Understanding Retail Trade
Analysis
by
Al Myles, Economist and Extension
Professor
Department of Agriculture
Economics
Mississippi State University
December 11, 2008
Presented at Oktibbeha County Leadership Forum
Retail Trade Analysis
-
Is a way to identify market trends within a local
community, including the degree of surplus or
leakage of dollars within specific retail sectors.
PURPOSE
•Gives an historical overview of a community’s or county’s retail
trade sector
•Provides a basis for comparison with similar size communities
and counties
•Is useful for identifying opportunities in the retail sector
•Similar to annual health physical at the doctor’s office. Tells you
what’s right and wrong.
Why Retail Trade?
Retail trade is one of the most important indicators of economic
activity in a community or county because local citizens spend a
large part of their incomes on goods and services.
The measures of retail trade and spending reflect consumers’
preference for the retail mix in the area and show how well the
economy is doing overall.
Since retail is one of the major economic forces in the country,
local officials often want to know how they compare with their
competitors.
.
Purpose of Retail Promotions
Keeping Local Dollars at Home
Indicators of Retail Activity
Sales Tax Collections
Market Capture
Gap Analysis (Potential sales-Actual Sales)
Pull factors
Sales leakage
Introduction
-Defining a town’s trade area is an important first step
in developing a strong retail sector.
-This is the foundation of retail market analysis. It helps
existing businesses to identify ways to expand their own
market.
-Increasing retail sales is one way an area can:
capture dollars
increase income
improve employment multipliers of its local
industries.
Defining the Trade Area
-Whatever the reasons for existing retail sales, city and
county leaders can help local businesses to improve
these trends.
-To determine the potential for increasing retail sales,
one should establish the trade area.
A trade area is the geographic region from which
a town draws the majority of its retail customers.
This can be done in several ways:
1. Conducting a traffic flow study,
2. Using a retail gravity model,
3. Using a zip code method, and
4. Using commuting data to define the trade area
boundaries.
Of these methods, COMMUTING and RETAIL
GRAVITY approaches present the least amount
of work to implement.
Traffic Flow….
Is the random canvassing of parking lots at major
locations in town at different times on different days
and over several weeks.
The locations might include
The downtown area,
 Major shopping destinations such as
shopping malls and centers, Wal-Mart
Super Center, Home Depot, Krogers’, and
 Other popular establishments in
town.
One should combined the results of vehicle license
plates from the different locations to obtain a composite
count of vehicles from surrounding counties and
compare them to regional commuting data.
Results from a traffic study will usually reveal
the major towns and counties that comprise the local
trade area or market.
To determine the major communities in the local
market one should:
1. Rank order the number of cars from various
counties in the region, and
2. Select the top five or six localities based on the
highest frequency and/or maximum percentage
(10% or more) of license plates in the area.
Commuting…
Commuting time to work by local residents is another
way of delineating a community’s retail trade area.
Converting commuting time to work into spatial
distances or miles and plotting these data on a map,
provide a visual picture of the geographic size of its
trade area.
Figure 1. Trade Area: Major Commuting Counties
Figure 1. Trade Area: Immediate Commuting Counties
Reily’s Law…
Another easy way of defining the retail trade area is to
use a gravity model. In retail trade analysis, the most
popular method is “Reily’s Law of Retail
Gravitation.”
Reily’s law is a rule-of-thumb used to ESTIMATE the
distance customers will travel to PURCHASE goods
and SERVICES after comparing price, quality, and
style.
Reilly’s Law
The law assumes that people desire to shop in larger
towns, but their desire declines the farther the distance
and time they must travel to get there. Thus, LARGER
TOWNS DRAW CUSTOMERS FROM FARTHER
DISTANCES THAN SMALLER TOWNS.
The maximum distance a customer will travel to shop in
a smaller town can be calculated using the following
formula.
Population and Travel Distances in Community A’s Trade Area
County
Total Population
Distance (FROM
Community A to
County Seat)
Trade Area Distance
Community A
22,000
Community B
1,543
27
5.65
Community C
23,799
23
11.73
Community D
2,145
27
6.42
Community E
7,169
33
11.99
Community F
8,489
17
6.51
Average
10,/8/
25.4
8.46
Figure 1. Picture of Community’s Trade Area
N
Community F
6.51 miles
6.42 miles
W
Community D
Community A
Community C
11.27 miles
5.65 miles
11.99 miles
Community B
Community E
S
E
Estimating Total Market Size
Once the physical boundaries of the trade area have
been identified, one should estimate the total market
size.
The total market consists of populations in the host
community plus population from surrounding towns in
the trade area.
Additional customers can be derived using the
formula:
3.14 X (Average Retail Trade Miles)2 X Average County
Population Density
Example:
Community A’s population = 22,000
Average trade area retail miles = 8.46
Average trade area population density per square mile = 51.45
Number of new customers = (3.14 x ((8.46)^2) x 51.45) =11,563
Total retail customer base = 33,372 (22,000 + 11,563)
In using this approach, there are a few caveats:
1.
Areas with large populations and densities per square mile
can distort the actual situation in retail trade analysis.
2.
Reily’s Law is less accurate when involving larger towns.
Trade Area Population Model
Answers the basic question: What is the probability that a consumer located
in communityi will shop in communityj, given the presence of competing
towns? The spatial interaction model takes into account such variables as
distance, attractiveness and competition in different sites.
The probability (Pij)1 that a consumer located in communityi will choose to
shop in communityj is calculated as:
Where:
Aj is a measure of attractiveness of communityj, such as total retail sales,
total personal income, or population of area.
Dij is the distance from i to j.
α2 is an attractiveness parameter from empirical observation.
Β3 is the distance decay parameter estimated from empirical observations.
Simply, it is a parameter that reflects the propensity to travel by
consumers.
n is the total number of communities including the host communityi .
The product derived from dividing
by
is known as the perceived
utility of communityj by a consumer located in communityi.
Using Information About
Market Size
After defining the trade area, one can ESTIMATE the
local sales potential and COMPARE them to actual
sales in the area. The following formula can be used to
estimate potential retail sales.
POTENTIAL SALES
•Potential sales for a given sector in a given county can be
PCIi
estimated as
PSij  Pi * SSPCj *
PCIs
•Where
-PSij is potential sales for commercial sector j in county i
-Pi is population for county i
-SSPCj is state sales per capita for commercial sector j
-PCIi is per capita income for county i
-PCIs is per capita income for state s
By comparing POTENTIAL with ACTUAL retail
sales, one can determine whether the city has room for
retail growth.
One should compare retail sales over SEVERAL
YEARS to determine the LONG-TERM health of retail
sectors in the city.
TRADE AREA ANALYSIS
Example:
•Pristine County, USA
•General Merchandise sector, 2005
•Figures for trade area capture estimation:
-ARSij (2005 taxable retail sales for Automotive sector in Pristine
Co.) = $1,011,060
-ARSsj (annual taxable retail sales for General merchandise sector for
USA) = $3,799,963,834
Pprstc (Pristine County population) = 4,896 people
Pu.s (USA population) = 2,412,301 people
Yprstc (Pristine Co. per capita income) = $26,363
Yu.s (USA per capita income) = $35,744
TRADE AREA ANALYSIS
Example:
Potential Sales
•The equation becomes:
 $3,799,963,834   $26,363 
PS  (4,896) * 
 *

 2,421,301   $35,744 
PS  $5,688,281
•The potential sales are considerably greater than the actual sales
of $1,011,060
Potential Sales: Interpretation
•Can compare estimates of potential sales for commercial sector j
in county i to realized sales of commercial sector j in county i
-Derive a value of captured or lost commercial sales for that
sector and county
Determining Retail Power
Trade Area Capture (TAC)
Information about the trade area can help one to
estimate the ability of community merchants to capture
the retail business of people in the area.
Trade Area Capture (TAC)
is an estimate of the number of people who shop in the
local area during a certain period.
Pull Factors…
Knowledge of the trade area is the first step in retail
market analysis.
Knowing the trade area, one can determine the size and
pulling power of local merchants in the market using a
concept call pull factors.
Pull factors are ratios that estimate the proportion
of local sales that occurs in a town.
The most common method of calculating pull factors is
as follows:
Pull Factor (PF) = Trade Area Capture
City Population
See slide 23
PF
Value
Interpretation
>
1
Retailers drawing customers from outside trade
area
<
1
Retailers losing customers from outside trade
area
=
1
Retailers maintaining customers in trade area
Pull factors for Selected Counties in Mississippi
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Clay
0.76 0.73 0.73 0.74 0.76 0.76 0.77 0.75 0.77 0.76 0.75 0.74 0.76 0.73 0.70 0.70 0.71 0.71 0.73 0.74 0.73 0.71 0.69 0.70 0.73 0.71 0.71
Lowndes
1.07 1.12 1.00 1.00 1.00 1.01 1.03 1.03 1.11 1.19 1.01 1.03 1.07 1.00 1.01 1.00 1.00 0.97 0.99 1.12 1.11 1.11 1.08 1.06 1.00 0.98 1.03
Oktibbeha
0.78 0.74 0.74 0.75 0.76 0.76 0.76 0.75 0.75 0.76 0.75 0.76 0.79 0.74 0.73 0.72 0.73 0.76 0.75 0.83 0.84 0.87 0.85 0.85 0.83 0.84 0.82
Mississippi
0.79 0.82 0.78 0.77 0.77 0.76 0.75 0.74 0.74 0.74 0.72 0.74 0.76 0.74 0.73 0.74 0.74 0.73 0.73 0.77 0.76 0.76 0.76 0.76 0.74 0.74 0.74
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1.40
1.20
1.00
0.80
0.60
Clay
Lowndes
Oktibbeha
Mississippi
0.40
0.20
0.00
igure 1. Weighted Average Pull Factors for Mississippi Counties, 2007
Mississippi
Total .74
PF>1.0
PF>.8<=1
PF>.6<=.79
PF>.4<=.59
White
Light Blue
Green
Yellow
PF>1.0
White
PF>.8<=1
Light Blue
PF>.6<=.7
9
PF>.4<=.5
9
Green
Yellow
Some questions to think about when interpreting pull factors:
1. How has the pull factor changed over time? If it has
increased, why do you think that is so? If it has declined,
what are some possible causes?
2. How does the local pull factor compare to other counties?
The state? Why do you think it is higher or lower?
3. What are some strategies your community can adopt to
increase the amount of money drawn in from outside the
county?
What Is Happening Locally?
Table 1. Oktibbeha County With and Without Federal Funds
Economic Strength Index
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Average
With
4.02
3.95
3.94
3.88
3.88
3.90
4.00
4.06
4.12
4.18
4.19
4.16
4.02
Without
3.77
3.69
3.68
3.63
3.62
3.65
3.70
3.74
3.83
3.87
3.86
3.86
3.75
Median State Index
3.57
3.56
3.57
3.57
3.58
3.56
3.55
3.56
3.55
3.55
3.57
3.52
Rank
24
27
26
28
28
28
25
26
26
24
24
23
Trade Area Capture
County
Clay
21,751
Lowndes
98,344
Oktibbeha
51,136
Region
Total
173,153
Current
Population2002
TAC to
Population
Projected
Population 2019
Ratio
21,979
61,586
42,902
22,840
65370
51200
98.96
159.69
119.19
126,467
139,410
136.92
Figure 1. Trade Capture
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
Series1
Clay
Low ndes
Oktibbeha
Region Total
21,751
98,344
51,136
173,153
Market Population
Figure 2. TAC and 2002 Population
Region Total
Oktibbeha
Low ndes
Clay
0
50,000
100,000
150,000
200,000
Clay
Low ndes
Oktibbeha
Region Total
Series2
21,979
61,586
42,902
126,467
Series1
21,751
98,344
51,136
173,153
Figure 3. TAC, 2002 Population, and Projected 2019 Population
200,000
150,000
100,000
50,000
0
Clay
Low ndes
Oktibbeha
Region Total
Series1
21,751
98,344
51,136
173,153
Series2
21,979
61,586
42,902
126,467
Series3
22,840
65370
51200
139,410
Figure 4. Market Capture Above Population
180.00
159.69
160.00
136.92
140.00
119.19
Percent
120.00
98.96
100.00
80.00
60.00
40.00
20.00
Clay
Lowndes
Oktibbeha
Region Total
Figure 5. County Retail Sales
$600,000,000
$500,000,000
$400,000,000
$300,000,000
$200,000,000
$100,000,000
$-
98
99
00
01
02
03
04
05
06
Series1 $363 $375 $398 $408 $435 $426 $447 $455 $529
Figure 6. Starkville Retail Sales
$400,000,000
$350,000,000
$300,000,000
$250,000,000
$200,000,000
$150,000,000
$100,000,000
$50,000,000
$-
98
99
00
01
02
03
04
05
06
Series1 $251, $272, $292, $300, $306, $302, $320, $328, $374,
Figure 7. Oktibbeha County Per Capita Sales Ratio
$9,000
$8,000
$7,000
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
$-
98
Series2 $5,967
99
00
01
02
03
04
05
06
$6,419
$6,799
$7,027
$7,203
$7,101
$7,447
$7,539
$8,499
Summary
This presentation shows how a few simple techniques
can be used to determine the geographic size of a town’s
trade area.
A trade area will often extend beyond its own
geographic borders.
CONCLUSIONS
•Trade area analysis shows how businesses can use existing data
to learn more about their business power
•Trade area analysis provides information about:
-The number of customers in a county
-A sector’s pull factor in the region
-Potential sales in an area
•This information can all be used to create a plan or strategy for
business owners
Shift-Share Results for Your
Area
In economics, there is a technique called shift-share
analysis. Its purpose is to take the change in employment for
an area and decompose it into the three sources that caused the
change.
National growth
Industrial growth
Competitive effect
The industries are ordered according to how many people they employed in the latest year selected
( 2007) .
During the period 1990 to 2007, employment in Oktibbeha County grew by 2,869 jobs. In terms
of employment growth, the most important industry was Professional and Business Services (1,411
jobs). It is followed by Education and Health Services( 1,376 jobs), and leisure and Hospitality (
1,929 jobs).
Table 1 presents the employment changes for the time period selected in Oktibbeha County, MS.
During the period 1990 to 2007, employment in the county grew by 2,869 jobs.
Table 1: Employment Changes in Oktibbeha County, 1990 to 2007.
Sector
Employment,
1990
Employment,
2007
Percent Growth,
1990 - 2007
Employment Change
Education and Health
Services
1,868
3,244
1,376
73.7
Trade, Transportation, and
Utilities
2,025
2,299
274
13.5
Leisure and Hospitality
1,207
2,136
929
77.0
396
1,807
1,411
356.3
Professional and Business
Services
Manufacturing
Table 1: Employment Changes in Your Area, 1990 to 2007.
2,111
1,582
-529
-25.1
1,369
809
-560
-40.9
Financial Activities
554
437
-117
-21.1
Construction
330
410
80
24.2
Other Services
249
234
-15
-6.0
Information
119
162
43
36.1
66
43
-23
-34.8
10,294
13,163
2,869
Public Administration
Natural Resources and
Mining
Table 2: Shift-Share Analysis for Oktibbeha County, 1990-2007.
Sector
National Growth National Growth Industrial Mix
Component,
Component,
Component,
Percent
Jobs
Percent
Competitive
Share
Component,
Percent
Industrial Mix
Component,
Jobs
Competitive
Share
Component,
Jobs
Professional
and Business
Services
24.7
98
44.8
177
286.8
1,136
Education and
Health Services
24.7
461
23.2
434
25.8
481
Leisure and
Hospitality
24.7
298
17.9
216
34.4
415
Information
Your Area, 1990-2007.
24.7Table 2: Shift-Share
29 Analysis for
-15.2
-18
26.6
32
Natural
Resources and
Mining
24.7
16
-20.3
-13
-39.3
-26
Trade,
Transportation,
and Utilities
24.7
500
-8.7
-176
-2.5
-50
Manufacturing
24.7
521
-47.2
-997
-2.5
-53
Construction
24.7
81
19.3
64
-19.7
-65
Other Services
24.7
61
3.1
8
-33.8
-84
Financial
Activities
24.7
137
-5.4
-30
-40.4
-224
Public
Administration
24.7
338
-10.0
-136
-55.6
-762
2,540
-471
800
1. The National Growth Component
The first source of change is the growth or contraction in the United States economy. This growth rate is listed in Table 2
as the national growth component.
Overall, the national growth component was responsible for a total of 2,540 jobs in Oktibbeha County.
An understandable goal of some local leaders is to make their economy more 'recession proof'. Economies
with more employment in government, military and education will experience less fluctuation because those
sectors are not directly related to the business cycle.
Also, economic sectors that are experiencing more growth will provide larger employment gains to a local
economy.
2. The Industrial Mix Component
The industrial mix component measures how well an industry has grown, net the effects from the business cycle.
Table 2 lists these components for each sector.
If the county's employment were concentrated in these sectors with higher industrial mix components, then the area
could expect more employment growth. After adding up across all eleven sectors, it appears that the industrial mix
component was responsible for decreasing Oktibbeha County’s employment by -471 jobs.
Thus, the area has a concentration of employment in industries that are decreasing nation-wide, in terms of
employment. The majority of these jobs can be attributed to decreases (-997 jobs) in the Manufacturing sector.
3. The Competitive Share
The third and final component of shift-share analysis is called the competitive share. It is the remaining employment change that is
left over after accounting for the national and industrial mix components.
If a sector's competitive share is positive, then the sector has a local advantage in promoting employment growth.
The top three sectors in competitive share were Professional and Business Services, Education and health Services, and leisure and
Hospitality. Across all sectors, the competitive share component equaled 800 jobs. This indicates the county is competitive in
securing additional employment.
A positive competitive share component indicates the county has a productive advantage. This advantage could be due to local
firms having superior technology, management, or market access, or the local labor force having higher productivity and/or lower
wages.
A negative competitive share component could be caused by local shortcomings in all these areas.
By examining the competitive share components for each industry, the development official can easily identify which local
industries have a positive competitive share component. This also indicates which industries have competitive advantages over
other counties and regions.
Local officials can then devise strategies to improve local conditions faced by particular industries selected for focus. These
strategies may include specialized training programs for workers and management, improved access to input and product markets
through transportation and telecommunications, or arranged financial alternatives for new machinery and equipment.
Questions?
THANK YOU!