Weather as a Determinant of Marketing Strategy: A Department-Store Case Study

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A&phe&s C r a t e s Steels

A dissertation admitted ia partial falflllment of the retirement# far the degree of Doetor of Dhilesephgr 1% the Department of Marie©ting in the Graduate College of the State Dhiversit^ of 1m m Aogaat 195C

ProQuest Number: 10907183

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ACEJOWLEBGlfmTS !hi« study would net have been possible without the basic sales and weather data which went late it. Mr. Morey Sostrin, President of Taunker Bros., lac., kindly put sales data of that organisation at the writer*® disposal.

Mr. 1. Stuart Kirk, Controller, assisted in the

selection of the sales data for the present study and supplied clerks to copy the data fro® the store*® records.

Mr. Boss M* Balbey, Publicity

Director, supplied advertising figures a© well as much of his time and many valuable suggestions.

She weather data was supplied by the Iftiited

State© Weather Burton, City Office, B®$ Moines.

Mr. B. C. S. fboai, who

was formerly Section Director in Das Moines, mad© available all the meteorological records in the Be® Moines office a© well as offering many valuable suggestions.

Mr. G. B. hamoureux, Mr. Ihos*s successor,

has been as cooperative as Mr. *fhom was.

Mr. Marvin B. Kagauson, First

Assistant at the Be© Moines City Office Station, has supplied large quantities of unpublished weather data. Dr. C. Frank Smith offered many valuable suggestions regarding the statistical technique*. Dr. Wendell B. Smith, chairman of the Committee in Charge, supplied Inimmemble very valuable suggestions sad

r IS

©pent a great deal of time discussing the various problems as they presented themselves.

Eis interest in the study and his understanding

of the problems of presentation were a constant source of encouragement.

li

Bean It* SU Hofffcaa of the College of Comerce asd finance, Drak© ffolrersiiy, provided entree to Imsineee exeoatives whose doors otherwise ad#** have heen ©losed* VaVlo&s official* of the Register aad TVib*m© PaMiaMng Coajwsoy have contrihttted tine and suggestions, especially Hr. Harry Piugh, Assistant Hasiaeea Manager. Hepeeial thanks go to sy wife* Katharine, who has helped la ianuiieraftie ways o d as uncountable miaber of times.

m&si os* c o n s page Acknowledgements

.......

.......

.

. . . . .

1?

fable of e x t e n t s ........ . ......... Sable of Figures

..........

Sable of tablets

If

. ........

r vii

Chapter X.

Introduction

. . . . . . . . . . . . . . . . . .

1

Chapter XX.

Semination of Previous Studies . . . . . . . . .

?

Chapter III.

She Relationship Between Weather and Total Sales of the Store

Chapter 17. Chapter 7.

.........

6l

Application of Statistical Results to Marketing Strategy of Store . . . . . . . . .

2&9

/ Bffects of Weather on the Sales of Individual Departments

........

Chapter 71. r Marketing Strategy in the Three ......... Individual Departments

IS? 219

...

ZkO

Chapter YXXX. Shmmary and Conclusion© . . . . . . . . . . . . .

ZkQ

Bihliogra^sr . . . . . . . . . . . . . . . . ............ . . .

257

Appendix

2 S5

Chapter 711 • General Economic and Social Considerations

............

iv

mBM m

n < n

1,

firae>-series Sraphs of fofeal Store Sales . . .

2.

Relationship of Daily Sesiperatnree (Difference from Ifortaal) and Bestifled -Store. Sales.* Excluding Rainy Says, faster Seasons, i . . . ........

..........

Relationship of Sally Precipitation Amounts and Rectified Store Sales, lister Seasons, 1945-48 . . . . .

Pag© Si

n 99

4*

Relationship of Sally Precipitation Amounts and Eectlfied Store Sales, Easter Seasons, 1945-48 . . . . . 100

5.

Relationship of Rally Relative Heaaldlty Me&gurestent® and Rectified Store Sales, Excluding Rainy Bays* Raster Seasons, 1945-48 * ............... 101

6.

Relationship Between Bally Sunshine Measurements and Ratified Sal©#, Excluding Rainy Bays, Raster Seasons, 1945-48 . . . . . . . . . . . . . . . .

102

7*

Relationship between Bally Wind Telocity Measure­ ments and Rectified Store Sales, Raster Seasons, ........ 104

0*

Relationship between Bally Wind-chill Figures and Rectified Store Sales, Rainy Bays Excluded, Easter Seasons, 1945-48 . . . . . . . . . . . . . . . .

1©5

Relationship Between Bally Wind-chill Figures and Rectified Store Sales (lagged One Ray), Rainy Bay# Excluded, Raster Season®, 1946-48 . . . . . . . . .

196

Relationship Between Store Sale® (As a Percent of Istlmated Sales) and Snow Depth, Raster Season, 1948 .....................

125

Relationship Between Store Sale® (A® a Percent of Estimated Sales) and Relative Hajaldity, Raster Season* 1948 . . . . . . . . . . . . . . . . . . . . . .

126

9*

10.

H.

12,

Relationship Between Store Sales (As a Percent of Estimated Sal©#) and Sunshine, Easter Season, 1948 . . . . . . . . . . . . ........... . . . . . . . 127

w

page 13.

15* I6-X9.

Estimated ©&d Aetna! Sales, Easier

Season, lfh-6 . . . . .

131

letimatodand Actual Sal®®, faster Season, 19**? . . . . .

I32

Estimated and Actual Sales, Easier

Season* 1 9 ^ S ......... 133

Helatlonshlp "betweenEstimated Store Sales aaA Actual Store Sales, Easier Seasons* 19^5* 19^7 and 19^8.. . . . . . . . . . . . . . . . . . . . . . .

137

20.

Relationship Between Estimated and Actual Moderate Priced Dress Department Sales*. Easier Seasons, I9A8 and 19A9 ................ 2D2

21.

Belationship Between the Estimated and the Actual Sales of the Candy Department, Easter Seasons* 1aratare (above By ar below 15 ) or with rainfall teing store hours* Justification for choosing 15° and 85° and rainfall as bases for •disagreeable3* weather I® contained in the following statements

9 3^.ftto$jrS«£' daily sale# records a w the five-year period, in ©onjimeilon with average noontime temperature, it m & ■ found that* on the average* sale# fell off vhm the noon­ time teTaperatnr© was 850 or higher* in guamor and 15 or lower in ©Inter* ■Tim number of selling days with temperatar© above or* below these critical points was therefore determined for each south of the period under stndy*

Tim effect# of precipitation were studied hy comparing dally sales with records of rain and snow during store hours. Snow appeared to have little or no ill effect upon sales* whereas rain was frecently associated with subnormal sales. Sh© number of days in which it rained during store hours in each month was therefore Included in the index of had weather. Explication# between the two criteria of bad weather were then eliminated, leaving a net count of the masher of selling day# with disagreeable weather each month, fhis involved ©goal weighting of day# regarded as uncomfort­ able from each of these condition#, but no a priori or empirical evidence was available to indicate other weights.* It would appear that merely counting the number of day# on which either the temperature was above 85° tor below 1 5 °) or rain was recorded during store hours (and can,celling one oat where the two occurred on the same day) Is an exceedingly erode method, at best, a® a measure of ‘*31eagrees-bl©** weather#

Nevertheless, it Is an objective

definition Hiking possible a quantitative measure. Dr* B#8HS*s next concern m s to see whether some of M s seven IMependesat variables might not have a high degree of intercorrelation, thereby making possible the elimination of some of them. Sach of the six independent variables mentioned above was correlated with volume by means of scatter diagrams.^

It was found, that8

number of selling days per month and disagreeable weather were clearly correlated with volume of ealee,

althou^i raaltlple-correlation analysis revealed m additional Independent effect upon cost.'

Q

Br* Bean used graphic multiple correlation.

S m

though lie

adapted a relatively erode definition of disagreeable weather* Dr* Bean did arrive at a significant correlation between nxrnber of days of dis­ agreeable weather a M monthly sales*

U s method is not applicable to

the present study inasmuch as this study will attempt to relate the effects of weather on a daily basis* lyftluatlon*

fhls study by Br. Bean indicates that on a

monthly basis quantitative methods hare succeeded in discerning that •disagreeable* weather as defined by Br* Bean did adversely affect the sales of three departments of a Hew Tort: department store* though correlation %

scatter diagrams and graphical multiple

correlation are not as methods completely above reproach, in the hands of a skilled statistician, which Br. Bean is* they may be considered an objective tool. Br. Dean93 contribution to marketing strategy seems to bo limited to the one sentence (previously quoted) to the effect that» •Bad weather* by ©eddeatly driving sales below estimated volume* may make unnecessary the services of sales-persons and stock persons prev­ iously ©apleyed**

It would seem then* that if sales could be predicted

one day ahead of time with a sufficient degree of accuracy, it would be possible to reduce the waste involved in having too many sales-persons end stock persons on the payroll on any given day.

11 ,.e£.MaHUM&m ,BfXact on Ratell Broad .Salas According to as article that appeared in a 'bnsinoss weakly early In 19&S* weather affects the nalect of a Hew York Baker, Onshrasais o Sons, Inc,4 Mar* Herbert Johnson, Sales Manager, claims that the bakery saves $250,000 per year by gearing production to the weather*

If the

weather Is predicted accurately by the Weather Bureau, Cushmans has very little day-old bread on hand,

Daily total sales are affected by

weather, but the greatest effect of weather i© upon the location of bread purchases*

People tend to buy mere bread at downtown stores and la lees at suburban ©tores In bad weather* According to Mr* Johnson: Per m a y years we knew that weather conditions affected ear. sales to a large degree and about 12 years ago started keeping a record of ©ale© with the weather conditions that prevailed, we found that sales were desaly related to the condition© of weather, that would either ©top people'frcm ©happing, or increase or decrease their appetite* Ife take into consideration the following weather .factors; #1. #2. #3*

#5*

feaperatur® Humidity la in A. hi^st - Moderate - Heavy B* 5irae during store hours rain will fall m»d Gentle — Moderate — Strong Snow Amount of snowfall

la estimating cur requirements for a given day we: 1st* Obtain the detailed weather report* 2nd* Average about 3 days where the same weather condi­ tions prevailed, as near as possible* 3rd.. Galonlaie the trend of sales*

She results we have received from the application of the above have boon most gratifying, we are able to save a great valuable material , as well as give oar cuatomers mUCEh. hotter service. BvaXmtlffiu

Mr. Herbert «Tohnson*a mthodology is of little

Use from the point of view of the present investigation*

She weather

elements that he has found to affect ’bread sales are significant* Baferene© will he made to then in Chapter III of this essay* However crude Mr. Johnson* a methodology his marketing strategy is excellent, and the accounts of the savings are is six figures.

He gets the weather forecast.

Shea he goes through M s

files until he finds three other days that had weather conditions very similar to those forecasted and averages the sales for those days. Finally he calculates Bthe trend of sales.H Presumably the final figare is in terms of predicted volume of sale® for the next day, and production and. distribution planning is based upon that forecast. A Btudv of ^ a a a s a s M a Jfrnthis^ In 19^6, because of the unseasonably warm fall, a chain clothing system got stuck with nearly $1,000,000 worth of m m *s fall suit®.*.. The chain clothing system puts its problem in the hand® of an applied meteorologist. The meteorologist sought a way to anticipate an unseasonably warm fall* 18 Hr. Hubert Bale of the U. S. Weather Bureau made a study of the problem*

He studied the weather record® of Hew York City for the

76 year period 1871-19^6.

He compared the September temperature anosia-

1& lies with the algebraic gums of the temperature anomalies of October,

13 Boveaber and December (hereafter called, •fall*1). He found a e o e fflo leat of correlation of ©*37, which he considered "highly significant for a sample of ?6 years* w*^ Shis mean® that there is a certain persistency in lew fork weather.

Mr. Bale attributed this persistency to the nearness c? Sow

Toxk to the Atlantic Ocean.

She shifting of the Oulf Stream consider-*

ably influences the water tompers.to.re near Sew York City.

Since the

t@Esperature of the ocean influence© the temperature in Mew fork City, the position of the Calf Stream considerably affects the te^emtnre in Mew York City, therefore* if the Qulf Stream is close to Mew York in September, the probability is that it will he close during at least a peart of the following three months.

the regull would he a warn

fell in Sow York City. Bering the 76 year period September anomalies hare ranged

from ~5 .9°2* to 4-5.5°F. She distribution of anomalies is considered normal.

Sis sum of the October, Moreiaher and Becemhsr anomalies (of

the asssse year) range fro® -*15*8 to 4*1?.

Shis distribution is also

considered normal. Mr. Bale fitted a least scares trend line to the ?6 data (the X sad® was time and the Y axis temperature) and found that there has been a slight rise in lew York normal temperature during the last 76 years.

On the basis of a coefficient of correlation of W

have been converted to these irnits for

pHfeotw*. &$'■ la his review of these formulae, except the last three* Stone ooaol^ied that the heat are probably the lehmnn Ho* 3.and th® Baettnar, and warns that fit is- probably very misleading to extrapolate the formula© very far beyond the m*%&m over which they were emlmied, i.e. * below -12 © and above 15 m/sec,.*' Sipl**© farmuXa* la turn, was evaluated for air temperatures of «*$**© to ~566C, said wind speeds to 12 si/sec. She wide difference between the coastste of earlier lavas* tigatora sad those adopted by Sipl© and Plummer are da® presumably to the different sizes and shapes on which the determinations are based, fhe katatherraoseter bulb Is about k cm long and 2 cm in dimeter; the Bave® frigorimeter is a adhere 7*5 «*■ i» diameter; Sipl©1® relative-comfort thermometer was a cylinder 1^,9 cm long and 5*7^ «a in dime tor; Plummer*© formula is based on an infinitely long cylinder 7*6 ©as, in diameter. Stone points out that bumaaarbody cooling ©caputed from, kafcathermmoter readings- avsntimes the actual (directly measured) She multiplicity of cooling power formula® requires son® additional ©aplenatiGiu irnold Court has the following to says Sesults from any one formula may differ from actual hoisan ..heat lose by varying .asaousts at different temperatures. Until a formula has been verified over its entire range, it. cannot be acoepbed completely* for exassplo, at the indicated wind greeds* Siple*® formula yields the same heat lose for varying fceaperatap©®.*-SttBd fab) , 1280 fewX/afar 1088 «

5 .-3 -50

'.SO. 15 « 2 *18 -28 -18

20 *23 -12

23 *25 -7

30 *27°*'• ~!T**

So far, there is no verification that a 10 Rph wind at

•artr* is Jaatt as mcomfortable as one of 30 mph at -5 ^•» perhaps it is wora®. ■Any fCrsEB&a for the convective heat transfer between men and the atmosphere asimaes, Aether Inplicitljr ©r % posfculation, a certain effective diameter. Shat is, the

tagmagi body stay bo considered to b© an assemblage of cylinders whose total reaction is equivalent to that of a single cylinder (or other shape) of a certain diameter, ©ailed the effective diameter. So far, the "best way to determine effective diameter has been by fitting observed data for actual body—heat loss* obtained in physiological testing* to an section such as KLtimmeris. for some time, physiologist® have been using a value of 3 Inches i that is, the rate of convective heat loss from the human body is about the same as that of a cylinder, 3 inches in diameter, of the same total area as the human body. Sbwever, this equivalence is valid only for th© conditions of the physiological experiments * which were almost entirely with nod© or lightlyHslad. men. The rather scanty data on heat loss of men wearing ordinary clothing indicate that a inch cylinder may be a better approximation. bhen the body is still more heavily clad, and especially when the individuality of ears and fingers la destroyed by cape and mittflsa*, the effective diameter of the body inoreases still further, and. the surface temperature more closely approaches that of the air. laboratory measurements are required to determine the effect!'*© diameter of a man dressed in Arctic clothing. The actual effect of wind in reducing insulation of a heavily clothed men is small, since at winds greater than 5 mph the layer of still, insulating air is practically negligible. The major heat losses at high wind speed are a resultant of the ventilation through the clothing assembly aywi the reduction in insulation because of compression.55 Court1s concluding statement: Vind-chill (i.e., dry cooling power) at present is only a qualitative description of the relative severity of combi­ nations ef wind and low temperatures. Its computation i® based on an empirical formula for certain observations of cooling at temperatures far lower than those of any other formula. Before an ©mot, practical, quantitative formula for solely the convective heat loss, or wind-chill, can be obtained, certain physiological data are required. Once such a formula is available, it may be used to compute true wind-chill values, bat such computations should be based on

actual instantaneous observations, mot averages, of teesperw atnr© -md wind. Only then will it he possible to draw 8®«ai»le tpaentitat^ laaps of wtm&~©MXl distribution for months or seasons.-*5 *» • 95&© conweefcir© he&t loss, or wind-chill, is only one of several avenues of body-he&t loss, and probably never aceoTcibs for more than 80 percent of the total body host loss* Also re^drin^: study in physiological laboratories are problems such as the relation ©f breathing rate at low temperature, and surface t^perature and ra&lafciv© effic­ iency of the ©osapletely ©lad body. Only when such questions hare boon answered will it be possible to comjmte accurately the body heat loss from all avenues, including wind-chill. Bven when it becomes possible to m2.ce such computations of theoretical bo&y-heat loss from all avenues,, it may prove difficult to relate them directly to human eosf©rt and to limitations of human activity. iHLfferences in individuals, ©esses, body builds-, ages, and races, as well as differences

in activity, body position, accliaatizatlon, physical con­ dition, diet, adequacy and efficiency ©f clothing, and above all in mentalL attitude, make one person f e d comfort&ble when another finds the weather unbearable* So matter how ©met m objective formula for body-heat less becomes, its results will always be attacked subjectively by those whose seaasaiiom* do not agree with the computed value of the wind©fell Most ©f the cooling power formulae are of th© general forms- 1 sr (a 4* b^2) (tg - ta), In which the symbols are the m m m- previously defined, lapamded this becoEsess S sr aig — atft $•

— b?®ta. la order to visualize better

the- variables.la the equations all -of.th© constants in the first term w i H he ©©Useted into one constant -e; all constants in the second term into one constant f; all constants in the third team into one constant gj aavl all constants in Us© fourth term Into one constant h.

©bgsmge© the e^mtion becomes;

With thase-

ft appears1that tbs toss main variables in th© cooling power are the tes^omtoe, the wind velocity and finally the product of temperature and the wind velocity.

It would appear then that if cooling power is

at least on© of the ©laments in discomfort caused by weather? t©s$»* erstfeur©, wind velocity and the product of temperature and wind velocity are three of the wither variables that enter into the functional rela­ tionship between weather ard the retail sales of a store* me, V^t..iCQqlipg. r>o^r.

All of the formulas depicted by the

table above make no provision for humidity.

The effective temperature

of th© American Society of Seating and Ven.tlla.ting Sngineers definitely took humidity into account, although the effective temperature covers such & narrow range of wind velocities as to he useless for outdoor use.

Various attests have been made to moisten the various cooling

power iheraioBseters to make bbem meagre the effect of evaporation and tha© give a wet cooling power*

tfefortunately th© wet c.p. Involves some tricky parameters affecting the ev ap o r a t i o n which cannot he easily taken into account. The 1923 wet-kata formula of Hill, %hm&i modi­ fied several times sine©-,, has been most widely used? a* z * + t M S ♦ s.ioz*®*3} where H is the dry kata.. V the' wind velocity in m/s©©*, © the actual vs^or pressure in saa, (45*3-©5 being the saturation deficit. This is an unhandy elation and requires two nomograras for graphical solution. A simplif­ ication by Gold •has th©.form -

B* r (O.Q6 * O.ii-TT1/3) » p. 130.

26. ^.h^d.,P- 131. 27. JBAtfff-r p. 132. 2S,

, P* 133*

29. Ihld.. p. i33* >, 8Gas Product Ion System Aided by Weather Man”, Gas Age, vol. 97* F e b r u a r y 7# I9b6, p. 97; HGaa Sondout and Weather”, Gas Age, vol. 99, March 6, I9b?, pp. 21—3; #Weather and Jlectric Power Systems”, j Lg$gte& wl. 93. April, If>b6 pp. 161-76; ”Weafcher Cycles”, Ifefcte J m & l & M m * ^oX* 53. June,. X9b&, p. 91? 0* A, Dunn, ”Weather Cycles”, Gas Age EeocrA. February 11, 1923, p. 2b; Halbert P. Gillette, ”Weather Cycles and their Causes”, Water and Sewage Works., vol. 93, June, 19^6, pp. 252-b. 31. Marlon £. Hogan, 4 s£ Ig&afi&d&iL Unpublished Mae tors thesis, Massachusetts Institute of technology, Feb. 11, 32 . Henry A. Bryar, ”I*oad Dispatching and Philadelphia Weather”, Bulletin

si. !&& to»Eto« 33. n w -

p- ***•

3&. lilt,, p. 162.

35. s m 36. m a . . p. 162

testate. TOl- 3°. Ma?.

^2* Walter I»» Badger said Warren A. McGage, _ _ _ *«» York, McGraw-Hill Book Go., Inc., 1936, pp. Il ff., W f f . . and m s t.i John H. Ferry, Chemical aggjnaer** jfandhaafc. Bow York, KeGraw-Bill Book Co., Inc., 19*&, pp. 1233* 236-40, 156, 237 and 25G—5^3 Mor&®cai lzaklel„ Methods of Sew York, Wiley end Sons, Inc., (Second Edition,, Chapter 16m Yhe generalIsstion regarding Qh&raoterlsfcle approach to problems of the engineer an contrasted with the physicist and the laaiheiaaticlan is based upon personal observation by the writer while taking courses at the graduate level from engineers, mthematieians and physicists? and also from personal association with them while in the Baval service. It has been the writer*s observation that engineers are not usually ac^alnted with the method of least %3
Seasons In the Deuartment Sfcors*sfear Shore are several fairly well-defined Mseasons'* in the depart­ ment—store retailer’s year, fhe outstanding season (from th© point of

▼tew of ytolwm of sales) is the Christmas season which might ho roughly considered to cover the maths of Woveraber and December (up to and Including Beeember 2A).

?

Second in importance is the Easter season

which may he considered to cover about one month said a half (or seven .

A

weeks) before B&ster*

She remainder of the year might he divided be­

tween the following categories:

mid-winter (between December 25 and

the beginning of the Easter season)* summer and fall, She effect of weather should be different in these different seasons,

the probable effects of weather on daily sales during the

different seasons mentioned will be discussed briefly,

ghe Ohristmaa Season 9 According to Dan fries of Prank©!1® Clothing Company in Dos Moines, mild weather during the months of November and December helps the baying of “Christmasy* goods (i.e.» Christmas presents* etc.). Such mild weather hurts the sales of the heavier expensive items. Bess Ealby*

Mr.

Publicity Director of Yconker Bros., Inc., is of the

opinion that, though mild and relatively pleasant weather is desirable during the Christmas season, such a situation is desirable onl# if there has been am early snow storm (preferably early In November) to

1st people know that Christmas and winter is upon them, and to get them “la the meed* for Christmas baying.

Just what “mild*1 weather is*

is net emctly clear (in quantitative meteorological terms), but the popular us© of the word seems to Indicate temperatures (for that time of year) in the 5©*s or above during the warmest part of the day, and

76 relatively lew wind velocities. A little snow from a fairly light (not dark an&gloGsy) overcast sky 1® not considered contrary to “mild* eendliixms.

On a day-to-day basis* more shopping and buying Is prob­

ably dans on relatively comfortable days than on those which are not; regardless of the total seasonal effect of a relatively cold season or & relatively warm season,

taking the Christmas season as a whole, it

la difficult to say whether a relatively warm season is bettor for a department store than a relatively cold season.

If there is some snow

with some cold weather early in the season and with relatively mild weather thereafter, it is likely that conditions may be thought to be Ideal, It is easily seen that the Christmas season offers interest­ ing possibilities far study, but also that the effect of weather on people*® day-to-day buying habits at that season is extremely complex. It is possible that a certain kind of weather at one time will have £ust the opposite effect fro® wfeat it may have at another time.

fhe Easter season has been defined as the seven weeks preced­ ing Easter.

$he worst complexity with respect to the laster season is

that the date of Easter changes so that an early lastor can be as early 11 a» March 22, and a late one can be as late as April Z$* Shore seems to be a general consensus of opinion that a late laster is a better shopping period thorn an early one.

3Rpcbb a weather point of view, however, the effect of weather during the Easter season is probably a simpler relationship than during the Christmas season. There appears to be no reason to suppose that snow, a stem or bad weather in any form should stimulate sales.

Thus,

It would appear that any day that tends to be milder (other things being the same) should be a day of higher sales.

Though the Easter

season does net consist of buying Easter hats, it is probably true that any type of bad weather temporarily dampens spring clothes buying and perhaps other buying also,

it seems likely also that good weather

should stimulate sales of less seasonal types of goods. More specifically. It would seem that the higher the temp­ erature, the lower the wind velocity, the less the amount of precipi­ tation, the acre desirable the weather.

There Is no tendency to rejoice

in the beauty of snowfall (people are tired of snow by March).

There

is nothing invigorating about a brisk breeze or a low temperature dur­ ing bant. Everybody is tired of winter,

the relationships should b®

B&ch more simple than those of the Christmas season. .%g.,$ m m „ The summer season might be thought of as the period from the day after las ter to the break of summer heat (usually in August). In all probability, if the summer season were to be chosen for intensive statistical study, it would have to be broken down into early summer (or spring) and late summer.

The early summer period would probably be

very difficult to handle because of the tendency for precipitation to

78 t* relatively sera important than temperature and wind in defining "bad shopping weather.

It is probable that as the temperature increases,

humidity would begin to be an appreciable element of interest.

With

the atari of spring plowing in the country, additional complexities weald begin to assert themselves. A rainy slay might (if it is rainy eatteugh) keep some of the city shoppers at horn© but might have the effect of drawing even more of the country shoppers than would other­ wise have been the case, since work in the fields is relatively diffi­ cult in the rain— and with some types of machinery* impossible, B&rly smssaer appears to be a period, then, that has very complex weather relationships. Late summer is also plagued with the farmer problem. will probably continue to com© Into town on rainy days.

Farmers^

By late summer,

however, due to the heat, a rainy day may be a welcome break to the city shopper so that precipitation (at least light precipitation) m y help sales.

Whereas, high wind velocity in the wintertime may be

expected to hart sales* In the late summer the higher the wind velocity (at leant below a certain maximum figure) the more comfortable should the day be.

Furthermore, a low temperature in the winter or Baster

season may be expected to lower sales; the relationship should be the reverse in the summertime. Humidity becomes an important element in summer.

So also i«sy cloudin©ss— a cloudy day becoming perhaps a welcome

relief from the tweaadeu® amounts of heat pouring on an already hot subject.

Insolation can, in Its effect © n the heat load on a person.

Increase the temperature by almost 30°F.12

Eh© fall season from a weather point of view start© with the end of hot weather.

In all probability fall Is very complex in the.

effect of its weather on sales as is the other transitional season—

early summer.

Ehe winter season (i.e., December 26 to seven weeks before Easter) should be fairly easy to handle.

It probably reacts in a man­

ner store or less similar to the Easier season, except that the effect of favorable shopping weather may not affect sales so markedly. As a matter of fast the winter season could probably be considered right along with the Easter season if m m means of indicating the somewhat stronger effect of favorable ©hopping weather on sale© could be indi­ cated as Easter approaches. After considering the various seasons the Easter season was chosen as the period to be covered by this study for the following reasons! 1.

It should be more sensitive to weather than any other

season of the year.

2 . there is no reason to believe that & certain type of weather should cause opposite effects on sales during different parts of the season. 3.

It is one ©f the two most important seasons of the depart-

mmfc*©tore year.

fo^essanA.of Ban^l Jteftt&to&aaft&Qs^

Eh© first step In the analysis m s to plot the sales data on a tine series lust as It is. inclusive, (figure l).

Ehis was done for the years 19^0-hS,

fhe first thing to be observed Is that there

Is a regular daily variation.

An examination of the data for the

year 19^2# for example, indicates that the Saturdays have, with few exceptions, the greatest sales of any day in the week. Mondays have the least sales.

In general the

Shere seems to be somewhat of a level­

ing off during the other days of the week.

Looking through the years

it m s noted farther that though the daily variation patterns for 19^1 and 19^2 look similar, there Is a sudden change in 19^3*

fhough

fee Saturdays continue to be important , Monday becomes the second important sales day*

Ehe pattern is distinctly TL-shaped.

“The reason

for this Is that the store (daring the war) decided to change its hours to a time more convenient for defense workers.

Ehe store opened

at noon and stayed open until nine p.m. on Mondays,

fhis was the situ­

ation daring the Easter seasons of 19^3« 1 9 ^ and 19^5-

post-war

years shows a return to the pre-war pattern* looking at all the years with the Idea of detecting whether or no*- fee same daily pattern prevails during all seven weeks of each Easter season, it appears that the seventh week of the pre-war and post-war years has a consistently different dally pattern as compared

STORE

SALES

(INDEX)

8.1

'(S T 'S

APRIL

MARCH

STORE

SALES

(INDEX)

FEBRUARY, 1940

MAR.

MAR „ 1943

APR.

STORE

SALES

(INDEX)

APR.

FEB., 1944

n m m u

MAR.

APR.

ghafhs

FEB., 1945

m w

MAR.

smsi m m

STORE

SALES

(INDEX)

82

APR.

MAR, 1946

FEB., 1947

MAR.

APR

X

a z hi

hi

4 i /i

if!

FEB., 1948

MAR.

m a m m

m m

m m

83 wtto

other »tx weeks.

toe seventh week dots m t appear to have a

pattor® differing from the other weeks -of the Foster season during the eaap years* however*

& b a resuit of the above observations it m s decided that several daily variations would have to be calculated. the three pre-war seasons have their own pattern*

h a m theirs.

First of all,

toe three war years

Furthermore, for the seventh week of the pre-war and

post-war years a separate pattern arista, as compared with the other six weeks.

toe first step in toe calculation of toe daily pattern was to nan a six day moving total centered on the third day*

The next

stop was to calculate to© percentage that each dayfs sales is of the moving total centered on that day*

She values so derived for toe

first six weeks were arranged in a table with toe columns labeled; Monday, Tuesday* etc.

The row® were labeled l^kO,first weak; I9&0,

second week; .... .19^1, first week;

etc.

In order to eliminate

extreme figure® that would exert too much influence on the magnitude of toe daily variation figure, toe highest three figures and toe lowest throe figures were eliminated from toe table,

toe decision to elimi­

nate three was based primarily on the fact that there had been three special sales that would have had toe ffect (If these sales figures had been averaged in) of increasing toe seasoned variation index of a certain day oat of proportion to what might be considered #nonaaltt for

84 that day,

toe remaining figure® In each column (i.e., the figures for

Mondays, toea&ay, etc.) were summed,

toen the summed figures were

reduced to percentage® in such a manner that they averaged 100 percent, toe seventh weeks of the three years were handled in a similar manner, except that extreme figures were not eliminated. and one low figure would have left on# figure,

SJllmimtlng on® high tots median figure

seemed lees representative of the three than the simple arithmetic mean.

In this particular case, however, the results would not have

been much different either way. Badly variation figures for the three war years were obtained In to# same my .

Bally variation figure# for the three posb-mr year#

were obtained in the same way except that the median was used on the seventh week because of one very low figure on the Saturday of 19^8, apparently caused by excessively bad weather.

As it turned out* toe

adoption of the median did not appreciably change the Saturday figure, toe arithmetic mean gave a figure of 108*1 percent whereas the media® gave a figure of 111.6 percent,

these figures compare with a Saturday

of toe seventh week figure ©f 12h«5 percent for to© pre-war years, and 119*7 percent for to# war years. toe daily variation figures as finally used are listed is tobl® XI. toe procedure for obtaining the weekly variation m s essen­ tially the mm # as that used to obtain the daily variation. moving total of weekly total sal©# was made,

First a

toen to® percentage that

85

Sable 11 Belly Variation Figures 3Nm ». Wed, Shur'‘ifnrn^nnfTrr' "Tnirrnrnrtri rrmiiiifrm* let 6 Weeks ?8.3$ 91.8$ 83-6$ 92.5$

7th Week

8Q.*$ 91-8$92.7$ 102.8$

Fri,

Set,

89.7$ 16^.1$ 108.3$ 12*K5$

1943^5 let 6 Weeks

116.?$ 85.6$ 85.7$ 89.5$ 88.5$ 13^.0$

7th Week

123.7$ 85.8$ 8^,1$ 8&.*# 102.2$ 119-7$

19%6-4g 1st 6 Weeks

85.2$ 106.0$ 91.5$ 93*2$ 89.0$ 135-1$

100$

eeeh week was of Hie moving total centered on it was taken. figures waps groupsd in a table. First Week, Second Week, etc.

2h.es©

2h© headings of the columns were:

fhe roes were labeled:

19^*0, 19^1* etc.

One extreme high and one extreme low figure were eliminated from each column and the remaining figures summed.

2he corns were changed to per­

centage* in such a way that the percentages averaged 100 percent.

The

weekly variation figures thus obtained were: 1st 2nd 3rd tei 5th 6th 7th

Week Week Week Week Week Week Week

fotal Average

96.9$ 96.9$ 98.5$ 99.# 99.# 1G3.# 105.# 700.8$ 100.0$

It was then necessary to combine the weekly variation and the daily variation percentages late one figure by Multiplication.

The

results are shown in Sable III. With the completion of the daily-weekly Index (hereafter called the seasonal Index), the original sales figures were expressed a# a percentage of the seasonal index figures.

Sack original sales

figure was divided by its appropriate seasonal index figure.

When statisticians speak of secular trend, the usual thought is the fitting of a trend line by the least scpares method,

fhis is

sometimes dim© without considering the appropriateness of the method to

SftM*

Ut

Combined Sally and Weekly Index

Week

Hen.

®fce.

fed.

{X948-42) first Second 7Mr& Fearth nm Sixth Swfj'fffeh

75*9 75.9 77*1 77*5 78.1 89.8 95-x

89*0 89*© 90.4 90.9 91.8 94.6 97*2

79.1 ?9a 82*3 82*8 83*4 86*1 97.6

19*>>*5) nrat 3Mid Foarth fiTth Sixth Seventh

1X3*1 113-1 114.9 H5.8 1X6.5 129.2 131.0

82*9 82.9 m.3 mjf 83.4 88*2 90*2

1946-48} Flxa* Second Third Fourth Fifth Sixth

8 2 .6 88.6 8 3 .9

84*3 $5 .0 87.8 93*5

102.7

102.7 104.4 104.9 IO5 .8 IO9 .2 100.6

Wi*

Sat*

89.6 89.6 91.1 91*6 92*3 95*3 108.9

86.9 86.9 88.4 88.8 89*5 92.4 114.7

I59.O 159.0 161.6 162.5 163.8 169*0 131.8

83*© 83.© 34.4 84.8 85*5 88.3 89*1

86.9 86.9 88*4 m.a 89.5 92.4 89*4

85.8 85*8 8?.2 87*6 88.3 91.2 108.2

129.8 129*8 132.0 I32.7 I33.7 138.0 126.8

88.7 8 8.7

90.3 90.3 91.8

86.2 86.2

92.3

88.1 88*8

130*9 130*9 133*1 133*7 X34 .S 139*2

90*1 98.4 91.3 94.2 106.2

t

e

.

93*0 96.0 103*9

87*7

9X.7 U 3 .0

118.2

the problem being considered.

On this point Boras and Mitchell ham

©stressed some words of eauti weather) might affect part of the faster season sales, but the Sfceter season Is sueh a email part of the year that faster season weath#y would act appreciably affect annual sales. Tounkera* annual sales for the years 1940 through 1948 are given below (Sots? Temnfears* fiscal year is from February 1 to January 3tf* Tear

Total Sales for the Tear (sum of dally indexes of sales)

1940 1941 1942 1943 1944 1945 1946 194? 1948

32,078*5 35,787.8 38,681.x 48,134.6 53,886.9 61,352.0 78,843.6 85,285.7 90,126.0

The above figures are, then, proportional to the tread. Below are given the total sales and average daily sales for the Hosier seasons for the years 1940-48, inclusive! Tear

194G 1941 W

1943 lf44 1945 1946 1947 1948

Total Hester Season Sales 3.876.6 h,369.I 4,867.0 6,070.8 6,837.6 7,897.6 10,l68.2 10,768.2 11,241.7

Average Baily Hasten Season Sales 92.3 104.0 U 5.9 144.5 162.8 188.1 842.1 256*4 267.7

Tor purpose* ©f comparison both the animal figures and the

Keeier season figures should be reduced to a common base,

A base of

1942 was decided upon because it appeared to be a rather normal year la the sense that the date of Heater is near the middle of the period daring which Hester might occur, and also since the weather seemed to he m l unusual.

The resulting figures are here presented:

Taar

Index of las ter Season Sales

Index of Annual Sales

19W

79*65 39.77 100.00 124.73 140.49 162.27 208.94 221.24 230.98

19^2 194-3 19441945 1946 1947 1548

Burly, Middle or Late Baster

82.93 93.04 100.00 124.44 139.33158*61 203.83 220.40 233.00

Early Late Kiddle Late Middle Early Late Middle Sarly

The last column classifies the Easters according to date. Barly Asters are between March 22 sad April 2, inclusive.

Middle

Saeters are between April 3 ami April 12, Inclusive, late Easters are between April 13 and April 25* inclusive. It would be interest­ ing to see if a late Has ter beads to be a better selling Easter than am early one.

In the following table are tabulated Easters according

to the Sarly, Kiddle and Late classification* a® well as according to whether or net the index of Easter season sales is higher than the Index of annual sale* In the table above. Bate of Easier

Ho. Easier Seasons Above Average Sales

Sarly

X 2 2

Middle:

Ho. Easier Seasons With Average Sales

Ho. Easter Seasons Below Average Sales 2

1 1

93 ffcongh, of coarse, the evidence of nine data is m t suffic­ ient to be definite proof, there does &mm t© he some reason to believe that there in a tendency in the direction of higher lastor sales when B a I s

late than when it is early*

furthermore, an early Easter is

probably a period of lower sales than a late Easter mainly because the weather is worse* It la desirable to produce a #trend1* figure that would take eat the fltrend" effect in such a manner that the finally adjusted sales figure would be in terms of percent of "normal0 (where 11normalu

1* 100 percent after seasonal variation and Htrend3* have been removed}* $&ks it will be necessary to divide the daily sales figures by a figure that is in magnitude comparable to the average daily sales of the Baster season,

Shis may be accomplished by multiplying the average daily sales

during Easter season by the ratio of the index of annual sales (19^2 base) to the index of Easter season sales B...'iaftthfti, .a w w ita fa ,.Ha

.

The following weather elements were plotted on the X axle of scatter diagrams, rectified store sales "being the X axis. 1.

Temperature!

The temperature was plotted in terms ©f the

number of degrees Fahrenheit the 12$30 p.m. temperature observation, was above normal*3^ 2.

(Figure 2).

Precipitationi

She amount of precipitation that fell

between 6iG0 a.m. and 6i0O p-.au was plotted* open from 9*GG a,a. to 5*3° p.m.

She store Is normally

The reason it was decided to include

in the preclpl feetion figure the amount that fell three hours before the opening of the store was the report that a rain In the morning before

17

the opening of a department store had a strong adverse effect on sales. r An examination of the data seemed to confirm this tendency.

The possi­

bility that rain falling at certain times of the day would have more influence m

sales than at other times was Investigated by various

graphical methods.

It was thought, for example, that the hourly pre­

cipitation figures recorded &t tea, eleven and twelve o1clock should be given more weight then precipitation falling during other hours.

So

96 fa&le V

Rectified fotal Store Sales, Easter Seasone, 19^3_lt8

x m K T

1st W««lc

V m t

Ji___ n t

2&d Week

3 M Week

4th Week

5tk Week

6%b

w $h F 1 ... M £ W Ste F 8 M £ W Th F s M £ W ffe F S.. . M f

W £h F ..a . .

% t

f%h Week

w F 3

_

m 98 89 88 r? 105 82 95 91 n 76 106 94 96 104 99 112 105 94 112 103 118 109 116 104 97 104 104 93 119 103 9© 96 104 103

108 93 95 99 96

101 X©3

19^*4

1945

1946

194?

1948

90 93 99 96 90 89 102 108 100 8? 86 79 73 68 88 92 102 100 73 71 104 9© 107 108 125 125 101 U3

109 103 108 97 116 98 117 107 76 83 95 93 $4 100 99 108 94 99 94 104 U5 116 114 114 120 108 103 91 115

113

95 97 97 95 84 100 81 96 101 90 93 105 98 107 104 101 102 10? 95 89 80 72 91 95 100 96 100 105 108 110 106 105 109 109 138 10? 108 106 98 102 1©6 108

81 96 92 102 96 86 85 107 104 97 13© 102 103 86 96 99 80 97 83 66 80

111 104 108

103 98 95 106 65 83 105 100 99 96 107 102 106 108 106 108 104 99 104 104 115 112 122 110 101 93 9? 105 115 105 118 94 104 102 100 95 103 105 107 10? 103 1Q3

108

106 116 147 , 110 113

in 111 114 105 104

122 114 114 128 109 112 112 105 98 97 90 98

91 88 76 90 89 90 72 88 1X0 104 109 108 118 103 126 122 138 118 118 111 100

97

140

I30

RECTIFIED

STORE SALES

120

100

90

80

70

40

30

20

-10

0

10

20

30

40

TEMPERATURE BELOW OR ABOVE NORMAL

m m m &

OF BAILY , ,

%mmim mim I9k5-W

,

«*®h tendency warn observed.

In the interests of simplicity, therefore*

it mao decided not to assign special weights to the precipitation that fell during certain period®,

Thou^i the store closes at 5:30 p.m.* it

wan necessary to use the six o*clock reading as no precipitation meas­ urement 1® taken at 5*30 p.m.

The precipitation figures were plotted

on sesd-log a® well as arithmetic graph paper.

%

Belativ© Humidity:

{Figures 3 and A)

Humidity is measured every six hours.

SSbe figures for 6:30 a.m. or p.m.* or 12:30 a.m.* were not considered Therefore* the 12:30 p.m. observation was

appropriate for this study. used, (stgure 5 ) h.

Sunshine:

Bureau Sunshine Seconder.

Sunshine is measured by the official Weather Percentages range from sero to 100 percent,

and cover the daylight hour®.

5 . Sfind Telocity:

(Ftgur© 6) The wind velocity from 12:00 noon to 1:00

p.m. was used as a representative wind figure.

Shis wind figure was

considered sore desirable than an average wind figure for the entire shopping day, as it is more representative of the meteorological situ­ ation than would a® average wind figure he.

6. Wind-Chill:

(figure 7)

The factor (9&~T)70*5 was also plotted.

%

was the 12:30 p.m. dry bulb temperature and 7 was the 12:00 noon wind velocity,

(figure 8) An examination of the scatter diagrams indicated that the

highest correlation seems to exist between wind-chill and rectified sales.

To test whether there may he a lag effect the wind-chill figures

were plotted against rectified sales for the following day.

(Figure 9)

130

RECTIFIED

STORE SALES

120

100

90

60

70

60 0.2

0.4

0.6

0.8

1.0

1.2

1.4

PRECIPITATION (6 a.m. TO 6p.m.) IN INCHES

yiCOSS %

BSXASMOSSIJXP Of M H * Y E8®CIPI£&3301f AMOWTS AK& HSCYIfW S®5B3 SALES, mSSSR SEASONS,

o - 00 CO

m

CM

(/) UJ

X

o

E Q.

CD

O CD

5 ? 8 ? o o CD

o IO

•L#_ 5 8

s s iv s

o

3sois aauuoau

CO

oo

Ll I

140

130

RECTIFIED aTORE SALES

120

100

90

80

70

20 30 40 50 60 70 80 RELATIVE HUMIDITY (PERCENT AT 12:30 p.m.)

100

hbkxbity kb&subotmts a h BSQftTTSB SfODS SAL1S, BXCLTIDISG EAIST BATS* EASTER SBASOIS*

n m m

5 , hblatxquship m

90

m t w

140

130

RECTIFIED STORE SALES

120

100

90

60

70

20 30 40 50 60 70 SUNSHINE (PER CENT DURING DAYLIGHT

nmm 6.

80 90 HOURS)

memm m m mt&mmmvs m> mcnnm tmmim m m mis, m s T m smso&s, 19145-^8

100

In comparing this plot with the unlagged scatter diagram of the same date* it appears that lagging one day redness the correlation consider­ ably. As was expected the precipitation figures formed a hotter s traight line cineter on semi—log paper than arithmetic graph, paper* Because of the amount of work Involved in running a multiple correlation* it was decided to limit the work to the four independent variables in the first estimating equation.

An examination of the four

scatter diagrams indicated that the following fear weather variables had the highest correlation with the rectified store sales4 precipita­ tion (log}* wind-chill, temperature (in degrees from normal), and wind velocity.

luring the years 19^6* 19^7 and 19h8 there were & total of 126 shopping days during the three Eaeter seasons.

(Bach Baater season

of each year is seven weeks lung and there are six shopping days in each week*} Table 71 presents Teunfcers5 total sales (in the form of m index number), the melted precipitation that fell between 6:00 a.®* and 6:00 p.m., the wind velocity during the first hour afternoon, the 12:3© p*m. temperature, and the normal temperature for the day* The general form of an estimating equation with one independ­ ent variable and three dependent variables is as follows:

\

= * * * 2*2 * b h *

where:

Vfc* V s

is rectified sales (the dependent variable)

.04 i/H

§ • •

o d O

M

o

00 P •

••

c o o c

•

5

m m

JSL 1 26 2?

..s US 233*9 242.? >7 188.8

188*5 ^ >?

I

Xplfcafcion *.«£ p.m. .per hr.)

226.9 209.8 275-6

© 0 ?f m

n

0 0 0 © 0 © 0 0 0.11 0 0 1.39 « 0.12 0.03 o .g 6 0 0 0.27

** 4- ** f«B5>©2W Wind Velocity 12 Sooiwl p.». ature (ml. per hr.) l2f30p.M.

28 ^ 20 12 9 10 19 9 9 9 *5 S 20 12 12 10 4 7 13 13 9 9 26 9 8 23 8 13 7 13 24 n i? 14 14 12 7

"

1 '' 31 33 4© 44 3? 42 41 42 44 35 00 15 21 23 fij 18 16 49 57 53 24 15 24 36 40 30 52 48 31 30 25 24 19 20 28

6 #* Honnal 'isjspsaew atnre '^ 40 % 41 42 43 s 44 45 45 46

22 22 22 23 23 8

24 24 25 25 26 26 2? 2? 27 28 28 29 29 3© 3©

3© 31

Ill SftKLe n

i

'. 2* "

'

8fe$tt TbwtaMr*# fetal Sal«« Clndez Nos.) s r r “ 2£ 6*I 253*0 9 16 223,6 2O4*0 11 12 269.9 402.0 "JJ,nW ’i: 246*0 il 321.9 27^6 t7 is 306,7 255,1 19 jgg. 472.? 22 307*2 23 375.2 24 337.7 25 33®*3 25 339*5 320.6 3?

3 **... Freolpifcafcion 6 a,a,-4 p.», (ml, per lay*.) 6 0

'

*

(Cont*d.)

4 ** '

Vflsd Velocity

'&**

fesqsaew

12 Sosa**! p«»« 'ate*® SHspsasv (sd. per hr.) IZtJQ p.st. afcure

'

2f 6 6 6 0 0 6 3$ 0,08 0 6 6 0 22? 0 6.61

15 12 16 7 13 9 7 14 6 20 16 IS

7 t 16 IS

7 23

19 , 11 2 0 m 30 % 37 43 59 49 5? 4? 5B 66 71 69 31

32 32 33 33 34 35 36 3& 37 3? 36 39 39 4© 40 m. 41

TopEfflfeer Brea. * Inc., store pggsg&g*

** *Hojsthly Meteorological Stnsm&ry,* Weather Bttreati, W. S* Cour Btwiso, ®«g Moines, Iowa. Jf * %tmm tear* with fmee

112 Sfefcla n t fetal Sales* and leather l&eme&ts •fa®- fcli© Setter Seasons* 1946-48 (lata ready for correlation)

1 Sat*

2 Seetlfled Stare

3 festpercitiurs (°3F. above or %mlm noraal)

4 flat Velocity

5 Wind f&Hl

6 Precipi­ tation Index

103 # 95 106 65 m l©5 10© 99 96 107 102 106 108 106 108 104 99 104 104 us 112 122 no 101 93 97 105 115 105 Hi 94 104 102 100 95

♦ 13 * 6 ♦ 9 ♦ H - 7 - 6 ♦ 14 a 26 ♦ 24 ♦ IS ♦ £t ♦ 14 % 7 ♦ 20 * H ♦ 24 ♦ 27 ♦ e .♦ 23 ♦ 19 ♦ 25 ♦ 29 ♦ 39 * 14 ♦ 33 + 26 ♦ 28 ♦ 8 ♦ 9 ♦ 12 - 3 ♦ 5

15 9 17 12 22 12 14 26 16 8 10 22 Ik 13 13 19 6 7 12 13 5 7 H 11 21 14 29 20 17 10 13 14 13 f 11 13

174 186 236 190 342 249 191 194 I60 127 133 225 202 148 177 157 78 3-35 121 12© 72 71

204 272

*946 5£riPS 5 6 7 6 "

ii 12

1 *5

.

/s|. .

*9 20 21 22 ___J a ­ il 27 38 29 _ .m %r 1 2 3 4 J H

9 10 n 12

.,.,W, A

■* $

*- 9

♦ 2 ♦ 15

7f 136 96 103 *25 201 177 126 195 172 209 177 156 123

&

0 261 160 0 211 0 223 © m 6© 0 O 0 0 273 0 0 0 0 185 0 0 0 0 0 48 0 0 0 0 16® 0 0

113 M f l n i (Coat*!.)

Joxmkiir*» $s>tsl Sales, &a& Wdatker BlBmeftt© for the faster Seasons, 1 9 & M S (0»fc& ready for correla'fcioft}

1 l&tt&fe MIMiiMM 19W

3 BasiifledL Store Salas

%r

fea^emture ( * * &^0f8 of ■below aortal)

♦ 6 ♦ $ 4 m ♦ ii ♦ 22

5 Wind Telocity

a ai a %£

*

^isd

mm

Itatioa ladex

119 12** 102

♦ 13 *5

97

n s

100 m 9&

101 90

93 103

98 107 1©&

im lot

m 93 89 80 72 m 95 %m 9$ W0 105

108 11#

* 12 -* h

♦

5 2

- 9 ♦ n

•* 7 **• 5 ** 5 -»• 9 «p 8 ♦

11

- 6 ♦ 1 , 0 ♦ 4 ♦ 5 »p 1 ♦ 2 ♦ 12 ♦ 6 •f 2 •p i5

~ 13 a ^ 5 p* l

- 3 - 3 * 19

11 19 9 5 a 13 15 17 18

n

* 16 8 9 12 9

292

2^* 159 229

170 1*18 ©

331

262 m 320 m wx 236 169 109

1 1# 1# 8 20 19

195 3® 0

236 212

9® 95 @5 3© IAS

0 Q 0 0 ^8 180 m 0

268

295 3l$© 32S

13«* 7 1** 17 3l

2^5 tm 2:

19a

0 225 0 30

1*8 0

114 tm» m

(eoat»d.>

Tcwa&er^ total Sales, and Weather Bleiaentia for the Saster Seasons, 19k6-48 (Bata ready far correlation) tats 1

t

'

Bate

R e c tifie d

m t

Sfcer© Sales

3*K2 22$ 17k

30 0 0 0 0 201 0 0

xm

2 5 1

10 19 9 9 9 1J

106

&

98 102

L tlar 1 2 3 k

Preamp-* ltatiom Index

W

%

2k 2^ 26 27

Wind Chill

28

138 im

fell 9 10 XX 12 13 ..»■ io 17 18 19 20 rn ra . ,.1. ^ .

5

Wind

1 9 8 1

105 X©9

%r

temperature (I. ah

-°-05817

It Is to he noted that the last factor, sunshine, has changed signs as compared with the second estimating equation.

3Ffeough It seems

reasonsible that wind-chill, precipitation and snow cover would have the effect of reducing sales (as indicated by the minus sign In the equation) ; it does not seem reasonable that the more sunshine there Is thelower the sales* As wasdiscovered in the analysis and of thesecond estimating equation

discussion

there is a high degree of negative

correlation between precipitation and sunshine.

$hls fact may la part

esqjlaln the unexpected sign of the sunshine regression coefficient. Shore may be a meteorological explanation.

In the winter-

time, sunshine Is almost as frequently associated with cold weather as with warm.

In the springtime sunshine 1® more often associated with

warm weather.

She coefficient of correlation between sunshine and wind-

chill increased from - 0.12 for the early las ter seasons to - 0.24 for

14.0 t o 1*1# Master seasons— thus seeming is bear out the generalization just DMds*

SJfee increased correlation "between sunshine and wind-chill

tw&es the latter a somewhat more reliable measure of unpleasant weather. fhe apparent instability of the sunshine factor may be merely & reflection of the inaccuracy of the Weather Bureau1* sunshine ?S$S3@&a9?V t o second most noted change was the M g drop In the effect of snow cover.

Shis is not particularly surprising In that the later

the Master season the 1ess would snow-cover have an effect on sales. She loss in percentage points in t o set effect of anow-eover Is more t o n m $ m v® by the increase of t o effect of precipitation.

One addi­

tional fact nay explain t o diminishing effect of snow-cover (when

eoarp&ring late Master seasons with early Master seasons).

For the

early totter seasons t o coefficient of correlation between wind-chill

end snow cover ie «► 0.25increased to a ©.47.

For the late Easter seasons this coefficient

t o w , wind-chill can explain, during a late

Master season, some of t o effect tot snow cover explains during an early laster season.

The coefficient of multiple correlation between rectified sales as calculated by elation number three and t o actual rectified sales is 0.5542 ( t o coefficient of multiple detorE&satlon is ©.3072). Shis coefficient of multiple correlation doe© not compare favorably with that of the second equation (which

G.6502). On© possible

1.4:1 e# this reduction in the Index is that weather is net

m Uspertaa* a determinant of tales when t o date of Master Is lata as when the date ef Matter i® early, There i® another possible explan­ ation: In the third equation two oat of three of the years examined are war years; whereas, in t o second equation only one of t o three i® a war year.

Weather may have had a lesser Influence on

habit® daring the war ton slsee. m.

the third compared with the seeond equation (early 1.

In comparison with the second equation the individual

of sunshine is considerably less and exerts its influence direction.

in the® 2.

Snow cover ha® considerably lee# importance, a® would he

eansiderlng the season of the year.

3* Both t o wind-chill factor and t o id in relative

In this chapter the department—store year was broken Into seasons, Because It was felt that the seven weeks before Master are: nest sensitive to weather, that season was selected for examination. Upon examining the various weather elements that were thought on sales, it wa® decided that the following

1/1.hv

weather elements played the most important role in influencing sales? 1*

temperature (in degrees from normal)

2*

FrseipltetiQn (logarithm of amount in inches)

3.

Wind x

01

mm

k x J t e & M U m ® bf,fffrftftftfflr w U ^ I t e S tra te g y may b e s t be d e fin e d la

Its

r e la tio n to p o lic y .

•M a rk e tin g p o lic ie s a re th e ra le s o f

conduct, e ith e r written or implied,

under w hich th e m a rk e tin g a c tiv itie s

o f a fir m

e re co n d u cte d .*2”

Bach Policy lilies a definite course of action, predeter­ mined for the purpose of insuring uniformity of procedure under substantially similar end recurrent circumstances. I t alm s to p ro v id e a u n ifo rm course o f a c tio n , b o th as between d iffe r e n t p e rio d s o f tim e so lo n g as the p o lic y is in fo rc e and between d iffe r e n t members of the organisation wise have th e r e s p o n s ib ility of acting within the c o n fin e s o f each p o lic y *

With respect to a department store, marketing policies may be th o u g h t o f as a *a e t co n n e ctio n w ith I t s

of prefabricated answers to questions arising In re la tio n s

In d is e a s in g

“the administrative task of adaptation to chang­

in g c o n d itio n s ,n P ro fe s s o r c a te g o rie s o f e x e c u tiv e

3 to Its customers, employees and resources.41

Copeland says, 44there are four successive

decisions to be m a d e . S h e y are?

(l) to s ic e

up the s itu a tio n , to diagnose the problem s to be met; (2) to determine th e course o f pose to

action to be followed —

the objective, or general p u r­

be aim ed a t; (3} to set up an organization and to educate Its

members to © a n y c u t th a t p u rp o se ; (h ) tfe© a c tio n , th a t is ,

to determ ine the ttstrategy o f

the sequence sad timing o f the moves.

150 ftpefe&wsr Copeland conalders all four of these categories as

■immm® stops la the development of business policy, thus tm ma^ aider© strategy so hat the last of four step® im carrying cut a policy. It spears, however, that policy a® it is usually defined 1® stplw®*lent to Professor Copeland1e second Hcategory of executive decision.41 (I.e. , *fh© course ©f action to he followed —

the objective, or general

purpose to he aiiaed at.*) Copeland defines strategy as *the sequence and timing of the

mmm* that are necessary to attain the objectives of the policy. Strategy is »©re specific than policy in the sense that it is aa pat­ tern of operation that Is activated In response to certain itemized and £ specified envirensfflfttal condition®.11 Policy la usually thought of as a long rang© decision; strategy a® a group of decisions covering a shorter tine period. In some respects Copeland*© definition of strategy may he considered toe narrow.

Sons of the decisions In connection with his

third category (i.e.* the setting up of the organisation and the educa­ tion of Its members to carry out the policy) are strategical in character. Presumably there are various possibilities as to the for® of the organisation and the method of education that might be consid­

ered somewhat strategical in character. Also, decisions with, respect to quantity (as la the case of the selection of media in advertising, for example) partake of strategy. Nevertheless, the problem of the ee^uMac® sad timing is marketing strategy in its purest fora.

1.51 however* problems regarding best form of organization, general quantitative and qualitative decisions can be made within limit*, os the basis of general principles that have been developed by thee* eh© hawn »«do careffcl scientific studies of the problems involved. Selpb ». Sewer la hie Blfltogy pft Macy*g of Haw York. 18«>BSttfer.tMi the tern policy correctly in the following quotation; ... S e major policies are four; dealing for cash only; selling: at one price to all customers alike, regardless of the bargaining ability; selling at very low prices (la today*a terminology, underselling competitors}; and aggressive advertising.8 las carrying out the policy of agpfssslvs advertising (for example) inKeeserable strategic decisions have to be made with respect to which medium be nee; how much space to buy; how to allocate the advertising appropriation throughout the year, the months, weeks and days, and as between departments and commodities;

as between sales,

♦events* and regular promotion. Some strategic decisions may be based upon quantitative infor­ mation net subject to quantitative evaluation; and therefore susceptible only to the nee of Judgment*

3&ere Is usually available both quanti­

tative and qualitative Information In most strategical decisions, thus, a combination of quantitative analytical tools and judgment most be used. In still other cases quantitative data is available but the qaasaiitaiive interrelationships are not yet known. Shis latter situation may exist because analytical tool© have not yet been sufficiently

Professor (hjpfl&mftA**

la Cfhapbefe

% and H I there has

%esfe a» attempt te diagnose the problems to be met.

la this chapter an

attest will be made to develop the ether three categories. la *si«iag up the situation* it has been shown (Chapter III) that weather affects the total dally dollar sales of lounkerc Depart­ ment Store.

Furthermore, a method m s developed whereby a sales fore­

cast m y be made based upon the weather forecast. $hs next step Is to adopt a general policy which will utilise ;eohnlqa« for the benefit of the firm. It is conceivable that the Improved method of forecasting the malms that has been developed stay he of use to the firm la the ways; 1.

it should he possible to adjust personnel needs more ade­

em a dally basis,

fhis may make possible, or necessitate, the

revision, of preseat personnel policies. 2»

It should he possible to allocate advertising

mere efficiently,

5Ms, too, say require some changes in. present

Presumably, one of the major, though

153 of th« fta» Is to keep easts as low as possible consistent with ado, p. 155-

5*

p*

^

Dr. Wendell R. Smith, personal interview-

?. Ralph H. lower, jg&t&Sg,, jb£ lanyard diversity Press, 19^3*

$1 M m %aX& 2&S&22I&* Cambridge,

8. Ibid.. p. h8. 9. 0. Preston Robinson end Borris R. Brisco, Store 0rasBgtig&tion and Q-peration. Hew York, Prentiee-’ -B&li, Inc., 19^9, pp. 178ff. 10. Malcolm P. McB&ir, Charles 1. Gragg and Stanley P. fsele, J& Retailing. Hew York, McCraw-Hill Book Co., 1937, P* 11. See Chapter 111 this essay as well as McB&ir, Gragg and feels, £$,. p. h-52| a®d Robinson and Brisco, jgg.. cit.. p. 187* 12. Robinson and Brisco,

pjt., p. 192.

13* i m * lb. IMd.. T). 19*K 15. McHadr, Gragg and Seele, pp. pjjL.* p.

b$2.

16. Robinson and Brisco, clt.. p. k6. fh© plan is folly discussed JaXftd. H. Hamr, PrlncltiLefl of Organisation AjmUbA la. M M s m Betailing. Sew Tork, Prentice-Kall, Inc., 192?. 17. Rdgar B. Bm9Lta< Control of the Retail Unite of Chain Stores, l & M i m gagfcVSSa vo1' 7, 1935. P* 26*

1.85 16, BoblMon

andBrlsoo,

alt.,p, 160.

M&S&m&Xm aa& tefilaUx £ t o m » »©w Tork, national £•£•11 Bry Goods Association, 19*i8, p, 97* s t r o lle r s *

20. Robinson

C ongress, 19*v? $&?x m

and B ris c o , o£. c l t . ,

pp. 182£Y.

21. Xbld.*P. I83. 22. HcHalr, Gragg and feele, gg,. pit.. p. ^52. 23. I$L4,. P* **56. 2k. Ib^d.. p. ^52.

23. Robinson and Brisco, op. ©It., pp. 180-81. For a discussion of a parallel situation gee lontolsgit Parketa^arkeA Moizmtisz lax A&TOMBa tigers^ booklet bO of the Advertising: nal wastes of over-and«^mder*-®Kpendl tarett on p. 10* 26, Moody1s Industrials, Beet. 1, October 27* 19^8, p. 238I* 2?* G. B* Davies and V. F. Crowder. -Methqda of Statistical toaly&l&. Hew York, John Wiley and Gong , 1933* P* '72. 28* Robinson and Brisco, £g> clt.. p. 1&9«

Iamd. ggwgfflas&r MTSUEfelglaa. lew Tork, Preatlce-Hall, Inc., 19^7, if* 290IT* 39? T^3bd». p. 299* %Qf Mwards and Howard, jg&. d t .. p. 125. .

m & ? * p* 3-3Q-

t2, Lund. ap. jsftt,. p. 299* ^3- P M . , p. 37* Bdwards and Howard, on. clt. > p. 6^2.

45. Mr. Hobs Balliey, Publicity Sirector, personal interview. Mvar&s and Howard, jjp. clt .. p. 6^1. tyf* Hr, Boss BaTbey, personal Interview, $8. See part B, Chapter 71, for a store complete discussion of the use of is&rk&t potentials.

charier is diTl&ed into the following sections: A* Definition® asst introductory statements B. Statistical chewing gam, s«&ll 'pembged infections, and probably most food products.^ -Convexso aids doe eraafi» ice, milk, cold drinks, and cooked food* sold by restaurant* to this deed* of this class tend to he bou^it at points cost to the consumer. Habit plays a leyg** pant in their procurement. Since no one purchase of such goods is likely to loon J ^ p p r J M ^ U budget* the consumer will not go far m % of hie way to procure them, nor is he li n ­ ing to exert hl&self particularly in making Investigations and easpsrisons in the proses* of selecting: them.® fhcre are esOy a few departments in the store in which all the

sales sd£it he Considered

©sod® sales* fhey;arc the fee

Boom, the 3Wc*ry*aad the Basement fouatain-Oaf© and tbs Sandy Bepart-

m m U M9m*9im with store official® revealed that the Bakery is so new in the store that Item problem has always been the baking of enough. ds*od* to

the demand1 * In short, the sales were determined core by

is w m m W O L W

the M

we but the palling power of a for style show

**#**• rn mrnm style item* for

Into the

i N # et» outside the .epee of this

mte m *

latter was considered the more desirable

of She two for the which ere indiviSteM dnallsed as to quality, service rendered, or as to features ether than price* They hare also been defined (Gowsitte© oik Beftaittons, Am. Marketing Assn.) as goods cm •which a typical buyer ek&ra£tsrl«£is«Hy insists and' for. which lie is trilling to make a special purchasing effort." their relatively

by W r attraction ether tbaapriee; and by the brand eonseiousaess of consumers and their insistence on particular brands. limit their distribution to the producers of

fever outlets with established exclusive agency arrangwaehts daa of the difficulties.to

*♦

good was

Mgulty of the definition* is fh* classification of commodities as specialty a aon^^royerelal point and mm contend that no article deserves a place in this classification unless at least one-

of this method of classification is the lack of -the habits of haying and the large number .of onset— marchandlae that might logically" fall Into more than and Aasben, Sleme&ts of Market-

191 ««*'* hi#-«rade ahoes, expensive pipes and tobaccos, and probsfcty each article* an aaboiacfbiX**, electric refrigerators, pianos, and

Warn w & T specialty products for which dcpartia^tal daily

figcy** »*» available (ef the shove list) are the sales figure* of the 8*fri**ya*«r Beparteaent and the Badio Beparimmt.

©afiartoately, the

'gale* ef both these departments during -the period I f M to 15*49 Inclusive waceaffacied. by the fact that during the war these products were net a w a tlie th X e ,

X s B a e d ia te ly a f t e r

sales were very M$*.

th e

w a r th ©

^ *w a » ^

««»d.

y

# i*

After 1ke demand had been partially satisfied,

sales fell off.

Of the two departments the Befrlgerator B^partaent is probably the tstler representative ©f a specialty good. To a considerable extent people have a greater brand preference for a refrigerator than for a

8*thed

t e

are several possible ways of handling the problem of

the effect of weather on the sales of the tee* individual departments that have been selected* Sne way is to handle each department as the problem has been handled in this study for the store as a whole. That would mean the calculation of the weekly and daily variation for the individual depart­ ment, m well as the trend for the individual department. Then the individual daily sale* would he rectified,, by using the dally and weekly

192 trend variations am oajLodated for the individual department. The remilting rectified calcs Could then be correlated with the individual weather elements aa wee done with the stored total figures. If the problem i* handled in this way, the possibility of encountering an excessive number of independent variables in the esti­ mating equation is a real one. four independent variables are need in the consideration of the effect of weather on the total store sales• Since suaaMu© was found to have little effect os sales, it is possible that the number could he reduced to three for present purposes* Also, since m attenpt Is to he made to detect th© presence of an optisana seles situation it would he necessary to increase the three independent variables to six in order to account for the squared part of the para­ bolic equation.

Sfcrlhermore, it would be necessary to add additional

Independent variables to taka car© of the effect of advertising linage. This would result In six or ei^it or more independent variables*

Since

the results are hoped to be in a form that a store manager can use in M s day-to-day «a»a®ema»h problems, the using of so many variables on eaeh of perhaps as many as one hundred departments is not feasible. A method cast he devised that will tie the sales of the individual department to the total store traffic. This is necessary for simplicity and in order to fit into the management pattern of most department stores. The whole problem may he considerably simplified (particularly from a management standpoint) by relating the sales of an individual department to the traffic of the store as a whole.

193 * pre®*dttr» my h* Justified on the following basis*

It

is probable that whatever affects th® sales of the store also affect*

the state \m\ In h i< ,.■ 11 '

la the ease of th« EafPigwator Department (tha specialty e»a> it would see* that the sane independent 'variables weald apply. ?hsr© is, however, a reason for sot taking late considBration the effects of advertising. There Is sen® question of th® extent to which it la possible to relate the linage of a specialty good to its sale, especially when it is fqjpliod to an iten that is as expeasiy© end which is purchased as to-

$mm antly as refrigerators, The existence of a specialty gped implies a strong Irand preference among at least nor® than half th© buyers. This brand preference has presumably been built up over a period of many years. It is based upon a long reputation of selling a reputable pro­ duct, and m widespread belief among many people that the product is actually superior to all others, To a certain exient, such a reputation nay be oEhenssd by the mae of advertising? thou# it Is nallkely that tile bad reffutatlea engendered by a eery inferior product can be overcome

advertising*

Be that as it may, insofar as th© product is a spa-

an Advertisement which appeared days, weeks, or even months Mgr have *owe influeaceaa a sals M a y .

Kars likely, however,

» former experience with the product (or that of may influence his preference. Saving decided act to consider the effect of advertising on the of the specialty goad the question arises as to father weather has effect o» the sales of refrigerators» The question involve* the reason for buying. Sens of the proximate reasons include? a* A young coupld have married and are haying their first fe-

b. The ©Id refrigerator of a family breaks down end the family to buy a new one rather than repair the old one. o. A

decides to replace its old refrigerator, even M u # eld machine Is still working as well as it ever did. th is

situation woULd arise

the now machine wet the

d e s ire s

beesaase

of

th e

for earn r«&»

purchaser

b e tte r

the eld one. Sither it was bigger, was acre beautiful er had other mere desirable features* d. A family decides to change from an Ice hex

to

an automatic

2%frigerater• This could he the result of a change in the scale of living of the family, or due

to th e fa c t th a t

electricity or gas had Just been installed, or for some other lean common reason.

211 M i relation of the sale of refrigerators to the- weather m e t then h# considered in the meaner In which, it afreets the above prexi«a*e V M M i er th# extent to which It keeps people- fro® the store or affects yaitfttft* psychology. With the emotion of proximal reason^. »hf*, It Is difficult to see how the weather ®i#t effect the sale of felbieemtox* a* all. It happens, however, I k l more refrigerators break do## on hot day# than on cold en##»^

the study node % Iferion G.

Began, on tide relationship covered only th# aumster ssQtta, however.

Wmm ##* It i* tmderstandable why there should be more refrigerator breakdowns M s f warn daya than cool days during' any ft*# of the year® Oaso, however, some proximate reason induces a willingness -to buy or «t least a frame of mind to ooaoider the possibility, %%appears that good shopping weather would lure more prospective buyers from the confer* of M i r home to the down town m e n of the city than would poor shopping weather,

yartfeenaore, since higher temperatures daring the

time of the year being considered apparently increases th© desirability of shopping weather, this tendency reinforces the effect of wsna weather % increase the number of broskdmms.

With th© prospect of summer com­

ing, each wa#« spell reminds a family with a defective or broken refrige­ rate# that makeshift means of keeping perishable food cool will m losgsr Is M l

la the not to# distant future. Thus It would see® that war®

wfwtthyr should t -nereese sales*

An examination of the daily sal®® data, of the aefrigerator B©partmont for # # B&ster seasons before World War II revealed very erratic # s

fi#r©#% ■for *3ebb$1», during th# month of February of all pre-War

2 ##•#* bwflt to *935 ‘'Beet of the day© after February 10 have almost ser©

W&m until M

M e * Saturday before the end at the month, *n whioh day

entrls* of fro* $MIG0 to $12,000 were made.

The explanation is that in

i#«i year* Mat* wore store wide promotions m the last Saturday of M

BefrigeraM Beparfement promoted the sale of a eortaia

siedel of refrigerator at a price reduction for M a t three weeks pre20 AiStag' tld« event and all sales were made- as of that day*' M H h e r iaqairy re^KJt^liag the sale© methods in the oem-

%StSMn& weather ami in the aarket for a refrigerator does net walk into the store and bey. At first the easterner is Just thinking about buying.

If possible the

aalesaaa in the Befrlgerator Bepartmeat tries to get the m m of people she are *fast looking^ and to ^follow the®

The probability is

that mmt sales are made one day or more Csometimes many more) following .the day th* easterner first examines th© various models in the store.

In

^following op* th© prospect# the salesman say have to visit the prospect at sohsequent time® In his hone or at hie plane of work* The salesman hay have to M B with the spouse of the person who looked at the re&lg*-

mtors In the first Instance. The majority of sales are and® e&' some day after th* first M i s s t o of refrigerators. The selection of a £2 nodal not at the Moment in stock farther delays the sale* It weadd appear that th© data available are of little use for the present study t M a n of the existence of the baying habits of sattos«r« and th# sales prwaetten policies of the store before the war.

Bn*

aft#**are sshjoct te th* same disadvantages. Unfortunately, outsideof th*field«£household appliances (Includingradio andradio-phonograph actiiatieaft) there areno specialtygoods soldat YoankerBros*, Is*, foridiftdallyfigores are available. Itwillbe impossibletherefore to lartsludelathis investigationa statistical studyof the effect of weatheron the saleof a specialtygoodat YounkerBros.»Inc. S..

and Gonclttgions

ft was decided to analyse the effect of weather on the of three departments of the store. After defining the tm m it was de­ cided to analyse the effect of weather on the sales of a department

was (isthe main)

selling

a shopping good, a

convenience good end one selling & The three departments chosen were as follows? The Moderate Priced Bros*

The An examination of therelationship between total store the salesof the ModeratePricedDress Department indicatedthat norml store trafficdidnotcause, 'beyondapoint, a levelingoffo: of therectified sales of theModerate PricedDressDepartment

214 **f the independent variable* examined only saov cover and the gut of M

M o # previous day*' advertising eoatrlbuied significantly to

the correlation between th©*© variables and the rectified «&•» of the Moderate Priced M s * Department.

la the eoaeideration ©f the effect a t

wsmmrnmUHfer rn d p I f M t ©orrelation between advertising handled as throe daily variable* and the ©alee of the Moderate Priced Drees Departm l «e* fwad.

An advertising figure which m s the result of summing

-the advertising linage of the previous M a e days, however, made a signi­ ficant COfttribtttiea to the correlation. On» of the reasons for the In­ crease of si^lficsne© of advertising handled as a m variable as against advertising handled as three variables Is the increased possibility ©f chance correlations made .passible by the use of three independent varishies, and thus requiring consideration in the analysis of Variance. Severer, there Is probably an indication also that the varying results Indicate* a real difference.

It is probable that due to- the very nature

of shopping goods and the varying distances that different individuals 1* the

i y nyirkfrt must travel to reach the store, no standard

dally pattern of response to the department's advertising may be ejected. The store's practice of adding the sales of the Item advertised for the three gay# following the appearance of an advertisement appears to be Justified in the ease of the particular shopping good under considor'

The figures examined indicated that the effect of advertising OH o U l was independent of th® weather situation. Whereas in the ease of the shopping good no significant

215 #®k* found between the rectified sales of the store and the « i w «f the department* the reverse is true in the ease of the Candy Department. A* §m th© ease of the shopping good, however, the conven­ ience ©sod iaddonbes no isn&oai*ey to level o ff or decrease (beyond a pctat) with increased store traffic.

Such were the coaelesions based

l p » the analysis ©f th© CanSyr Department sales vlthout between early Baster seasons and late mm* After the data had been classified as to earls seasons the gsneralisationa made in the above paragraph had to be modi­ fied am feUowst 1* With reboot to the early Easter seasons only wind-chill m & snow mover contributed significantly to the correlation.

Beatified store

sales, sunshine and precipitation did sot make a significant contribution* 2* With respect to the late Bag&or seasons only reetified store sales contributed si^iiYleantly to the relationship. It would appear, then, (if future early laster seasons evoke customer M i s r

similar to that brom#t forth by the Easier seasons of

19^5, 1 9 % and 19^8) that weather is influential In affecting the vari­ ation of the Candy Department's sales during Easier seasons but that it Is not during late Faster seasons. During late luster seasons th© de­ partment's sales are significantly affected by store sales only.

Indirectly,

of course, the Candy Department's sales are influenced by weather daring late Sente# seasons inasmuch as the store* s sales are influenced in part by weather (as indicated by the third store estimating equation and the associated coefficient of multiple correlation of O.j

probability is that there is little or m re­ sales on & day-to-day baels tetftg %»*#» CMMROnS. StseaM? aoatJis*

a generalisation stay met be true daring

217 Pootnotes

1.

m Definitions, American Marketing Association. as ^pasted la P m l £# Bjrstro* (editor), Marketing Handhflftk. B©w York, Ybo Sossa&d ftpti* Ooj^aay, ipb®* p. 20.

1»

S. Jaoaandor, Frank M. Surface and Wroe Aiderson. Market!** (SeTised Edition) * Boston, Oiun and Gongany* 19*19* p. 3,62.

3* B a d H* Bystreat 2D* £*!•• p. 20* *. Alasuulor ai. «X-> fl». Sll-> p. 57. 5* Dan! B. Converse and Bsrrey W. Huegy, ^u» Elements g£ Marketing. Bov iQSk. Prwatico-Hall In®., 19*16. p. 181. d. Mvma8m* *&» &»• sa* fill** PP* 57-58. 7* Tanl H. Bystroo, SSL* ^L&** P* 28* ®» J&2&** P* 28.

f. A1candor, s£. si*,

fi&.

fill.,

p.

6&

10. Charles C. Peters and Walter B. f m feerhis, jjjftfaftfaaft. g&99$te.a& jpd ffady Sfattkaaaa/fcicfli Bases. loir York, McSrasf-Hill Book Company, Iae.7^0* p. 227*

11* Sao Appendix 7. 12. See Appendix 7.

13- SfeiAX*. Ikld. 15- 2ess Balby, Publicity Director, personal Interview. Id. Frederick 2. Croxton and Godley J. Covdea, Applied tics. Bov $»?&, Prentice-Ball, inc.» lf^6, p. 878.

SfcoMsr

17* Goo Appendix B. 18. Ibid. 19. Albert Weslsy Iteey. Advertising. Bov lot, She HonsXa Proas Gosgsay, X9*7* PP* 3®-32*

218 M * Harloa

Hogan, A tjteftr of lafoatrl**! Meteorology. tf»pobltshed H&ater* The9la, Massachusetts Inutitttt® of Technology, 11, 19*6*

21# Charles (Hl&wrbloom, Baparfcmenfc Head, personal interview. See % peadix & for the sales flgsres for tho Sefrigeraior Bepariment. 22* ©hades fcfiaadlooai, personal interview. 23. &|&. 21k ja*i»

219 f t

muebsii®

$mmm

is

mm tm-nwm, mgBUMMtaws

9NU* chapter la divided into the following main sectIonas

X* Marksting Strategy in the Hoiemts Priced Dress Department. D.

Harks ting Strategy la the Sandy Department.

$» Mark©ting Strategy in the Hefrigerator Department, She use of the ©st.Imiing equations la evaluating the causes for sales variation B.

Conclusions

la Its Strictest sense, strategy has keen defined as 8the 1 Sequence sad timing of the moves.# It is in this strictest and most limited sense that the word strategy applies to the operation of indivi­ dual departments of a department store. By using the relationships observed in the preceding chapter. It should he possible for the departmental buyer * W $ ^ 6B0 358 690 957

!94f

Aetna! Sales

Apr 12 $ ?64 13 896 l6 1€N37 15 1366 is

Sstisated Sales ( 3 h m $ 772 693 m 786 12X1

SGTJECSS; Actual Sales: store records* Sstimteft Sales: Gandy SNspartJaeat final estimating eqn*ttion for late Saeter seasons.

233

(smmoa

no

saivs

•*

(suxnnoa no

sanvs

235

236

1600

| ACTUAL

1400

SALES

0 ESTIM ATED

SALES

SALES

(IN

DOLLARS)

1200

1000

800

600

400

599595

944995

200

M/tmA

,_______

a

2g I 2 3 4 5 |

FEB. MAR.

m m m 25*

mama

d m L B d S u B o M l C iw ifld B Q n la d i

8 9 10 II I2 (^ 15 16 17 18 19^ 2 2 2 3 2 4 2 5 2 6 ^ 2 9 3031 I 2 ^ 5 6 7 8 9 j'| 12 13 14 15 16

APR.

gf s®s a w , r a m

m m m m

m s m m *m

237 itilfelfc ©banging ©e&sut&er desires*

With respect to advertising copy prep­

aration, management would be in a better position to evaluate fSlliag power of different specific advertisements and/or the pulling

$&me of different types of advertisements.

(See figures 22 and 23

far a graphical presentation of the data contained In fable XIII)*

With respect to the Candy Department* management is in a

better position to evaluate the equality of merchandising in a manner similar fee its evaluation of merchandising and advertising In the Moderate Priced Dress Department,

She actual and estimated figures

for the Gandy Department are listed in parallel columns in Sable H?. She same data are diagrammed graphically in Figures 2k and 25* One of the chief difficulties that may be encountered daring periods of rapidly changing general business conditions will , be to determine the trend figure- to be used*

.If,, for ©sample, esil-

mted sales tend to ran for a fairly long period of time rather eon~

aisfeently higher than actual sales# considerable difficulty may be tMtperienced in determining Aether the cause of the- difficulty is one Which the management can correct or is due fee a shift in general bust—

ness conditions*

1*

though it is not practicable to vary salespeople1®

salaries with sales in the Moderate Priced Dress Department alone, it Is practicable to do so with respect to the Ladies1 Apparel Kerch-

238 andis© Division.

Z*

So attest n®«d be m d e to modify bh© amount of adver­

tising linage in relation to the expected weather.

%

Warnmu£feer &$ salespeople in the Gandy Department can

be varied frost day to day*

}

However, since daily and weekly variation

and trend are an overwhelming determinant of sales a® compared with the effect of weather, it Is questionable whether the attempt to vary

the number of salespeople in response to weather forecasts is worth while.

Saking into acccmnt the effects of dally and weekly variation

and trend should he adequate. h.

She departmental estimating equations sake possible the

calculation of a daily sales potential figure which can (after the day is passed) be compared with the actual sales figure for the pur­ pose of aiding in the determination of the quality of the merchandis­ ing m & promotional effort*

In this use any Inaccuracy of weather

forecasts does not reduce the validity of the results.

It may be that

the calculation of a market potential after the day Is passed. In the ease of Individual departments, is the moot useful application of the

technique*

239 footnotes it* Melvin 3J. Copeland, *$he Job of an Bs®coitive*w' Hayv&rd Bnstlngma

JSeview* *®1, IS, 1940, p. ‘

1

2* D. A, Besaet, Divisional Merchandise Kianager of the Koften*a Apparel Division,, personal interview. 3* Boas K. Balbey, Publicity Director, personal interview*

4* W. A, Bonnet* personal Interview. 5* lefts H* Dalb«y, personal interview* 6, Oontroller^e Congress of the National Detail Dry Ooods Association* Bind Qoer^tlng Results of Beaaarteent §Jm£& tel:, National'Detail Dry Cools Association, 1949, p* m» ? *

See note on weather forecasting accuracy. Appendix 1*

S. Controller*® Congress, £%> eit*

%

See Appendix H.

W . Ibid* 11. B, H. H4 Cowan, Chicago, She diversity of Chicago Dew York, J-hg.JHA DC ■% J&ft ^ Columbia University Press, 1940* itery A* fcsdal, Ma-n^i^iggaept^ Dew York, McOraw~HllX Book Co., 1933, PP» 79YY•

240 S&uapisr M Z

w m m m icosokic m b soem, o m r a u f s o w t e t e t e * that have been discussed is this dissertation t e w t e a i t e t e *f only interns .of their applicability to the s t e tegp of aanagensnt.

the t*ehnl., Iitd., Ifl^. >* H, L. , Ltd., 1923.

saise. Bow York, 5 Circles. Hew York, MacMillan. & Co,.,

Panl H., (editor),

0 fhs Honal& Press

Go., __ §■2. Perry. «Toh?i 5., H S 1 leek GO., Yb ©,v Y9tu

Ebook* Hew York,. t?cGrasws~-

f. Peters, Charles S., and Walter B. Yaa Yoorhis, How York, McGraw-Hill Book Petersen, WIlliasF., ffi&tu Charles 0,

111. , , Bow York, Henry Holt & Coxnpany, 19^9


IMXadeiphia Weather,*'^ 1^ 2'* 3©* 19%.

B&stesan, Paul, •■tt,« Profitable to Watch the Weather*11 Aimrltsm. ^ ^ 19^7* p®. 22-25. ^5. Carcia—M&ts., Carlos, and Pelir I. Shaffher* w$olar and Beonossic HelatlOf&sMps, a SYelirainary Report*# JfmmSm*

vol. ^9, 1935*

ep *

1-51*

66. Canlt, l&uar S. , ^Control of the Retail Chits of Chaim Store® ,11

vol* 7* 1935* pp. 1-3?. fi?. Gillette, Halbert P., #W«abher Cycles and their Causes,» Mater and jWMpaaau 93* 19^6* PP. 2 5 ^

68. #&as Production System Aided by Weather Mm.** Cam Age. vol. 97* 39*4* p . 97# * *Qas Sendout aad Weather,* § m &m* vol. 99, 19^7, pp. 21-3. ?&. iSeher&en, W., ^Account of the Heat of Jsly, X025, with toms- laaitt

«pffw Sensible Cold,* BfoIi^^rgrdftAaa^ ffieap-ga&tlftiaft pf frfra Ss^al. ffiaeiaty 1S25, m * . •

71. £13.1, X>»» and others, #£b0 Measareetent of the Bate of Sgai lose at Pesporatore by Convection, Radiation '.and Sysporabiom,* fffelleff’’ pTftaT'gej.t^rmn of goolatv of Loodan^ vol. 207. 1916 72.

f* 0., and S. P, Tagloa, #BeterJaiaimg Mmes of B{g&al Corafort," Aajwet&tlaa flf Haatlaa a g & . A f i l m g a $ » * % •ffjumm. Ml. 29,. 1923. pp. 159-172 aafi pp. 361-’.

73. Klim, m U n S. , •Caleultttion of Solar Hadiatlon and the Solar Ha»t iead ca Has.* fffff-ff nf Mataofalogg. vol. 5« 19**8» PP7A. Kimball, S. S. , #Axaount of Solar Radiation that Beaches the Shrfaee of the Sarth on land and Sea and the Methods by Which it is Meas­ ured ,* Ifopthl^r jBeyiefeir.. vol. $6, 1928, pp.

262 J&*

»*,. t o k review of William F. Petersen, » l . r Charles C. Thoms, 19**?, vox. 30» 19^9. P. H i

?&, ton, G, A.*,%«fctor Cycles*, jag Age Saeord. Feh. 11, 1923. p* 2&* Slatoy, C. 0., and L, T, Wright, «t o Sol-Air tormas0ter*~A Saw toiiuwent; A M Research Escort Ho, l29S,ttA$t©ri31. ® U Stone, B* fr», *0n the Practical Hvalmtlon and Interpretation of the Cooling Power In Bioclimtology,rl Blue Hill {toermtory, Harvard University, Eeprint Ho. 1C, 19**3«

82. “Store Sbcee Warns PH*®,* Tide. Parish 3,

, p. kz,

83, fan Cleef, Bugene, “The Influence of Weather on Sbreet-Car Traffic in Botath, Minnesota,," Ssa&EmbX'&l P&tiM' »»1. 3, 191?, PP- 126-3**. 8A. “fertlme Bevelopsenta in. Allied Climatology.11 Meteorolpigl^^l Monographs, rciOT ■Vfc” * vol. 1, The American Meteorological Society, 19**?» pp. J«3* 3 S S S 0 ° " SCM H 8 ° a

1AvO

I

“ ^ a s s a b s s s & s

ESI CA-^J'TkA’'© P * H W CM CM Csi CM CM

NQWlf\(*VO

0\ita4 toildity during day- of snow I213O p»tt» 12 M. to tiaa 6 a.m. 12:30 p.m. light hours 6:30 p.®. 9F. 1 p.m.Cmph) to 6 p.®,, (Percent) (Percent)

16 23 40 51

s 56 67 *& 42 47

53 6? 6?

70 |3 6o 63 J2*

43 36 40 4-3 25 26 47 60 58

5 20 14 9 9 15 6 3 19 12 17 17 14 17 20 5 15 17 12 20 20 17 6 9 19

15 9 17 12 22 12 14 26 16

0 0.15 8$ 0 0 0 0 0 G 0.17 O .57 0.1G 0 4f 0 0 0 0 0*75 0 0 0 0 0 0

71 87 82 61 65 48 48 41 40 83 84 58 50 82 40 26 38 50 82 % 37 46 49 41 28

o.ll 0.52 ® 0 0.41 0.04 0 0.13 0

71 95 68 73 87 69 53 64 80

100 0 0 100 98 100 100 100 100 12 I? 75 88 0 ■ 95 100 93 85 5 94 100 91 94 90 96

41 0 48 9 54 31 100 48 60

0 0.3 0 G 0 0 0 0 0 G 0 0 0 0 0 0 0 0 0 G 0 0 0 0 0

0 0 0 0 4.1 5*0 0 0 ©

yca

A B m m i X X (Canted.) msxc m m

280

APPSHDIX A (Cont*d.) E6.SXC H A M

feiaperature 12*3© x m u *

II « 57 49 62 66

f

60 66 71 75

J52L 77 70 73 53 55

jma. 52 4© 4©

WlBdtt Velocity Precipita­c 12 M. to tion 6 a.m. 1 p.m.(fflph) to 6 p.®,

8 10 22 14 13 13 19

6

7

12 13 5 7

11 11 21 14 29 20 17 10 13 14 13 9

11

4 1

71 6x 74 65

*5T 20

13 17 a 21

8 16

Q.l? Q

0.66

98 91 98 87 55

0 0

0

68

0

42

If 0.04 0 0 0 0 0 0 2f

68

0 99 50 62 96 0 42 8$ 100 100 54 79 79 100 78 100 28 100 77 6? 11

82 67 51 46 40 36 32 42 48

ISO 8? 99 100 100 85 74 100

0.09 2f

83

47 0 0 If o 0.54 0 0 0 0 0. 0? 0

P 60 57 98 71 56

§ 48 6? 33 26

If 0

3*

0

19

11

Eolati’ro Sunshine Depth^ taildtty during day12530 p.». light hours of snow (Percent) 6:30 p.m. (Percant)

63 54

68

7

0

n

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 O 0 0

0 0 0 0 0 0 0

0 0

AEPHSDIX A {Go»t*d.) BASIC 22&*EA

HqII&T Sil«#'■

m * x7 18 X9 20 21 22 2k

26 27 28 J&3L~X~ 3 i

7 --- JL 10 11 12 13 1* ___ M,

l? 18 19 20 21 22 2k

25

26

27 28 ..SSL

Apr 1 2 3

201.0 253-9 220.3 219-5 185-3 33&.0 170.6 251.0 229.& 207.7 2M .6 35®-^ 211.1 285.9 238.2 236.I 229.0 362.9 20^.3 239.** 185-5 170.2 20K$ 323.7 217.0 259.0 23^-1 2£*8.8 2^5*7 379.** 237.3 293.^ 262.9 268.0 32^.5 381.3 271.8 265.3 269.^

$ 508 319 $89 3IO M2 700 320 I# 552 320 620 ¥& km. 10k 532

Ml m

313 %>6 373 251 125 759 k?7 2k9 209 ?Qk 526 903 259 MO 718 566 1162 633 399 508 719

$ 267 310 387 31® 3®7 519 290 359 381 3019 33^ 560 323 365 3# 397 376 588 318 353 290 283

0

55& 221

2ft&

£^8o

fT t’ Jt * Tii t.

562 275 592 396

385 M6 318

3398

mf

k6? m?

Ml 601 652 231 727

2368

3.012 738

282 APEffiBttX A (Goni*’d«) basio

m m

StoSr SalafclW^ Sunshine temperature Velocity Preeipita-C ftmaidlty during day12:3® P-®12 M. to tion. 6 a.m. 12:30 p.m. light hours °2V X p.m.(mph) to 6 p.m. (Percent) (Percent) 2® 27 v?

ISL 20 22 22 W 20 i§~ 2** 31 3® 35 35

SI35 M m 32

20 .Illj.iSS'iir

"5® 32 35

35 % 33 %© m m.

kz

9 5 8 13 18 17 18 11 6 16 8

9 12 9 3 6 1% 16 8 20 19 18 13 8 7 Ik 17

2k

28 26 12 9 16 «A 19 9

9 9

0 0.05 0.03 0 8$ 9$ 7f 2f 0.03 0.03 0 0 0 0

Jt 0.01 3t IS o.*m 0.18 re at 0 0.18 It 2f 3f O 0.0% 2t 0 0 0 i 0h o.%x 0 0

p m 82 77 78 80 70 65 83 8% 60 5S 61 53 77

m

61 57 99 97 7© 73 52 90 68 72 72 57 63 65 53 36 its **3 95 87 83

$5 0 2 71 0 0 3% 51 0 0 %5 6% 6 78 G G 1% 8 0 0 %% %? 98 G 1% 1 12 99 %6 66 73 59 100 f t5

Department [Moderate Priced Bi^jarteent Dresses) (Candy) Dollar Sales Dollar Sales

$Qb 33©

1

735

Dollar Sal#®

270

284 M m m t X A (Confc*!■y^ x s ^ s a ^ ^ « M e B«Xativ»° Sunshine Velocity Frecipifca- Humidity daring day- Eeptir 0apartment 16^ 12 K. to tion 6 a.m. 12:30 p.m. light hoars of snow Advertising 1 ?>»».'(aglt) to 6 p.m. (Percent) (Percent) 6*30 p.m. Agate lines

frwwwwwnwrnmff.w^

tuss aaa^.axrtg^a^'^Bnrag^aaigassai'gs«xT.xvff*agwg^^

0 0 0

?

7 16 19

7 jsi

22 0.01 G

12 8 n 13 17

___ 3&

20 16

xb a 13

102

9 13

10 16

17 13

63

0

85 62 72

68 88 m

72 58 73

22 3*

77 50

0 0.20 12

3*

60 86 56

87

0 0

90 ^3 57 93 39

12 0.23 0.19

78 96 96

i& __ 5%

O.hh

0

0 0

0 0 0

E 1

0 3^2 292

0

0

0

100 100

0 © 0

0 100 5% 60 0 & 12

0

0 a 22^ a :saA 0 0 0 0 0 0 a 27^ 0 © m xm6 m & 2m a 125 2 7G 0 0 0 G e 252

79

0

0.12

17

100 100 100 82 80 68

3

20

12 15 23

75

58

m 8

hb b9 62

Q

0 82

39

0 0 0 0 0 0 0*13 32

12 8

5h

79

0 66 0

50 100 O G 0 0 30 60 Q &* 0 0 0

0 0 0 © G 0

0 0 0 © 0 0 0 0 © 0

© 0 0

0 0 ©

0

0 0

2 E

1^9 b2$

0

0

0

0

289

m m m ix a (c©nt#d.} msie m a t

19 ^ Apr 1 2 k 5 6 7 S . -& 11 12 13 Ik 15

"■“ r T Department l6^ _ Store (Moderate Priced Department 66 Department 97 ature (Sefrigerators) 12#3© Brasses} (Sandy) Sales Dollar Sales . Dollar Sales Bellar.Me^ ■ *Slie- "4» (Indez)

291.0 *51.1 232A 269.1 27S.4 2^2,6 267Jb ^30.5 23B.2 270.2 263.3 259.9 306.5 356.3

$

39* Ml 6* 622 *22 337 555 73* ^96 662 *18 375 **0 213

$

573 731 3§5 511 *08 ms 632 8*8 635 76* .896 1007 % m 1656

f 3*7 m o 1*6? a® 2 60* 3J 95 1899 578 633 #8 1289 9*2 1760

% ^

'

50 60 62 52 '»■ 67 m

n m

a. Youhker Bros., Inc., store records. b. U. S. Weather Bureau, Monthly Cllmtolo^icaa Snffiaarr. m 0 S* Courthouse, Bos Moines, Iowa. c. Ibid. 2 indicates frace, or saouut to© siaall to m m *#*. fh* number preceding the 2 Indicates the nuiaber of hours that ft fraee occurred between 6:00 a.fia. and 6:00 p.a. d. U. S. Weather Bureau, hOO U. S. Courthouse, Bos Moines, leva, unpublished cliiaatological records. e. Register and fribune Publishing CoHrpany, Bee Moines, Iowa, adver­ tising records. S stands for Begister, 2 for fribune, SB for Sunday Register, and C for "city only.8

290 APFSKBIX A (fenfc'd*) BASIC DAS& ffii,7!‘WiiifBattaaeaBaftftgas^ ^

Wind Telocity Precipita* XZ M. to tien 6 a.m. 1 p.m.(mph) to 6 p,m. IB

13 15 9 15 8 ___ I S 8 10 21

m ***43

0 © 0 0 0 0 0 0 0 0 0 0* * * 0 G

i7jiosfflrJB^avOT.7'

Bel&tivm Sunshine Humidity during day* Depth Department 166 12:30 p.m. li^ht hours of e»ew Advertising (Percent) (Percent) 6:30 p.m. Agate limes 60 57 3* 5* *1 36 39 *0 25 *2 *2 87 77 53

20 39 89 *9 72 71 100 ** 100 99 77 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0

392 0 m e 13* 0 0 B 366 B 336 0 0 0 0 B 21* 0 8 I

oq \

rfcv/eTj JL

method used to correct the date for daily and weeklj ation sad tread, is that of Frederick B* Croxton sad Bu&ley J. Cowden, g%g££Ae&&. Ik&lne&a lfeti,at,lfa» Em? fork, fresilce^all* lac*, 1938* ^apters XXI-XVX, Inclusive*

She daily variation 1© arrived at in the

same way as the seasonal described in the shore reference*

In the ©ass

of the moving: total of an even number of data, Groxton and Cow&en util­ ise a rather elaborate centering operation requiring a large immber of computations*

It -was felt that a great deal of effort could be saved

if this elaborate centering method were not used-* According, to Arthur B. Sams and Wesley G, llichell* Me&gurinar Business gvalaa. Hattons! Bureau of Economic Besearch, Ifar fork, 19^6, p. kfMt

A thAi^eoii-month moving average centered on the ©©weufe month* the first and last months receiving half weight, is preferable in principle to a simple twelve-month moving average? but experiments by one of our colleagues , Julius Shiskin-, have demonstrated that the 0&&n is negligible and definitely is not worth fee extra cost. Wv m a simple four-cpiarter moving average centered on fee third, uuarter Is not appreciably Inferior' to a flve~