3)Extrapolation Handbook

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EXTRAPOLATION

HANDBOOK

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Table of contents

1. The functional principle of a retail panel in contrast to consumer research 4

2. Universe 7

2.1. Definition 7

2.2. Information 8

2.3. Assignment of the shops to the distriution channels one to one 8

2.4. !hannel fusions "

2.#. $%ceptions 13

3. &ample construction 14

3.1. Accurac' of samples 14

3.1.1. The term inaccurac' 1#3.1.2. The total error 1#

3.1.2.1. The s'stematic error1(

3.1.2.2. The sampling error17

3.2. &hop profiles 18

3.3. )ethods 1"

3.3.1. *racticale approach 1"3.3.2. &tatistical approach 21

3.4. *ractical aspects 24

3.4.1. +efusal of panel participation 243.4.2. !hanging sample consistenc' and structure 2#

3.#. Difference et,een target and actual sample si-e 2#

4. $%trapolation 27

4.1. $%trapolation channels 27

4.2. $%trapolation cells 27

4.2.1. !ell definition 284.2.2. Assignment of the shops to the e%trapolation cells 284.2.3. !omputation of the e%trapolation factors 2"4.2.4. !arr'ing and not carr'ing outlets 34.2.#. )odification of the cell definition 34.2.(. Identical e%trapolation for more than one product sector / product group 31

4.2.7. 0andling of outliers and at'pical outlets 314.3. $%trapolation for individual retail companies 31

4.4. 0andling of aggregated data 32

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4.#. &ample data versus census data 33

4.#.1. &ampleased e%trapolation 334.#.2. !ensus data 344.#.3. !onclusion 3#

4.(. 0andling of entries and leavings 3#

4.(.1. $ntries 3#4.(.2. eavings 37

4.7. *roceeding in case of prolems ,ith the data 37

4.7.1. )issing deliver' periods 374.7.2. ate data deliver' 384.7.3. Incomplete data 384.7.4. Incorrect data 3"4.7.#. $%treme changes in the data 3"

4.7.(. Unreproducile changes in the data 3"4.8. Test of the e%trapolated data 4

4.". !overage 4

4.1. +epresentativeness of the e%trapolation ,ith respect to the universe 4

4.11. )aintenance and updating of e%trapolations 41

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CHAPTER 1

1. The functional !incile of a !etail anel in cont!ast to consu"e! !esea!ch

In the follo,ing the functional principle of a retail panel in contrast to consumer research isdescried ,ith the special e%trapolation method for retail panels as the main focus. &ince onl'three maret research institutions are ,oring ,ith a retail panel A!5 and I+I ,ith a food paneland 6f ,ith a nonfood panel there is not much literature concerning this suect. 9n this accountthis special e%trapolation handoo ,as created.

The suect of a research proect determines ,hether it is to e regarded as demographic :people ased; research or research on maret statistics. Demographic proects concern those people

involved in the maret and consider their ehaviour :u'ing ehaviour opinions etc.;. +esearch onmaret statistics concerns maret developments as to maret si-e sales units sales value sales prices numer and structure of defined companies operating in the maret etc. *anels can e usedto generate oth demographic data :e.g. household panel; or maret statistics :retail panel;.

In a retail panel data :e.g. sales units sales value sales prices stocs; of a sample of retailers arecollected and anal'sed in order to create a special reporting on the considered maret. Thisreporting supplies decisionmaers of manufacturers and retail companies ,ith informationnecessar' for planning and monitoring maret decisions.

The retail panel of 6f )areting &ervices is a monitor of sales of product groups ased on single

articles< level. The functional principle of the retail panel is the panel methodolog' ,hich ischaracterised ' three main terms=

universe sample e%trapolation.

The target of the panel methodolog' is to set up an e%trapolation out of an appropriate sample of retail outlets ,ith respect to the universe ,ith the result of eing ale to mae representativestatements in the statistical sense concerning the maret situation and the maret development ason

the maret si-e ' sales units and sales value the maret structure ' technical features the situation of the different distriution channels prices average prices and price categories rand shares hitlists of the most sold models distriutions of models rands features etc.

In order to get a satisf'ing reporting >ualit' out of the panel some preconditions have to efulfilled.

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The universe must e ,elldefined. As a rule universe is a s'non'm for one distriution channel.As to this channel as much information as possile should e availale. Therefore from time totime studies of the universe have to e realised in order to get all the re>uired information=

numer of shops per region

address list of the considered shops and for ever' shop=

percentage of retailer and ,holesaler turnover percentage of selling turnover and turnover ,ith service installation and repair assortment structure total turnover and turnover ,ith assortment parts respectivel' product sectors sales area organi-ation t'pe distriution t'pe :ric?mortar clic?mortar pure pla'er;.

The sample should e constructed in such a ,a' that a representative e%trapolation ,ith respect to

the universe is possile. This does not mean that the sample itself has to e representative for theuniverse. It is a ,idespread miselief that the sample has to e representative for the universe. Inmost of the cases the sample is not representative for the universe and this ,ould even e ,rong.The sample has to e appropriate for a representative e%trapolation ,ith respect to the universe.

5ormall' the outlets ,ithin a distriution channel are ver' heterogeneous and there are lots of outlets ,ith a small turnover man' outlets ,ith a medium turnover ut onl' a fe, ,ith a largeturnover. In most cases a stratified sample is used ,here the turnover classes represent the strata.@ithin the turnover classes there is a higher degree of homogeneit' of the outlets.

ecause of the heterogeneit' of the outlets in the total universe the variance in the upper turnover

classes is consideral' larger than in the lo,er ones. ut since the statistical accurac' ,hichdepends on the statistical variances to a great e%tent should e large in all turnover classesespeciall' in the upper ones ,hich are highl' relevant for the maret the sampling fractions in theupper turnover classes have to e larger than in the lo,er turnover classes. This leads in nearl' allcases to a disproportional sample structure.

+egarding the e%trapolation ,ith respect to the universe ever' outlet in the sample gets ane%trapolation factor. &o ever' sample outlet is representative for a certain numer of other ones,hich are in the statistical sense similar outlets.

!onse>uentl' in turnover classes ,ith a small turnover share :and a small variance; onl' a small

sampling fraction is necessar'. This leads to a large e%trapolation factor per shop. In turnover classes ,ith a large turnover share :and a large variance; a large sampling fraction is necessar',hich leads to a small e%trapolation factor per shop.

This issue is demonstrated ' the follo,ing e%ample=

strata boundaries universes sampling sample extrapolation

(turnover classes) fractions sizes factors

0,05 - 0,5 Mio. € 7.500 ! 75 00

0,5 - ,0 Mio. € ".500 " ! 50 50

# ",5 Mio. € ."00 5 ! $0 "0

",5 # 5,0 Mio. € 500 % ! &0 ",5

5 # 0,0 Mio. € '00 0 ! '0 0

0 # "5,0 Mio. € "00 "5 ! 50 &

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"5,0 Mio. € 00 &0 ! &0 ",5

0o,ever these are onl' theoretical figures. In practice there are outliers and at'pical outlets ,hichthe computed e%trapolation factor cannot e applied to ut onl' a smaller one. This means there isa loss of the numer of the degrees of freedom i.e. the sample si-e has to e enlarged in the

corresponding e%trapolation cell if the computed e%trapolation factor should e ept for the other outlets :see 4.2.7.;.

9n the other hand there are often chain stores or several stores elonging to one and the samecompan' ,hich provide 6f ,ith census data. In these cases the e%trapolation factor per outlet is1.

In each case the e%trapolated data have to e tested in order to e sure that the specifiede%trapolation is correct and the re>uired accurac' has een achieved. If there is a definite shortfallthe chosen method cannot e used ecause there ,ould e too man' inaccuracies. In this case thee%trapolation or even the sample design has to e modified.

$%trapolation is done channel ' channel. The aggregation of all retail channels forms the audited

panel maret.

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CHAPTER #

#. $ni%e!se

As a rule universe is a s'non'm for distriution channel. In order to fi% a precise definition of thisdistriution channel it is necessar' to no, ,hat is actuall' ,anted from it. Therefore as muchinformation aout the channel respectivel' the outlets elonging to the channel as possile should

e availale. @hen the channel definition is fi%ed the assignment of the shops one to one can tae place. It is also possile that shops ,ill e e%cluded from a channel ecause of the specifieddefinition. In the reporting the distriution channels can e sho,n separatel' or as a fusion of t,oor more channels.

The universe is also determined geographicall'. These restrictions relate to a certain countr' 6f regions ,ithin the countr' etc. Burthermore there is a restriction in time. In most cases the universe

is fi%ed for one 'ear and ,ill then e adusted to the ne, situation. The information aout theuniverse have to e updated continuall'. If the retail scene is changing ver' fast the universe isupdated ever' >uarter of a 'ear or even ever' month.

#.1. Definition

The universe respectivel' the distriution channel must e ,elldefined. 6f )areting &ervicesfi%ed precise definitions for all channels of the retail panel specific to the special re>uirements.These definitions are valid for all countries ,hich leads to a harmonisation in the internationalreporting. +egarding for e%ample the electrical retailers the follo,ing conditions have to e

fulfilled ' each shop=

the turnover per shop is more than #. C more than # of the turnover is realised ,ith electrical products

more than # of the turnover is realised ,ith sales and less than # ,ith serviceinstallation and repairs

$lectrical retailers then are devided into three shop t'pes=

u'ing groups= retailers ,ho are memers of u'ing groups as $uronics $lectronic*artner $%pert $.D.A.

independents= retailers ,ith ( outlets at the most ,ho are not memers of u'ing groups

chains= retailers ,ith more than ( outlets ,ho are not memers of u'ing groups :)edia )art &aturn Di%ons !urr's ut!omet Dart' etc.;

The definitions of the distriution channels are specified in such a ,a' that there is nooverlapping. This is an infle%ile rule. It must e guaranteed that a certain shop can onl' eassigned to one channel. Bor instance is it not allo,ed that a certain shop elongs to the electricalspecialists as to consumer electronics and to photo specialists as to photo.

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#.#. Info!"ation

In order to dra, an appropriate sample it is necessar' to collect as much information as possileaout the corresponding distriution channel=

numer of shops per region address list of the considered shops and for ever' shop=

percentage of retailer and ,holesaler turnover percentage of selling turnover and turnover ,ith service installation and repair assortment structure total turnover and turnover ,ith assortment parts respectivel' product sectors sales area organisation t'pe distriution t'pe :ric?mortar clic?mortar pure pla'er;.

In distriution channels ,ith fre>uent changes in the retail structure studies of the universe have to e realised ever' 'ear in order to get all the re>uired information up to date. In distriutionchannels ,ith rare changes studies of the universe can e carried out over a longer period. As tone, distriution channels studies of the universe are a must.

+egisters of memers from u'ing groups and client lists from ,holesalers and manufacturers arehelpful for this as ,ell as structural data ,hich chain stores send to the e%ecutives of the 6f retailservice. In most cases information from the previous stud' can e considered too. Additionall'surve's have to e carried out and the est ,a' of intervie,ing has to e found=

&uestionin' b( fiel) *o!+e!s

used if the >uestions are long and complicate ,hen e%planations are needed )isa)%anta'e= this ,a' is e%pensive and taes a lot of time

&uestionin' b( lette! used if the >uestions are long ut not too complicate and don


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an annual turnover of less than #. C ,ill not e included in this channel. &hops ,hich realisemore than # of the turnover ,ith service installation and repair are also not assigned toelectrical retailers. The' are classified as service shops.

#.-. Channel fusions

In the reporting the distriution channels can e sho,n separatel' or as a fusion of t,o or morechannels. The names of the current fusions are fi%ed and correspond to an international standard.The follo,ing channel fusions are defined=

Fusions Channels included

Panelmarket all Channels available in the product group

Department Stores/ Mail Order Houses Department Stores

Mail Order Houses

Hypermarkets/ Cash & Carry Hypermarkets

Cash&Carry

Hypermarkets / Supermarkets / Cash & Carry Hypermarkets

Supermarkets

Cash & Carry

Hypermarkets / Cash & Carry / DI Superstores Hypermarkets

Cash & Carry

DI!Superstores

Hypermarkets / Supermarkets/ Cash & Carry / "ariety Stores Hypermarkets

Supermarkets

Cash & Carry

"ariety Stores

Mass Merchandisers Department Stores

Mail Order Houses

Hypermarkets

Cash & Carry Supermarkets

"ariety Stores

Mass Merchandisers #ithout "ariety Stores Department Stores

$only DI% Mail Order Houses

Hypermarkets

Cash & Carry

Supermarkets

Mass Merchandisers / DI Superstores Department Stores

Mail Order Houses Hypermarkets

Cash & Carry

Supermarkets

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"ariety Stores

DI!Superstores

DI!Superstores / "ariety Stores DI!Superstores

"ariety Stores

lectrical 'etailers / Iron Mongers / lectrical Installers lectrical Specialists

$only MD(% )echnical Superstores

lectrical 'etailers / Iron Mongers Iron Mongers

$only SD(% lectrical Installers

lectrical 'etailers lectrical Specialists

)echnical Superstores

Consumer lectronic Stores lectrical Specialists


Photo Specialists

Photo 'etailers incl. )echnical Superstores Photo Specialists


Minilabs

*inocular Specialists

Photo studios/ateliers

Photo 'etailers excl+ )echnical Superstores Photo Specialists

Minilabs

*inocular Specialists

Photo studios/ateliers

Computershops Computer Hard#are Shops

Computer So,t#are Shops

Computershops / )oys Specialists Computer Hard#are Shops


)oys Specialists

Systemhouses I)!'esellers $,ormerly Systemhouses%

I) Mail Order $Hard#are%

I) Mail Order $So,t#are%

Computershops/Systemhouses Computerhard#are!Shops

Computerso,t#are!Shops

I)!'esellers $,ormerly Systemhouses%



Computershops / )oys Specialists / Systemhouses Computerhard#are!Shops

Computerso,t#are!Shops

)oys Specialists

I)!'esellers $,ormerly Systemhouses%



)elecom Specialists )elekom Specialists

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0urniture / 1itchen 'etailers 0urniture Specialists

1itchen Specialists

Sports / Shoes 'etailers Sports Shops

Shoes Shops

Drugstores/Chemists/Pharmacies Drugstores

Chemists

Pharmacies

0ood 'etailers/'ural )rade $)raditional% 0ood Stores

Discount 0ood Chains

Drugstores

'ural )rade

0ood 'etailers $)raditional% 0ood Stores

Discount 0ood Chains

Drugstores

Supermarkets

)yre (ccessories 'etailers Car (ccessories Specialists

Car (ccessories -holesalers

)yre Specialists

Po#er )ool Specialists Iron Mongers


lectro -holesalers

Industrial Suppliers

Iron Mongers / Motorists Iron Mongers / DI!Shops

Motorists

Household 'etailers Household Specialists

Iron Mongers / DI!Shops

*usiness Channels O,,ice uipment Specialists

Stationers

Stationers -holesalers

Copier Specialists

I)!'esellers $,ormerly Systemhouses% I) Mail Order $Hard#are%


)elekom Specialists

Mobile Phone Specialists $,ormerly 'adioSp+%

Car (ccessories Specialists


Car (udio Specialists

Car Dealers

Car .arages

Consumer Channels Department Stores Mail Order Houses

Hypermarkets

Cash & Carry

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Supermarkets

"ariety Stores

DI!Superstores

lectrical Specialists


Photo Specialists

Computer Hard#are Shops


It is also possile to define further channel fusions ut the name should e cleared ,ith theinternational methodolog' department of 6f )areting &ervices in 6erman' in order to use thesame name in all countries.

A distriution channel cannot e sho,n separatel' if it includes less than three retailers ecause inthis case these retailers ecome transparent. 9f course this situation ma' occur in case of three

four or even more retailers ,ithin a channel. &o this can onl' e a guideline ,hich should not econsidered as a rule ecause it al,a's depends on the special situation in each countr'. If theturnover share of a retailer ,ithin a channel e%ceeds # this retailer should e ased ,hether heagrees ,ith this channel sho,n separatel' in the reporting.

#.. E/cetions

As to the assignment of the shops to the distriution channels there are some e%ceptions e%isting.In some cases the outlets of a retailer ,ould e assigned to a certain channel according to thedefinition ut this is not possile since it ,ould e the onl' retailer in this channel and this retailer

,ould ecome transparent then.

An e%ample for this is $l !orte Ingles in &pain. The outlets are department stores ut $l !orteIngles is the onl' compan' in &pain ,hich carries on department stores. Thus $l !orte Ingles is notassigned to department stores ut to the electrical retailers.

CHAPTER ,

,. 0a"le const!uction

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!onsiderations of costs and time usuall' restrict the spectrum of the surve' so that onl' parts of a product sector product groups are audited from a limited numer of retailers according to certainguidelines. An appropriate sample of retail outlets has to e dra,n in order to e ale to do arepresentative e%trapolation ,ith respect to the universe. The sample should e constructed in sucha ,a' that representative statements for the universe as to the maret situation and the maret

development as on

the maret si-e ' sales units and sales value the maret structure ' technical features the situation of the different distriution channels prices average prices and price categories rand shares hitlists of the most sold models distriutions of models rands features etc.

can e made.

&ince a retail panel ,ill e used over a long period of time a good sample design is fundamental.&ometimes a retail panel is created according to the re>uest of a specific manufacturer and itsdesign strongl' reflects the ,ishes of this client ,hich ma' differ from the needs of other futureclients. Bor this reason the sample design should meet a large variet' of different needs. Thismeans some criteria have to e satisfied= sample si-e selection of the retail outlets samplestructure data accurac' reporting >ualit' costs etc. 9f course there is an interrelationship et,eenthese criteria.

,.1. Accu!ac( of sa"les

The suscriers of the reporting ased on a retail panel place great importance on the >ualit' of thedata as accurac' precision and representativeness of the results. 9f course the target of a retail

panel should e a high degree of accurac' so that the maret situation and the maret developmentis sho,n correctl' in the reporting. &ince the panel data is ased on samples hundred per centcorrect results cannot e achieved and the' are not necessar' at all.

Bor e%ample if panel data is used in order to chec the future usiness polic' of a compan' acertain degree of inaccurac' is not disadvantageous since ust ac data are considered ,ithrespect to decisions concerning the future. As a rule at the moment of the decision the actual

maret situation has alread' changed. It is onl' deciding that the maret situation and the maretdevelopment is not sho,n distortedl' and gives reason to ,rong decisions. In most cases it doesnot pla' a role ,hether the maret share of a retail compan' is 1# 1( or 17 per cent. It is moremeaningful to see ,hether the maret share increases or decreases.

The degree of inaccurac' to e accepted cannot e defined generall' ut the target of the highest precision is not onl' uneconomic it can also detract from relevant prolems.

,.1.1. The te!" inaccu!ac(

Accurac' respectivel' inaccurac' can e defined as the difference et,een the sample results andthe GtrueH data of the universe. This difference is also called GerrorH in the statistical terminolog'.

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The difference et,een the computed sampleased value and the actual value of the universe isthe asolute error and the percentage difference of these t,o values is the relative error.

The term error can lead to a misunderstanding ' people ,ho are not concerned ,ith statisticaltheor'. ut incorrect statistical data are not to e regarded as eing ,rong and useless. There is a

,ide and et,een complete correct and complete ,rong data.

The panel data cannot e precisel' correct ut for the purpose for ,hich the data are intended theerror can e irrelevant and an' attempt to reduce it ,ould not ring an' real enefit. This isespeciall' the case ,hen precise information aout the GtrueH situation is not re>uired ut onl'

road information aout maret si-e and trends are ,anted. 5ormall' it ,ould e irrelevant for e%ample ,hether sales units of a particular product amounted to (#.1(7 or (#.. It is alsosufficient to no, that sales units increased at # and not at #4.

If the inaccuracies of the data are igger the usailit' of the data ma' e reduced ut the' mustnot e ,orthless or misleading. In such cases the data should e used ,ith caution i.e. ,hen

interpreting the data possile errors should e considered. In general data incorrect data or asthe' are called in practice appro%imate data are still etter than no information at all. Thissituation is comparale ,ith driving a car in the fog. To e ale to recognise even the shado,'image of the road marings at either the centre or the side of the road is a great help.

Data are onl' useless if the' sho, a distorted picture of the realit' and lead to ,rong decisions.ut this ris can largel' e avoided if the research is carefull' planned and carefull' carried out.

The fundamental prolem in the assessment of the accurac' of the data is that in most cases theGtrueH data are unno,n. 9ther,ise there ,ould e no reason to do the surve'. &ince the GtrueHdata are unno,n the error level cannot e specified in concrete individual cases. The est that can

e done is to estimate potential errors on the asis of e%perience or if certain re>uirements are athand to calculate ho, large the error is liel' to e on average as to such surve's.

5evertheless plausiilit' checs should e carried out implicitl'. This does not mean ust to chec the results in order to see ,hether the' are internall' consistent i.e. that there are no discrepancies

ut also to compare them ,ith oneJs o,n ideas and e%periences. arger differences give reason toclear up the discrepancies. ut it should e selfevident that the panel results are not onl'mistrusted or reected on that account ecause the' are contrar' to the e%pectation.

,.1.#. The total e!!o!

The inaccurac' of the data i.e. the total error consists of t,o different components the s'stematicerror and the sampling error. In cases of census data there is no sampling error ut onl' thes'stematic error. In cases of sample surve's there are oth the s'stematic error and the samplingerror. ut this does not mean that census data are al,a's more precise than sample data. Bor thedifferent error components can compensate partl' or totall' and the si-e of the s'stematic error can

e completel' different as to census data and sample data.

,.1.#.1. The s(ste"atic e!!o!

The s'stematic error is largel' ignored in scientifical literature and in practice although it is moreimportant since it usuall' has a larger effect on the data >ualit' than the sampling error. The reason

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for this negligence ma' e that possile sources of error though can e sho,n ut their effect isnot measurale. In contrast the sampling error can largel' e ept under control provided a>ualified proceeding.

In order to e%plain the s'stematic error the most important reasons are descried in the follo,ing.

9ne reason is an imprecise definition of the relevant universe. The surve' should onl' cover theoutlets ,hich are relevant to the oective. This re>uirement is easil' met onl' in theor'. In practicethere are alread' difficulties in classif'ing the distriution channels ,hich are relevant for a certain

product categor'. Bor e%ample the universe of shops ,here a private individual can u' a photofilm consists of

photo specialists photo studios department stores supermarets h'permarets

cash?carr' marets petrol stations mail order drugstores chemists ioss etc.

0o,ever defining the universe is onl' the first step. It is also necessar' to assess the numer andimportance of the outlets in the universe. Directories from official or other sources are usuall'incomplete and are ased on different classifications. Therefore much ,or has to e done in order to complete the data ase. 5evertheless it must e recognised that part of the universe either largeor small cannot e considered ecause the' cannot e identified. This leads to an inevitale undercoverage of the universe.

9n the other hand overcoverage can e ust as much a source of s'stematic error as undercoverage. The reason for overcoverage can e doule count or the inclusion of outlets ,hich donot or do onl' partl' elong to the universe. Bor e%ample this ,ould e the case if regarding acertain retailer it is not possile to separate retailer from ,holesaler turnover.

Another ig prolem in maret research is the socalled nonans,ering ,hich can cause significant

distortions in the data. There are al,a's retailers or retail companies ,hich refuse to cooperate. Inother cases there is no data deliver' in one or more periods ecause there is some troule ,ith theelectronic data transfer or the merchandise management s'stem itself :see also 4.7.1.;. If theseretailers cannot or can onl' partl' e considered the sample results ma' e distorted.

In case of census data the nonans,ering is a much igger prolem. If a retailer refuses to cooperate or stops data deliver' the result is a corresponding gap an undercovering. In cases of e'accounts the >ualit' of the data can e put at ris. In order to produce relief the outlets of thisretailer have to e created out of selected outlets of the other retailers. ut here a prolem canappear namel' if this retailer differs from the other ones significantl' for instance a e' retailer ,ith strong private laels in the assortment. &o regarding the s'stematic error as a rule sample data

is advantageous in comparison ,ith census data.

Another source of s'stematic error are incorrect data as

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,rong sales prices negative figures differences et,een the delivered turnover and the product of sales units and prices ,rong article numers :error of posting;

,rong assignment of products to product groups unrealistic sales units purchase units stocs or sales value.

Inaccuracies can also occur ,hen the data are evaluated and ,hen the reporting is set up. Theseinaccuracies are further sources of s'stematic error.

,.1.#.#. The sa"lin' e!!o!

+egarding sample surve's there is also the sampling error e%isting. *ossile distortions arise ,henthe results of the sample are e%trapolated up to total maret i.e. the sample results are generalised.

A particular sample is onl' one of lots of possile samples ,hich ma' e more or less appropriatefor a representative e%trapolation to the universe. Therefore ,hen the sample results aree%trapolated to the universe inaccuracies and oversimplification can occur.

In the e%treme case there is such a large sampling error that the e%trapolated results are useless.&ince this cannot e recognised ,ithout plausiilit' checs one could run the ris of maingdecisions ased on ,rong information.

There are t,o fundamental ,a's of reducing the ris of getting such an e%treme sample= increasingthe sample si-e or stratification. Increasing the sample si-e ma' lead to a etter sample and areduction of the sampling error ut this is not assured. 9n the other hand increasing the samplesi-e can increase the s'stematic error.

!onse>uentl' stratification is the etter method and is applied as to the retail panel :see 3.3.;. Bor e%ample the universe is stratified ' the annual turnover of the shops. A sample is dra,n fromever' turnover class. This helps to prevent highl' se,ed samples in ,hich ver' small or ver'large outlets are overrepresented. &tratification of the universe leads to a significant reduction of the sampling error.

The sampling error depends on the sample si-e and on the heterogeneit' of the outlets in theuniverse ,hich is measured ' the standard deviation. The larger the standard deviation the larger

is the sampling error. If all outlets in the universe ,ere identical it ,ould e sufficient to selectonl' one outlet for the sample and the results ,ould e completel' correct. ut in practice theuniverse is normall' ver' heterogeneous. There are lots of shops ,ith a small turnover man' shops,ith a medium turnover and onl' a fe, ,ith a large turnover. &tratification causes morehomogeneit' ,ithin the turnover classes and therefore helps to reduce the sampling error significantl'.

The sampling error is onl' an average error ,hich sho,s ho, large the difference is et,een thesample result and the GtrueH value in the universe regarding the average of all possile samples,hich can e dra,n out of the universe. The numer of possile samples is in case of the retail

panel astronomical large. The sampling error does not state an'thing aout the individual case it is

simpl' a gloal measurement of accurac'. In the individual case the inaccurac' can e smaller or significantl' larger than the sampling error.

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esides the sampling error there is the ma%imum sampling error ,hich is much more concrete.The ma%imum sampling error sho,s the ma%imum difference ,hich can occur for a particular

percentage rate of all samples for instance "#. )entioning the ma%imum sampling error increases the secureness as to the si-e of the possile inaccurac' ut the statement ,ill e moreimprecise since the ma%imum sampling error ,hich ,ill not e e%ceeded in "# of the samples

is roughl' t,ice as much as the average sampling error. Burthermore it is not guaranteed that theconcrete sample elongs to the # of all samples in ,hich the sampling error is possil'e%tremel' larger than the ma%imum sampling error.

Unfortunatel' there are no general rules e%isting as to ,hat the optimum sample si-e is. It can onl' e appro%imatel' estimated in e%ceptional cases if target significance levels are no,n as ,ell asthe structure of the universe the costs of the surve' and the e%tent of the s'stematic error.

The computation of the sampling error assumes the no,ledge of the standard deviation in theuniverse ,hich is unno,n in most cases. &o the standard deviation has to e estimated on the

asis of the sample. ut it has to e considered that suect to the individual sample different

estimated values for the standard deviation ,ill result. The rule is that the standard deviation in theuniverse is the average value of the estimated standard deviations for all possile samples dra,nfrom the universe.

In practice onl' one value for the standard deviation ,ill e calculated. This value can e smaller or larger than the GtrueH value. Then the calculated sampling error ,ill e too small or too large.9nl' the average of all possile samples ,ill lead to the GtrueH sampling error. Thus theinformational value of the sampling error should not e overestimated.

,.#. 0ho !ofiles

Bor each shop in the sample a shop profile ,ith all relevant characteristics as

shop t'pe total turnover or turnover class assortment structure / composition of the carried product sectors turnover per product sector memership in a u'ing group memership in a franchise organisation outlet of a e' account

head>uarter ,ith susidiaries percentage of retailer and ,holesaler turnover percentage of selling turnover and turnover ,ith installation service and repairs percentage of ecommerce turnover sales area

has to e dra,n up. This is necessar' so that the shop can e a assigned to

the correct distriution channel :e.g. electrical specialists; the correct strata :e.g. turnover class 2# # million C region north independent; the correct e%trapolation cell :see 4.2.2.;.

The shop profile also provides information aout if the shop is at'pical or an outlier. This isimportant for the e%trapolation ecause an at'pical shop or an outlier cannot stand for a large

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numer of other shops so that the computed e%trapolation factor cannot e applied to it. In thee%treme case such a shop can onl' stand for itself. The handling of outliers and at'pical outlets isdescried in chapter 4.2.7.

&hop profiles should e updated if necessar' since there are sometimes changes. &hops can e

enlarged or do,nsi-ed. The' can also change the assortment structure ' including ne, productgroups or ne, product sectors in their sales program and ' e%cluding others.

,.,. etho)s

In principle diverse methods of sample construction e%ist=

>uota procedure cutoff sampling

focused sampling simple random sampling stratified random sampling clustered sampling.

These methods are descried in the panel guide retail and technolog'. 0ere t,o estalishedapproaches are introduced.

,.,.1. P!acticable a!oach

As a rule a stratified sample ,ith at least three dimensions is used for the retail panel of 6f )&.The dimensions can e distriution channels organisation t'pes regions turnover classes salesarea classes etc. The result is a certain numer of cells ,hich are characterised ' these features.An e%ample for such a cell is

Gelectrical specialists / independents / north / # 1 million CH.

Bor all these cells resulting from the stratification the numer of outlets and the turnover of thesesoutlets have to e estimated. After having fi%ed the total sample si-e depending on the costs thenumer of sample shops per cell can e calculated according to the follo,ing formula=

n:i; K f1 L : 5:i;; M f2 L : E:i;; N f1 M f2 K 1

n:i;= sample si-e in cell i n:i;= percentage of the sample si-e in cell i 5:i;= numer of shops in the universe of cell i 5:i;= percentage of the numer of shops in the universe of cell i E:i;= turnover of the shops in the universe of cell i E:i;= percentage of the turnover of the shops in the universe of cell i f1= factor 1 f2= factor 2

This is a ver' simple formula ut ecause of the various alternatives of determining the factorssatisfactor' results can e achieved in all situations. If the sample si-e of a cell should depend on

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the numer of outlets in the universe of this cell ' the maorit' factor 1 must e larger than factor 2. If the sample si-e of a cell should depend on the turnover of the shops in the universe of this cell

' the maorit' factor 1 must e smaller than factor 2. Bactor 1 and factor 2 ma' also e identicalif the same ,eight is given to the numer of outlets and the turnover. ut it has al,a's to econsidered that the sum of factor 1 and factor 2 results in 1.

The follo,ing e%ample ma' clarif' the calculation of the sample si-es in the cells :f1 K f2 K #;.Assuming the universe consists of 1#. outlets.

cell turnover numer of shops percentage of the numer no. classes in the universe of shops in the universe

1 O # mio. C 8.4 #( 2 # 1 mio. C 3.( 24 3 1 2# mio. C 1."# 13

4 2# # mio. C ( 4 # P # mio. C 4# 3

total 1#. 1

cell turnover percentage of the turnover percentage ofno. classes of the shops in the universe the sample si-e

1 O # mio. C 8 32 :K :#( M 8; / 2;2 # 1 mio. C 1 17 :K :24 M 1; / 2; 3 1 2# mio. C 17 1# :K :13 M 17; / 2;4 2# # mio. C 12 8 :K : 4 M 12; / 2;# P # mio. C #3 28 :K : 3 M #3; / 2;

total 1 1

If the total sample si-e is fi%ed ' # outlets the sample si-es in the cells are

n:1; K # L 32 K 1( n:2; K # L 17 K 8#

n:3; K # L 1# K 7# n:4; K # L 8 K 4 n:#; K # L 28 K 14

and the sampling fractions sf:i; K n:i;/5:i; :i K 1234#; are

sf:1; K n:1;/5:1; K 1(/8.4 K 1" sf:2; K n:2;/5:2; K 8#/3.( K 24 sf:3; K n:3;/5:3; K 7#/1."# K 38 sf:4; K n:4;/5:4; K 4/( K (7 sf:#; K n:#;/5:#; K 14/4# K 311

The e%ample sho,s that this procedure leads to a disproportional sample structure. In smallturnover classes there is onl' a small sampling fraction and in large turnover classes the sampling

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fraction is large. It ,as the target to achieve the disproportional structure of the sample ecause thevariance of the outlets in large turnover classes is ver' much larger than the variance of the outletsin smaller ones. ut the outlets in the larger turnover classes are highl' relevant for the maret andin order to get the same statistical accurac' the sampling fractions in the larger classes have to elarger than in the smaller ones. In practice this means that the percentage sample si-e in the larger

turnover classes and especiall' in the largest one has to e large and comparativel' man' shopshave to e recruited here. Though in most cases this ,ill not raise a prolem since in theseturnover classes there are chain stores delivering census data.

ut not onl' the numer of shops in the sample is important. It is also necessar' to have shops of different companies in the sample. 9ther,ise the degree of heterogeneit' ,ill e undervalued.

9n the other hand this does not mean that the sample should include shops of ever' retail compan'.This is a ,idespread miselieve. If there are no shops of a certain compan' in the sample the' can

e considered in the e%trapolation ' appl'ing corresponding e%trapolation factors to the other sample outlets.

,.,.#. 0tatistical a!oach

Another practical method to construct a sample is ased on a statistical approach the socalled 5e'man principle. In a first step this method is used in order to calculate the needed total samplesi-e in dependence on a fi%ed degree of accurac'. In a second step it is used to calculate the samplesi-es ,ithin the strata :e.g. turnover classes sales area classes etc.;. This method can also e usedfor the calculation of the sample si-es ,ithin the strata if the total sample si-e is fi%ed.

In principle t,o different procedures e%ist in the statistical theor'. 9n the one hand the udget isfi%ed so that the total sample si-e is predetermined and the statistical accurac' of this procedure isma%imised as far as possile. In this case the target is minimisation of the sampling error andma%imisation of the >ualit' of the results generated. The other alternative is to achieve a specifiedaccurac'. The total sample si-e then is not fi%ed and the minimum needed total sample si-e ,ill ecalculated in dependence on the specified level of accurac'.

In order to mae statements in the statistical theor' a statistical distriution of a significant parameter value of the regarded units has to e assumed. This is also necessar' if the minimumneeded total sample si-e should e calculated in dependence on a fi%ed level of accurac'. The

parameter value in terms of ,hich the sample should e constructed should have a highrespectivel' the highest possile correlation ,ith the characteristic to e researched. In the retail panel the shops are the regarded units and the parameter value usuall' is the turnover.

According to the central limit theorem of statistics the 6aussian normal distriution can eassumed if the universe is large. As a rule the degree of heterogeneit' of the universe i.e. theshops in the distriution channel is e%tremel' high. The empirical densit' function of thedistriution of the turnover follo,s in most cases a pattern of a large gradient on the left side and adecreasing gradient on the right as there are lots of shops ,ith a small turnover man' shops ,ith amedium turnover and onl' a fe, ,ith a large turnover. This means it is not similar to the densit'function of the 6aussian normal distriution.

Bor this reason a logarithmic transformation has to e carried out. @ith this procedure the and et,een and 1 is transferred into the and et,een Q and 1. &o especiall' the and et,een

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and 1 i.e. the turnover class ,here the percentage numer of outlets is the largest is lengthened.' means of the logarithmic transformation the empirical densit' function ecomes similar to thedensit' function of the 6aussian normal distriution. The arithmetic mean of the sample :i.e. the,eighted average in a stratified sample; can then e used as an uniased estimate of the universemean.

0aving fi%ed the confidence level and the ma%imum sampling error the minimum needed totalsample si-e n:5e'man; can e calculated after this formula=

n:5e'man; K RS 5:i;L&:i; / ReLE/>:1V/2; M S5:i;L&:i;

The legend of the figures in the 5e'man formula is as follo,s=

e= ma%imum sampling error 1V= confidence levelE= total turnover of all outlets in the universe

5:i;= numer of outlets in the universe of stratum i&:i;= standard deviation in stratum i>:1V/2;= percentile of the normal distriution for the confidence level 1V

The follo,ing e%ample should illustrate the 5e'man principle.

Assuming an appropriate sample should e selected from the universe of 1#. electrical retailers.If the ma%imum value of the sampling error allo,ed is 2 the confidence level should e "# atleast. These figures are common in the usiness ,orld. If the sample si-e ,ould e calculated for asample ,ithout stratification more than 3. outlets ,ould e needed for the sample in order toachieve the re>uired statistical accurac'. This means more than 2 of the outlets in the universehave to e included in the sample. This is a result of the e%tremel' heterogeneous universe. Thereare shops ,ith an annual turnover of #. C ,hereas others realise a turnover of more than #mio. C. &ince the calculation of the sample si-e depends on the variance of the arithmetic meansuch a large sample si-e results.

Therefore the universe has to e divided into strata here turnover classes. Then a homogenisationof the universe ,ill e achieved :see 3.3.1.;. In the e%ample the follo,ing turnover classes ,erefi%ed=

# million C # million C

# million C 1 million C 1 million C 2# million C 2# million C # million C # million C 1 million C 1 million C 2# million C P 2# million C

The calculation of the at least needed total sample si-e according to the 5e'man principle andconsidering a ma%imum sampling error of 2 an a confidence level of "# results in 372 outlets.9ther results are possile if the values of the ma%imum sampling error and the confidence levelare modified.

If the confidence level is fi%ed ' "# and the ma%imum sampling error is varied e%tremel'different sample si-es are the result. If a ma%imum sampling error of 1 is re>uired the sample

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si-e increases from 372 to 8( shops. If on the other hand a ma%imum sampling error of # issufficient the sample si-e could e reduced to 1"( shops.

1V K "# e K # n K 781V K "# e K 4 n K 118

1V K "# e K 3 n K 1"( 1V K "# e K 2 n K 372 1V K "# e K 1 n K 8(

If the ma%imum sampling error is fi%ed ' 2 and the confidence level is varied the follo,ingsample si-es ,ill e calculated=

e K 2 1V K "# n K 372 e K 2 1V K "( n K 3"" e K 2 1V K "7 n K 431

e K 2 1V K "8 n K 471 e K 2 1V K "" n K #3"

In the follo,ing tale more results of the calculation are sho,n. sample size 1-α = 95% 1-α = 96% 1-α = 97% 1-α = 98% 1-α = 99%

e = 5% 23 34 56 783 797

e = 4% 773 7:5 7;9 747 75;

e = 3% 754 :79 :9; :47 978

e = % 92: 955 ;97 ;27 695

e = 1% 384 392 327 578 545

All these results are correct in the statistical sense. In practice it has to e ,eighed ,hich values touse as to the ma%imum sampling error and the confidence level. 6enerall' the values e K 2 and1V are used.

According to the formula

n:i 5e'man; K n:5e'man;L5:i;L&:i; / S5:;L&:;

the sample si-es ,ithin the strata respectivel' the turnover classes are calculated. In the e%ample

this leads to the follo,ing sampling fractions :n:5e'man; K 372;=

turnover classes sampling fractions

# million C # million C 1# million C 1 million C 2

1 million C 2# million C # 2# million C # million C 8 # million C 1 million C 1 1 million C 2# million C 34 P 2# million C 48

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9nce more this e%ample sho,s that the 5e'man principle leads to a disproportional samplestructure too. In small turnover classes there is onl' a small sampling fraction and in largeturnover classes the sampling fraction is large and this ,as the target :see 3.3.1.;.

ut there is one ,ea point of the 5e'man principle. If the degree of heterogeneit' is ver' high

the 5e'man formula can re>uire a sample si-e ,hich is larger than the universe in this turnover class. This can appear in the largest turnover class. This is a t'pical phenomenon of the 5e'man

principle. In practice this prolem is solved ' the follo,ing ,a'. In the turnover class ,here thesample si-e should e%ceed the universe all outlets are taen for the sample. The difference et,eenthe re>uired and the actual numer of outlets in this turnover class can e added to the re>uiredsample si-e in the ne%t larger turnover class. It can also e allocated to more turnover classes. Inorder to eep the re>uired accurac' the total sample si-e and the sample si-es of the other turnover classes should e recalculated using the 5e'man principle.

As to sample optimisation there are t,o more interesting documents on the &tarTrac platform=

3. )ethodolog' &ample 9ptimisation 9ptimum Allocation of &tratified +andom &amples

3. )ethodolog' &ample 9ptimisation 9ptimising &tratified +andom &amples

,.-. P!actical asects

,.-.1. Refusal of anel a!ticiation

In practice it can happen that a retailer or a retail compan' refuses to participate in the retail panel.This prolem of the nonans,ering concerns sample data as ,ell as census data.

If sample data are used and the refusing retailer is less important for the panel these outlets ,ill esustituted ' other ones provided that comparale outlets are availale. If the sample si-e is largeenough in the corresponding cell it is also possile to modif' the e%trapolation factors of the other outlets in such a ,a' that the loss of data can e compensated.

If census data are used and the refusing retailer is less important for the panel the outlets of thisretailer have to e created out of selected outlets of the other retailers. ut this method can onl' eapplied if data per outlets are availale. 9ther,ise the data of the rest of the retailers have to e,eighted correspondingl'.

If the refusing retailer or retail compan' is important for the panel the >ualit' of the data in thereporting can e put at ris no matter if sample data or census data is used. The results can edistorted especiall' if the lost retailer differs from the other ones significantl' for e%ample a e'retailer ,ith strong private laels in the assortment.

In general it is not necessar' to get outlets of all retailers or retail companies into the panel since

,ith respect to the distriution channels there are lots of similar outlets ,hich can stand for anumer of other ones. This should e considered ,hen constructing the sample in order to eep thecosts controlled.

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,.-.#. Chan'in' sa"le consistenc( an) st!uctu!e

In most cases the consistenc' of the sample changes from period to period. There are sometimes

retailers ,ho stop the data deliver' ecause the' do not ,ant to provide 6f )& ,ith their dataan' longer or 6f )& stops the cooperation ,ith a retailer ecause of ad data >ualit' or ecausethe data deliver' is too late. +etailers can close their shop:s; or some of their shops ,hich then aremissing in the sample. 5e, shops are included in the sample. &hops can e enlarged or do,nsi-ed.The' can also change the assortment structure ' including ne, product groups or ne, productsectors in their sales program and ' e%cluding others.

All these activities lead to changes ,ithin the sample ,hich in turn can lead to variations of thee%trapolated results as to the universe. ut on the other hand the universe itself is permanentl'changing over time. &o it is not possile to differentiate et,een the GtrueH variations and thevariations caused ' changes in the sample.

*anel samples as the retail panel have the advantage that the prolem of nonans,ering or refusalof panel participation essentiall' occur in the initial phase. The variations caused ' changes in thesample decrease in panel samples too. The handling of these changes ,hen e%trapolation is done isdescried in chapter 4.(.

,.. Diffe!ence bet*een ta!'et an) actual sa"le si2e

5ormall' there is a :possil' large; difference et,een the target and the actual sample si-e. Thereare t,o main reasons for this= the e%istence of outliers and the deliver' of census data. In othcases the actual sample si-e ,ill e larger than the target sample si-e.

It often occurs that retail companies especiall' chain stores deliver census data. ut regarding thesample it ,ould not e necessar' to consider all outlets of this retailer in the evaluation. 9n theother hand census data ,ill e processed if this retailer is provided ,ith his e%clusive performancein the reporting. Then there is no sampling error :see also 3.1.2.;. &o the actual sample si-e ,ill elarger than the calculated one. ut the processing of census data is a >uestion of costs. Therefore incases of retailers ,ho are not supplied ,ith the e%clusive segment a sample ,ill e preferred. The other reason for that the actual sample si-e is normall' larger than the target sample si-e is the

e%istence of outliers. In practice it happens again and again that there are shops ,hich cannotrepresent the numer of other ones according to the computed e%trapolation factor ecause thesales structure is too different. Then the computed e%trapolation factor cannot e applied to such anoutlier or at'pical outlet ut onl' a smaller one. In the e%treme case such a shop can onl' stand for itself ,ith e%trapolation factor 1. ut this means there is a loss in the numer of the degrees of freedom i.e. the sample si-e in this cell has to e enlarged if the computed e%trapolation factor should e ept for the other outlets. Therefore the sample si-e in practice is mostl' larger than inthe theoretical model :see also 4.2.7.;.

It can also occur that in a special e%trapolation cell the actual sample si-e is smaller than the targetsample si-e. Bor instance if a retailer ,ith several outlets or a chain store stops data deliver' the

sample si-e can ecome too small in this cell. Then ne, shops have to e recruited. The handlingof such cases is descried in chapter 4.(.2.

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CHAPTER -

-. E/t!aolation

The target is to set up a representative e%trapolation ,ith respect to the universe. In all cases thee%trapolation taes place either for distriution channels or for retailers ,ho are mostl' supplied,ith an e%clusive segment in the special reporting. +egarding the e%trapolation for distriutionchannels the channels are estimated completel' that means the coverage rate per channel is 1.!onsidering as e%ample the electrical retailers then the sales units and sales value of this channelare estimated hundred per cent. As a matter of course ,hen e%trapolating ,ith respect to a retailer the sales data of this retailer are estimated completel' too.

-.1. E/t!aolation channels

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The e%trapolation channels can e identical to the countr' channels ut it is also possile to put allcountr' channels ,hich elong to the same production proect into the same e%trapolationchannel. Bor instance the t,o countr' channels Gelectrical specialistsH and Gtechnical superstoresHma' e put together into the e%trapolation channel Gelectrical retailersH. In order to e ale torecognise the e%trapolations in the D@0 at a later date there should e given reasonale names to

the e%trapolation channels. The name should e structured as follo,s=

countr' identification code e.g. GD$H for 6erman' sector identification code e.g. G!$H for consumer electronics name of the channel e.g. Gelectrical retailersH.

Then the name of the e%trapolation channel in the mentioned e%ample ,ould e

GD$ !$ electrical retailersH.

In man' cases there are changes in the retail scene or in the sample during the 'ear. &hops are

closed ne, shops open retailers in the panel stop data deliver' ne, retailers oin the panel other retailers in the panel deliver data of ne, shops etc. These changes have to e considered updatesof the e%trapolation are necessar'. In practice a first e%trapolation version is set up at the eginningof the 'ear. If then there are changes in the universe or in the sample the' ,ill e considered in asecond e%trapolation version and so on.

It is al,a's necessar' to set up a ne, e%trapolation version if an e%trapolation variant is uilt upfor the first time or if there is a change in the e%trapolation caused ' a change in the universe or inthe sample. This means it is a must to set up a ne, e%trapolation version even if there is onl' onechange.

The versions of an e%trapolation should e laelled ,ith reasonale names. It is advisale to assignthe name of the reporting period e.g. Wanuar' 2#. If e%trapolation versions do not change for several periods it is etter to choose another name e.g. 12#.

-.#. E/t!aolation cells

&ince in most of the cases the outlets ,ithin a distriution channel are ver' heterogeneous and thevariance is ver' large the sample ,ill e stratified in order to get more homogeneit' ,ithin thestrata :as mentioned in chapter 3.;. Instancing the electrical retailers the spectrum of the turnover of

the outlets is ver' large and there is a large degree of heterogeneit'. Turnover classes should eestalished.

!oncerning the e%trapolation there is also a segmentation of the channels ,hich need not eidentical to the stratification structure. $ach channel is separated into diverse segments ,hich arecalled e%trapolation cells. These cells can e completel' different regarding the different channelsand product sectors.

-.#.1. Cell )efinition

The e%trapolation cells are constructed in dependence on several features as regions or turnover classes or sales area or organisation t'pe etc. A comination of features is also possile. Bor e%ample an e%trapolation cell can e defined ' a special region a special turnover class and a

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special organisation t'pe :e.g. north / 1 2 million. C / u'ing group;. In order to constructe%trapolation cells according to the desired features it is an essential condition that these featuresare listed in )D). 9nl' the listed features can e used for the cell construction.

Then an e%trapolation cell is defined ' the follo,ing criterions=

cell name cell features :turnover class region sales area organisation t'pes etc.; numer of outlets in the sample tale of the itemised outlets in the sample ,ith shop numer e%trapolation universe distriution universe e%trapolation factors distriution factors.

The cell name should correspond to the features ,hich descrie the e%trapolation cell e.g. a

comination of a special region and a special turnover class as

Gelectrical specialists / independents / north / # 1 million. CH.

The e%trapolation cells have to e defined in such a ,a' that there is no overlapping. This meansthe cell definitions have to e fi%ed one to one so that overlapping is not possile. It must eguaranteed that a certain shop can onl' e assigned to one e%trapolation cell.

-.#.#. Assi'n"ent of the shos to the e/t!aolation cells

@hen the e%trapolation cells are defined the shops can e assigned to the cells. In each cell the celloutlets are selected from the tale of the itemised outlets in the sample ,ith shop numer. Theassignment of the shops to the e%trapolation cells ma' differ from product sector to product sector respectivel' product group to product group if the cell definitions differ itself. As a matter of course the characteristics of the shops as turnover sales area etc. remain the same. This is clarified

' an e%ample.

Assuming an electrical specialist ,ho elongs to the u'ing group $uronics is situated in thenorth realises an annual turnover of 1# million C and has a sales area of 2# m the follo,ingassignments are possile.

As to consumer electronics this shop ma' elong to an e%trapolation cell characterised '

Gelectrical specialists / $uronics / north / 1 2 million C / 2 # mH.

As to photo the same shop ma' elong to an e%trapolation cell characterised '

Gelectrical specialists / u'ing groups / north / 1 # million C / 2 3 mH.

ut the assignment of the shops to the distriution channel does not depend on the panel or other criteria. It depends e%clusivel' on the channel definition. This means for e%ample it is not allo,ed

that a shop is assigned to electrical specialists regarding consumer electronics and to photospecialists regarding photo.

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&ince overlapping of cells must e e%cluded each shop can onl' e assigned to one e%trapolationcell. 9nl' if a shop fulfils the conditions of the specified cell definition it ,ill e assigned to thiscell.

An e%trapolation cell must not necessar' consist of lots of outlets. It is also possile to create a cell

,ith onl' one outlet. This is often done if ne, recruited shops should e integrated into the samplein the ne, reporting period ut not into an e%isting cell. Then a ne, e%trapolation cell can e setup ,ith an o,n e%trapolation factor.

-.#.,. Co"utation of the e/t!aolation facto!s

Theoreticall' the e%trapolation factor in each e%trapolation cell is the reciprocal value of thesampling fraction in this cell :e.g. sampling fraction # e%trapolation factor 2; and all sampleshops in this cell get the same e%trapolation factor. ut in practice there are mostl' some cases

,here an individual factor is attached to a shop ,hich is different from the factors of the other outlets. Among other things this rule has to e applied to at'pical outlets ,hich the computede%trapolation factor cannot e placed to ut onl' a smaller one. In some cases there are shops,hich cannot represent a large numer of other shops for reasons of their sales structure. In ane%treme case such a shop can onl' represent itself i.e. the e%trapolation factor is 1.

The e%trapolation universe and the distriution universe ma' differ as ,ell as the e%trapolationfactor and the distriution factor. Bor e%ample if a certain retail compan' ,ith # departmentstores transmits the data for all # outlets in aggregated form i.e. not per outlet the e%trapolationfactor is 1 the e%trapolation universe is 1 the distriution factor is # and the distriution universeis #. Thus a difference has to e made et,een the e%trapolation universe :factor; and thedistriution universe :factor;.

In an e%trapolation cell either the universe is fi%ed :fi%ed universe; or the factors are fi%ed :fi%edfactors;. This can e done for the e%trapolation universe and for the e%trapolation factors as ,ell asfor the distriution universe and the distriution factors. It often occurs that distriution universesor factors for a product sector or for certain product groups are predetermined ,ithin a channel.That depends on the numer of outlets in this channel ,hich carr' the corresponding productsector or product group.

+egarding the first method :fi%ed universes; the e%trapolation factors and the distriution factors

are calculated automaticall'. Bor e%ample if a cell universe consists of 1 outlets and there are #sample outlets the s'stem calculates 2 as e%trapolation factor for each shop. 0o,ever it is also possile to attach an individual factor to a shop. If in the e%ample aove factor 4 is attached to oneoutlet the s'stem ,ould calculate automaticall' factor 24 for the other shops.

The universes per cell and per cell definition are fi%ed once at the eginning of the reporting 'ear.As a rule the universes are not modified ,ithin this 'ear unless there are relevant changes. 9n theother hand there are distriution channels ,ith fre>uent changes in the retail scene so that thee%trapolation has to e updated during the 'ear.

+egarding the second method :fi%ed factors; the e%trapolation universe and the distriution

universe are calculated automaticall'. Bor instance if there are # sample outlets in an e%trapolationcell ,hich factor 2 is placed to each of them the s'stem calculates a universe of 1 outlets. If

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there are four outlets ,ith factor 24 and one outlet ,ith factor 4 the s'stem also calculates auniverse of 1 outlets.

-.#.-. Ca!!(in' an) not ca!!(in' outlets

If onl' a part of the outlets in a distriution channel carries a certain product categor' :productsector product group rand feature etc.; a difference can e made et,een carr'ing and notcarr'ing outlets. Then the e%trapolation taes place for the carr'ing outlets. ut the not carr'ingoutlets have to e considered in the e%trapolation ecause e%trapolation is al,a's done for thecomplete channel ,ith a coverage rate of 1 . &o as to the not carr'ing outlets one or morefurther simulated e%trapolation cells are created. The follo,ing e%ample ma' demonstrate this

proceeding.

Imagine a universe of #. outlets in the distriution channel h'permarets. 8 of the outletscarr' products of the product sector consumer electronics. Accordingl' the universe of the carr'ing

outlets in this channel amounts to 4. outlets. These 4. carr'ing h'permarets are assigned toe%trapolation cells e.g. according to sales area groups. The 1. not carr'ing h'permarets arealso assigned to simulated e%trapolation cells. The reason for this proceeding is to get a correctdistriution. If the simulated e%trapolation cells for the not carr'ing h'permarets ,ere not createdthe s'stem ,ould fi% the universe of all h'permarets at 4. outlets :K 1; automaticall' andthe distriution of the carr'ing h'permarets ,ould e invalid ecause it is onl' 8. @ithout thesimulated e%trapolation cells the s'stem is not ale to recognise that the universe of allh'permarets is #..

-.#.. o)ification of the cell )efinition

A cell definition can remain changeless during the ,hole reporting 'ear ut it can also e modifiedin one t,o or all reporting periods. There are several reasons for the modification of celldefinitions. Bor e%ample if t,o regions ,hich ,ere e%trapolated separatel' in the past no, are puttogether the cell definition has to e modified. 9r if t,o turnover classes ,hich ,ere comined inthe past are separated the cell definition has to e modified too.

-.#.3. I)entical e/t!aolation fo! "o!e than one !o)uct secto! 4 !o)uct '!ou

+egarding a special distriution channel or a special part of a distriution channel the e%trapolationcells often are identical for more than one product sector / product group. Then the e%trapolation issimplified. There are also e%trapolations e%isting ,hich are applied to all product sectors. Thisoccurs primaril' in distriution channels ,hich carr' products of various product sectors e.g.h'permarets or department stores.

In order to mae e%trapolations less difficult the target should e to use the same e%trapolationrespectivel' e%trapolation cells for as much product groups as possile. This saves a lot of ,or concerning maintenance and updating of e%trapolations.

-.#.5. Han)lin' of outlie!s an) at(ical outlets

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In practice it happens again and again that there are shops ,hich cannot represent the numer of other ones according to the computed e%trapolation factor ecause the sales structure is toodifferent. Then the computed e%trapolation factor cannot e applied to such an outlier or at'picaloutlet ut onl' a smaller one. In the e%treme case such a shop can onl' stand for itself ,ithe%trapolation factor 1. ut this means there is a loss in the numer of the degrees of freedom i.e.

the sample si-e in this cell has to e enlarged if the computed e%trapolation factor should e eptfor the other outlets. Therefore the sample si-e in practice is mostl' larger than in the theoreticalmodel :see also 3.#.;.

The prolem of the reduced numer of degrees of freedom can usuall' e solved easier in cells,ith the more important outlets :large turnover large sales area etc.; since in these cells there arenearl' al,a's retail chains ,hich deliver census data and the numer of sample outlets e%ceedsthat re>uired ' the sample design. +egarding cells ,ith the less important outlets the sample si-eshould e enlarged in order to eep the re>uired accurac'. ut as a rule the numer of lessimportant outlets in the universe is ver' large so that it ,ould not e a prolem to recruit somemore shops. 9n the other hand in cells ,ith less important outlets there is more homogeneit' and

the variance is not ver' large. 0ere trivial inaccuracies ma' e neglected.

Bor e%ample assuming in the turnover class # 1 )io. C the e%trapolation factor is # and thereare 3( shops in the sample. T,o shops can onl' stand for themselves i.e. e%trapolation factor 1t,o shops can onl' represent 1 others i.e. ma%imum e%trapolation factor 11 and t,o further shops can onl' represent 2 others i.e. ma%imum e%trapolation factor 21. The result is a factor sumof (( for ( outlets. &ince the cell universe is 1.8 :K 3( L #; the other 3 sample outlets ,ouldget factor #78 :K 1.734 / 3;. In order to eep e%trapolation factor # for these 3 outlets at least #more shops are needed in this cell :1.734 / # K 34(8 3 M #;.

-.,. E/t!aolation fo! in)i%i)ual !etail co"anies

e' accounters are often supplied ,ith an e%clusive segment in a special reporting so that the' cansee their maret share in the different product sectors product groups and feature categories andcan compare their assortment structure directl' ,ith that of the corresponding distriution channeland the panel maret. In such a case the sample of outlets of the corresponding retail compan' hasto e dra,n ,ith regard to representative aspects concerning this compan' and not torepresentative aspects concerning the distriution channel. This means an appropriate sample of outlets is re>uired in order to get a good estimation for the retailers o,n data.

If the outlets of a retail compan' are largel' homogeneous i.e. the turnover and the assortmentstructure is nearl' the same for all outlets the choice of outlets is rather eas'. ut the higher theheterogeneit' of the outlets is the choice ecomes more and more difficult ecause then one outletcannot e representative for a large numer of other ones.

Another prolem ,ith the reporting >ualit' can appear if such a retailer closes e%isting outlets or opens up ne, outlets. The e%trapolation has to e modified immediatel'. A >uic information onthe part of the retailer is asolutel' necessar'. It is the same if an outlet is enlarged or do,nsi-ed.

As a conclusion a sampleased e%trapolation ,ith respect to the data of a retail compan' ,illnever ensure asolute accurac'. !ompletel' correct data for the e%clusive segment can onl' e

guaranteed ' census data assumed a complete and correct data deliver' and processing :see alsochapter 4.#.;. If in case of census data a retailer closes e%isting shops or opens up ne, ones thischange is taen into account automaticall'.

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+egarding the e%trapolation for individual retailers the outlets of such a retailer are assigned to oneor more separate e%trapolation cells. If there is a split in regions turnover classes sales areas etc. inthe reporting for the corresponding distriution channel an e%trapolation cell for each segment isre>uired. That means for e%ample if there is a reporting for 3 turnover classes und 2 regions the

outlets of the corresponding retailer have to e assigned to ( e%trapolation cells. If no split taes place one e%trapolation cell ma' e sufficient.

-.-. Han)lin' of a''!e'ate) )ata

&ometimes a retailer ,ith more than one outlet delivers the data not per outlet ut in aggregatedform. The reason ma' e that this retailer is not ale to separate the data or this is too e%pensive for him. In other cases data are delivered per outlet and 6f aggregates them in order to save costs.This procedure is allo,ed if there is no :e.g. regional; split in the reporting for the correspondingdistriution channel.

Then the e%trapolation factor is 1 and the e%trapolation universe is also 1. The distriution factor and the distriution universe correspond to the numer of outlets of this retailer. Bor e%ample if acertain retail compan' ,ith # department stores delivers the data of all # outlets in aggregatedform the e%trapolation factor is 1 the e%trapolation universe is 1 the distriution factor is # andthe distriution universe is #. If onl' the data of 1 of the # department stores is delivered inaggregated form the e%trapolation factor is # the e%trapolation universe is # the distriution factor is # and the distriution universe is #. In cases of data deliver' in aggregated form it is not

possile to use single outlets of this retailer representative for outlets of other retailers regardingthe e%trapolation.

$ven if there is a split in the reporting for a distriution channel an aggregation of the data isallo,ed ut onl' ,ith respect to this segmentation respectivel' to these e%trapolation cells. Thenthe data of the outlets of this retailer ,hich elong to the same segment can e aggregated.

Assuming for e%ample a distriution channel ,ith a reporting for 3 turnover classes and 2 regionsand a retail compan' ,ith # department stores ,hich delivers the data in aggregated formcorresponding to these ( e%trapolation cells=

e%trapolation distriutionturnover class region X outlets factor factor

O # )io. C 5orth # 1 ## 2 )io. C 5orth 12 1 12 P 2 )io. C 5orth 8 1 8 O # )io. C &outh 4 1 4# 2 )io. C &outh 1# 1 1# P 2 )io. C &outh ( 1 (

-.. 0a"le )ata %e!sus census )ata

-..1. 0a"le6base) e/t!aolation

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If all retailers ,ithin a distriution channel deliver census data these data are aggregated. Ane%trapolation is not necessar'. The data of a certain retailer can e used directl' for the e%clusivesegment in the reporting. If not all retailers ,ithin a distriution channel deliver census data asample has to e dra,n in parallel.

A)%anta'es

&ince in cases of complete census data ,ithin a distriution channel there is no e%trapolation andno sampling error an optimal >ualit' of the reporting can e achieved. If a retail compan' closese%isting shops or opens up ne, ones this change is taen into account automaticall'. This meansthat e%tensive e%trapolation tests ,ith modified e%trapolation factors are not necessar'. Thedistriution channel ,ill al,a's e represented ,ith a coverage rate of 1.

Disa)%anta'es

If a retailer stops the data deliver' the result is a corresponding gap a coverage rate less than1 in dependence on the importance :turnover; of this retailer. In cases of e' accounts the>ualit' of the data in the reporting can e put at ris. In order to produce relief the outlets of thisretailer have to e created out of selected outlets of the other retailers. ut this method can onl' eapplied if data per outlet are availale. 9ther,ise the data of the rest of the retailers have to e,eighted correspondingl'. ut here a prolem can appear if the lost retailer differs from the other ones significantl' for e%ample a e' retailer ,ith strong private laels in the assortment.

Another prolem appears if a ne, retailer enters the maret ,ho has to e assigned to adistriution channel ,ith complete census data. Then this retailer ,ill e completel' transparent inthe reporting. !onse>uentl' in cases of complete census data ,ithin a distriution channel there are

asic advantages ,ith respect to the reporting >ualit' assumed a complete and correct datadeliver' and processing ut there is also a ad ris since one is dependent on ever' single retailer in this channel.

-..,. Conclusion

The ma%imum of >ualit' and confidence in reports ,ith e%clusive segments can onl' e achieved

if the corresponding retail companies transmit census data. +egarding the retailers ,ho are notsupplied ,ith an e%clusive segment in the reporting census data certainl' guarantee the highest>ualit' and confidence ut the processing of census data is a >uestion of costs. Therefore in thesecases a sample ,ill e preferred. This means if possile census data concerning retailers ,ithe%clusive segment and sample data concerning retailers ,ithout e%clusive segment in the reporting.

-.3. Han)lin' of ent!ies an) lea%in's

&ometimes retailers stop the data deliver' ecause the' do not ,ant to provide 6f ,ith their dataan' longer or 6f stops the cooperation ,ith a retailer ecause of the ad >ualit' of the data or

ecause the data deliver' is too late or ne, retailers could e recruited for the panel. Theseactivities lead to changes ,ithin the sample.

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9n the other hand retailers close their shop:s; or some of their shops ne, retailers come into themaret and other retailers open up ne, shops. &ome shops are enlarged others are do,nsi-ed.&ome shops change the assortment structure. The' include ne, product groups or ne, productsectors in their sales program and e%clude others. &o there are al,a's changes in the universe.

&ince a panel has to e adusted permanentl' to the :changing; universe there must al,a's echanges in the sample si-e and the cell populations so that e%trapolation cells have to e modifiedin order to get a representative e%trapolation further on. 5ormall' the cell population is fi%ed onceat the eginning of the reporting 'ear and is not modified ,ithin this 'ear unless there are relevantchanges. ut there are a couple of cases ,here a modification is necessar'.

-.3.1. Ent!ies

5e, shops of e%isting countr' channels ,hich are alread' included into )D) shop master arelisted in the e%trapolation s'stem ,ith a proposal for ever' ne, shop ,hich e%trapolation cell

according to the cell definition it ,ould e assigned to and ,hich e%trapolation factor it ,ould get.The decision concerning the e%trapolation factor is ased on the cell population and the universedescription for the corresponding cell. Bor the calculation of the proposed e%trapolation factor itma' e specified on cell level ,hether the e%isting shop factors or the cell universe should remainunchanged.

In e%trapolation cells ,ith a fi%ed universe the factors ,ould change automaticall' i.e. the' ,oulddecrease. Bor e%ample if # ne, shops oin an e%trapolation cell ,ith a fi%ed universe of ( shopsand a population of 2# shops the e%trapolation factor per shop ,ill decrease from 24 to 2.

If the e%trapolation factors remain unchanged the ne, shop increases the cell universe. Bor e%ample if # ne, shops oin an e%trapolation cell ,ith a population of 2# shops and a fi%ede%trapolation factor of 24 the universe increases from ( to 72.

Thus a ne, shop can e included into the e%trapolation model ,ith a single mouse clic 'accepting the proposals for this shop. $%ceptions are at'pical shops ,hich are not representativefor as much as shops as the e%trapolation factor specifies. These shops need a special treatment.The e%trapolation factor has to e smaller than those of the other shops i.e. at'pical shops ,ill getfi%ed e%trapolation factors. An at'pical shop can also e assigned to a separate e%trapolation cell of its o,n. ut as a rule at'pical shops do not appear alltoo often.

)ost of the ne, shops are included automaticall' into the e%trapolation model and the e%ceptionsare specified e%plicitl'. This proceeding limits user interaction to a minimum ut still guaranteesthat e%trapolation models are modified under user control.

A precedent condition for the decision ,hether a shop is at'pical or not is that ne, shops have to e e%amined ,ith minuteness. Bor each ne, shop a socalled shop profile ,ith all relevantcharacteristics as

shop t'pe total turnover or turnover class assortment structure / composition of the carried product sectors

turnover per product sector memership in a u'ing group memership in a franchise organisation

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outlet of a e' account head>uarter ,ith susidiaries percentage of retailer and ,holesaler turnover percentage of selling turnover and turnover ,ith installation service and repairs percentage of ecommerce turnover

sales area

has to e dra,n up. @ith the help of these data it should e possile to decide if the shop isat'pical or not and if not ,hich e%trapolation cell it has to e assigned to.

In the normal case the decision on ,hether and ho, to include a ne, shop into the e%trapolationmodel is made efore data of this shop are availale for the first time. ut it happens that thedelivered data of ne, :and also some old; shops are not 'et usale. Bor these cases the s'stem isale to recompute automaticall' the cells according to the cell population after the amendments inthe BactTool are made.

Bor e%ample if the e%trapolation model e%pects and orders from IDA& data for 1 shops for a cell,ith a universe of 7 outlets resulting in an e%trapolation factor of 7 per shop and onl' # of thedata deliveries arrived or are usale some of the missing data deliveries ma' e compensated inthe BactTool e.g. ' cop'ing the data from the previous period. Assuming in this e%ample that thisis done for 2 of the # missing shops this function ,ould recalculate the e%trapolation factors asedon 7 shops resulting in a shop factor of 1. 9f course this is ased on the assumption that thereare no shops ,ith a fi%ed factor in this cell.

-.3.#. Lea%in's

If a retailer stops the data deliver' or 6f stops the cooperation ,ith a retailer the missing datacan e compensated ' modification of the other e%trapolation factors. This method is useful incases of large cell populations ,here the missing shop can e set aside.

In e%trapolation cells ,ith a fi%ed universe the factors ,ould change automaticall' i.e. the' ,ouldincrease. Bor e%ample if a shop is missing in an e%trapolation cell ,ith a fi%ed universe of (shops and a population of 2# shops the e%trapolation factor per shop ,ill increase from 24 to 2#..If the e%trapolation factors remain unchanged the missing shop decreases the cell universe. Bor e%ample if a shop is missing in an e%trapolation cell ,ith a population of 2# shops and a fi%ed

e%trapolation factor of 24 the universe decreases from ( to #7(.

It is also possile to cop' the data of the previous period ut this cannot e done for man' periods ecause the data then ecomes too old particularl' for features and models. This method onl'maes sense if a ne, shop as a sustitute for the lost shop is e%pected soon.

If the cell population is small the reporting >ualit' and consistenc' is put at ris. Incorrect dataand e%treme values as special offers of products ,ith large sales units or products ,ith a pricehigherthanaverage and onl' little sales units ,ill e multiplied ,ith an increased e%trapolationfactor. In this case a ne, shop must e found as a sustitute for the missing one as fast as can. If this is not possile it has to e considered ,hether the corresponding e%trapolation cell can e put

together ,ith another one. It is also possile to create dumm' outlets in this cell. Then for eachdumm' outlet a shop numer is defined. The complete data of another reall' e%isting shop is

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copied permanentl' into this shop numer. This procedure is carried out until the cell population,ill e enlarged.

-.5. P!ocee)in' in case of !oble"s *ith the )ata

-.5.1. issin' )eli%e!( e!io)s

In most of the deliver' periods the data of one or more retailers are missing ecause the datatransfer could not e realised. There are various reasons for that=

There are compan' holida's and no contact person is gettale. The responsile person is ill or on holida' etc. and another person is not ale to transmit

the data. There is some troule ,ith the electronic data transfer or the merchandise management

s'stem itself ,hich cannot e solved in time.

There is some troule ,ith the update ,hich cannot e solved in time. A ne, merchandise management s'stem is installed at the time.

In these cases there is a loss of the data concerning the corresponding retailer and deliver' period.Then it has to e decided aout the further proceeding i.e. if the data of the previous deliver'

period could e duplicated perhaps ,ith a ,eighting or if the data of t,o or more previous periods could e taen into account or if this period can e compensated ' the average of the previous and the follo,ing period :if possile; or if the data of another retailer :or other retailers;should e applied alternativel' or if the e%trapolation factors of the other outlets in thecorresponding e%trapolation cells should e modified correspondingl' :as descried in 4.(.2.;. Thesmaller the turnover of this retailer the lesser is this prolem. +egarding a retailer ,ho is relevantfor the corresponding distriution channel prolems ,ith the reporting >ualit' ma' appear.

As to this prolem there is another document on the &tarTrac platform=

2. )anuals 4. D@0 Data@arehouse D@0 BactTool :$nglish;

-.5.#. Late )ata )eli%e!(

If the data of a retailer ,ere not transmitted until the latest deliver' date according to agreementne%t da' he ,ill e ased for the reason and a ne, deliver' date ,ill e fi%ed. The ne, date of datadeliver' should e determined so that the data processing concerning this retailer is guaranteed insufficient time. If the data transfer until this ne, deliver' date is not possile for one of the aovementioned reasons or for another reason the data concerning this retailer and this deliver' periodare lost. In this case the proceeding is as descried in 4.7.1.

-.5.,. Inco"lete )ata

In some cases the data of a retailer are not complete i.e.

the data of one or more outlets are missing the data of one or more product groups are missing

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facts as stocs or sales prices are missing data sets are missing the data do not include the total deliver' period :for instance instead of a month onl' 2

,ees;.

Then the retailer ,ill e ased for the deliver' of the complete data. If this is not possile thefurther proceeding has to e fi%ed.

In case of missing outlets the data of other outlets of this retailer could e duplicated ut onl' if the assortment structure and the turnover is similar to that of the missing outlets. 9ther,ise it is

etter to duplicate the data of the previous deliver' period perhaps ,ith a ,eighting. If the samplein the e%trapolation cells of the missing outlets is large enough and the other outlets are not toodifferent the e%trapolation factors of the other outlets could e modified correspondingl' :asdescried in 4.(.2.;.

In case of missing facts the calculation rules ma' help to compute sustitutional figures. If the data

do no

3)Extrapolation Handbook

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