46 Admap • February 2009 © World Advertising Research Center 2009

PRICE IS OFTEN overlooked as oneof the least interesting aspects ofmarketing. But as Robin C John-

ston said in her article, Making Sure thePrice is Right: “Price, one of the marketingmix’s ‘Four Ps’, is an often-misunderstoodweapon in the marketer’s arsenal.

“Too often, small- and medium-sizedenterprises (SMEs) rely on Price to carrythe weight for its ‘little brothers’ – Prod-uct, Place and Promotion – in the firm’smarketing battles.

“This is because of many firms’ mis-conception that they have little choicebut to make sure their prices are com-parable to those of the competition.” (Seewww.evancarmichael.com.)

It is a given that too high a price ren-ders products unaffordable. However, lowprices reduce profit margins and lead to aperception of poor quality. Consumersbuy products for their value, not necessar-ily for their low price. Because mostorganisations continue to manage pricingin a vacuum – without due considerationof the other three ‘Ps’, they risk lost profitsas well as long-term damage to theirbrand image. Given that pricing strategy

is a risk, what can be done? The answerrests with having a risk managementstrategy for pricing research.

Pricing research offers a variety ofquantitative methods to measure the‘right’ price. The most effective quantit-ative approach addresses a range of re-search problems, such as customers’ will-ingness to pay, price sensitivity and per-ception of value. As with anything else,the benefits of a quantitative approachmust be balanced against its costs; pricingresearch should be used as a supplementto, not in place of, other types of strategicresearch and brand positioning.

But pricing research must be doneright. This article examines five commonpricing strategies and how to most effect-ively take advantage of them.

Gabor-Granger/price wheelGabor-Granger pricing research is namedafter the economists who invented it inthe 1960s. Customers are surveyed to seewhether they would buy a product at aparticular price. The price is varied until itreaches the level where customers saythey would not buy the product, resultingin the optimal price for each person.

A variation of the Gabor-Granger isoften referred to as the price wheel. Con-sumers are given a starting point, either atthe top or bottom of an array of a set ofprices, and asked whether they would buy

the product. When beginning at the bot-tom half, the researcher records when therespondent indicates that the price hasrisen too high. When the price begins atthe top end of the scale, the research noteswhen they say ‘yes’. What results is a pric-ing curve (Figure 1).

The weaknesses of the Gabor-Grangerapproach are that consumers may under-state, or overstate, the price they are will-ing to pay. The phrasing of the question,“Would you buy?”, may be taken out ofcontext because the consumer is furnish-ing the answer independent of other con-siderations, and Gabor-Granger is onlyuseful for a product in isolation, withoutconsideration of competitive products ormarket position.

Price elasticityPrice elasticity of demand provides, whenused with Gabor-Granger, a single num-ber that summarises price sensitivity, par-ticularly between different customer seg-ments. In Figure 1, the average elasticitybetween Tweens and Soccer Moms isshown at the top, and elasticity is cal-culated below.

For example, if, in response to a 10%fall in the price of a service, the quantitydemanded increases by 20%, the priceelasticity of demand would be:

20% /(–10%) = –2The average elasticity of demand for

snack food items is the mean change frompoint to point. In general, a fall in theprice of a service is expected to increasethe quantity demanded. The larger theabsolute number (generally negative), themore price-sensitive is the item.

So if a snack food has a price elasticityfor a particular item of, say, –2.67, thatmeans percentage demand falls roughly2.7 times faster than the percentage priceincrease. Looking at Figure 1, we see that Tweens are more sensitive to theprice of the snack food than Soccer Moms– probably because they have less extrachange to spend.

Van Westendorp The Van Westendorp method, introducedin the 1970s by Dutch economist PeterVan Westendorp, is a slightly more

Setting the correct price for a product is essential, but which sort of analysis shouldyou use? Michael Lieberman, Multivariate Solutions, explains the tools available

Use the correct set of tools tomake sure the price is right

productpricing

Average elasticityTotal respondents –2.67Tween lunch boxers –3.60Soccer mom snackers –2.18

9080706050403020100

FIGURE 1

Gabor-Granger price wheel – cupcakes

$2.09$2.05$1.99$1.95$1.89$1.85$1.79$1.69$1.59$1.55$1.49$1.45$1.39$1.35$1.29

Total sample

Snake cake consumer groups

Tween lunchboxers

Soccer momsnackers

How important is price in a buying decision?

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February 2009 • Admap 47© World Advertising Research Center 2009

sophisticated approach to pricing. Thismethod is often used in conjunction withthe Revenue Forecast Extension to ascer-tain the optimal price.

The Van Westendorp method asks foursurvey questions:1 At what price would you think this

product is good value?2 At what price would you think the

product is getting expensive?3 At what price is the product so inex-

pensive that you doubt its quality?4 What price would you think is too ex-

pensive for you to consider buying theproduct?Van Westendorp yields the following

price definitions:● PMC: Point of Marginal Cheapness

Price point where more sales would belost because quality is questionablethan would be gained from ‘bargainhunters’.

● PME: Point of Marginal ExpansivenessPrice point above which cost is a seri-ous concern, where it is felt that theproduct is too expensive for the valuederived from it.

● OPP: Optimum Price PointPoint at which the same percentage ofcustomers feel the product is too ex-pensive as those who feel it is so lowthat the quality is questionable.

● IDP: Indifference Price PointPoint at which the same percentage ofcustomers feel that the product is get-ting too expensive as those who feel itis at a bargain price. This is the point atwhich most customers are indifferentto the price.

● RAI: Range of Acceptable PricingThe difference between the PMC and the PME.The results are often shown as depicted

in Figure 2.

Revenue Forecast Extension (RFE)This useful extension of the Van Westen-dorp adds the following two questions (in italics below).● At what price would you think this

product is good value?● How likely are you to purchase the

product at this price?● At what price would you think the

price for the product is gettingexpensive?

● How likely are you to purchase the productat this price?Using the purchase intent results of

these two questions to calculate pen-etration of the product at various pricepoints (by dividing the number of peoplewho expressed interest at each level bythe sample size), we can calculate revenueper 100 customers by multiplying priceby penetration by 100.

A display of the RFE chart is shown inFigure 3 with an overlay of Van Westen-dorp price points.

Despite the Van Westendorp termin-ology, the Optimal Price Point is notalways at the top of the revenue curve.Market penetration and revenue tend to

fall sharply above the Point of MarginalExpansiveness (PME); the PME is usuallythe recommended price point.

Conjoint AnalysisConjoint Analysis is particularly useful inshaping new products, determining max-imum levels of product enhancementand predicting market share. It providesdata for determining whether to add feat-ures, as opposed to lowering prices.

Conjoint Analysis is a revolving con-cept test. Respondents are asked to rankvarious product qualities, followed by aseries of product purchase interest ques-tions. Running the data gives utilityscores for models that allow the research-er to simulate the market in detail.

Within the price toolbox, ConjointAnalysis can be used to test various pricesof a concept as a trade-off against otherproduct features. The model is construct-ed to simulate a large amount of potentialscenarios while gathering information ononly a few. Below is an example of a con-joint scenario:

“On a one-to-five point scale, how likelyare you to purchase this olive oil with the following features?”● Premium, select quality olive oil● Full-bodied olive oil, adding a rich layer

of flavour● Contributes to a healthy

cholesterol ratio● Select, high quality olive oils from

California● 16 oz – $5.89.

Then the same respondent is asked torate another scenario:

“On a one-to-five point scale, how likely areyou to purchase this olive oil with the followingfeatures?”● Extra light olive oil by adding virgin olive

oil to other oils● Extra light olive oil with a light flavourful

touch● Contains antioxidants● Select, high quality olive oils

from California● 16 oz – $4.89.

And so on. A respondent might seenine to 15 conjoint measures.

Conjoint Analysis allows us to examinethe relative importance of price against

Michael Lieberman is founder andpresident of Multivariate Solutions,

a statistical and market researchconsulting company that works with

major advertising, public relationsand political strategy firms.

michael@mvsolution.com

FIGURE 3

Revenue forecast extensionRevenue per 100 customers (right axis)

190

19 Marginalcheapness

Optimumpricepoint

Indifferencepricepoint

Point ofmarginalexpansiveness

119

9

1821 23

315

460 450

330 315

240

0

10

20

30

40

50

$40$35$30$25$20$15$10050100150200250300350400450500

Market penetration (left axis)% $

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48 Admap • February 2009 © World Advertising Research Center 2009

the other factors in the model. In Figure 4,we see that, while price has the largestshare of attribute importance, the otherfactors are significant.

The next step in the Conjoint Analysisprocess is to develop a simulator to modelany combination of factors – purchaseintent not only for the scenarios shown inthe survey, but for any scenario, so helpingto design the optimal product.

A respondent might see nine scenarios,but, with the conjoint output, respondentswill be able to evaluate 240 different product levels. Afterwards, it is possible tomeasure the effect of raising prices on pur-chase intent.

Conjoint Analysis provides a level ofrealism. As the scenarios change, respond-ents make sub-conscious (or even con-scious) choices between product altern-atives. Market share can then be modelled.

However, conjoint is not as realistic asan actual purchase situation and dealswith intent, not volume. Also, conjointshould be used when a product’s finalmake-up is not yet certain and the re-searcher is asked to examine price withinthe context of product design.

Discrete ChoiceDiscrete Choice is a more realistic con-sumer choice exercise than ConjointAnalysis. Discrete Choice is used when

the productsthemselves are fixed,

which is most often in pricing research.The main objective is then to determinemarket share in various competitive andpricing situations.

Discrete Choice analysis consists of aseries of questions that ask respondents tochoose between two or more hypotheticalproducts or services at different price lev-els. The resulting model is a simplifieddescription of reality providing a betterunderstanding of how consumers makeproduct decisions. The model simulatesfuture market states to support productand price level decisions. A well-con-structed model:● Allows for multiple what-if scenarios

within the context of the model.● Optimises price or brand positions

within existing market realities.● Takes into account ‘non-purchase’.● Gives customers ‘real world’ choice

with the inclusion of competitivebrands, which can be set at differ-ent prices.

● Can target specific competitors withproducts designed to take share specif-ically from them.For example, Bart’s Bait Company

wants to introduce new bait into the localmarket. With discrete choice, Bart will beable to project his market share amonghis chief competitors. Bart specifies thecompetitors and a range of prices. Beloware two sample scenarios: ● Scenario 1Please choose one of the following:1 Bart’s Skinny Chunk priced at $2.39.2 Zoom Fat Albert Twin Tail priced

at $2.19.3 E-Bait Big Salty Chunk priced at 2.39.4 Bracken Bait’s Big Critter Craw priced

at $1.89.5 None of the above.● Scenario 2Please choose one of the following:1 Bart’s Skinny Chunk priced at $2.39.2 Zoom Fat Albert Twin Tail priced

at $2.39.3 E-Bait Big Salty Chunk priced at $1.89.4 Bracken Bait’s Big Critter Craw priced

at $2.19.5 None of the above.

After running the model using a logis-tic regression, we create a simulator,which allows Bart to plug in prices for hisSkinny Chunk, as well as for the threeother competitors in the market.

productpricing

More on product pricingat www.warc.com

FIGURE 4

Average importance of attributes

17

13 12

21

31

12

22 23

19

30

0

5

10

15

20

25

30

35

Olive oil users

General population

Sizeand price

SourcingHealthFlavourPosition

Initial price findings – all brands at $2.19

FIGURE 5

Discrete choice – Bart’s Bait – Skinny Chunk market entry

0 10 20 30 40

None of the above

Bracken Bait’sBig Critter Craw

E-Bait Big Salty Chunk

Zoom Fat AlbertTwin Tail

Bart’s Skinny Chunk 13%

30%

40%

12%

4%

FIGURE 6

Tested price points for Bart’s Bait Skinny Chunk

Estimated sales(right axis)

$152,038

35.6%

35.6%20.2%

16.3%13.0%

10.3%8.1%

6.3% 5%

35.6%

$127,663

$105,608

$86,206

$69,558

$55,577$44,044

$34,671$27,146

$21,162

Price points ($)

% $’000

0

5

10

15

20

25

30

35

40

2.592.49

2.392.29

2.192.09

1.991.89

1.791.69

20

40

60

80

100

120

140

160

Estimated market share(left axis)

Zoom Fat Albert Twin Tail $2.19E-Bait Big Salty Chunk $2.19Bracken Bait’s Big Critter Craw $2.19

The baseline output is shown in Figure5. With all bait held at a middle price of$2.19 (median market conditions), Bart canexpect about a 13% entry market share.

With the model and a working simu-lator, Bart can project his Skinny Chunksales. To keep things simple, Figure 6shows a graph of Skinny Chunk sales ifcompetitors are all priced at $2.19.

Clearly, the lower the price Bartcharges, the greater the market share hewill gain. However, the lower prices maynot be realistic price points, and the high-er ones may give Bart too low a sales fig-ure. Moreover, Bart’s competitors mightraise or lower their prices.

One of the strengths of the discretechoice model is flexibility. The graph inFigure 6 can be reproduced countlesstimes if market conditions change. Forexample, if E-Bait and Bracken’s pricetheir bait at $2.29 and Zoom prices his at$1.99, a new chart can easily be created.

Discrete choice modelling is best whentesting ‘price only’. That is, when theproduct is past the concept test phase.Bart’s Skinny Chunk product is set, nowonly the price determination remains.

ConclusionThere are many applications for theaccurate measurement of pricing. In thetoolbox shown, we are searching forrobust pricing methodologies designed toyield extraordinarily accurate price elas-ticity measures. Powerful pricing sim-ulators give our clients unparalleled flex-ibility in modelling ‘what-if’ pricing scenarios. The methods explored hereconstitute a useful price optimisation sys-tem, determining optimal price pointsthat maximise revenue, share, pene-tration and margin, with less risk.

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