retail market measurement0001

Part 2: PROCESSES

Chapter 8

Retail marl{et measurement

James BrooksTim Bowles

INTRODUCTION

The purpose of this chapter is to explain how market information based uponretail sales is derived and used by manufacturers and retailers to make betterbusiness decisions. Since the emergence of mass marketing, manufacturers offast turnover packaged goods have wanted information about the performanceof their brands, both against key competitors, and the market as a whole. Forthe fifty years prior to 1980, such information came from two main sources:consumer panels and retail audits.

In the retail audit, information on sales through a sample of shops wascollected regularly by auditors working for the research company. Consumerpanels approached the task by recruiting a sample of consumers who kept arecord of all their packaged goods purchases, usually in the form of a purchasediary.

In both cases, the information collected, from shops or consumers, could beanalysed and statistical methods used to project from the sample to what hadbeen sold through all shops, or bought through all households. Estimates oftotal market size, and the share of major brands, could then be provided forproduct categories ranging from detergents to savoury snacks.

The principal advantage of retail audit measurement was accuracy. In a sampleof affordable size many more purchase observations could be obtained from asample of shops than from a sample of consumers, in the same data collectionperiod. This advantage was particularly important for tracking infrequentlypurchased products or brands. The retail audit also avoided potential errorarising from human memory and accuracy in recording purchases, which canaffect consumer panel results.

241

242 Tim Bowles, James Brooks

Consumer panels, while limited in the ability to provide accurate marketmeasurement, are a rich source of information on the consumer; who buys aswell as what is bought. Through consumer panel data one can gain insight intothe household profile for purchases of different products, and how brandchoices are influenced by the characteristics of buyers themselves. The twomain sources of market tracking information are therefore complementary.

The process of collecting information on retail sales has undergone arevolution in the last twenty years. This revolution was driven by theprogressive adoption by retailers of electronic cash registers equipped withscanners which can recognise bar-codes printed on product packaging. EPOS(Electronic Point of Sale) equipment is now widespread in the United States,Western Europe, Japan and other developed economies, where it has largelyreplaced the traditional audit as a source of information on retail sales ofpackaged goods. This availability of scanner information is a by-product ofthe retailers' drive for efficiency through the use of EPOS equipment, as thebasis for stock control and other administrative systems.

The impact of EPOS developments is enormous, in that information isavailable at an unprecedented level of detail for products and brands, for everyshop in a retail chain, and for any time period, however short. Whereas thechallenge for the retail audit was to collect reliable data, the challenge in thescanning era is to manage the data and reduce them to usable reports formanagement.

While scanner penetration of retail outlets in developed countries is nowwidespread, goods in many countries are still mainly sold through traditional,privately-owned shops. Even in some developed markets, the traditional retailtrade is still significant. International manufacturers and retailers of consumergoods can therefore expect to have to deal with both traditional retail audit andscanner-based information for the foreseeable future, in order to gain anunderstanding of their markets.

In the remainder of this chapter we will explain the basis of retail marketmeasurement as it is practised today, and how the data are analysed to supportbusiness decisions.

A BRIEF HISTORY OF RETAIL DATA SUPPLY

The science ofretail sales tracking was invented by Arthur C. Nielsen in 1933,when he set up his Drug Index service in the United States to measuredrugstore sales. This development was followed in 1934 by the creation of theFood Index to measure sales in the grocery trade sector.

The first European developments occurred in 1937, when the British MarketResearch Bureau set up a panel of 1,000 grocers in major towns in the UnitedKingdom. Soon afterwards Nielsen's company took the first step in anaggressive programme of European expansion when it set up the UK FoodIndex in 1939.

Retail market measurement

Over the following fifty years ACNielsen followed the global expansion of itsmulti-national clients to establish a powerful network of companies providingretail audit services in more than 120 countries. Competition to Nielsen wasmainly local and the company became the dominant worldwide supplierexcept in certain niches such as specialist markets. (The company also becameactive in media audience research with the supply of radio and TV ratings).

EPOS scanning equipment, and the associated adoption of bar-codes onproducts, was developed during the 1970s. The Article NumberingAssociation (ANA) was formed in 1977 as an initiative by Europeanmanufacturers and retailers. This organisation has now expanded across theworld and currently comprises seventy-nine national product numberingassociations representing eighty-six countries. This system of bar-coding,described in a subsequent section, is now employed by 600,000 consumergoods companies.

The appearance of EPOS data in the late 1970s created the conditions for theemergence of a serious competitor to Nielsen in the United States: InformationResources Inc. (IRI). IRI recognised the potential for scanner data and, in1979, created a revolutionary test market service, based upon scanner data,called BehaviorScan. IRI then exploited its early capability in the analysis ofscanner data to launch a national retail tracking service in the United States,competing directly with Nielsen, called InfoScan, in 1986.

At the time of writing ACNielsen and IRI are the dominant suppliers of retailtracking data in the United States, and IRI is challenging Nielsen's pastdominance in Europe and other parts of the world.

BASIC PRINCIPLES

243

The underlying principle of retail market measurement is that sales data aretaken from a number of retailers and combined to represent a larger tradesector: individual trade sectors can then be combined in order to represent thetotal market. This has meant that retailers can provide their sales data to bepooled into a tracking service whilst maintaining the confidentiality of eachparticipating retailer's sales.

Figure 1

data provided data reported


For example, the top five grocery retailers in a country might be combined toform a trade sector called key account retailers. The data released to the clientwould be the 'Top five retailers' trade sector, which preserves dataconfidentiality for the individual retailers participating in the service.

In developed markets, for the main trade sectors, this information is obtaineddirectly from retailers in the form of tapes containing EPOS records of salesthrough all, or a sample of their outlets. Tapes typically contain data for salesweek-by-week although, in theory, data could be collected day-by-day orhour-by-hour.

Where EPOS data are not available, sales information has to be derivedthrough a retail audit. This process involves going into stores to count thestock within a store both on the shelf and in the stock room. Comparing thisinformation with the stock holding on the last visit, together with deliveries inthe time period, means that sales can be derived:

opening .. closingsales = k + dehvenes - kstoe stoe

Because a retail audit involves shop visits by trained auditors it is quite anexpensive procedure. For this reason most retail audits have collected andreported data on a bi-monthly basis. In comparison with more frequentscanner data, retail audit data are therefore more limited in their ability to trackthe effects of marketing actions such as advertising and promotions.

The issue of retail data confidentiality

Retailers have, in the past, been reluctant to provide details of their ownindividual sales for distribution to manufacturers or competitive retailers,concerned that they could thereby sacrifice competitive advantage. This is thereason they have insisted that research companies should only report pooleddata, at total market and trade sector level. Manufacturers have therefore had

to rely upon consumer panels to provide estimates of the shares of categorysales held by different retailers. This is vital information when manufacturersare negotiating with retailers to increase their share of scarce shelf space.

The agencies which analyse and supply retail data have long argued for moreopen data exchange, so that they can provide 'named account' or 'keyaccount' data which show the individual sales performance of different retailchains.

By the end of 1997 most retailers in the United States and in The Netherlandsand a minority of retailers in other European countries had agreed to therelease of their own, identified sales data in the marketplace. This trend islikely to continue to a point in the future where there will be total transparencyof data exchange between retailers and manufacturers, but the process maytake some years.


THE BAR-CODE

The allocation of bar-codes is supervised by the Article NumberingAssociation (ANA). The international standards for the EAN (EuropeanArticle Number) were developed as tools for improving business efficiencythrough:

o providing a system for identification of products, services, etc.o standard bar-codes to represent information which can easily be read by

scanners.

It is this product identifier which is the focal point for scanning-based retailmarket measurement studies.

The bar-code typically consists of thirteen digits, which are constructed in thefollowing manner:

50 12345 12345 7

The first two digits signify the country of origin.

The next five digits are a manufacturer's identifier which is allocated by theANA. The next five digits are the product code, which is assigned by themanufacturer. The final digit is a check digit, which is calculated by analgorithm applied to the previous numbers. This check digit ensures thecorrect reading of the bar-code at the point of sale. This is achieved by thescanning device reading the bar-code and re-calculating the check digit, whichis then compared with the check digit in the bar-code; if they do not matchthen the item will be rejected by the scanner. The process ensures full dataiptegrity.

Occasionally small products do not have the space for a thirteen digit barcode. In these instances an eight digit bar-code may be used. Due to thelimited number of these codes, they are issued directly by the ANA.

Although bar-codes are numbers, they are represented by a series of blacklines on a white background which can easily be read by scanning technology,as in Figure 2.

Figure 2

0"0'51111"12881"" 7

245


This convention for the allocation of bar-codes ensures that:

o bar-codes are unique

o the bar-codes are non-significant, i.e. it is the EAN which is the key toaccess the database

o they are consistent across countries, i.e. international

o they are read securely.

To give an indication of the scale of the data processing exercise, in the threeyears to 1997, IRI (in the United Kingdom alone) has processed in excess ofone million different bar-codes.

Bar-codes are allocated for each variant of a product. For example, a lemonflavoured product will have a different bar-code from the same product whichis orange flavoured. Even special offer packs (e.g. 10% extra free) will havean individual bar-code. This allocation system enables maximum productdetail to be available to data users, who can select the level of detail theyrequire in analysis.

Figure 3

Accuracy

Cost

Timing

Back data

Client reports

Delivery time

Outlet coverage

Product detail

Audit

Audits rely on manual counting ofstock and stores recording deliveries.As such they are subject to humanerror. They also measure storethroughput. Shrinkage (theft, damageetc.) is recorded as a sale.

As audits rely on personnel visitingstores to count stock, costs arerelatively high.

Due to the cost being linked to thefrequency of store visits, data aretypically available montWy or bimontWy.

Due to the costs, data are usuallycollected when a client commissions a

study, meaning that there are no backdata available for previouslyunreported categories.

Syndicated trend reports.

Typically four weeks after period end.

Cost constraints mean that sample dataare collected and projected to representa total retailer's sales.

Data may be collected at 'audit codelevel' to reduce the number of

individual items being counted, e.g.different scents of shampoo may becombined.

Scanning

Scanning data are a true representationof what has actually passed through aretailer checkout and therefore are themost accurate measurement of sales.

Sales data can be supplied directly bythe retailer thereby reducing the needfor store visits and therefore the costs.

Data are usually collected weekly. Dailydata are now also becoming available.

Data are stored on tape, therefore backdata are more easily accessible.

Customised reports driven by softwareapplications.

Ten to twenty days (may be shorter insome cases).

Census data (data for all stores) areavailable.

Data are collected at EAN level.

Retail market measurement-AUDIT AND SCANNING DATA

The primary purpose of retail sales tracking is to provide a reliablemeasurement of sales, covering all significant types of retail outlets. Scanninginformation is rarely available from smaller outlets, such as small shops,kiosks and market traders. Sales through such outlets may nevertheless besignificant for certain product categories. Retail audits are often the onlypractical way of collecting data from such outlets, which do not have scanningequipment.

A comparison of audit and scanning data, on key criteria from the user's pointof view, is made in Figure 3.

The availability of scanning data varies considerably by country. The chartbelow demonstrates the percentage of food sales which pass through scanningcheckouts, varying from the high 90s to as low as 10%.

Figure 4PERCENTAGE SCANNING PENETRATION

100

90

80

70

60

50

40

30

20

10

247

o

i ·~~f j j J ~ J ~ j.[~~~.~~

< (/) ~ ...• ~ ~

Source: ACNielsen estimates 1996

It was expected that the introduction of scanning would reduce the costs of theprovision of retail market measurement services. This has not been the casefor a number of reasons which are identified below:

o retailers' requirement for payment to access their data either in cash orinformation services (and in some cases both)

o the requirement to continue store visits to collect information on in-storemerchandising conditions (i.e. factors which influence sales)


o the requirement to continue audits in trade sectors and/or countries with lowscanning penetration

o the scanning has vastly increased the size of databases as a greater level ofproduct detail has become available

o the requirement for continual investment in information technology tosupport the advanced analysis of scanning data

o the maintenance of a product dictionary for an ever-changing universe ofproducts and product variants.

With the development of consistent bar-codes and more portable technology,it is becoming more common to replace paper-based in-store audits with handheld scanners. With this technology, the fieldworker scans the product andthen enters the volume of stock, together with the selling price. Historicallyaudits have been conducted at an item level (e.g. flavours may have beenmerged to reduce the number of items for which data were being collected)but audit data can now be collected and reported at the same level of productdetail as scanning data. Using the bar-code also ensures that audit andscanning data can be aligned perfectly in an integrated database.

STRUCTURING THE DATABASE

Retail market measurement is unlike many other forms of market research.The end product is not a research report but a database which is delivered toclients usually in electronic form. The database will typically be updated withnew data each reporting period (weekly, four weekly, monthly etc.).

This database is then used by clients on a continuous basis to monitor andinvestigate both their own and competitors' performances. Both Nielsen andIRI, the principal data suppliers, also offer proprietary software which theirclients can use to select and manipulate data from the database.

The databases are constructed with two primary purposes in mind:

o to monitor the trend of sales for different products, over time and in varioustrade sectors

o to investigate how various other measures of causal conditions in the storeinfluence sales; such measures can include distribution levels for theproduct, price level and the presence or absence of certain types ofpromotion.

It is sometimes helpful to think of the database as a cube in which each cell isa combination of specific:

o trade sectors, i.e. the combination of retailers

o time periods

o measures, e.g. unit sales, volume sales, rate of sale

o products, e.g. which products are being reported.


Figure 5

Product

Trade Sector

Time

Structuring a database in this fashion means that it is possible to cut the data indifferent ways to meet the analysis needs of the end user. Some examples ofhow the data cube may be 'cut' are shown below:

Figure 6

249

Financial Managers Product Managers Regional Managers Ad hoc

Given that the dimensions for structuring the database are a critical part of itsutility, the next sections will concentrate on how these dimensions are

,constructed, and how sales and distribution measures are defined.

Trade sectors

In defining the trade sectors to be monitored within a retail tracking study,there are three key considerations:

o client requirements for trade coverage and segmentation of the trade

o structure of the retail trade in the country

o willingness of the retailers to contribute their data within different levels oftrade sector aggregation.

There are likely to be several stages in defining the trade structure:

1. Client requirements

2. Defining the universe

3. Selecting the sample

4. Projecting the stores to represent the universe.


Client requirements

The requirements of clients in terms of trade coverage can vary significantly.For manufacturing clients, coverage of the outlets which distribute theirproducts is likely to be the critical factor. As a result of this, clients whomanufacture goods which are distributed through a wide network are likely torequire coverage which represents the total market. On the other hand, clientswho manufacture products for distribution through a network of specialistretailers are more likely to be satisfied with coverage of that specific tradesector. The relative importance of different trade sectors varies considerablyby product field and by country. Table 1 shows the relative importance ofdifferent trade channels for health and beauty products across a number ofdifferent countries (i.e. the percentage of sales each trade sector represents). Inpractice it is difficult even to develop a cornmon classification of retail outlets,some of which are specific to a few countries.

Table 1

Czech andUnited

SlovakFrance

KingdomGreecePolandTurkeyRepublics

Grocery stores

355970204025

Department

7755510

storesPerfumeries

31020104410

Drug stores /

3I

25 030030. chemists

Pharmacies

2263595

Other, e.g.kiosks and

23230220

speciality stores Source: IRI

In the above example the requirements for a health and beauty manufacturerwould be different across different countries, and this needs to be reflected inthe definition of the trade sectors used in the study.

Achieving the right segmentation within the trade structure is particularlyimportant as in many countries retailers have increased or are starting toincrease their presence in 'non core areas'. A good example of this is thehealth and beauty market in the United Kingdom. Over the past five years, thetraditional grocery chains have invested heavily in this area and havecontinually increased their market share at the expense of the traditional


chemists. A trade sector which combined grocery stores with chemists wouldnot have been able to monitor this change over time.

The requirement to define trade sectors tightly is sometimes at odds with theneeds of the retailers. A number of retailers are very protective of their dataconfidentiality and prefer them to be aggregated with data from a largenumber of retailers. This a situation which the research agencies need tomanage continually.

A further issue is the availability of data by trade sector; for example in theUnited Kingdom the grocery stores can be monitored through the provision ofscanning data whereas the drugstores/chemists require a combination of auditand scanning data to provide complete coverage of this trade sector.

Having identified the trade sectors of significance, the next stage is tostructure them in a manner which is logical for the client. This processtypically involves grouping like stores together and placing them in ahierarchy. An example for the health and beauty market is shown in Figure 7.

Figure 7

251

TOTAL ALL OUTLETS

GROCERS & DRUG STORES

CO·OPS IND. GROCERS & DRUG

Once the broad trade sectors to be covered have been identified, the next stepis to establish the universe.

Establishing the universe

Having established the trade sectors which the client wishes to measure, thenext key task is to define what the universe will be, i.e. which store types dowe aim to cover.

Sources of information to establish the relevant universe vary significantly andcan include:

o government statistics or census on the number of stores

o information sourced directly from the retailers

o original research on the universe structure.

Obtaining universe information on the centralised retailers which participate inretail market measurement services is relatively straightforward as they areable to provide universe details for their own stores, i.e. number of stores,sales per store. The difficulty arises in defining the universe for the lesssophisticated end of the retail trade. This is a particular issue in countries such


as the eastern European countries where market stalls and governmentcontrolled pavilions are significant outlet types.

Where information is not directly available from the retailers or governmentsources (even when it is, it can be less than reliable or out of date) the researchagency will need to carry out an establishment survey to determine the numberand size etc. of retail outlets which are in the universe for a given trade sector.The establishment survey for any retail measurement service is a significantpiece of work and underpins the accuracy of the service as a whole, because itwill be used as the basis for projecting from the research sample to the totalmarket estimate.

The establishment survey can be carried out by taking a random sample ofpostal districts in the country being measured. Researchers will then count thenumber of shops of each type within that postal sector. They will also collectinformation such as store size, products sold, number of checkouts andaverage sales. These characteristics can then be used to aid the selection of arepresentative sample.

This random sample of postal sectors can then be projected to represent thetotal country.

The key output of an establishment survey is the number of stores within eachtrades sector. An example is shown in Table 2.

Table 2

Trade sector

ScanUniverse sizeSource

Major multiples

100%2,900Retailer

Minor multiples

100%l,800Retailer

Co-ops

100%2,400Retailer

Independent grocers

9%33,200Survey

CTN's

10%26,800Survey

Multiple

20%4,370Survey / retailer

Independent

0%22,430Survey

Garages


Off-licences


All other impulse

0%57,500Survey

The Table also shows how the universe information was obtained, whetherdirectly from the participating retailers or from the establishment survey itself.


Selecting and projecting the sample

The sample of stores used to represent the universe will be dependent on thelevel of detail required for each trade sector. The sample needs to beconstructed for each individual trade sector for which data are to be reported.In the trade structure shown in the previous section, this would entailconstructing individual samples within each of the trade sectors such asmultiple chemists and major multiple grocers. The individual trade sectors canthen be combined in order to give an aggregate measurement of total marketperformance.

For trade sectors where there is 100% scanning and a high level of retail cooperation, it is possible and in many cases desirable to process census data, i.e.process data for all stores and not just for a sub-sample projected to representall stores. This has the advantage of being 100% accurate in terms ofmonitoring sales. Whilst this route may be possible it may not be pursued, dueto cost constraints either in terms of the fees levied by the retailers for the dataor the costs incurred in processing such high volumes of data (a chain of 500stores selling in excess of 20,000 lines represents a very large amount of datato be processed!).

Where census data are not being used, it is necessary to select a sample ofstores to represent the universe very accurately. The aim is clearly to select aprofile of stores which matches the profile of the universe. It is then arelatively straightforward process to project the sample stores to represent thetotal universe.

The first stage in defining the sample is to establish which criteria are to beused to balance the sample. These criteria will be factors which influence thesales within a given store and may include:

'0 store size

o location

o sales volume

o presence of certain departments (e.g. fresh foods, pharmacy)

o age of store.

The larger the number of factors used to stratify the sample, the higher theaccuracy which can be obtained in projecting to the universe. A highernumber of stratification factors will also, however, necessitate a larger sampleSIze.

Once the sample has been identified, the sample can (using the relationshipbetween the sample and the universe) be projected to represent the totaluniverse. Where available this relationship can be established based on actualsales. Where it is not available it can be based on the number of stores.

253


In its most simple form, the relationship between the total sample and universewill be applied to each individual store. For example if the universe sales are$1,000 million and the sales at the sample stores are $120 million, then thesales of the total sample will be multiplied by 8.3 ($1,000/$120).

In the above scenario each individual store in the sample is given the sameweighting. It is however possible to assign different weightings to differentstores in the sample. This technique is more likely to be used in situationswhere it is difficult to select the sample with a profile identical to that of theumverse.

Time periods

The time periods reported within a study are driven by the requirements of theclient, together with the data sources which are being used.

Scanning data are typically collected and processed on a weekly basis,therefore it is feasible (and most commonly done) to report weekly data on afour weekly basis (i.e. every four weeks, four individual weeks of sales will bedelivered). This enables the client to align the data more closely with theactivity in the marketplace or any other conditions which may impact sales. Inaddition weekly databases also allow the client to align the data to timeperiods which they may use internally for reporting data. Whilst it is possibleto deliver weekly data each week, this is not yet common practice due to thecosts involved in such delivery.

Where audit data are used to represent a trade sector it is very unusual for datato be reported weekly. Since auditing costs are a major issue, it is typical toreport audited trade sectors on a four weekly, monthly or bi-monthly basis.

Where trade sectors combine audit and scanning data to provide broader tradesector coverage, data will usually be reported at the lowest possibledenominator, i.e. if weekly scanning data are being combined with fourweekly audit data, the trade sector will typically be reported four-weekly. Thisis illustrated in Figure 8.

In some cases where the less frequent audit data represent a small proportionof the data which are to be combined, the audit data can be weighted and splitinto weekly blocks weighted to the profile of sales shown in the weeklyscanning data. This can only be done when:

1. the audit data are a small proportion of the total

2. the audit stores are similar in profile to the scanning stores.

It should be stressed that this is not an alternative to scanning data but is amethod for integrating relatively small volumes of audit data into scanningdata.


Figure 8

255

Products

Trade sector' A'

Week]

Week 8

+

Trade sector 'B'

4 week

period

4 week

period

Combined trade sector

4 week

period

4 week

period

A significant element in structuring a retail market measurement database ishow the products are organised within the database. A typical database for oneproduct category can have in excess of 5,000 product codes. Failure toorganise the products effectively will result in a database that is not of use tothe client - imagine trying to find a single product code in a list of 5,000 items!

The structure of the product dimension of the database will be dependent onthe individual market and the client. The first stage in organising the databaseis to define the attributes which will be used to segment the data. Typicallythese attributes are the product characteristics that the consumer uses in apurchasing decision (or how the client segments the market for marketingpurposes).

Examples of product attributes are:

o pack type, e.g. bottle, can or jar

o fragrance, e.g. lemon, strawberryo size, e.g. 200 ml, 300 ml.

In addition to the product characteristics the brand and manufacturer areusually treated as attributes to help in structuring the database. The attributescan then be used to construct a product hierarchy that can in turn be used tostructure the order of the products in the database. An example producthierarchy for shampoo is shown in Figure 9.

In some cases it is necessary to group different attribute values together inorder to prevent the database becoming unwieldy, and to enable the user toemolov the attrihlltes to strncture the analysis An examnle is :lir frp."hpnpr"


with different scents such as lavender, rose and honeysuckle, all being definedas 'floral', thereby removing unnecessary detail.

Figure 9AN EXAMPLE PRODUCT HIERARCHY FOR SHAMPOO

Total category - shampoo

Type e.g. medicated versus non-medicated

Supplier e.g. Henkel, Procter and Gamble

Brand e.g. Timotei, Wash and Go

Hair Type e.g. permed, greasy, normal

Size e.g. 200 - 250 ml, 251 - 350 ml

Bar-Code

The products are then sorted according to the product hierarchy allowingstructured analysis of the data. For example, as well as examiningperformance of specific lines or products, it is also easy to analyse theperformance of total sectors; for example how fast are 2-in-l shampoosgrowing versus standard shampoos. This prevents the user having to sortthrough individual lines and allocate them to different sectors. An example ofhow this may be done is shown for the stir fry sauces market (Figure 10).

Figure 10

100%

90%

80%

70%

60%

50%

40%

30%

10%

0%

D Other Sauce

III Stir Fry

IIlITex·Mex

lIT] Traditional

DOriental

• Indian

• Italian


Collection of attributes

By necessity, attributes are usually collected in the stores involved in thestudy. A field worker will identify the product that needs coding and thenrecord all the attributes for that individual product. In order to ensureconsistency, all attributes must be observable and tangible. Subjectiveattributes, e.g. premium versus standard, are not typically used as this requiresjudgement by the field workers and may make allocation inconsistent.At the initial database construction, all products will need coding and this canbe a large task. Coding of products which are no longer being produced needssome judgement to be exercised by a market expert. Recent developmentsinvolve field workers video-recording products and coding attributes from thevideo image. This helps to ensure consistent attribute coding, but also meansthat databases can be re-coded if a new attribute emerges as being significantin the future.

As new products are launched, they will also need to be incorporated into thedatabase. Usually the first the research agency knows about a new product iswhen it appears in the data which are supplied by/collected from the retailer.Speed is of the essence in collecting the attributes in order for the product tobe incorporated and accurately reported within the database.

Given the fact that it takes time to collect the attributes, products may notimmediately appear in the database. For this reason it is usual each period tore-process the previous period's data, to ensure that new product sales arefully included in the database. Clearly the objective of the agency is tointroduce the product as quickly as is possible in order to minimise these datachanges with respect to the previous period. The client can assist by providingexample products before they are launched. This ensures that the product isplaced in the database prior to it appearing in the shops.

Measures

To some extent, data measures are the most important part of a retail marketmeasurement study, as these are the data facts which are being reported for thecombination of a specified time period, trade sector and product.

The availability of measures varies considerably with the source of data beingused. A scanning database can potentially have in excess of 160 measures.This coupled with weekly data for 5,000 products can generate a huge volumeof data for the client. The key to data utility is in distilling the measures whichare of value to the client to ensure that they are not swamped.The core measures which form the basis for all other measures can be defined

in the following groups:1. sales

2. distribution / rate of sale

3. price

4. promotional incidence and response.

257


Each of these measures is taken in turn in the following sections.

Sales measures

Typically, three types of sales will be used:

o unit sales: the number of packs sold

o value sales: the value (local currency) of products sold

o volume sales: expressed in volume equivalency, e.g. kilograms, litres etc.(value sales will typically be reported net of any promotional discount).

Sales measures are the basis of calculation for many of the share measureswhich are used within a study. The most commonly used share measure whichallows a manufacturer to track his relative performance within the market is'category sales share'. This can be defined as:

Sales of product(s)Category sales share = --------x 100%

Sales of total category

A manufacturer may choose to use this measure with an individual line, brandor total supplier. Similarly, retailers may use this type of measure to monitortheir share of the market by dividing their sales with the sales of the totalmarket.

Distribution measures

Distribution is the reach a product has within a given trade sector and can bederived in one of two ways:

o numerically

o weighted by sales.

Numerically

Numeric distribution refers directly to the percentage of stores carrying aproduct. For example in a universe of 2,000 stores, if a product is listed in 620stores its numeric distribution would be 31%.

Given that not all stores are the same size (for example, compare a smallconvenience store with a hypermarket) the client may wish to weight thedistribution according to the relative size of the different stores, i.e. reflectingthe importance of different stores in calculating the product distribution.

The stores may be weighted according to sales of all products stocked (AllCommodity Volume: ACV). Distribution is then calculated by adding theACV of all stores which sold the product during the period and dividing thatby the total ACV of the trade sector which is being analysed, i.e.:

Total ACV of all stores which sold the productACV weighted distribution = ---------------- x 100%

Total ACV of all stores in the trade sector


This is illustrated with the example in Table 3 (yes/no indicates if that

particular store sold that product in that week).Table 3

Store IStore 2Store 3Store 4

Annual value sales (acv)

BOrn£20m£ISm£3Sm

WeekI

YesYesYesNo

Week 2

YesYesYesNo

Week 3

NoNoYesNo

Week 4

YesNoNoNo

Four-week period

YesYesYesNo

30+20+1S

ACV weighted distribution = -3-0-+-2-0-+-1-S-+-3-S)<100% = 65%

259

However, All Commodity Volume is not always the most appropriate way inwhich to analyse weighted distribution. In cases where a trade sector containsa mix of store types, so that the ACV s represent completely different productmixes, e.g. where chemist chains are reported with grocery stores forshampoo, it may be appropriate to weight the distribution according tocategory sales. In this scenario, exactly the same formula and methodology isused as above, but ACV sales are replaced with the sales of the total category,e.g. total shampoo sales.

When using the distribution measure, care should be taken over the time'periods and products selected. The lower the level of detail used in definingthe product and time period dimension, the more validity the distributionmeasure will have. For example, using the distribution measure for a totalcategory is likely to result in a high distribution, in many cases 100%.Similarly, using distribution over a long time period increases the likelihoodthat a store will have sold a given product and therefore increase its level ofdistribution.

Weighted rate of sale

Distribution measures are often combined with sales measures to give anassessment of how well a product sells in a store where it is stocked. Themeasure, weighted rate of sale, calculates the average sales in an average sizedstore. This measure takes out the effect of a product being distributed acrossdifferent store types and calculates sales for a particular product in an averagesized store:


Sales for product

Weighted rate of sale = Distribution * x Total number of stores

*either ACV weighted or category weighted distribution depending on the data base.

To illustrate the calculation, consider the following example, which shows thevalue sales for Product A in each of five stores during each week in a fourweek period:

Table 4

Store 1

Store 2Store 3Store 4Store 5

Annual value sales (acv)

£30m£40m£ISm£ISm£2Sm

Category value sales

£ 10k£ISk£Sk£1Ok

Week 1

£SOO£400£4S0

Week 2

£200£300£2S0

Week 3

£100

Week 4

£200-Four- week period£900£800£700

Total ACV of all stores which sold the product 00ACV weighted distribution = ------------------ x 1 %

Total ACV of all stores in the trade sector

30+1S+]Sx 100% =48%

30 + 40 + IS + ] S + 2S

This means that an average sized store selling Product A had sales of £1,000of the product in the four-week period.

Rate of sale measures are used most commonly in understanding the relativeperformance of products in stores where they are stocked. This can help theidentification of products with high potential as well as those which areperforming poorly. Using rate of sale in conjunction with distribution canenable the client to identify high potential products, i.e. those with a high rateof sale and low distribution, and potential products for de-listing by a retailer(those with high distribution and low rate of sale).

Price measures (see also Chapter 20).

As sales for the stores contained within the sample are a combination of salesfrom different stores, the price which is reported from a retail market


measurement survey will be an average price. An average price can bereported per unit or per volume (e.g. per litre).

The price per unit is simply the value sales divided by the unit sales andreflects the price paid by the customer. It is at its most useful when reported ata product line level, as it can be ntisleading if reported at a higher level. Forexample the introduction of a small pack size within a brand can cause theaverage price per unit of the brand to fall, but it does not mean that the brandhas reduced its price.

Further pricing measures are also available as part of the promotional elementsof retail market measurement which are covered in the next section.Promotional measures are of such significance that they will be covered in anindependent section.

PROMOTIONAL ANALYSES

The aim of the promotional element of retail market measurement is to link theincidence of in-store causal activity (i.e. in-store promotions) with the salesachieved. This enables two key measures to be derived:

o incidence of promotional activity (i.e. reach and depth of activity)

o response to promotional activity (i.e. the incremental sales generated).

Due to the fact that weekly store level, EAN level data are required forpromotional analysis (as will be explained later) this form of analysis isrestricted to trade sectors for which there are scanning data.

Methodology

Causal data are collected at weekly level by individual bar-code from storeswhich are within the scanning universe. These causal data are then linked tothe sales data in order that the sales can be split into base and incrementalsales. Base sales are those which would have been expected withoutpromotional support, whilst incremental sales are those which can beattributed to in-store promotions.

Causal data are typically collected by a fieldworker going into a sample ofstores and scanning the products which are on promotion with a hand-heldscanner. The details of the promotion are then keyed into the scanner. Thisensures that the promotion details are collected at the EAN level and cantherefore be directly matched to the sales data provided by the retailer.

Figure 11 shows the sales for an individual bar-code and the incidence ofcausal data for the same bar-code.

261


Figure 11

600

500

400300

200

100o ."""" 1'1'1'" I'" I" ,"",'" 1'1'1'1"""

1- Actual sales • Promoted weeks

The next stage is to use an algorithm to calculate the 'base sales'. One methodis to take the store level data and create an exponentially smoothed curve ofsales during weeks without promotion, and then to apply this to promoted andnon-promoted store weeks to generate total base sales. Incremental sales canthen be calculated as the difference between total sales and base sales. Since

incremental sales are the sales attributed to promotional support in weeks withno promotion, base sales are equal to actual sales. An example of base andincremental sales is illustrated in Figure 12.

Figure 12

600

500400300

200

1001~ "o II II II II II II II II I'll II II II I' IIII I' II II I' II

- Actual Sales • Promoted Weeks ••• Base Sales

The key thing to note is that for a base sales calculation to be effective it needsto be conducted at both the bar-code and store level and then aggregated to thetotal market.

Causal conditions

Experience suggests that the impact of different types of promotions variessignificantly. In order to appreciate the impact of and to quantify the volumeof promotions, different promotions are classified into different groups,although all would trigger the base line calculation.


Causal conditions to be collected and monitored will be dependent on thosewhich are evident in the country or market which is the subject of the study.Example conditions may be:

o price reduction

o displayo multi-offer

o loyalty card points given with product

o print advertisements.

The purpose of classifying causal conditions separately is to allowpromotional incidence and response to be monitored for individualpromotional mechanics.

As the causal data are collected in the store this ensures that what is reported isin fact what was on offer to the customer. Whilst it is possible to base-lineusing promotional programmes provided by a retailer, these are not always inreality implemented in the store.

Reporting

As outlined, the two key measures of a promotional study are:

o depth and reach of promotion, increase in sales due to a promotion.

Depth and reach of promotions

In order to establish the reach of a promotion it is normal to look at thepercentage of non-promoted sales which were sold with the given promotion.This is illustrated in the equation and example below:

, Base volume sales for promotion% of base volume sales for a promotion = ------------ x 100%

Total base volume sales

Consider the volume sales for Product A that were due to print ad, no display:

263

Week 1 Week 2 Week 3 Week 4 Quad Wk

Total base volume

Base volume sales with display

33 g

10 g

36 g

llg

25 g

8g

26 g

9g

120 g

38 g

38% of base volume sales with display = - x 100% = 31.7%

120

The use of this measure allows the client to understand the extent to which

brands or products are promoted compared with competitor products withinthe marketplace.


Increase in sales due to promotion

This measure identifies the percentage sales increase which was due to thepromotional condition being considered. A key factor in calculating theincrease is that the calculation should only be done using the promoted storesas a base. This allows the true incremental effect of the promotion to beidentified. The calculation can be defined as:

Incremental volume + week after incremental volume% increase in volume sales = x 100%

Base volume sales in stores promoting

Consider the volume sales for Product A that were due to any deal:

Week 1

Week 2Week 3Week 4QuadWk

Base volume

33 g36 g25 g26 g120 g

Incremental volume

5g6g4g3g18 g

18% increase in volume sales with any promotion = -x 100% = 15%120

In the above equation, the week-after effect has been included in theincremental sales. Where it is thought that the effect of a promotion may lastlonger than the time when it was in evidence e.g. print ads, the incrementalsales of the following week of the promotion may be included.

Figure 13VOLUME OF PROMOTION VERSUS SALES UPLIFT

% Volume increase

100 -, ----------------------l(ll-I-ta-Ii-a-n-------,90

80

70

o Stir Fry

oIndian

°Oriental

.• 0Traditional

60

50 o Tex-Mex

50454035302520

40 I I15

% Volume sold on promotion


The use of this measure allows the client to understand what is the most

effective promotional mechanic for a brand or product and which products orbrands respond best to promotions. This coupled with information on the levelof promotion helps the client to understand which products it may be mostappropriate to promote more or less in the future. This concept is illustrated inFigure 13.

The arrows demonstrate products for which the client could either considerincreasing or decreasing the level of promotional support, i.e. stir fry productsgenerate good response when promoted but receive a relatively low level ofpromotional support.

DISAGGREGATE USE OF SCANNING DATA

The previous sections of this chapter have focused on how retail marketmeasurement studies are constructed through aggregating data provided fromindividual retailers, i.e. sales are combined at an EAN level across all storeswithin a trade sector so that total sales are reported for an individual productline. The use of disaggregate level data focuses on using store level data at theEAN level to perform more sophisticated analyses.

The purpose of using disaggregate level data is to increase the number of salesobservations which can be used, thus increasing the ability to build a causeand effect relationship between sales achieved and the factors affecting theme.g. price, promotions and advertising.

Taking a single product with two years of weekly data, there will be 104 salesobservations (fifty-two observations per year). Using store level data canincrease the number of sales observations to the tens of thousands and

therefore means that the ability to conduct sophisticated analyses is greatlyincreased.

As well as increasing the number of observations, using store level dataensures that averages are not used. Table 5 illustrates why the use ofdisaggregate analysis can be important.

Table 5

Week one

Week two

Price

SalesPriceSales

Store 1

£2.999£2.9915

Store 2

£2.883£2.88 4

Store 3

£2.8212£2.8213

Store 4

£2.2417£2.2414

Total market

£2.5941£2.7046

265


U sing the total market data this table would suggest that as the price hasincreased from £2.59 to £2.70, sales have gone up from 41 to 46 units - anideal outcome! The reality is, however, that the sales in stores charging thehighest price have increased. So although no stores changed the selling price,the total market data would imply that there has been an increase in price. Afurther key issue is that because the market price is an average, it is not a pricewhich was ever paid by a customer. This is why disaggregate level data arevaluable in planning and evaluating the impact of different marketinginitiatives.

Multiple regression techniques can be used to isolate the impact of sales ofdifferent marketing conditions. Often the output of these models can be builtinto software packages which allow the end user to play out 'what if'scenarios. This provides ultimate utility for clients, allowing them to assess theimpact of changing an element of the marketing mix without incurring the costof doing so.

In addition to regression analyses, it is also possible to group stores accordingto different characteristics to understand the impact different conditions haveon sales performance. These analyses are often referred to as store groupanalyses and together with regression analyses will be discussed further in thissection.

Regression analyses

Sales of a brand are influenced by a variety of different factors - price,promotions, TV advertising, competitor activity, etc. The impact of each factorwill differ in magnitude.

Using store level data, which enable the measurement of the within storechange in sales against the within store change in price/marketing condition, itis possible accurately to quantify the impact of each variable on brand salesvia a custom multiple regression model.

Sales = f (base price + promotional price + in store materials + print ads +displays + multi-buys + link saves + special packs + television advertising+ consumer promotion + seasonality + etc.)

Store level data are not only the most accurate sales data available, they alsoavoid having to use averages such as 'average price'. The issues associatedwith using 'average price' were outlined in the previous section.

Most importantly, store level data have an abundant supply of salesobservations. One brand will typically have well in excess of 30,000 robustsales observations for inclusion within any model, allowing the differentmarketing variables to be very accurately isolated and their impact on salesquantified.


Following construction of the model, simulations can be run to identify theimpact on sales of a change in the marketing mix - a reduction in the presenceof special pack (e.g. a pack with 10% extra free), for example.

Dedicated studies to understand the contribution of everyday price,advertising or promotions, using the same techniques as above, are describedbelow:

Base price elasticity

One of the most important issues in marketing is understanding how salesrespond to changes in everyday (base) price. To understand this, an analysis ofbase price elasticity can be conducted assessing:

o How sensitive are a product's base sales to a change in base price?

o How sensitive are a product's base sales to changes in the competitor's baseprice?

The answers to the above questions can help manufacturers better manageeveryday price. This analysis is useful for strategic marketing decisions.Specifically, a manufacturer can use this analysis to evaluate historical salesresponse to changes in price and use this to measure what may result fromfuture price changes.

The base price elasticity (degree of sensitivity) of a product is determinedthrough a store-level custom multiple regression model. The regression modelexamines within-store changes in base sales as a function of within-storechanges in base price.

The output of the regression model can be built into a software package,which will allow the client to play out 'what if' scenarios.

Promotional price elasticity

Consumers respond differently to everyday (long-term) price changes thanthey do to promotional (short-term) price changes. It is important tounderstand these two elements of pricing: everyday and promotional.

The base price elasticity analysis addresses the long-term price elasticity of theproduct, while promotional analysis will explicitly address the short-termprice elasticity of the product. Typically, most clients wish to have bothelasticities quantified for their products when issues regarding price need to beaddressed.

Similarly to the base price elasticity, the promotional price elasticity (degreeof sensitivity) of a product is determined through a custom multiple regressionmodel built using store-level sales data. The regression model examines theincrease in sales associated with a temporary reduction in price only, or atemporary price reduction accompanied by other trade conditions (e.g. in-storematerials, display, etc.).

267


An example of this form of analysis is shown in Figure 14 which demonstratesthe increase in sales which are achieved through different levels of pricediscount in conjunction with other promotional initiatives.

Figure 14

600

Sales

500

40030020010000

5 10 15 20 25 30 35 40 45 50

% Reduction in price

TPR = Temporary price reduction

Effectiveness of TV advertising on sales

The premise behind reading the effects of television advertising is that salesover time are a function of marketing stimuli over time. In order to determinethe element that television advertising contributes to sales, a store level custommultiple regression model is utilised.

The analysis will, typically, enable the following questions to be answered:

o What contribution to sales did television advertising make?

o What would sales have been, had the television advertising never takenplace?

o Are our television advertising campaigns more effective this year than lastyear?

o Are there certain regions of the country which are more responsive to ourtelevision advertising than others?

Knowing the answers to the above will provide extremely valuableinformation. By precisely understanding the impact of television advertising,manufacturers are equipped with the knowledge to make more effectivedecisions with respect to future television advertising spending.

Store group analyses

Store group analyses have many different applications. The underlying feature


of these analyses is the division of the sample into sub-sets, e.g.:

those stores featuring a certain conditionvs.

those stores where the condition is absent.

An example of a store group analysis is comparing the rate of sales for storeswith different price points for a given product. An example of this is groupingthe stores by the price charged for a given product. In Figure 15 the averagesales per store week are shown for each of the price points, together with thefrequency with which the price point was evidenced. This helps to identifycritical price thresholds for a given product.

In this example several price thresholds can be seen, at which sales dropsignificantly when the price is increased. This helps the client in positioningthe price of a given product.

Figure 15

269

300

250

200

150

100

Average base salesper store week % Frequency

- Product A

• % Frequency I 25

20

15

10

50

o o

Base price

Optimal mix analysis

The mix of individual lines that can be found on shelf is an important elementin maximising the overall strength of the business for a brand. For example, amanufacturer may produce eleven variants of a given brand. However, not allretailers will carry the entire range. So understanding the mix of these itemswill assist the manufacturer in answering the following questions:

o How do my sales respond to an increasing number of items carried?

o Does a common pattern exist, where the same 'core' items should always becarried?


o What is the optimal mix of products when seven items are carried?

In-store custom audits

Observable data are collected, at bar-code level, by the fieldforce using handheld scanners (typically at the same time as collecting causal data outlined inthe previous section). This can be matched to sales data for the same group ofstores in order to provide insight into the impact of the in-store positioning ofa brand on sales.

The information can be used in the following ways:

o Determine the number of facings for a selection of chosen products.

o Ascertain the position on shelf of a product or range of products.

o Identify if a product is under or over-faced given the share of shelf spaceallocated versus brand share.

o Assess if a product performs better when it is located above, below oradjacent to a complementary or competitor product.

o Determine how a product performs in stores where there is one facingversus in stores where there are a greater number of facings.

RETAIL MARKET MEASUREMENT: COMMERCIAL REALITY

The previous sections of this chapter have demonstrated that the transition ofretail market measurement from audit to scanning based technology has led toa 'sea change' in the services available. This in turn has dramatically changedthe context in which these services are used.

The primary use of these services is at the retailer and manufacturer interface.The retailer is the custodian of shelf space and hence the sales opportunity formanufacturers. As competition between brands increases the battle for shelfspace becomes ever more critical. In addition these brands are also competingwith the retailer's own products. This fierce competition, together with a muchenhanced service base, has driven the concept of data-driven marketing. In anenvironment in which marketing decisions are made on fact-based data it iscritical that retailers and manufacturers truly understand the factors which aredriving sales. In reality retail market measurement has changed radically froman industry concerned with tracking and monitoring sales to one which isconcerned with understanding the factors which drive sales.

The examples of the services provided in previous sections allow themanufacturer and retailer to manage the key areas of:

o pncmg

o rangmg

o promotional planning

o new product introductions.


The above have been identified as four of the key areas for focus withincategory management and efficient consumer response initiatives by bothretailers and manufacturers alike.

The advent of category management over recent years has put greater focus onusing fact-based information to tackle some of the issues highlighted below:

Pricing

a What is the significance of different price points to consumers?

a What is the optimum price gap between brand X and brand Y?

a How will sales respond to a 10% increase in the base price?

a Will a drop in price of 10% generate sufficient sales to cover costs?

Ranging

a Identify items which are performing well in the market but not stocked by aparticular retailer.

a Identify high growth areas with the potential for own label development.

a Assess which categories over or under perform compared with the norm.

a Identify which categories offer the greatest potential for growth.a Identify items with a high rate of sale with a low distribution and vice versa

to determine which items could be substituted.

a Determine optimum product mix to generate the highest rate of sale for thetotal category.

Promotional planning

a Establish which products respond best to promotions.

a Identify the most effective promotional mechanic for an individual product(e.g. is' a gondola end more effective than a 10% price reduction)?

a Assess the profitability of any given promotion.

a Forecast likely stock requirements for an up-coming promotion.

a Forecast impact of different levels of promotional price.

o Identify the impact at the category level of promoting an individual productor line.

New product introduction

o Weekly sales data, by store, allow the impact of sales of new items to bemonitored.

o Identify high growth areas with the potential for own label development.

As can be seen, the answers to the issues raised represent a great opportunityboth for manufacturers and retailers alike, in more efficiently managing theirrespective businesses. The reality is that rather than being a research

271


mechanism, continuous data have now become an integral part of businessmanagement.

The analyses only really scratch the surface in the power of retail-based data.The next stage is sure to be an even greater exploitation of store-by-store datawhich allows decisions to be taken, not just at a total chain level but at anindividual store level. This will allow both retailers and manufacturers fully toexploit the wealth of information by tailoring their marketing programmes tothe individual store environment.

There is, however, a sting in the tail. These types of services are only possiblewhere retailers agree to participate. They have their greatest value the moretransparent the data in the marketplace becomes. At its most transparent, aretailer will make its own sales available to a manufacturer in order that themanufacturer can invest in developing both its own and the retailer's business.This is an increasing trend within the data market.

The thirst for information is not limited to retail-based data. Given the issues

which data are now being used to address, integrating retail data with otherdata sources, e.g. internal data, geodemographics, profit information andadvertising spend, is critical in order for clients to be able to take informeddecisions.

ACKNOWLEDGEMENTS

The author appreciates the contributions of Mario Lesser, AC Nielsen; and fromPeter Buckley, Mike Campbell, Bruce Dove, Mark Dye, of IRI InfoScan.

Documents

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