Targeting Sales Effort 9780231891622

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DETROIT BOSTON SAN FRANCISCO PITTSBURGH ST LOUIS

Targeting

Sales Effort

WASHINGTON D C CLEVELAND BALTIMORE

By CHARLES W. SMITH Β Β Β Β Β

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M INNEAPOLI S-ST PAUL BUFFALO KANSAS CITY HOUSTON CINCINNATI

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MILWAUKEE DALLAS SEATTLE MIAMI ATLANTA

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PORTLAND ORE DENVER INDIANAPOLIS SAN DIEGO PROVIDENCE

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NEW ORLEANS LOUISVILLE COLUMBUS OHIO ROCHESTER Ν Y MEMPHIS

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DAYTON FORT WORTH SAN ANTONIO BIRMINGHAM AKRON

SENIOR CONSULTANT. McKlNSEY 4 C O M P A N Y . INC.

NEW Y O R K , 1958

COLUMBIA UNIVERSITY

PRESS

ACKNOWLEDGMENTS

THE METHOD presented in this book has gradually evolved over a period of two decades, during which m a n y individuals m a d e substantial contributions to its development. A n u m b e r of my associates at McKinsey & C o m p a n y helped to test the concepts embodied in the m e t h o d ; others encouraged me to bring the method to its present stage of development. Marvin Bower, Carl H o f f m a n , Phillip Babb, Everett Smith, A r c h Patton, H o w a r d A d a m s , A r t h u r Caruso, Gould Jones, Harvey T h o m a s , C a m e r o n Caswell, John Flint, R o b e r t Axtell, Stuart N i m m o , James M a r k e r , and Charles Fish have all aided me. Others who have been of help in the project w h o m I should like to specifically thank by n a m e are: Charles A. Kornheiser, President, D a t a C o m p u t i n g C o r p o r a t i o n ; ΑΙ Ν. Seares, Vice President, Remington R a n d Division, Sperry R a n d C o r p o r a t i o n ; C. A. Brewer, C. G. Klock, a n d William H . Bloodworth, General Electric C o m p a n y ; E . P. Salvas,

Controller, Special Products Division, National Biscuit C o m p a n y ; Ralph K. Guinzburg, President, I. B. Kleinert R u b b e r C o m p a n y ; Mrs. Rachel Bard, Douglas Fir Association; J o h n White, Vice President, General D r a f t i n g C o m p a n y ; Mrs. Jessie A s p d e n ; W a r r e n W . C a m p bell; A r t h u r L. Scaife; F r a n k l i n Cawl, Sr.; Joseph Boyajy, Vice President, J o h n Felix & Associates; Henry H . Wiggins, C o l u m b i a University Press; and Harvey Kailin, H e n r y Wulff, H o w a r d G . Brunsm a n , A. Ross Eckler, and H o w a r d C . Grieves, Bureau of the Census. Finally, I should like to express especial t h a n k s to C a r o l i n e Bird of Dudley-Anderson-Yutzy, who is virtually the author of this book. It was she w h o finally expressed the thoughts of a technical specialist in words that should make the method understandable to any executive w h o is faced with the kinds of problems this method can help to resolve. CHARLES

New York, New York December 16, 195 7

COPYRIGHT

©

1958

COLUMBIA

UNIVERSITY

PRESS,

NEW

YORK

P U B L I S H E D IN GREAT BRITAIN, CANADA, INDIA, AND PAKISTAN BY T H E OXFORD UNIVERSITY P R E S S L O N D O N . T O R O N T O , BOMBAY, AND KARACHI LIBRARY O F CONGRESS CATALOG CARD N U M B E R :

58:6864

M A N U F A C T U R E D IN T H E U N I T E D STATES O F AMERICA

W.

SMITH

CONTENTS Part

I.

TRADING

CENTER

GEOGRAPHIC

METHOD

SALES

Figures

OF

ANALYSIS

1.

Introduction CHAPTER 1.

CLASSIFICATION OF MARKETS

CHAPTER 2.

MECHANICS OF THE TRADING CENTER

CHAPTER 3.

DISTRIBUTION OF TOTAL UNITED STATES POPULATION, RETAIL SALES VOLUME, AND NUMBER OF RETAIL ESTAB-

3

LISHMENTS BY CLASS OF TRADING CENTER AREA

8 2.

TYPICAL REPORT OF SALES BY CUSTOMER TYPICAL INDIVIDUAL DEALER WORK SHEET

METHOD

1 1

3.

USES OF THE TRADING CENTER METHOD

20

4.

EXCERPTS FROM TYPICAL REPORT OF SALES BY TRADING CENTER

Part II.

TOOLS

OF

TABLE 1.

THE

TRADING

CENTER

METHOD

5.

TYPICAL SUMMARY ANALYSIS OF SALES PERFORMANCE BY CLASS OF TRADING CENTER AREA

CITIES OVER 2,500 POPULATION: ALPHABETIC KEY BY STATE

27

TABLE 2.

COUNTIES: ALPHABETIC KEY BY STATE

27

TABLE 3.

URBAN TRADING CENTERS BY RETAIL SALES VOLUME

6.

BY INDIVIDUAL TRADING CENTER AREAS 7.

15 6

TYPICAL SUMMARY ANALYSIS OF SALES PERFORMANCE

DISTRIBUTION OF SALES VOLUME BY CLASS OF TRADING CENTER AREA FOR A MAJOR ELECTRICAL APPLIANCE COMPANY

TABLE 4.

COUNTIES AND CITIES OVER 2,500 POPULATION BY TRADING CENTERS

TABLE 5.

165

DISTRIBUTION OF TOTAL UNITED STATES POPULATION, RETAIL SALES VOLUME, AND NUMBER OF RETAIL ESTABLISHMENTS, ALPHABETIC BY STATE

Table

6.

393

SUPPLEMENTARY DATA TO BALANCE DISCLOSED CITY AND COUNTY INFORMATION WITH STATE AND NATIONAL TOTALS, ALPHABETIC BY STATE

395

Trading Center Method of Geographic Sales

Analysis

INTRODUCTION

THIS BOOK introduces a statistical tool for developing information

opportunities. One problem is to know where people are purchasing

about where people buy. Its purpose is to help marketers of nationally

their requirements, for all marketing strategy necessarily is based on

distributed products target their sales efforts more efficiently. T h e

assumptions about where people buy. Such assumptions

tool is a classification of markets which reflects the concept that most

management decisions regarding the geographic allocation of direct

influence

products are purchased within a relatively small area surrounding

selling and advertising effort, the location of factories, warehouses,

each major center of population, and that these areas logically fall

and dealers, and the appraisal of individual or group sales perform-

into a small number of classes having common commercial charac-

ance.

teristics.

Those who made the first attempts to determine where people buy

While the concept is simple and not original with the author, devel-

were handicapped by lack of information regarding the distribution

opment of a method of classification that would resolve the problems

of retail sales volume. Because population statistics were available to

involved in its application has not been easily achieved. T h e tool

indicate where people lived, initial efforts at geographical sales analy-

presented here represents the experience gained in trial and error

sis involved the tabulation of city sales data according to categories

application of the method to practical business problems over the past

determined by city population size. In most instances, these efforts

fifteen years.

were stimulated by publications or advertising agencies concerned

Geographic sales analysis is both difficult and important because the great American market is diverse and constantly changing. In the

primarily with problems involved in determining the most effective patterns of advertising coverage.

early history of this country, people bought where they lived. Only

Improved methods of geographical sales analysis which these same

the very wealthy traveled any distance to shop. Then the growth of

agencies and publications later developed utilized privately gathered

railroad networks and urban area transportation systems stimulated

information about the distribution of retail sales potential. They were

the development of downtown area stores in major cities. Later the

designed to disclose the areas of influence around principal cities in a

automobile and improved roads gave impetus to this movement of

network covering the entire country. Manufacturers who tried to

consumers into major centers by enabling them to shop easily at long

apply these methods to problems involving the distribution of sales

distances from their homes. Now congestion of downtown areas has

outlets and salesmen, however, found them to have a number of serious

caused city dwellers to move into surrounding suburban areas in

shortcomings.

increasing numbers, with the result that major shopping centers have been developed to make shopping easier for suburbanites. From the viewpoint of the marketing strategist, this constant change in the structure of the national market creates both problems and

Since the method presented in this book grew out of a search for a way of overcoming the difficulties involved in using existing methods, it may be helpful first to review the historical limitations of city methods and area methods.

4

INTRODUCTION

1. A complete analysis by cities requires the tabulation of data for over 30,000 cities and towns. The great number of individual cities runs up the cost of the study and makes it difficult to interpret results. T o reduce costs and simplify tabulation, data are usually accumulated only for a selected list of cities. While apparently a simple and sound step, the use of an abridged list of cities involves three difficulties.

Whenever city sales figures for any company are directly compared, it is always necessary to take into account the size of the primary market area served by the city. For example, the incorporated cities of Pittsburgh, Pennsylvania, and Milwaukee, Wisconsin, are approximately equal in population. The primary market area served by Pittsburgh, however, has approximately twice the population and retail sales of the area served by Milwaukee. On the basis of per capita sales in the two incorporated city areas, Pittsburgh would appear to be a much better market than Milwaukee. Computed on the basis of their primary market areas, however, per capita sales in the two cities are approximately equal.

The first difficulty is that of selecting the cities to be included in the list. Population size is commonly used, but it fails to distinguish between independent cities and suburbs. Also, important unincorporated places (e.g., Weirton, West Virginia) are often omitted from such a list.

Similarly, a comparison of advertising circulation in a city with the retail sales in the same city may produce a misleading result. A truer measure of the effectiveness of advertising is obtained when total circulation in the primary market area served by the city's stores is compared with total retail sales in the same area.

The second difficulty is that of making certain that all sales in a given city are actually credited to the city. In New York City, for example, there are a great many unincorporated communities (e.g., Flushing, Forest Hills) that are not easily recognized as parts of the city. A company's volume in a particular city thus may easily be understated as a result of clerical failure to identify all sales transactions originating in that city.

3. City boundaries frequently change. Sales trends for a given city are hard to plot over a period of time during which the boundaries of the city itself have changed, or the population of the city has been shifting to the suburbs.

CITY METHODS Because of the relative ease with which sales can be identified by cities, the incorporated city methods are the most widely used. A number of serious limitations in these methods are recognized, however, by informed analysts.

The third difficulty is that all sales made in cities not included in the selected list are automatically grouped together. In many cases, a large "all other cities" figure serves to confuse the results of the analysis. 2. The use of city methods can lead to serious errors of interpretation. One of the most common errors results from comparing per capita sales in cities of similar population that differ in commercial or buying importance. For example, Yonkers, New York, with a population of ] 53,000 and retail sales of $164,000,000 is much less important commercially than Albany, New York, which has a population of only 135,000 but retail sales of $221,000,000.

4. Market potential data for a given product are seldom available for all incorporated cities. This is due not only to the great number of cities, but also to the disclosure rules of the Bureau of the Census. By law, the Bureau cannot give retail sales volume figures for any city where the release of such data would disclose the operations of an individual establishment or business organization. If a city has only two furniture stores the Bureau cannot release furniture sales figures for that city. Thus, while retail sales figures are normally available for all important counties and for states, they are often not available for some important cities. This creates the problem of estimating potentials for a number of cities without the benefit of source data. Where city estimates are necessary, they can be developed on a reasonably accurate basis, but the cost of developing such estimates is rather high.

INTRODUCTION In view of the foregoing factors, the use of city methods of geographic sales analysis has tended to decline in favor of other methods that are either more accurate or less costly to use. AREA METHODS Over the years, a number of different area methods of geographic sales analysis have been used. In some instances these methods were originally developed to overcome the obvious shortcomings of city methods. In other cases they were developed to meet special needs where the degree of detail developed by city methods was not required. Area methods involve the tabulation of basic data by carefully defined geographic areas. The areas vary in number and size according to the concepts that underlie their definition. The largest areas are the nine major geographic regions used by the Bureau of the Census in reporting summary data for broad sections of the United States. States are also commonly used as geographic areas. To analyze geographic sales data in certain industries in which manufacturers sell primarily through wholesale outlets (such as the drug, wholesale dry goods, and food industries), special wholesale trading areas 1 have been developed to show the primary area of influence around each major wholesale trading center. Other types of trading areas have been developed to show the territory served by retail outlets located in each major city.2 In most cases these areas reflect the pulling power of the large shopping goods out1 See " A t l a s of W h o l e s a l e D r y G o o d s T r a d i n g A r e a s , " by E l m a S. M o u l t o n , B u r e a u of F o r e i g n a n d D o m e s t i c C o m m e r c e . D e p a r t m e n t of C o m m e r c e ( 1 9 4 1 ) ; " A t l a s of W h o l e s a l e G r o c e r y T e r r i t o r i e s , " by J a m e s W . M i l l a r d , B u r e a u of F o r e i g n and D o m e s t i c C o m m e r c e , D e p a r t m e n t of C o m m e r c e ( 1 9 2 7 ) ; " T h e N . W . D . A . Dist r i b u t i o n M a p , " by N a t i o n a l W h o l e s a l e Druggists A s s o c i a t i o n , A m e r i c a n M a p C o . (1948).

- See " C o n s u m e r T r a d i n g A r e a s of the United S t a t e s , " by L. J . M c C a r t h y , D i r e c t o r of t h e M a r k e t i n g D i v i s i o n , I n t e r n a t i o n a ] M a g a z i n e Co., Inc. ( 1 9 5 2 ) ; " M a r k e t A r e a s in t h e U n i t e d S t a t e s , " b y R e s e a r c h D e p a r t m e n t . T h e C u r t i s P u b l i s h i n g C o m p a n y (1952). A m o r e c o m p l e t e list of m a r k e t i n g m a p s is a v a i l a b l e in t h e a n n o t a t e d b i b l i o g r a p h y " M a r k e t i n g M a p s of t h e U n i t e d S t a t e s . " by M a r i e C. G o o d m a n and W a l t e r W . R i s t o w of t h e L i b r a r y of C o n g r e s s ( 1 9 5 2 , 100 p a g e s ) .

5 lets in each city, such as the department stores. Typically, these retail trading area systems involve the use of a standard index of sales potential as a basis for comparison with the manufacturer's own sales figures. Area methods of geographic sales analysis have many useful applications. A major advantage of such areas over cities as a basis for analysis is the relatively smaller number of area figures. Furthermore, because such areas cover the entire United States, the resulting figures include all sales. Another advantage is the availability of sales potential data for comparison with company sales figures. For this reason, area methods are useful in appraising variances in the level of sales performance and in analyzing advertising coverage. They can also be helpful in laying out sales territories and developing a sound plan of field sales organization. In spite of many useful applications of area methods of geographic sales analysis, a number of factors limit their usefulness for certain types of problems. One of the more important limitations is the fact that it is difficult to classify trading areas by type and size on any basis that has commercial significance. Two areas having the same total sales potential can present radically different sales coverage problems. For example, an area around a city like Denver, Colorado, may contain a single important city with a large surrounding rural territory, whereas another area of equal potential around a city like Youngstown, Ohio, may consist of a cluster of smaller cities with little or no strictly rural territory. The number and type of potential outlets and customers for any product in these two areas would be quite different. Thus, any comparison of average performance in the two areas would have only limited value as a basis for determining sales policies or controlling selling effort. A second limitation of certain area methods is the fact that they involve the use of split counties. When two or more major trading centers draw trade from a single county, the county must be divided

INTRODUCTION

6 between them to define the T h e increased accuracy of county, however, is seldom sales transactions that must area c o d e is applied.

precise area of influence of each center. definition to be gained by splitting the worth the extra cost of tracing individual be done to m a k e certain that the proper

Actually, there are only a few counties'' in the United States where failure to split the county will make a m a j o r difference in the total sales potential of any area. Normally, the m a j o r part of any split county logically falls in one area and the balance of the county represents a very minor adjustment of the potential in the adjoining area. T h e Bureau of the Census has now recognized the practical difficulties involved in using split county areas by establishing standard metropolitan areas o n a full county basis in the 1950 Census to replace the split county metropolitan districts used in the 1940 Census. NEED

FOR

TRADING

CENTER

METHODS

T h e need for a method of geographic sales analysis that would overc o m e the basic disadvantages inherent in the city and area methods of determining market coverage was recognized in the 1930s. As late as 1938, however, the only comprehensive breakdowns that classified markets o n the basis of their relative importance as integrated retail trading centers were private systems evolved by advertising agencies and publications on the basis of population. When the available methods were applied to evaluate dealer coverage for electrical appliances, for example, a n u m b e r of weaknesses were disclosed in every method. N o n e of them provided a basis for determining the number of points at which dealer outlets should be franchised for o p t i m u m coverage of a distributor's territory. Without clear-cut trading areas, it was difficult to appraise the effectiveness of each distributor's sales coverage. Even when sales were broken down o n a county-by-county basis, it was necessary to make an individual investigation of each county that showed suspiciously low ;t

Principally located

in t h e N e w

England states, w h e r e the n u m b e r

r e l a t i v e l y s m a l l in r e l a t i o n t o t h e n u m b e r o f l a r g e c c n t e r s o f

o f c o u n t i e s is

population.

sales in order to determine whether it was outside the territory of any dealer, whether the dealers supposedly covering it were doing a poor job, or whether people in that particular county habitually bought their appliances in an adjacent county. T h e Second World W a r intensified the need to determine natural market areas so that appliance service centers could be located with the greatest possible efficiency. Such areas were also needed to m a k e rational plans to help good dealers stay in business pending the eventual resumption of appliance production. These and other problems facing analysts of geographic sales information in the war years focused attention on the desirability of determining trading center areas based on the concept that each major center of population in the United States serves as a primary shopping center for the residents of a small surrounding tributary area. T r a d i n g center areas, if they could be determined accurately, would indicate the points at which a m a n u facturer of consumer goods should consider establishing distribution at the retail level. DEVELOPMENT IN

THIS

OF METHOD

PRESENTED

BOOK

T h e first step was to determine the n u m b e r , type, and location of trading centers that could be classified as u r b a n as contrasted with those that were predominantly rural in character. T o do this, the author analyzed the n u m b e r and location of independent cities as contrasted with suburbs, and the cities in which daily newspapers and department stores were located. When another researcher discovered that people living in rural areas spend the m a j o r part of their income within the county of their residence, the basis was established for the initial classification of trading centers into seven classes of a r e a s — six urban and one rural. This classification was then applied successfully in the analysis of a n u m b e r of problems. Observation in the field eventually established the fact that any rural county containing a town of over 5 , 0 0 0 population was usually an important rural trading center. As a result of this finding, the rural

INTRODUCTION trading center classification was broken down into two classes, "key" and "secondary" areas. A map subsequently published to show the location of every trading center provided the first publicly available description of the method. 4 Publication of the final 1950 population figures by the Bureau of the Census required a second revision to allow for the effect of population changes between 1940 and 1950 on the relative importance of various classes of trading centers. At this time, it was decided to analyze the correlation between the concentrations of population and retail sales volume as reported in the 1948 Census of Business. This analysis resulted in two further changes in the method of classification: ( 1 ) three groups of rural trading centers were set up in place of the former two, and ( 2 ) retail sales volume was considered along with population in classifying all trading centers. Tests of the revised method of classification proved it to be more accurate than the previous methods. Since publication of the retail sales volume data in the 1954 Census of Business, the classification of trading centers has been •»"Market Areas for Consumer Goods," McKinsey & Company (1947).

7 further revised to reflect changes in retail sales volume since 1948. Population

figures

were not changed because no revised official

population data will be available until the results of the 1960 Census of Population are published. Those analysts to whom interim population estimates are of vital concern know that Sales Management

magazine has published esti-

mates of current population for each county annually for a number of years. These data provide one means of checking the change in importance of individual trading center areas. As a rule, however, significant changes in the relative importance of individual trading center areas occur only over a period of years. Thus, the rankings assigned to individual trading centers (particularly the class in which they fall) provide a reasonably accurate basis for appraising their relative importance during the period between the decennial censuses. Part I of this book describes the method in detail, explains its use in general, and illustrates its application to specific types of marketing problems. The tables in Part II provide a complete listing of cities, counties, and trading center areas needed to apply the method.

CHAPTER 1 CLASSIFICATION OF MARKETS THE METHOD of geographic sales analysis presented in this book reflects two basic concepts: ( 1 ) that each major center of population in the United States serves the residents of a relatively small surrounding geographic area, and ( 2 ) that these trading center areas can be categorized into a relatively small number of classes because they have certain commercial characteristics in common that are reflected in the population and retail sales volume statistics. The method thus rests fundamentally on a classification of markets that is described in this chapter. In developing this classification of markets, a number of practical considerations affecting the usefulness of any system of geographic sales analysis were kept in mind. Specifically, the classification was devised to: ( 1 ) permit the use of existing company sales records; ( 2 ) facilitate comparisons of company information with outside market data; ( 3 ) encourage direct comparisons among areas having similar commercial characteristics regardless of their location geographically; and ( 4 ) facilitate the coding of all data for machine tabulation. BASIS FOR DEFINING TRADING CENTER AREAS The first decision was to define all trading center areas in terms of whole counties. The number of counties is relatively fixed and manageably small compared with the number of cities, towns, villages, townships, and parishes in the United States, and county boundaries are the most stable political units smaller than states. In addition,

more market data are available on a county basis than for cities and minor civil divisions. Finally, the county has been recognized as the natural unit for defining larger areas, since even the Bureau of the Census has established its standard metropolitan areas in terms of whole rather than split counties. The next decision was to group counties in metropolitan areas in accordance with the boundaries established by the Bureau of the Census. This served to insure a reasonable degree of uniformity of definition among areas of comparable importance. CHARACTERISTICS OF TRADING CENTER AREAS The trading center area classes shown in Figure 1 reflect the commercially important factors of total population, degree of concentration of population within the area as indicated by the population of its largest city, and retail sales volume in the area or in its largest city. POPULATION SIZE. Analysis of all trading center areas indicated two critical points in population size: 2,000,000 and 350,000. Any area with more than 2,000,000 population is automatically a Class A area. To qualify as either a Class Β or Class C area, any other trading center must have a total population greater than 350,000. CONCENTRATION OF POPULATION. Population concentration proved the key to differentiating between markets with similar total population but with substantially different commercial characteristics. The proportion of a trading center's population concentrated in its largest city determines its character. An area with a total population between

CLASSIFICATION O F M A R K E T S

9

Figure 1. DISTRIBUTION OF TOTAL UNITED AND NUMBER OF RETAIL ESTABLISHMENTS

Description of Trading Center Area Class A.

B.

C. D.

E.

F.

All areas with a population of over 2,000,000 or with a city that accounted for more than .65 percent of United States retail sales All areas with a population from 350,000 to 2,000,000 with a city that has either more than 250,000 population or more than .2 percent of United States retail sales All other areas with a population from 350,000 to 2,000,000 All areas of less than 350,000 population with a city that has either more than 100,000 population or more than .1 percent of United States retail sales All other areas of less than 350,000 population with a city that has either more than 50,000 population or more than .05 percent of United States retail sales All other areas of less than 350,000 population with a city of 25,000 population Total Urban

All other areas of less than 350,000 population with both a city of more than 5,000 population and more than .025 percent of United States retail sales H. All other areas below 350,000 population with either a city of more than 5,000 population or more than .01 percent of United States retail sales I. All remaining areas

STATES POPULATION, RETAIL SALES VOLUME, BY CLASS OF TRADING CENTER AREA

Number of Trading Center Areas

Retail Sales ' Volume (thousands Percent of dollars) of U. S.

Population b Percent Number of 11. S.

Number of Retail Establishments

13

$ 56,181,069

33.0581

43,345,341

28.7624

488,928

27 13

23,223,864 7,579,264

13.6663 4.4596

17,044,757 6,517,972

11.3100 4.3254

185,950 78,678

45

13,822,213

8.1333

10,687,277

7.0914

119,432

83

13,449,106

7.9136

10,952,407

7.2682

128,286

130

10,068,112

5.9244

9,168,369

6.0843

106,889

311

$124,323,628

73.1553

97,716,123

64.8417

1,108,163

198

$ 12,609,633

7.4201

11,288,774

7.4910

141,025

930 1,509

22,322,458 10,692,412

13.1337 6.2909

25,073,900 16,618,564

16.6380 11.0293

295,828 176,775

G.

Total Rural

2,637

$ 45,624,503

26.8447

52,981,238

35.1583

613,628

Total United States

2,948

$169,948,131

100.0000

150,697,361

100.0000

1,721,791

' Based on latest (1953) Census of Retail Sales.

"Based on latest (1950) Census of Population.

10 350,000 and 2 , 0 0 0 , 0 0 0 was designated Class Β if it had a city of more than 2 5 0 , 0 0 0 population, and Class C if it did not. This effectively separated areas with a high concentration of retail trade potential (Buffalo, Milwaukee, and Kansas City) f r o m areas with a low concentration of retail trade potential (Scranton, Norfolk, and Worcester). The significance of the population of the key city can be illustrated by comparing the Class Β Rochester trading area with the Class C Albany-Schenectady-Troy trading center. Both have about the same total population and retail sales volume, but there are nearly 25 percent fewer retail establishments in the Rochester area. The higher concentration of retail trade potential in Class Β areas tends to restrict the total number of retail outlets and increase their average volume size. Depending on the nature of the product involved, the factor of trade concentration in an area can have a marked influence on the action needed to establish satisfactory distribution. If a manufacturer of household appliances decides to follow a policy of selective distribution, for example, he may be able to cover an area dominated by one key city with only one or two outlets. T o establish equally satisfactory coverage of a more dispersed area of comparable total population, however, it may be necessary to franchise three or more outlets. Population concentration was also used to differentiate between the smaller markets. An area with less than 3 5 0 , 0 0 0 total population was automatically designated Class D if it had a city of 100,000; Class E if it had a city of 5 0 , 0 0 0 ; and Class F if it had a city of 25,000. Rural areas with a city or town of between 5,000 and 2 5 , 0 0 0 population were designated either Class G or Class H. RETAIL SALES VOLUME. Areas within each population class were listed in order of their retail sales volume, so that this factor could be used to assign to areas of exceptional commercial importance higher classi-

CLASSIFICATION OF MARKETS fications than would be indicated strictly on the basis of population size or concentration. Although Dayton, Ohio, had less than 250,000 population, it was assigned Class Β rather than Class C because of its relatively high concentration of retail sales volume. The factor of retail sales also helped to identify rural trading centers with either an unusually high resident per capita income or a largerthan-normal surrounding trading area. T o cite only one example, Roseburg, Oregon, had more than twice the retail sales volume of Danville, Kentucky, although its population is about the same. SIGNIFICANCE

OF

CLASSIFICATIONS

Since no two markets in this country are exactly of classification necessarily involves a number of apply with varying degrees of accuracy to individual reason, it may well be argued that the importance of markets has been understated.

alike, any system assumptions that markets. For this certain individual

From the viewpoint of the marketing strategist, however, the method provides a very practical basis for determining significant concentrations of sales potential or variations in sales performance. F r o m the viewpoint of the sales administrator, the orderly ranking of areas of similar size provides a basis for identifying quickly the areas in which sales performance is out of line either on the high side or on the low side. Figure 1 shows the distribution of population and retail sales volume by class of trading center. It also discloses the number of retail establishments and number of trading centers in each class. Specifically, it shows that more than one-half of the total retail sales volume in the United States was generated in only 53 trading centers, while only 6 percent came from 1,509 Class I counties. These summary figures alone are sufficient to indicate the potential importance to any company of knowing the pattern of its distribution in relation to such a classification of markets.

CHAPTER 2 MECHANICS OF THE T R A D I N G CENTER τ ο SHOW HOW the classification can be used, let us assume that a manufacturer of quality children's underwear with national distribution wants to know where he can profitably expend additional effort to increase his sales. He might also want to test some theories about his distribution by getting precise answers to a number of typical questions: 1. Is the company doing better in the big metropolitan markets than it is in smaller cities? 2. Is the Bridgeport, Connecticut, salesman doing as good a job as the salesman in Norfolk, Virginia? 3. How much additional business could the company reasonably hope to gain in areas where its share of market is now low? 4. Is the potential rural market volume worth the cost of getting it? T o answer such questions, the manufacturer must determine the extent to which his present sales volume reflects the true sales potential for his products in every local market. SELECTING THE SALES POTENTIAL YARDSTICK The first step is to select a suitable yardstick for measuring sales potential. In this case, let us assume that industry-wide sales volume figures are available, but that there is no breakdown of the total volume by cities. The manufacturer, therefore, must select an index that will provide a basis for estimating the approximate volume of quality children's underwear being sold in each county in the United States. In selecting such a yardstick of sales potential, the manufacturer keeps in mind that his products require a special kind of customer—

METHOD

babies. If he were selling dog collars or dog food, his sales volume would be influenced by the number of dogs in an area; if encyclopedias, by the educational status of the population; if wallpaper, by the volume of residential construction; if work pants, by the level of factory a n d / o r agricultural employment. The manufacturer finally concludes that since every mother with loose change in her pocketbook is a potential customer for his product, the amount of income left over after the purchase of shelter, food, and clothing will provide a fair measure of sales potential. Such an index not only reflects the number of families in an area, a proportion of whom may be expected to have babies, but also the amount of money they have to spend for niceties of life such as high quality children's underwear. U p o n investigation, the manufacturer finds that Sales Management magazine publishes an annual Survey of Buying Power that gives an estimate of the percent of total disposable income in the United States for each county. By comparing his present sales with the index of disposable income, the manufacturer can determine whether his sales consistently parallel disposable income. If they do, then he will have strong evidence that his sales efforts are properly related to the potential for his product. If he finds, on the other hand, that there is little correlation between his sales and disposable consumer income, he will then have to assume either (1) that his sales efforts are not being targeted properly or (2) that disposable income does not accurately reflect the distribution of the quality children's underwear sales potential. In the latter event, he must initiate a study of the detailed factors that can be used to measure sales potential. In such a study, the number of children by age brackets, weather conditions, the types of retail outlets available

12

to promote the sales of the product, and other factors may be analyzed to develop a custom-made index of sales potential for the product line. For the present, however, the manufacturer decides to compare his sales during the period under consideration with disposable consumer income in each trading center area. CODING SALES DATA The second step in using the trading center method of analysis is to code the basic sales data. To simplify this task, the method provides a six-digit code for each trading center area. The specific number assigned to each area is shown in Table 4, which lists all areas in numerical order. The first digit in the code number for each area indicates the class of trading center area: 1 for A, 2 for B, and so on down to 9 for I. The next four digits indicate the rank of the trading center area in its class based on total retail sales volume. The sixth digit is used only to designate individual counties in multiple county urban areas. Thus, code number 1-0001-1 shows that the area is a Class A market; that it is in the most important trading center area in the country (New York City); and the final 1 indicates that it is in the most important county in that area (New York County). The code number for Kings County, the second most important county in the New York City area, is 1-0001-2. The number 1-0002-1 stands for the first county (Cook) in the second most important trading area (Chicago), also a Class A area. This digital system can be used with either mechanical or electronic data processing methods. It facilitates summarization of data by classes or by individual areas. To obtain summary data for the nine classes it is necessary only to sort by the first digit. Should a summary figure be needed for each area, then the first five digits are used in sorting. All six digits need be used only when a complete breakdown by counties within each area is required. The coding procedure that is used will depend on the kind of sales

MECHANICS OF THE METHOD records kept by the company. Any form of sales record may be used that shows the city or county location, provided the records of shipments or orders show the point of ultimate sale for use—in this case, the location of the retail outlets selling the company's children's underwear. When a company sells to wholesalers who in turn sell to retailers, it is necessary to obtain a record from each wholesaler of sales to each retail outlet. When sales records are not available that will permit identification of the point of sale for use, wholesale sales records can be analyzed to show concentration of sales for resale in each trading center area. Such summary data provide the basis for a number of types of secondary analyses. To determine the point of sale for use, it may even be necessary to go back through a file of invoices or orders, coding each transaction by hand. In most "companies, however, invoice or order information is routinely transferred to a customer account record in which the volume sold to each customer is summarized at regular intervals. In some companies, sales to each account are posted by hand on cards or sheets; in other companies it is kept on machine-tabulating punchcards and reports. Sales may be summarized by month or by quarter, or cumulated at each posting so that the total for the period is always shown. In the case of quality children's underwear, let us assume the manufacturer is selling direct to 5,000 retail stores and that he records his cumulative sales of each major product to each of these customers monthly on a dealer sales summary report that lists the accounts in each sales territory (Figure 2). Under these circumstances the simplest way to code is to transfer the sales information to an individual work sheet for each dealer. The work sheet shows the dealer's name, city, county, state, type of outlet, sales volume, and trading center code number (Figure 3). When the work sheets are made out, they are sorted alphabetically by state, and then by city within each state. They are then ready to receive the trading center area code number.

MECHANICS OF THE METHOD Figure 2.

TYPICAL

REPORT

CUMULATIVE

Territory

Customer Number

Class

CUSTOMER

Department Code Number*

80

5,035

1

33 35

80

5,610

1

21

80

5,903

9

21

80

6,167

9

11

80

6,173

9

11

80

9,599

2

13 OF SALES BY

11 21 27 33 38 44 50 60 82 84

80

9,990

2

12 84

80

9,990

9

11 12 21 27 31 33 35 41

CUSTOMER

REPORT

Dollar Sales to Date Last Year

This Year

996,930 179,760 1,176,690· •

36,033 36,033* 37,780 37,780* 3,100 3,100* 4,050 104,470 90 12,000 7,500 180 71,851 11,844 2,706 27,690 242,381* 600 850 1,450* 11,843 129,474 1,030 325 12,195

Territory

Number

Department Dollar Sales to Date Code _ Last Year This Year Number'

43 44 50 57 82 84

1,077,852 128,760 1,206,612* 295,200 295,200* 40,680 40,680* 7,700 7,700*

80

CR *

5,850 CR CR CR CR CR CR 26,808 3,775 8,920 45,353* 1,800 10,960 12,760* CR 1,580 19,113 15 4,059 3,580 19,752 2,360

Customer

9,998

11 12 21 22 27 31 33 35 36 38 40 41 43 44 50 51 52

780 800 26,710 183,157* 74,573 58,471 121,336 11,429 175,530 49,165 40,781 13,536 4,815 1,480 25,804 77,888 31,886 63,094

613 12,385 120 401 10,290 18,390 92,658* 186,882 67,560 132,075 31,608 5,141 202,014 65,695 62,883 29,641 317 8,637 12,468 88,249 71,477 49,889 3,257 17,460

* D e p a r t m e n t c o d e n u m b e r s i d e n t i f y d i f f e r e n t m a j o r p r o d u c t s sold by the c o m p a n y .

T o illustrate how the coding is done, assume that the first work sheet is for a variety store in Bridgeport, Connecticut. We look up Bridgeport in Table 1, which lists all cities over 2,500 alphabetically by state. There we learn that Bridgeport is in trading center area 3-0042, the nation's 42d biggest market. This number is entered in the space provided on the work sheet.

14

MECHANICS OF THE METHOD Figure 3. TYPICAL

Customer Number

INDIVIDUAL

DEALER

WORK

SHEET

discover that Bridgeport has a population of 5 0 4 , 3 4 2 and $ 7 0 9 , 4 4 6 , -

Class Number

0 0 0 retail sales, while Norfolk (4-0044) has 5 8 9 , 4 2 7 population and $ 6 6 9 , 6 4 4 , 0 0 0 retail sales. The two markets seem similar to the casual observer, but the results achieved in each market may prove to be

Type of Store

Name of Customer City

Code

County Name

Code

State

Code

Salesman Code

Trading Center Area Code

GRAND

TOTAL

importance with both population and retail sales. From Table 3, we

quite different on analysis of actual sales. As the coding procedure is carried out, it may be found that all _ _

SALES

Product Group A—Total Product 11 Product 12 Product Group Β—Total Product 31 Product 38 Product 33 Product 35 Product Group C—Total Product 44 Product 40 Product 41 Product 43 Product Group D—Total Product 60 Product 64 Product 21 Product 50 Product 84

places are not listed in Table 1: Beaverton, Alabama, for example, is not listed because it is a place with fewer than 2 , 5 0 0 people. By reference to the United States Postal Guide, available at a nominal charge at any post office, or to a copy of Dun & Bradstreet's credit reference book, Beaverton is found to be in Lamar County. By reference to Table 2, which lists all counties alphabetically by state. Lamar County is found to be in trading center area 9 - 2 3 4 4 , the 2344th in importance in the entire country. SUMMARIZING

THE

DATA

When all the trading center area codes are added to the work sheets, they are then ready to be sorted by trading center areas. If this is done manually, they are simply arranged in order by number. All dealers in the biggest market will automatically be on top and those in the smallest market on the bottom. From this sorted stack, a summary can be prepared which will show the total volume of sales and total number of dealers in each trading center area. If it is decided that the number of dealers of each type and their sales volume should be determined, the work sheets are then sorted by type of dealer within each trading center area and subtotals by type of dealer are prepared in tabulating the total sales and total number of dealers in each area. For ease in making such a summary, three open columns have been provided at the right on Table 3 and Table 4. Because the hand method of tabulation is relatively slow and costly,

T o determine where Bridgeport stands in relation to other trading

the tabulating job will usually be done either by the company's own

center areas, it is necessary only to refer to Table 3. There we find

tabulating department or by an outside tabulating service. The stand-

listed all of the 311 urban trading center areas by number in order of

ard procedure in machine tabulation is to punch the information from

15

MECHANICS OF THE METHOD Figure 4. EXCERPTS SALES

FROM TYPICAL

BY TRADING

REPORT

OF

CENTER Volume of Sales Current Year to Month Date

Trading Center Area Number Name (FROM CLASS C)

38 39 40 41 42 43 44 45 46 47

Akron Toledo Omaha Hartford Bridgeport New Haven-Waterbury Norfolk Albany-Schenectady-Troy Tampa-St. Petersburg Youngstown

72 73 74 75 76 77 78 79 80 81

Canton Spokane Des Moines Utica Peoria Tacoma Duluth Reading Chattanooga Charlotte

$2,161

420 1,699 5,264 1,112 3,491 2,603 1,785 867 303

$20,259 16,526 14,425 41,521 24,278 26,967 15,833 33,597 17,525 8,592

(FROM CLASS D)

$

126 2,054 467 101 295 768 209 624 1,095

Lubbock Kalamazoo Eugene Columbia, S. C. Poughkeepsie Charleston, S. C. Yakima Jackson, Miss. Winston-Salem

14,375 $

(FROM CLASS E)

127 128 129 130 131 132 133 134 135

$ 1,423 9,446 9,155 8,369 5,384 3,781 5,008 11,949 4,814

167 213 996

2,767 98 2,430 3,600 13,575

261

1,456 2,649

57

the work sheets (Figure 3) into a tabulating card for each dealer. A tabulating machine is then used to sort the cards, add the sales figures, and even print the table of information by trading centers, as illustrated by the excerpts shown in Figure 4. Such a report immediately discloses a number of important relationships because a glance down the list of areas to the first area with no sales directs attention automatically to the biggest "open spot." Sales figures should decline more or less regularly down the second column, so that a figure which is out of line will immediately catch the eye. In reviewing this report, for example, it can be seen very quickly that sales in the Canton area were only $1,423. In the slightly smaller Utica area sales were $8,369. Farther down the line in the report, there were a number of "open spots." For instance, there were no sales reported in either the Lubbock, Texas, or Yakima, Washington, areas. Quick reference to sales in other areas of similar importance suggests what is being lost in sales because no dealers in these towns carry the line. Such an analysis provides the basis for a study of the factors that determine the point in size of trading center area below which it is not profitable to seek distribution. ANALYZING THE DATA Formal analysis of the results of the sales tabulation begins with the preparation of a summary of sales and buying power by the nine major classes, as shown in Figure 5. To obtain this summary, the percentage of disposable income is tabulated for each trading center area. If manual procedures are used, percentages for each county are first posted from a copy of the latest Survey of Buying Power issued by Sales Management to a list of counties in each trading center area as shown in Table 4. Then the percentages are added for each multiple county area and for each class of area. A county summary work sheet may be used as a posting and sorting medium when manual tabulating methods are employed. If mechanical or electronic data processing methods are used, the disposable income percentages for each county are punched into a

16

MECHANICS OF THE Figure

5.

TYPICAL

PERFORMANCE

BY

Class of Trading Area

SUMMARY CLASS

OF

ANALYSIS TRADING

OF

SALES

CENTER

Percent of U.S.

Figure

AREA Sales Performance Index

Potential

Sales

A

34.4

24.5

71

Β

16.5

14.5

88

C

6.5 9.4

Center

5.1

78

8.2

11.6 12.6

122 154

6.5

10.6

162

81.5

78.9

97

G

7.2

9.1

H I

6.0

8.0

126 134

5.3

4.0

76

18.5

21.1

100.0

100.0

D E F Total Urban

Total Rural Total United States

6.

TYPICAL

PERFORMANCE

BY

SUMMARY

INDIVIDUAL

Tratling Center Area Number

Name

ANALYSIS TRADING

OF CENTER

Percent of U.S. Potential

Sales

METHOD SALES AREAS Sates Performance Index

CLASS A

1

New York

7.1659

5,0156

2

Chicago L o s Angeles

4.5716 4.1907

3.5117 2.5314

70 77

Newark

2.8737

1.5655

60 54

5

Philadelphia

2.7258

1.3013

48

6

Detroit

2.5096

2.2422

89

7

Boston

2.1096

2.1696

103

8

San Francisco

1.9589

1.4043

72

9

Pittsburgh

1.5276

.8765

57

10

St. Louis

1.2327

.7289

59

114

11

Washington, D. C.

1.3611

1.7197

126

100

12

Cleveland

1.2477

.8077

65

13

Baltimore

.9131

.6163

67

34.3880

24.4907

71

3 4

tabulating card that also includes the name and c o d e number of the

Total Class A

county. O n c e the county cards are all punched and verified, they are arranged in c o d e number order by a tabulating sorter. T h e n the cards

CLASS Β

14

Minneapolis-St. Paul

.8765

.7171

82

15

Buffalo

.7588

.6046

80

16

Kansas City

.6735

.5162

77

17

Houston

.7307

1.2679

174

R e f e r r i n g again to F i g u r e 5, it is obvious at o n c e that sales per-

18

Cincinnati

.6807

.4848

71

f o r m a n c e is not as high in the important Class A markets as in the

19

Milwaukee

.6898

.5906

86

are run through a second tabulating machine which adds the percentages f o r each multiple county trading center area and class of area and prints a table showing the results, as in F i g u r e 6.

smaller cities, f o r these top areas, which hold 35 percent of the nation's disposable income, account f o r o n l y 25 percent of sales. T h i s rela-

CLASS C

tionship can b e conveniently expressed as a "sales p e r f o r m a n c e i n d e x . "

41

Hartford

.4671

.8221

176

I f the percentage of sales m a d e in Class A markets exactly parallels

42

Bridgeport

.4702

.5877

125

the percentage of disposable i n c o m e in these m a r k e t s — t h a t

43

New Haven-Waterbury

.4351

.6119

140

44

Norfolk

.4173

.3255

78

45

Albany-Schenectady-Troy

.3537

.2116

60

46

Tampa-St. Petersburg

.3481

.2826

81

47

Youngstown

.3753

.0743

20

is, if

35 percent of sales w e r e in Class A m a r k e t s — t h e n the index would be 100. H o w e v e r , these markets supplied only 25 percent of the company's sales, so the sales p e r f o r m a n c e index is 25/35 or 71. W h e n manual procedures are used, computation of the sales per-

MECHANICS OF THE METHOD formance index for each trading center area must be done area by area—a necessarily time-consuming job. When mechanical or electronic data processing methods are used, however, both the sales and disposable income data are punched into a single card for each county and then summarized by tabulating machines. These machines will also compute the sales performance index for each trading center area and print a summary of the results in a finished table, as in Figure 6. INTERPRETING THE RESULTS Since the markets in each class are similar in size and character, wide variations in sales performance among groups provide a clue that coverage is uneven or that some sales policy may be favoring one class of markets. The poor showing in Class A markets, for instance, may be the result of one or two badly handled big markets that can be brought up to par by better execution of existing sales programs, or it may indicate failure to provide for special promotions in department stores, which are the most important type of outlet for quality children's underwear in that class of trading center area. Variations among markets within each class indicate the points at which to look for improvement opportunities. Tabulation of the sales performance index for individual trading center areas (Figure 6) makes it possible to pick out several directly comparable markets for further study. For example, Houston, the 17th market, has $62,000 and Kansas City, the 16th market, only $25,000. The detailed work sheets for these areas (Figure 3) show that there are six dealers in Houston and twelve in Kansas City. Does this indicate that the company is fortunate in having one or two outstanding dealers in Houston? Does it suggest that the company does not have the most important dealers in Kansas City? If not, have the key outlets been properly solicited, or are they handling competing lines because the company is selling too many small, price-cutting competitors? Only a field check in Kansas City can determine whether the company should have franchised fewer dealers in that area, but an across-the-board comparison of the num-

17 ber and types of dealers in each market area in comparison with the sales potential in each area can tell the points where poor sales performance appears to be the result of soliciting too few dealers, or too many, or the wrong types. To find out what steps are needed to increase sales, it is necessary to select a number of below-par trading areas in each classification and then set out to answer two questions about each area: (1) Are there good outlets available for quality children's underwear in this area? (2) If so, are these dealers carrying the company's line? To answer the first question, the analyst first determines the most important population centers in each below-par trading center area by referring to its code number in Table 4. There he finds the names of the principal cities in the area. He then refers to a copy of Dun & Bradstreet's credit reference book and notes the top-rated department stores, variety stores, and other types of outlets for the company's products that are listed in each city. By comparing this list of retailers with the company's dealer list, he can pinpoint the dealers who are not presently carrying the company's line. Similar analysis of some of the comparable areas in which the company's sales performance index is over par will indicate the kinds and numbers of dealers who have done the best selling job in the past. The sales performance index for individual trading centers can be used as a basis for resolving local problems. For example, it provides a real comparison between the performance of the salesman in Bridgeport and the salesman in Norfolk. By referring to the detailed tabulation shown in Figure 6, the percentage of U. S. total disposable income and sales in each area can be seen. The sales performance index in Bridgeport is 125, in Norfolk 78. While such a statistical comparison obviously does not tell the whole story about the two men, it does pin down an important factor in the choice between them. SUMMARY The hypothetical case of the underwear manufacturer illustrates only one of the more common types of geographic sales analysis to

M E C H A N I C S O F THE which the trading center area method can be applied. It does serve

M a n y such service organizations were contacted by the author to determine whether they now have any county statistics available on

to show, however, that two steps are basic to the use of the method: I . Sales data are first coded

METHOD

by trading center area. T h e coding

system has been designed to facilitate summary tabulation either by

tabulating cards. T h e following companies reported that they maintain statistical data files:

class of trading center area or by individual trading center areas. It is adapted to hand, mechanical, or electronic data processing methods. 2. Sales data are then compared

with any selected

market

data.

F o r example, sales may be compared with sales potential, present outlets with potential outlets, or number of outlets with sales potential. OTHER

APPLICATIONS

Because

so many

OF

THE

METHOD

market factors are statistically

measured

by

Name of Company Barnard, Inc. Data Computing Corp. Market Statistics, Inc. Statistical Tabulating Corp. The Service Bureau Corp. Wilmotte Research Tabulating, Inc.

Headquarters New York, New York Hempstead, New York New York, New York Chicago, Illinois New York, New York Chicago, Illinois

county, a w i d e variety of analyses may be made. It is possible to test many theories that might explain the low performance in some areas. F o r example, a hunch that low sales had something to d o with inches

T h e kinds of county data reported to be available from one or more of these sources include: Magazine circulation Newspaper circulation Number of manufacturing establishments and related information for twenty industry groups Volume of retail sales for seven types of stores Number of dwelling units and other related data Number of households and families Number of families by fifteen income groups Number of persons by standard age groups Number of T V households

of rainfall, or the incidence of home ownership, or variations in freight -rates that affect "landed" product costs, can be c h e c k e d at low cost. In some cases, wanted county statistics may not be readily available. F o r example, a manufacturer of animal medicines once analyzed the list of members of the A m e r i c a n Veterinary M e d i c a l Association to determine the number of veterinarians resident in every county in the United States. D a t a on the value of farm animals in every county, however, were obtained from the latest Census of Agriculture. In a great many cases, ready-to-use county statistics can be obtained f r o m organizations that maintain files of such information on tabulating punch cards. T h e service fee charged for reproducing the data

Other kinds of data are undoubtedly available from other organi-

on a duplicate set of tabulating cards is much less than the cost of

zations that were not contacted. A n y information about such sources

c o p y i n g the figures from original sources. T y p i c a l l y , such organiza-

will be welcomed by the author. O n e source, not listed, of course, is

tions develop their statistical files as a by-product of work done by

the Bureau of the Census in Washington, D . C . , which will supply for

regular clients. Consequently, it is often impossible to obtain every

a fee upon special request many types of county data on tabulating

kind of county data in ready-to-use form f r o m any one source. T o

cards.

determine whether wanted data are actually available on cards, it

T h e above listing should not be regarded as certification of the

thus may be necessary to contact a number of different service organi-

service provided by any listed organizations. T h o s e who wish more

zations.

specific information should write directly to e a c h source, and take

MECHANICS OF THE METHOD whatever steps are normally considered necessary to reach agreement on the nature and cost of any services that may be offered. ROUTINE ANALYSIS OF COMPANY SALES DATA When a company wishes to determine its sales by trading center areas as a part of its regular sales analysis procedure, the trading center area code number can be entered on each invoice or order as it is prepared for processing. If orders are headed up by an addressograph plate, for example, the trading center code number can be shown on

19

the plate. This precoding at the time a sale is made greatly simplifies and speeds up the preparation of the summary reports and cuts down coding errors. Frequently used data may be recorded on tabulating punch cards or electronic data processing tape to provide a master information file that can be drawn upon in preparing current reports. Last year's sales in each trading center area may be kept in this way to facilitate comparisons with the current quarter's results for a quick check on a fast-moving sales situation. Results of analyses can also be plotted on a m a p for graphic presentation to interested groups.

CHAPTER 3 USES OF THE T R A D I N G C E N T E R THE TRADING CENTER METHOD of analysis is a statistical tool. T h e information it develops solves no problems, but it can help the analyst to recognize situations that seem to hold promise for improving profits. T o diagnose the factors that control the solution of any problem, the analyst usually has to go to the field to get the facts behind the figures. The value of the information the classification can yield thus depends to a considerable degree on the imagination and skill with which it is used. KINDS

OF

PROVIDED

INFORMATION BY

THE

METHOD

(1) market potential in particular trading centers and by m a j o r market classifications; (2) share of market in particular trading centers and by m a j o r market classifications; (3) number and type of outlets carrying the product compared with share of market in particular trading centers and by major market classifications compared with sales performance; (4) volume of local advertising compared with share of market performance in particular trading centers and by major market classifications.

METHOD

T h e basic geographical information provided by the method may be used for different purposes. Line sales managers use it to check operations. T o p management, marketing managers, and management consultants use the data to frame long-term plans and policies. A sales manager, for instance, will use a geographical breakdown of sales performance to determine where additional salesmen can be used to greatest advantage. A marketing consultant looking at the same breakdown may start to wonder whether company policy has been adversely influencing selling effectiveness in a certain size of market. His hunch may lead eventually to a basic policy change that will help to increase sales or lower selling costs. Both line sales managers and market planners need the kinds of geographical information the trading center method can develop, but they need it for different purposes. T h e variety of information that can be developed by use of the method is very wide, but four types of geographical data are used frequently:

USE OF DATA

BY LINE

SALES

MANAGEMENT

Line sales managers can make effective use of geographical information in checking their operations. They want to know when sales performance in a particular trading center area is substantially below performance in other comparable centers, because investigation of the reasons for the low share of market may disclose an opportunity to help salesmen. Information on the number and type of outlets in high performance trading centers of each classification also enables the line executive to see more clearly the distribution patterns that are most successful in each size of center. This information provides a basis for modifying the dealer structure in low performance trading centers that deviate from the ideal pattern. For example, analysis of the ratio of local advertising expense to sales may indicate that performance in a trading center is below par because dealers in the area are not doing enough local advertising.

21

USES O F THE METHOD USES OF DATA BY OTHER EXECUTIVES T o p management executives, marketing managers, and management consultants use geographical information to diagnose opportunities for improving basic policy and to organize the national sales job. They are more interested in variations among types of markets than they are in individual trading centers whose performances are out of line. The basic information the method provides can be used to lay out sales territories, establish policies on the type and number of retail or wholesale outlets to be franchised in each class of trading center, select trading centers to be used as distributing points, and determine the best mix and amount of sales promotion and local advertising for each class of trading center. Such decisions are not lightly made nor frequently revised in a company that has clearly defined its objectives and policies. Indeed, basically, the method was devised to provide the soundest possible informational basis for making them. D E T E R M I N I N G SALES V O L U M E O B J E C T I V E S . T h e m a r k e t

classification

is a precise tool for analyzing the distribution of a company's market potential. It also provides a means of measuring the trends and shifts in the geographical distribution of sales potential that must be watched if sales volume goals are to be set realistically. For instance, how would you measure the sales potential for ethical drugs? Does it reflect the number of physicians in a community, the number of hospital beds, the volume of retail drug sales, all of them together, or some other readily available measure? T o answer this question, a pharmaceutical manufacturer once analyzed the distribution of all these factors and decided that since all factors were closely correlated, total retail drug sales provided a practical index of sales potential. When a company markets several products through the same sales organization, it is important to know the variation in market potential by type of market. Such information is needed to appraise

Figure 7. DISTRIBUTION OF SALES VOLUME TRADING CENTER AREA FOR A MAJOR APPLIANCE COMPANY

BY CLASS OF ELECTRICAL

Percent of Tolal Soles

Volume

Product A

Product Β

Product c

Product D

A Β C D E F

28.0 17.9 7.4 7.0 7.9 5.8

25.4 18.6 10.4 10.2 6.9 5.6

30.8 16.6 8.8 6.9 7.4 5.3

13.9 18.2 5.0 9.7 9.7 6.9

Total Urban

74X)

77.1

75.8

63.4

53?6

Total Rural

26.0

22.9

24.2

36.6

46.4

100.0

100.0

100.0

100.0

100.0

Class of Trading Center Area

Total United States

Product E

7.3 14.3 3.7 10.8 8.8 8.7

selling effectiveness and to allocate advertising expenditures. Figure 7 shows a breakdown of sales by major market classification for each product sold by an electrical appliance manufacturer. The varying patterns of distribution by size of market dramatize the need for varying the type and amount of promotional effort rather than pursuing an across-the-board selling strategy on all lines in every type of market. A n apparel manufacturer once used the method to determine whether the potential market for his products justified a major plant expansion program. T o estimate the volume he was losing on his established lines in below-par urban centers, he first determined the actual shipments made on his three product lines into every urban trading center. Then he calculated an index of sales performance for each product in each trading center and estimated the sales volume required to bring sales up to the national average in every below-par center. T h e analysis disclosed that lost volume on established product lines in below-par centers amounted to about $4,500,000 a year. Field

22

USES OF THE METHOD

investigation confirmed the finding that many key accounts in belowpar centers were simply not carrying the full line of company products. The analysis assured management that there was a potential market for the projected increase in output. At the operational level, the analysis disclosed the need for establishing a sales quota on each product line for each trading center to provide a stronger incentive for balanced line selling. It also pointed to the need for a substantial increase in the number of full line key accounts in below-par trading centers. MEASURING VARIANCES IN SHARE OF MARKET. C o m p a n i e s t h a t

sell

direct to retail outlets should know whether their sales policies are equally effective in all sizes of markets. To do this, they need only to measure their share of market by class of trading center area. Whenever a company's share of market varies widely by class of trading center area, further steps should be taken to identify the factors that are.causing the variation. A company selling jewelry products used the trading center method to analyze its sales performance. The results of the analysis disclosed that sales performance was relatively weakest in the larger centers and progressively improved as the size of the centers decreased. Further analysis of data for individual Class A trading centers disclosed that sales performance was above par in only one area. These findings stimulated the company to make a careful study of the factors that were limiting its sales in large cities. The investigation disclosed that large prestige jewelers in these cities were carrying the line but selling very little of it. It turned out that the company's sales promotion policies were geared not to the requirements of prestige jewelry stores, but to the requirements of smaller city jewelers. A further limitation was the lack of representation in major department store outlets. Discovery and correction of these biases in the company's distribution policies substantially improved the company's share of market in large trading centers within a relatively short time. Another example of the use of the method to appraise variances in

share of market by class of trading center is provided by the experience of a rubber goods manufacturer who sells a number of related products direct to retail stores. Analysis showed that its share of market was highest in large trading centers and lowest in small trading centers. The company field checked its dealer coverage in a number of smaller trading centers and found that many worthwhile potential outlets for the company's products were not being sold because the company's salesmen had not been given sufficient time to solicit orders from them. The company realigned its sales territories and added a substantial number of salesmen. Within twelve months, the new territories were all showing sizable sales gains over the preceding year and the company's share of market in the smaller trading centers was substantially increased. ESTABLISHING

A BASIS FOR R E A L I G N M E N T

OF

SALES

TERRITORIES.

Whenever a company reorganizes its sales territories on a comprehensive basis, information regarding market potential by trading center areas is necessary to ensure equitable distribution of sales manpower. A leading pharmaceutical manufacturer once used the trading center method to establish a factual basis for a thoroughgoing realignment of its sales territories. This analysis disclosed a wide variation in the geographical pattern of market potential for the company's two principal lines. With this information it was possible to develop a plan of organization for the sales force that permitted the use of special representatives for each product line in areas where market potential warranted two mei. In areas where sales potential was too dispersed geographically to make the use of special representatives economical, both lines were soxl by a single representative. The market potential data for individual trading centers were then used to lay out sales territories of approximately equal size. ESTABLISHING DISTRIBUTION POLICIES. Because distribution poicies can have an important influence on share of market, analysis of a :ompany's dealer structure by class of trading center can be very profiable.

USES OF THE METHOD By determining the number and types of dealers or distributors in high share-of-market centers, the coverage pattern required in each class of center can be established. This pattern can then be used to determine the weaknesses of the dealer structures in low share-of-market centers. A manufacturer of industrial chain, for example, used the trading center method to determine his pattern of wholesale distribution. This analysis showed that: 1. The number of distributors required in relation to market potential varied widely by size of center. 2. The number of distributors the company was using in high share-of-market centers was substantially greater than in low share-ofmarket centers. As a result, the company took steps to increase the number of its distributors in the below-par centers. A similar analysis by a jewelry manufacturer pointed up the need for increasing the number of outlets in below-par centers as one important means of increasing its sales volume. The analysis further disclosed the point beyond which it was not profitable to go in franchising additional outlets in each size of center. LIMITA TIONS OF THE METHOD The amount of information that can be developed from the trading center classification of markets depends on the type of company and

23 the ingenuity of the analyst. A company that can trace its sales to the retail level can, of course, develop more specific information than a company that has sales data only at the wholesale distributor level. Methods can be developed, however, to estimate the location of customers for products that are sold through wholesalers and jobbers. Such information can help them do a better job and provides a better basis for the evaluation of their individual performance as distributors. Obviously, the amount of outside market data available for analysis on a county-by-county basis also affects the usefulness of the method. In the final analysis, the value of the method to any company will depend on the use to which the information it can develop is put. It is always necessary to check statistical indication of sales weakness by field research. If more dealers seem to be required, do the potential dealers actually exist? If the volume of local advertising is unsatisfactory, does some local factor limit the amount of advertising that can be used? When these limitations are clearly understood, there are few companies that cannot develop new and useful information with the help of the method. Up to the present time, only a few of the potential uses have been explored. As more companies devote greater effort to the development of their long-range sales plans, however, the advantages offered by the method should be exploited more fully in developing useful geographical market information.

PART II

Tools of the Trading Center Method

TABLE 1

TABLE 2

CITIES OVER 2,500 POPULATION

COUNTIES

A L P H A B E T I C KEY BY STATE

A L P H A B E T I C KEY BY STATE

ALABAMA

ALABAMA

City

McKinsey Trading Center Number

Population

Number of Retail Establishments

Retail Sales ($000)

County

McKinsey Trading Center Number

ALBERTVILLE ALEXANDER CITY ALICEVILLE ANDALUSIA ANNISTON

H H I H F

8 8 9 8 6

0629 1004 1815 0896 0252

5.397 6.430 3.170 9,162 31.066

144 141 54 163 514

12*441 14.930 4.520 12.816 55.782

AUTAUGA BALDWIN BARBOUR BIBB BLOUNT

I H H I I

9 8 8 9 9

2245 0682 1367 2057 1634

ATHENS ATMORE ATTALLA AUBURN BAY MINETTE

H H E H H

8 8 5 8 8

1220 0909 0180 0827 0682

6.309 5,720 7,537 12,939 3,732

160 129 107 118 88

14,794 10.305 6.797 7.489 6.795

BULLOCK BUTLER CALHOUN CHAMBERS CHEROKEE

I H F H I

9 8 6 8 9

2384 1339 0252 1337 2314

BESSEMER BIRMINGHAM B0AZ BREWTON BRUNDIDGE

Β Β H H H

2 2 8 8 8

0037 0037 0629 0909 1292

28,445 326,037 3,078 5,146 2.605

515 3.105 109 124 54

41.003 424.156 8.629 10.504 2.998

CHILTON CHOCTAW CLARKE CLAY CLEBURNE

I I H I I

9 9 8 9 9

1548 2229 1183 2165 2499

CHICKASAW CHILDERSBURG CLANTON CORDOVA CULLMAN

D H I H H

4 8 9 8 8

0087 0556 1548 0837 0757

4,920 4.023 4,640 3.156 7.523

38 56 133 42 233

3.378 2.984 10.717 1.702 24.692

COFFEE COLBERT CONECUH COOSA COVINGTON

H H I I H

8 8 9 9 8

1307 0688 1973 2723 0896

DALLAS MILLS-EAST SIDE DECATUR DEMOPOLIS DOTHAN ELBA

G H H G H

7 8 8 7 8

0388 0588 1356 0476 1307

2.768 19.974 5,004 21,584 2,936

27,751 7.499 39.780 3,832

CRENSHAW CULLMAN DALE DALLAS DE KALB

I H H H H

9 8 8 8 8

2084 0757 1406 0562 1127

ENTERPRISE EUFAULA EVERGREEN FAIRFAX FAIRFIELD

H H I H Β

8 8 9 8 2

1307 1367 1973 1337 0037

7,288 6.906 3.454 2.717 13.1.77

ELMORE ESCAMBIA ETOWAH FAYETTE FRANKLIN

I H E I H

9 8 5 9 8

1524 0909 0180 1976 1338

U

349 85 383 61

U

165 131 104

10.360 8.344 6.272

145

11.602

T A B L E 1: C I T I E S

ALABAMA McKir m'y TrutlinR Number Center

Cm

Sum her of Population

T A B L E 2: C O U N T I E Relui!

Retait

Suies

rsoool

Establishments

County

McKinsey Tradir, Cenrer Ν umher

FAIRHOPE FAYETTE FLORALA FLORENCE FORT PAYNE

H I H H H

8 9 8 8 8

0482 1976 0896 0526 1127

3 »354 3.707 2.713 23.879 6.226

64 90 60 298 170

5.552 6 .8*9 3.343 37.941 11.547

GENEVA GREENE HALE HENRY HOUSTON

I I I I G

9 9 9 9 7

1545 2204 2157 1845 0476

GADSDEN GENEVA GREENVILLE GUNTER5VILLE HALEYVILLE

F I H H I

5 9 8 8 9

0180 1545 1339 0629 2143

55,725 3 .579 6.781 5.253 3.331

631 88 130 103 88

57.453 6.670 9,952 8 .903 5.398

JACKSON JEFFERSON LAMAR LAUDERDALE LAWRENCE

I Β 1 Η I

9 2 9 8 9

1496 0037 2344 0526 2203

HARTSELLE HOMEWOOD HUNTSVILLE JACKSON JACKSONVILLE

H Β G H F

8 2 7 8 6

0588 0037 0388 1183 0252

3.429 12.866 16.437 3.072 4.751

101 77 462 72 56

6.208 8.730 53.696 8.773 2.935

LEE LIMESTONE LOWNDES MACON MADISON

Η Η I Η G

β 8 9 S 7

0827 1220 2409 1410 0388

JASPER LANETT LANGDALE LEEDS LIPSCOMB

H H H Β Β

8 8 8 2 2

0837 1337 1337 0037 0037

8.589 7,434 2.721 3,306 2,550

178 74

17.195 5.238

MARENGO MARION MARSHALL MOBILE MONROE

H I H D I

8 9 β 4 9

1356 1713 0629 0087 1670

MARION MFRRIMACK MIGNON MOBILE MONROEVILLE

I G H D I

9 7 8 4 9

1948 0388 0556 0087 1670

2.822 3,035 3.053 129.009 2.772

MONTGOMERY MORGAN PERRY PICKENS PIKE

D H I I H

4 8 9 9 β

0098 0588 1948 1815 1292

MONTGOMERY M O U N T A I N BROOK NORTHPORT OAKWOOD-LINCOLN ONEONTA

D θ F G I

4 2 6 7 9

0098 0037 0272 0388 1634

106.525 B.359 3.B85 4.447 2.802

RANDOLPH RUSSELL ST CLAIR SHELBY SUMTER

H E I I 1

θ 5 9 9 9

1430 0136 1747 1526 2046

O P E L IΚ A OPP OZARK PHENIX CITY PIEDMONT

H H H E F

8 8 8 5 6

0827 0896 1406 0136 2 0252

12.295 5.240 5.238 23.305 4,498

230 104 121 222 74

17.210 6.237 7.005 12.101 4.8 04

TALLADEGA TALLAPOOSA TUSCALOOSA WALKER WASHINGTON

H H F H I

8 β 6 8 9

0556 1004 0272 0837 2451

PRATTVILLE PRICHARD ROANOKE RUSSELLVILLE SCOTTSBORO

I D H H I

9 4 8 8 9

2245 0087 1430 1338 1496

4.385 19,014 5.392 6.012 4.731

88 220 91 131 123

4.661 25,444 5.422 9.823 8.910

WILCOX WINSTON

I 9 22*2 I 9 2143

MILLS

υ

78 18

υ

77 υ υ

υ

4.700 613 5.123

υ υ 1 .381 84

182.694 7.762

1 .101 56 68

148.250 5.776 4.230

124

28

υ

8.686

T A B L E 1: C I T I E S City

ALABAMA McKinsey Trading Center Number

Population

T A B L E 2: C O U N T I E S

Number of Retail Establishments

326

Retail Sales (S000)

SELMA SHAWMUT SHEFFIELD SYLACAUGA TALLADEGA

H H H H H

β 8 8 8 8

0562 1337 0688 0556 0556

22.840 3 .266 10.767 9.606 13.134

TALLASSEE TARRANT CITY TROY TUSCALOOSA TUSCUMBIA

I Β H F H

9 2 8 6 8

1524 0037 1292 0272 0688

4.225 7.571 8.555 46.396 6.734

105 96 168 533 125

5.029 7.664 11.346 51.749 10.670

TUSKEGEE UNION SPRINGS WEST END-ANN ISTON-COBB TOWN WEST HUNTSVILLE WETUMPKA

H I F G I

8 9 6 7 9

1410 2384 0252 0388 1524

6.712 3.232 3.228 8.221 3.813

114 94

7»875 4.595

ALA BALANCE OF STATE

I 9 9999

1.800.078

AJO AMPHITHEATER AVONOALE ΒI SBEE CASA GRANDE

D D D Η Η

4 4 4 8 S

0092 0092 0054 0581 0516

5.817 12 »664 2.505 3.801 4,181

45 53 149

CHANDLER CLIFTON COOLIDGE DOUGLAS ELOY

D H H H H

4 8 8 8 8

0054 1413 0516 0581 0516

3.799 3.466 4.306 9.442 3.580

FLAGSTAFF GLENDALE GLOBE KINGMAN MESA

H D H I 0

8 4 8 9 4

0636 0054 0834 1827 0054

2:î*i 6.419 3.342 16.790

MIAMI MILLER VALLEY MORENCI NOGALES PASQUA VILLAGE-EL RIO

H H H H 0

β 8 8 8 4

0834 0890 1413 1289 0092

4.329 2.953 6.541 6,153 5 .466

U

181 200 214

U U

96

U

U U

McKinsey Trading Center Number

County

33.270 18.527 17.331 15.797

6.440 402.784

9.323

ARIZONA

ARIZONA

3.872 6.571 14.018

APACHE COCHISE COCONINO GILA GRAHAM

I H H H I

9 8 8 8 9

1825 0581 0636 0834 1659

124 70 114 135 91

10.990 5.093 9.596 12.894 4.937

GREENLEE MARICOPA MOHAVE NAVAJO PIMA

H D I H D

8 4 9 8 4

1413 0054 1827 0714 0092

150 146 132 87 251

l}.?96 11.199 8 • 386 34.556

PINAL SANTA CRUZ YAVAPAI YUMA

H H H G

8 8 8 7

0516 1289 0890 0449

83

U U

92

u 29

U U U

7.732 15.160

T A B L E 1: C I T I E S dry

ARIZONA McKinsey Trading Center Number

Population

PHOENIX PRESCOTT SAFFORD SUNNYSLOPE TEMPE

H I 0 0

4 β 9 4 4

0054 0890 1659 0054 0054

106 »818 6 »764 3 »756 4 »420 7,684

TOLLESON TUCSON WAKEFIELD WARREN WEST YUMA

0 0 0 H G

4 4 4 Β 7

0054 0092 0092 0581 0449

3 »042 45»454 8 »906 2»610 4»741

WINSLOW YUMA ARIZ BALANCE OF STATE

H β 0714 G 7 0449 I 9 9999

6,518 9.145 418.333

0

T A B L E 2: C O U N T I E S

Number of Retail Establishments

υ

υ υ υ

2*405 158 111 142 44 1.235

(SOOOI

υ

υ υ υ

103 280 3*528

McKinsey Trading Center Number

County

315»033 14·181 10*532 8*464 2*102 169*992

17.332 36*948 242.073

ARKANSAS

ARKANSAS

ARKADELPHIA ASHD0WN BATESVILLE BENTON BENTONVILLE

H I H H H

8 9 8 8 8

1298 2455 1310 1273 0615

6.819 2.738 6»414 6.277 2.942

137 46 179 149 101

BLYTHEVTLLE BRADLEY QUARTERS BR INKLEY CAMDEN CLARENDON

G I I H I

7 9 9 8 9

0437 1742 1684 0920 1684

16.234 2.880 4.173 11.372 2.547

292

CLARKSVILLE CONWAY CR0SSETT CULLENDALE DE QUEEN

I H I H I

9 Β 9 8 9

2064 1349 1464 0920 2275

4.343 8*610 4*619 3.225 3.015

112 186 58

DERMOTT DE WITT DUMAS EL DORADO EUDORA

I H I G I

9 8 9 7 9

1644 0753 1523 0466 1644

3.601 2.843 2.512 23.076 3.072

47 75 64 417 58

95 237 57

70

30

U

U

10.901 3.045 13*265 13.483 5.323

ARKANSAS ASHLEY BAXTER ΒΕΝΤΟΛ BOONE

Η I I Η Η

8 9 9 8 8

0753 1464 2211 0615 1383

25.173

BRADLEY CALHOUN CARROLL CHICOT CLARK

I I I I Η

9 9 9 9 8

1742 2745 2126 1644 1298

CLAY CLEBURNE CLEVELAND COLUMBIA CONWAY

I I I Η Η

9 Ο 9 8 8

1608 2548 2781 1089 1426

CRAIGHEAD CRAWFORD CRITTENDEN CROSS DALLAS

Η Η Η I I

8 8 8 9 9

0630 1420 0664 1627 1936

6.928 19.038 2.727 6.677 12.345 7.757 4.904 2.930 5.418 5.270 38.129 3.720

T A B L E 1: C I T I E S City

ARKANSAS McKinsey Trading Center Number

T A B L E 2: C O U N T I E S

Population

Number of Retail Establishments

Retail Sales (S000}

County

McKinsey Trading Center Number

FAYETTEVILLE FORDYCE FORREST CITY FORT SMITH HAMBURG

G I H F I

7 9 8 6 9

0456 1936 1067 0225 1464

17.071 3.754 7.607 47.942 2.655

232 99 196 804 56

24 »888 6,833 14.476 82.459 3.813

DESHA DREW FAULKNER FRANKLIN FULTON

I I H I I

9 9 8 9 9

1523 2027 1349 2396 2725

HARRISON HELENA HOPE HOT SPRINGS JONESBORO

H H H F H

8 8 8 6 8

1383 0808 1369 0295 0630

5.542 11.236 8.605 29.307 16.310

147

10.101 17.291 11.797 46.672 27.925

GARLAND GRANT GREENE HEMPSTEAD HOT SPRING

F I H H H

6 9 8 8 8

0295 2680 1302 1369 1355

LITTLE ROCK MC GEHEE MAGNOLIA MALVERN MARIANNA

D I H H I

4 9 8 8 9

0089 1523 1089 1355 1665

102.213 3.854 6.918 8.072 4.530

1 .404 95 189 173 120

167.295 8.066 16.790 11.844 9.889

HOWARD INDEPENDENCE IZARD JACKSON JEFFERSON

I H I H F

9 8 9 8 6

2178 1310 2707 1084 0282

MARKED TREE MENA MONTICELLO MORRILTON NASHVILLE

H I I H I

8 9 9 8 9

0970 2032 2027 1426 2178

2.878 4 .445 4.501 5.483 3.548

59 98 106 146 83

3.762 6.168 6.726 7.934 4.802

JOHNSON LAFAYETTE LAWRENCE LEE LINCOLN

I I I I I

9 9 9 9 9

2064 2302 1770 1665 2610

NEWPORT NORTH LITTLE ROCK OSCEOLA PARAGOULD PARIS

H D G H I

8 4 7 8 9

1084 0089 0437 1302 1831

6.254 44,097 5.006 9.668 3.731

586 95

160

14.882 49.456 12.037 14.039 4.226

LITTLE RIVER LOGAN LONOKE MADISON MARION

I I H I I

9 9 8 9 9

2455 1831 1242 2673 2760

PIGGOTT PINE BLUFF POCAHONTAS PRESCOTT ROGERS

I F I I H

9 6 9 9 8

1608 0282 2090 2247 0615

2.558 37,162 3.840 3,960 4.962

61 612

123

4.636 49.898 6.407 5.396 11.791

MILLER MISSISSIPPI MONROE MONTGOMERY NEVADA

F G I I I

6 7 9 9 9

0234 0437 1684 2841 2247

RUSSELLVILLE SEARCY SI LOAM SPRINGS SPRINGDALE STAMPS

H H H G I

8 8 8 7 9

1325 0859 0615 0456 2302

8,166 6,024 3,270 5,835 2,552

136 185 89 150 41

12.357 17.585 7.384 14.452 2.623

NEWTON OUACHITA PERRY PHILLIPS PIKE

I H I H I

9 8 9 8 9

2918 0920 2872 0808 2533

STUTTGART TEXARKANA TRUMANN VAN BUREN WALNUT RIDGE

H F H H I

8 6 8 8 9

0753 0234 2 0970 1420 1770

7,276 15,875 3,744 6,413 3,106

178 337 91 103

20.870 29.186 5.122 6.261 7.317

POINSETT POLK POPE PRAIRIE PULASKI

H I H I D

8 9 8 9 4

0970 2032 1325 2304 0089

188

191 649 301

218

73

94

82

81

31

T A B L E 1: C I T I E S City

WARREN WEST HELENA WEST MEMPHIS WYNNE ARK BALANCE OF STATE

ARKANSAS McKinsey Trading Center Number

I H H 1 1

9 8 e 9 9

1742 0808 0664 1627 9999

Population

2.515 6.107 9.112 4. 142 1.286.253

T A B L E 2: C O U N T I E S

Number of Retail Establishments

Retail Sales iSOOO)

9.702 3.493 22.567 8.636 316.745

121

71 154 92 7.134

CALIFORNIA A A A G I

1 1 1 7 9

0008 2 0008 2 0003 1 0313 1884

64.430 17.590 51.359 16.714 2.819

46 5 140 590

45.267 14.053 95.019

73

6.691

ANAHEIM ANT I OCH ARCADIA ARCATA ARVIN

A A A G E

1 1 1 7 5

0003 2 0008 4 0003 1 0322 0100

14.556 11.051 23.066 3.729 5.007

289 194 332 110

37.216 18.167 42.339 17.788

ATASCADERO ATHERTON ATWATER AUBURN AVENAL

2 î un G 7 0340

AZUSA BAKERSFlELO BANNING BARSTOW BAYVIEW-ROSEWOOD-CUTTEN

A E E E G

1 5 5 5 7

0003 1 0100 0107 0099 0322

McKinsey Trading Center Number

RANDOLPH ST FRANCIS SALINE SCOTT SEARCY

1 H H I I

9 8 β 9 9

2090 1067 1273 2540 2620

SEBASTIAN SEVIER SHARP STONE UNION

F I I I G

6 9 9 9 7

0225 2275 2818 2716 0466

VAN BUREN WASHINGTON WHITE WOODRUFF YELL

I G H I I

9 7 8 9 9

2561 0456 0859 1947 1842

CALIFORNIA

ALAMEDA ALBANY ALHAMBRA ALISAL ALTURAS

G 7 0427 G 7 0431

County

3.443 3.630 2.856 4.653 3.982 11.042 34.784 7.034 6.135 2.779

υ

β 63 146

5.191 15.736

139 1.193 105 143

16.706 166.504 10.387 20.201

32

υ

« 1 0008 2

ALAMEDA ALPINE AMADOR BUTTE CALAVERAS

I I G I

9 9 7 9

2909 2003 0337 2112

COLUSA CONTRA COSTA DEL NORTE EL DORADO FRESNO

H A I H D

8 1 9 8 4

1184 0008 4 1444 1060 0066

GLENN HUMBOLDT IMPERIAL INYO ICERN

H G G H E

8 7 7 β 5

1012 0322 0329 1140 0100

ICINGS LAKE LASSEN LOS ANGELES MADERA

G I H A H

7 9 β 1 β

0431 1492 1108 0003 1 0564

City

T A B L E 2: C O U N T I E S

CALIFORNIA

T A B L E 1: C I T I E S McKinsey Trading Center Number

Population

Number of Retati Establishments

tiOOO)

McKinsey Trading Center S umber

County

BEAUMONT BELL BELMONT BEN1CIA BERKELEY

E A A A A

5 1 1 1 1

0107 0003 0008 0008 οοοβ

1 3 5 2

3*152 15.430 5.567 7.284 113.805

71 221 64 56 1.156

3.966 2B.169 5.000 3.860 126.015

MARIN MARIPOSA MENDOCINO MERCED MODOC

A I G G I

1 9 7 7 9

ΟΟΟΒ 6 2506 0417 0340 1884

BEVERLY HILLS BISHOP BLYTHE BRAWLEY BREA

A H E G A

1 8 5 7 1

0003 1 uto 0107 0329 0003 2

29.032 2.891 t.089 11.922 3.208

614 86 118 178 46

141.795 10.294 11.904 21.533 3.202

MONO MONTEREY NAPA NEVADA ORANGE

I G G H A

9 7 7 8 1

2645 0313 0423 0893 0003 2

BUENA PARK BURBANK BURLINGAME CALEXICO CARLSBAD

A A A G Β

1 1 1 7 2

0003 2 0003 1 0008 3 0329 0027

5.483 78.577 19.886 6.433 4.383

130.799 41.101 15.415

PLACER PLUMAS RIVERSIDE SACRAMENTO SAN BENITO

G I E D I

7 9 5 4 9

0427 1650 0107 0057 1525

CARMEL-BY-THE-SEA CARMICHAEL CARPINTER IA CHICO CHICO VECINO

G 0 E G G

7 4 5 7 7

0313 0057 0147 0337 0337

4.251 4,499 2.864 12.272 3.967

E Β A E G

5 2 1 5 7

0099 0027 0008 1 0104 0355

CHI NO CHOWCHILLA CHRISMAN CHULA VISTA CLAREMONT

E H G Β A

5 8 7 2 1

0099 0564 0316 0027 0003 1

5.784 3.893 4.211 15.927 6.327

30.039 5.776

SAN MATEO SANTA BARBARA SANTA CLARA SANTA CRUZ SHASTA

A E D G G

1 5 4 7 7

0008 3 0147 0055 0339 0383

CLOVIS COACHELLA COAL INGA COLTON COLUSA

D E D E H

4 5 4 5 8

0066 0107 0066 0099 1184

2 .766 2.755 5.539 14.465 3.031

72 40 95 172 87

6.153 2.058 8.140 18.196 426 154 245 14 316 122 595 232

36

23.489 8.902 587.183 29.433 3.935 38.936 14.849 20.237 44,417 9,530 22,455 74,413

213 328 92 191 496 υ

20.555

υ

Υ

240,698 16,367 36,725 1,797 24.457 12.086 84,497 27,747

McKinsey County

Center

Trading Number

T A B L E 1: C I T I E S McKinsey Trading Center Number

City

T A B L E 2: C O U N T I E S

CALIFORNIA Population

711 158

Retail Saler ($000)

RIVERSIDE ROSEVILLE RUP.ERT SACRAMENTO SALINAS

E G G D G

5 7 7 4 7

0107 0427 0457 0057 0313

SAN SAN SAN SAN SAN

A E A G A

1 5 1 7 1

0008 6 0099 0008 3 0316 0008 3

9,188 63,058 12,478 16,534 14,371

110 1,029 177 361 214

12,252 144,276 20,418 56,702 23,612

SAN DIEGO SAN FERNANDO SAN FRANCISCO SAN GABRIEL SANGER

Β A A A D

2 1 1 1 4

0027 0003 1 0008 1 0003 1 0066

334,387 12,992 775,357 20,343 6,400

3,829 353 10,045 334 117

524,870 46,397 11 172,221 41,578 11»735

SAN SAN SAN SAN SAN

D A G A A

4 1 7 1 1

0055 0008 2 0355 0003 1 0008 3

95,280 27,542 14,180 11,230 41,782

1,748 577 290 106 587

228,078 59,854 30*732 13,734 76,210

SAN PABLO SAN RAFAEL SANTA ANA SANTA BARBARA SANTA CLARA

A A A E D

1 1 1 5 4

0008 4 0008 6 0003 2 0147 0055

14,476 13,848 45,533 44,913 11,702

194 312 805 776 260

12,778 47,452 116,678 84,927 19,366

SANTA SANTA SANTA SANTA SANTA

G E A G 5

7 5 1 7 7

0339 0147 0003 1 0316 0312

21,970 10,440 71,595 11,049 17,902

492 240 1 ,069 161 527

42,846 27,072 151,946 15,789 67,749

5AUSALI TO SEAL BEACH SEASIDE SEBASTOPOL SELMA

α A G G D

1 1 7 7 4

0008 6 0003 2 0313 0312 0066

4,928 3,553 10,226 2,601 5,964

SHELL POINT SIERRA MADRE SIGNAL HILL SOUTH BA