Area
Anytown,
USA
Site Trade
Area
Total
Population
58,975
71,483
Total
Households
20,219
25,583
Ben & Jerry’s
Dominant
Segment
Count
9,205
15,350
Dominant Segments
Trade Area Does Not Match Retailer
Trade Area
Comparison
AutoZone
Average
Trade Area
Anytown, USA
Site Trade
Area
Total
Population
74,260
71,483
Total
Households
27,269
25,583
AutoZone
Dominant
Segment
Count
9,595
5,361
Dominant Segments
Step 4: Tools for Selling The Trade
Area
Retail Leakage/Surplus Analysis




How many dollars are leaving
What stores attract outside dollars
How strong is your retail sector
What are our retail opportunities
Example of Major Store
Type. Buxton analysis
includes details within
Major Store Types and
analysis by Product Type
Sample Tenant Match
Retailer and Restaurant
Preferred GLA (sq, ft)
Ben & Jerry’s
600-1,200
Best Buy
18,000 – 40,000
Blockbuster Video
2,500 – 4,800
California Pizza Kitchen
2,500 – 6,000
Casual Male
3,000 – 5,000
Chili’s Grill & Bar
5,000-7,000
Cinnabon
800
Famous Footwear
8,000-10,000
FedExKinko’s
300 – 3,000
Gap
2,750 – 16,800
Hallmark
3,500 – 5,000
Limited
1,000 – 5,000
New Balance
2,500 – 3,500
Staples
10,000 – 25,000
Starbucks
100 – 2,000
Subway
300 – 1,500
Target
126,000 – 175,000
U.S. Cellular
1,800
Walgreen’s
14,560
Zales
1,500 – 1,600
Custom Pursuit Packages –
Sample Score Sheet
12
12
XYZCompany
Company
XYZ
PotentialLocation
Location
Potential
9
9
66
33
12
12
16%
XYZ Company
Potential Location
XYZ Company
Potential Location
9
9
12%
6
8%
6
00
11
3
4%
3
33
55
77
99
11
11
13
13
15
15
17
17
19
19
21
21
23
23
25
25
27
27
29
29
31
31
33
33
35
35
37
37
39
39
41
41
43
43
45
45
47
47
49
49
51
51
53
53
55
55
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
63
65
0%
0
1
3
15
37
59
711 913 1115 131715 1917 2119 2321 25
23 27
25 29
27
29
31
31
51 53
53 55
55 57
57
33 33
35 35
37 37
39 3941 4143 434545 4747 4949 51
15,350
59
59
61
61 63
63 65
65
Speak the Retailer’s Language
57
57
59
59
61
61
63
63
65
65
Custom Pursuit Packages –
Sample Score Sheet
15,350
16%
12%
8%
4%
0%
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
49
51
53
55
15,350
57
59
61
63
65
CommunityID SCOUT 2.0
Market your community using SCOUT’s
dynamic online tools
Enter SCOUT
together with your
target retailer
Click to show your
site
Click to show the
trade area
Click to show the
retailer’s dominant
segment
households
Click Match Report
to show the retailer
you are their target
market
CommunityID SCOUT 2.0
Market your community using SCOUT’s
dynamic online tools
Click on a
Retailer
Marketing
Package to
show all the
data relative to
the target
retailer and your
community
You can send
this and other
reports
electronically to
your prospect
Ongoing Client Support
Buxton @ ICSC Spring Convention
Ongoing Client Support
Buxton @ ICSC Spring Convention
Summary
 Identify The Trade Area

Identify The Customers

Match The Customers to Retailers

Aggressively Market to Matching Retailers

Get Retailers & Developers Interested
 Bring New Retail to Your Community
Sample Projects
Downtown San Jose, CA
One of the most affluent trade areas in
America


Many restaurants, NO retail
Daytime Profile overlaid onto Residential
profile to see if any retailers fit


Can we build a case for Border’s? Yes!
Benchmarked downtown against Long
Beach and San Diego (perceived competitors
for retail)

Midtown Alliance – Atlanta, GA
Retail Analysis for Peachtree Street Corridor
– The “next Michigan Avenue?”


High growth residential development
Adjacent to Georgia Tech campus – what is
the impact of the college student?

Huge increase in daytime population – who
are these people?

Downtown San Diego Partnership
Profiled eight distinct neighborhoods –
both residents and work place

Psychographic Profiling and Analysis of
Petco Park (San Diego Padres) visitors


Padres provided specific data sets for
analysis: Season ticket holders, suite ticket
holders, single game walk up purchases,
internet purchases, etc.

“Game Day Profile” - We can then overlay the
Padres customer profile onto the existing
trade area profile
Barriers to Downtown Retail
 Identifying the customer – who is it?
 Parking, Access, Visibility
 Assembling viable sites
 Unmotivated property owners
 Unmotivated brokers
 Existing retail outside of downtown