Measuring PriceElasticity

Researchers today have a choice ofmethodologies. Here's how to make

the right trade-offs.

By Bashir A. Datoo

For years, marketing researchers have been trying to estimate market share forproducts and services by using a research design that permits calculation ofprice elasticities. Price elasticity methodologies based on experimental designshave evolved rapidly in the past couple of decades. The author focuses on thesalient phases of that evolution and, by pointing out the key limitations of eachgeneration, highlights the improvements that distinguish the next.

One particular class of price elasticitymeasurement methodologies—dis-tinguished by the use of experimentaldesigns that require collectionof primary data through survey

research—has been used by researchers for decadesto predict market share at different price points.

Such designs involve exposing survey respon-dents to a series of future scenarios in which thepriee of products or services is varied systematical-ly, and (hen asking these respondents to projecttheir behavior under eaeh scenario. On the basis ofrespondent.^' preferenees or ehoiees. researcherscan estimate market share—or demand—forbrands included in the design and calculate prieeelasticities.

However, it is important that researchers under-stand the evolution of this class of methodologiesso they can determine which technique to employunder diiferent market conditions.

FIRST GENERATION

Conjoint ScalingPrior to Ihe introduction of trade-off techniques,

market researchers relied on the use of convention-al rating scales to measure the role of different fae-tors. ineluding price, in brand selection decisionmaking. The advent of full-profile conjoint scalingin the 1970s revolutionized the measurement pro-cedure and, in the process, gave researchers thecapability to do market simulations and estimatethe market share of brands under different "whati f scenarios.

Conjoint scaling offered two key benefits overthe rating-seale methodology:

• The measurement focus .shifted from individ-ual factors lo product profiles; that is. insteadof evaluating one factor at a time, respondentsnow evaluated one product profile at a time (a

3 0 Vol. 6 No.2 MARKETING RESEARCH:

profile being a "bundle" of factors, each with aset of descriptors).

• Concurrently, the tneasurement proeess shiftedfrom attitudes to preferences; that is. instead ofindicating the extent of importance of eachfactor, respondents now indicated the degteeof their preference for eaeh product profile.

Exhibit I shows a hypothetieal profile of anagricultural herbicide. The factors used to describethe herbicide remain the same from profile toprofile, only the descriptors are systematicallyvaried in accordance with an experimental design.Respondents are called upon to make trade-offsamong the factors as described, for instance, theextent to which ''unacceptable" control of somegras.ses is fine in return for "fair" to "good" controlof some other grasses.

Two key outcomes of conjoint sealing bothrelate to the issue of pricing. First, in quantifyingthe contribution of each factor to brand selectiondecision making, marketers ean determine theoverall importance of price. Exhibit 2 shows thatprice accounts for 15% of the decision proeess inthis case, being about half as important as controlof grasses (28%) but almost twice as important ascrop injury risk (8%).

Second, in t"neasuring the sensitivity to eachdescriptor of a factor, the price elasticity functionof the product category emerges. Exhibit 3 showsthat the same percentage price ehange results intnuch greater loss in preference with a priceincrease (a difference of -.40 utile) than a gain inpreference with a price deerease (a difference of-I-.25 utile).

From the perspective of pricing research,although conjoint represents a major advance overprevious methodologies, it suffers from two majorlimitations:

• Cotijoint typically understates the itnportanceof price in brand selection. This is particularlyso when brand is not included as one of thetrade-off faetors (as is often the ease), sorespondents tend to use price as a proxy forquality, thereby dampening price elasticity.

• The technique yields a generic priee elasticityfunction for the product category as a whole.The same curve is assumed to apply to allbrands within the category, regardless of theirpereeived performance and/or imagery.

SECOND GENERATION

Brand/Price Trade-offTo overcome the limitations of conjoint scaling

Exhibit 1

Sample conjoint product profileProfile #6

Mode of application:

Crop injury risk:

Soil residual activity;

Broadcast cost;

Good control for:

Fair control for:

Unacceptable control for;

Post-emergent

Moderate

Up to 3 weeks

$14 per acre

Foxtail, quackgrass

Shattercane,velvetleaf, pigweed

Volunteer corn, cocklebur,johnsongrass,morningglory

Exhibit 2

Contribution of price to brand selection

Broadcastcost per acre

Crop injuryrisk

Soil residualactivity

Mode ofapplication

Control for4 broadleaves

Control for5 grasses

Exhibit 3

Product category price elasticity

1.0

0.8

0.6

0.4

0.2

0.0

(+.25)

-.40)

S6 $14(Current)

Broadcast cost per acfe

S22

MARKETtNG RESEARCH: Vol. 6 No. 2 3 1

Exhibit 4

Sample brand/price trade-ojf scenario

BasagranBlazerDualFusiladeLassoLoroxPoastProwlRoundupSencorTreflan

Exhibit 6

Scenario #9

Broadcast cost per acre

$15.00$15.00$16,25$15,00$17,50$17.50$ 9.00$11.25$33.75$ 6,00$ 9,00

Exhibit 5

Brand's self-elasticities: typicalpricing function

Brand A50% - Market share

(+15.7)40%

30%

20%

10%

0

38,1

22,4

$15 $20

(Current)Broadcast cost per acre

$25

Brand's self-elasticities: other pricing functions

Brand D

$15 $20 (C)

Broadcast cost per acre

$25

Brand B Brand C

50%

40%

30%

20%

10%

Market

—^^(+1C

47,1

-

-

—

$7,50

3 2 Vol. 6 No. 2

share5)

^ ^ . ^ ^ ^

36.6 "̂ •

$10 (C)

Broadcast cost per acre

50%

40%

30%

25.4 20%

10%

1 n

$12.50

C = Current price

MARKETING RESEARCH:

Market s

-

(-4,1)

•9,2

$7,50

nare

13.3

$10 (C)

Broadcast cost per acre

(-2.9)

•10.4

$12.50

for pricing research, a few market research compa-nies and/or practitioners developed an alternativeapproach that can be characterized as a directbrand/price trade-off. Total Research Corp. wasone of them, introducing its Price ElasticityMeasurement System (PEMS*) in 1982.

The underpinning of this methodology is thesame experimental design u.sed in conjoint scaling.However, in the scenarios presented to respon-dents, brands now replace factors, and price pointsreplace descriptors, as ean be seen in Exhibit 4.

This represents a shift from profiling a productto profiling a market, in which respondents* task isno longer to indicate which product they wouldprefer but. rather, whieh one they would buy.Hence, there is a shift in the method of evaluationfrom preference to choice.

The brand/price trade-off technique offers twoadditional benefits over eonjoint sealing:

• It permits the development of brand specificself-elasticity effects, not just one genericfunction for the entire product category. Theeffect of changes in the price of a eompany^brands can have a differential impact on thedemand of those brands.

• It measures brands" cross-elasticity effectsdirectly, rather than just inferring them fromdifferences in the end results of market simu-lations. The beneficiaries of a given brand'sprice increase may be somewhat different fromthe losers of that brand's priee decrease.

Self- and cross-effects are developed for everybrand whose price is manipulated in the design.Exhibit 5 shows a typical pricing function; it is elbow-shaped beeause brands typieally gain more share(+15.7 share points) than they lose (-5.2 share points)with the .same propoitional change in price (25%).

Exhibit 6 shows several other functions thatmay be observed within the same produet catego-ry: Brand D shows a reversed elbow curve charac-terizing a brand that basically has saturated themarket; Brand B shows a linear function that maybe applicable to eommodity-type products; andBrand C shows a counter-intuitive curve typifyinghigh-status brands.

Exhibit 7 illustrates the effects of one pricechange, a 25% inerease. Base shares at the bottomof the exhibit represent market shares of thebrands at their current prices (e.g.. Brand A has ashare of 22.4%). The numbers in the diagonal ofthe matrix indicate the impact on the share of abrand in the wake of a priee inerease (Brand Aloses 5.2 share points, resulting in a new marketshare of 17.2%). The figures off the diagonal showthe beneficiaries of a price increase (for example.

Exhibit 7

Brands' cross elasticitiesEffect on:

Effect of 25%price increase for:

Brand A

Brand B

Brand C

Brand D

Brand A Brand B Brand C Brand D

-5.2

+1.2

+0.3

+8.1

+0.4

-11.2

+2,0

+0.8

+ 1.6

+8.4

-2.9

+2,6

+3.2

+1.6

+0.6

-11.5

Basic share 22.4% 36.6% 13.3% 27.7%

Brand D gains the most, 3.2 share points, resultingin a new share of 30.9%).

Notiee that the relationship between Brands Aand C is asymmetrical: Brand A loses to Brand Cproportionately more than Brand C loses to BrandA when they respectively raise their prices.

Armed with self- and cross-effeets, it nowbecomes possible to simulate how market shares ofthe brands would be reconfigured in the event thatseveral brands simultaneously or sequentially altertheir prices. Sueh ''what it̂ ' seenarios can be run forany conceivable combination of prices that fallwithin the price range tested for each brand.

The brand/priee trade-off technique has becomeextremely popular in price elasticity research, inpart because it has been successfully tailored toaddress a wide variety of market conditions. Thisflexibility notwithstanding, the technique doeshave some constraints:

• Brands are presented to respondents as'•gestalts," as an assemblage of whatever brandperceptions they bring to the task or whateverbrand descriptions are provided with the task.Thus, the technique eannot directly measurethe added value of a new or improved feature.

• Brand set is fixed across all the scenarios, withno brand being added or deleted. Hence, theleehnique cannot readily test the effect ofsequential new product introductions.

THIRD GENERATION

Discrete Choice ModelingThe constraints mentioned above mean that the

brand/price trade-off technique cannot be used in afew situations. Companies that pioneered the use ofdiscrete ehoice modeling have pressed this tech-nique into use in the "90s for such special situations.

MARKETING RESEARCH: Vol, 6Nc. 2 3 3

Exhibit 8

Sample DCM choice setSet #12

BasagranNew indication for controlof (broadleaf)

BlazerNew/improved formulation

Dual

FusiladeCurrent packaging

Lasso

LoroxNew indication forcontrol of (grass)

Etc.

Broadcast cost per acre

$25.00

$18.75

$16.25

$11.25

$14,00

$17,50

Bashir A. Datoo is Senior VicePresident and Head of iheStrategic Research Support andDevelopnieni Group (SRSAD)at Total Research Corp..Princclon. N,J,

Discrete choice modeling involves the develop-ment of choice sets that show product choicesavailable in the market, together with their descrip-tions (in terms of factors/descriptors). Thus, theyrepresent a combination of conjoint scaling andbrand/pi'ice trade-off scenarios. However, themethod of evaluation remains the same as inbrand/price trade-off, namely choice of, not prefer-ence for brands.

Exhibit 8 illustrates one of the special situationsthat calls for discrete choice modeling by showinga choice set that focuses on two product improve-ments: Blazer is assumed to come out with a newformulation, whereas Fusilade is assumed to intro-duce a cuiTent packaging. To disguise our interestin these two specific changes, a couple of other fea-tures also are altered from one scenario to the next.

The outeome from such an approach is shown inExhibit 9, with findings indicating that theimproved feature adds significant value to thebrand. First, the function for the "improved" prod-uct is higher on the graph than that of the "current"product (a difference of 4 share points at the pro-posed/current price of the products). Second, witha price decrease, the curve of the "improved"' prod-uct is steeper than that of the "current"' product(gains more share): with a price increase, (he curveis gentler (loses less share).

CURRENT STATUS

The move toward newer generation methodolo-gies has ihu.s provided greater realism and Hex-ibility—from conjoint scaling (CS) throughbrand/price trade-off (BPT) to discrete choice

Exhibit 9

Elasticity of current vs. improvedproduct

30% r

20%

10%

0

Market share

26,7

12.3

-25% (Current) 2 5 %Broadcast cost per acre

modeling (DCM). Specifically, the evolution intosecond generation methodology has added realismto the task that is set up for respondcnt.s, whereasthe development of third generation methodologyhas offered greater flexibility in addressing theneeds of pricing research:

• In terms of realism, respondents are shown aprofile of the market (BPT/DCM), as opposed toseparate profiles of products (CS): they make achoice of products or services from among alter-natives presented (BPT/DCM), as opposed togiving a preference for each alternative (CS).

• In terms of flexibility, changes in product pro-files can be described (DCM). as opposed tostaying with fixed profiles (BPT): changes inchoice alternatives can be introduced (DCM),as opposed to assuming a stable market (BPT).

Even though conjoint scaling is still an impor-tant tool in the understanding of product and ser-vice feature trade-off.s. it is an outmoded techniquefor the measurement of brand-specific price elas-ticity.

Brand/price trade-off is currently the most pre-feiTed method for determining elasticities associat-ed with current products and limited marketchanges: discrete choice modeling may be the pre-ferred technique for changing products and chang-ing markets.

In other words, brand/price trade-off is applica-ble in a wide variety of situations and discretechoice modeling jumps in where the former isfound to be wanting.EDI

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