consumer-driven development: coffee beverage mindsets for three commercial venues

© 2007, The Authors

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Blackwell Publishing IncMalden, USAFRIFoodservice Research International1524-8275© 2007, The Authors; Journal compilation © 2007, Blackwell Publishing

? 2007

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Original Articles

Coffee beverage mindsets for three commercial venuesH. Moskowitz et al.

Original article

Consumer-driven development: coffee beverage mindsets for three commercial venues

Howard Moskowitz,* Rachel Katz,* Bert Krieger* and Roberto Cappuccio

†

*Moskowitz Jacobs, Inc., 1025 Westchester Avenue, White Plains, NY 10601-3522, USA;

†

Illycaffè s.p.a., via Flavia 110, I – 34147 Trieste, Italy

Abstract

This paper presents results from part of a larger-scale development study oncoffee. The study deals specifically with new concepts of coffee beveragesdesigned for three specific coffee venues: Burger King, Dunkin Donuts andStarbucks. The method used internet-based conjoint measurement, where therating scale was ‘appropriate’ for the specific venue. This ‘bottom-up’approach allows the researcher to identify which characteristics of a coffeebeverage are suitable for a particular venue, and whether there exist segmentsthat show different response patterns, even for a beverage designed for aparticular venue. The results suggest modest differences in the features of acoffee beverage that would fit each venue, but with the existence of segmentsof individuals who want radically different products. These segments existin all venues.

Introduction: coffee consumption and innovation opportunities

Coffee is said to be the world’s most popularbeverage after water. The general trend for coffeeconsumption in the USA has been an increase inthe number of coffee drinkers on a daily andoccasional basis (ICO 2001). This growth is prob-ably due to the wide choice of coffee-based prod-ucts, ranging from gourmet whole beans or roastand ground coffees, to ready-to-drink beverages,such as iced and cold coffees. Thus, any data thatcan help the manufacturer fine-tune the coffeeproduct for a new end use become relevant. Forexample, during the past 10 years, new brewingmethods have been successfully introduced intothe US market. One that is popular is the typicalItalian brewing method

espresso

, which canaccentuate the sensory characteristics of a gour-met blend (Illy & Viani 1995). Moreover,completely new coffee-based products have been

developed. Ready-to-drink coffees have been oneof the fastest growing beverage categories, withan estimated growth rate of more than 20%, asreported by Beverage Marketing Corporation(Berry 2002).

Out-of-home coffee provides a major opportu-nity. Dunkin Donuts has 6000

+

shops, of whichover 4400 are in the USA, and approximately1700 are in 29 countries, making it the largestcoffee, bagel and doughnut shop in the world(Dunkin Donuts 2006). Burger King, a fast-foodgiant, has over 11 100 shops in 65 countries(Burger King 2006). Starbucks continues toincrease, year after year, penetrating into mostmajor world markets. These three venues repre-sent different situations for the out-of-homeconsumption of coffees, and perhaps for coffeebeverages. Thus, coffee represents a major oppor-tunity for new product development. The ques-tion is simply and specifically what type ofproduct to develop.

Correspondence:

Howard Moskowitz, Moskowitz Jacobs, Inc., 1025 Westchester Avenue, White Piains, NY 10604-3522, USA.Tel: (914) 421 7400;Fax: (914) 428 8364;E-mail: [email protected]

Keywords:

consumer research, conjoint measurement, modeling, segmentation

Coffee beverage mindsets for three commercial venues

H. Moskowitz et al.

© 2007, The AuthorsJournal compilation © 2007, Blackwell Publishing


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The field of coffee beverages is ripe for innova-tion. According to Sharma (2005), coffee and teadrinks have a 1.1 billion dollar market (2004–2005) and a low 4% growth, meaning that not toomuch effort is focused on innovation. A lot morefocus has been placed on other beverages such asfruit-based drinks and dairy drinks starting at least10 years ago, but probably a lot longer (Young1995). Coffee and tea drinks are about half the sizeof dairy drinks. Areas of possible beverage inno-vation include pleasure and indulgence, lifestyle,functional health, adult complete nutrition andsports/performance. Coffee beverages more likelywould support the pleasure and indulgence prod-ucts, rather than health, nutrition or sports.

Innovation, whether for coffee beverages inparticular or for products and services in general,is the single most important factor in the futuregrowth of any business venture; innovation is amindset – a new way to think about businessstrategies and practice (Kuczmarski 1996). Onequestion in the foodservice business is how todrive innovation using consumer input. Canhigher level innovation be carried out by availabletechnologies, such as product concept develop-ment, using current, state-of-the-art methods,executed on the Internet? Furthermore, the inno-vation process is not only a question of newproduct development, but also technologicalapproach, new markets and organization changesin the company (Horska & Ubreziova 2003). Thisview, which is commonly held by many businessthinkers in a variety of industries, suggests thatthe opportunities for coffee-beverage innovationmay transcend the product itself, and can profit-ably look for the combination of product andconsumption venue.

Developing new coffee-based beverages for out-of-home consumption using consumer research

Coffee-based beverages appear to be a growingbusiness sector, with potential space for inventionand product design. In a typical European coffeeshop, one can observe a range of customers thatmay be satisfied with a range of coffee beverages,beyond simple coffee. Many of these customersare still in their school years. Other individualsare business people with their coffee invigorating

them before a meeting. Still others are shoppersor doing errands, and enjoying coffee alone orwith friends, and chatting. These individuals mayrepresent very different segments with differentneeds and requirements. The design of such bev-erages for out-of-home consumption leaves roomto satisfy many different consumer needs, rangingfrom the simple coffee to a relatively low-costexperience provided by a relatively exotic bever-age. The question is how to systematize the devel-opment of such beverages for this targeted,‘venue-based’ segment. As the consumer in theUSA becomes increasingly accustomed to coffeeshops, the need for such beverages will increase,because product innovation is one way to keepcustomers interested and to maintain growth inthe face of competition.

The approach presented here provides oneadaptation of the so-called ‘voice of the con-sumer’, where consumers help drive the develop-ment of product features (see Van Kleef

et al

.2002; Sorenson & Bogue 2003).

Consumer researchers use a variety of tools forinnovative development. One of the most basic isthe creation of ‘raw materials’ (in other words,ideas) that can later be refined and combined intonew product ideas. Brainstorming is usually fol-lowed by qualitative research such as focusgroups, and quantitative research such as formal-ized concept and product testing.

A new, possibly alternative approach thatmixes the qualitative and quantitative methodsprovides a cost-effective, rapid developmentscheme. The method combines ideation and con-joint analysis. Conjoint analysis measures the rel-ative importance of different attributes pertainingto a product or service, and does so at the levelof the individual respondent (Molteni 1993).Descriptions of conjoint measurement abound inthe literature because it is one of the more popularmethods (Gustaffson 2001). The starting point isa set of elements or variables pertaining to theproduct or service. These concept elements aresystematically varied, allowing an equation orstatistical model to be created relating the pres-ence/absence of the individual elements to theresponse to the concept. The typical analysis usesone or another form of regression, applied eitherat the individual respondent level if the experi-mental design is constructed that way, or often at


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the group level (e.g. larger-scale breaks such asmale/female).

The large amount of literature pertaining toconjoint analysis is easily accessible in the public,refereed papers as well as in many books. Evi-dence for the usefulness of conjoint measurementin marketing research can be found in many well-known papers spanning a 35-year period (Green& Rao 1971; Green & Wind 1975; Cattin &Wittkin 1982; Green & Krieger 1991; Moskowitz

et al

. 2005).

The mega study to assess latent interest in a product end use and to design a product

A recent enhancement of conjoint analysis is the‘wall studies’ or ‘mega studies’ (Beckley & Mosk-owitz 2002; Moskowitz

et al

. 2005). The wallcomprises a list of related conjoint analysis stud-ies. The respondent is brought to the wall byanswering an e-mail invitation. At the wall, therespondent is shown a set of study topics and isinstructed to choose a study in which the respon-dent is interested. Figure 1 shows an example.The positions of the different studies on the wallchange as a function of the number of respon-dents who participate in each study. The leastpopular study, in other words, the one with thefewest respondents, appears in the top left posi-tion where it is more likely to catch the respon-dent’s eye. The most popular study appears at the

bottom right. This approach allows the researcherto measure latent interest in an end use by mea-suring the number of respondents who choose toparticipate in a study, and the number of respon-dents who actually complete the evaluation. Stud-ies having a large number of respondents arethose that are more interesting, at least by title. Ifthe study deals with a single topic, as in the caseof a new coffee beverage, and the name of thestudy differs (e.g. coffee for teenagers vs. coffeefor people ages 51

+

), then the number of respon-dents choosing to participate in one study or theother may indicate the prevalence of interest inthat aspect of this one topic. This paper uses thewall approach to identify the frequency of interestin different types of coffee beverages, as well asto direct the respondent to a study of interest.

Scope of the paper

The primary objective of the research was to iden-tify the features of coffee beverages designed withthree different and popular venues in mind:Dunkin Donuts, Starbucks and Burger King.These three venues are quite popular in the USA,and represent locations where coffee and coffeebeverages are consumed. They are different typesof venues, however, and may be associated withdifferent types of coffee beverages. The secondaryobjective was to uncover segments of like-mindedindividuals and to see how these segments fall inthe different venue-related studies. The approachused the ‘wall’ approach to present the differentvenues (along with other coffee-related situa-tions), and employed conjoint measurement withthe same set of concept elements in each of thethree studies.

Method

The coffee beverage ‘design’ study

These data for the three venues are taken from amuch larger development study, comprising 28related but different surveys. All of the topicsdealt with a coffee beverage. Each of the 28 con-joint studies comprised the same set of 36 conceptelements (combined into unique sets of 60 con-cepts) and the same set of classification questionsfollowing the conjoint analysis.

Figure 1

The coffee beverage ‘wall’. Respondents choose the study that interests them. Their choice trans-lates into a latent interest for that beverage concept. The studies are identical except for the rating question and the wording of some of the classification questions.

Welcome to the Coffee StudyPlease choose the coffee study you find

most interesting

CoffeeBurger KingStarbucksDunkin DonutsMaxwell HouseFolgersTaster’s ChoiceBefore BreakfastBreakfast CoffeeMid-morning CoffeeLunch-time CoffeeAfternoon CoffeeEarly Evening CoffeeLate Evening Coffee

Relaxation Coffee Energizing CoffeeCoffee (Teenagers) Coffee (Ages 20-30)Coffee (Ages 31-50) Coffee (Ages 51+)Coffee (Friends)Coffee (Family)Coffee (Happy)Coffee (Sad)Coffee (Restless)Coffee (Tired)Coffee (Social)


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Studies on the wall

Figure 1 shows the wall structure for the differenttypes of studies. Table 1 lists the studies in a morestructured form. The studies dealt with coffeesappropriate to age groups (ages 21–30, 31–50,51

+

), coffees appropriate to different emotionalstates (e.g. sad, happy) and coffees appropriate todifferent social situations, different consumptionvenues, etc. A respondent could only participatein one study. This study will concentrate on thethree venues.

Unbeknown to the respondent, the 28 studieswere identical, except for the rating question. The

same test elements for the conjoint study werecombined into different concepts. Each respon-dent had a relatively unique set of 60 combina-tions, but the set of 36 elements was identicalfrom respondent to respondent, from study tostudy. A total of 85 sets of combinations werecreated, so that the results would not be biasedby any combination that performed particularlystrong or poor. Furthermore, all concepts wererandomized for a respondent, so even if the samedesign set of combinations was used for fiverespondents (e.g. the 393 respondents for BurgerKing), the respondents would see the test conceptsin different orders.

This structured approach permitted the com-parison of the same concept elements across dif-ferent studies (in other words, end uses), andpermitted the comparison of the different enduses across the same elements. The classificationquestionnaire was the same across studies as well,changed only in a few places to be appropriatefor the particular end use.

Concept elements

The specific test stimuli for the concepts com-prised 36 elements relevant for a

new coffee bev-erage

. The elements were selected by a contentanalysis of different websites, analysis of the com-petitive frame as well as a short ideation session.The basic structure for the coffee elementsappears in Table 2. The concept elements wereselected so as to be appropriate for each of the 28studies. That is, there were no clear apparentcontradictions between the concept elements andthe end uses, although the results show that someof the elements were clearly more appropriate forsome end uses than they were for others.

When looking at the elements, it is importantto keep in mind that each element stands by itself,as a simple declarative phrase. This structure per-mits the elements to be mixed and matched byexperimental design, because there are no connec-tives linking one element phrase to another.

Experimentally designed concepts

The test concepts comprised two to four elements,one or none from each silo, combined accordingto an experimental design. The design ensured

Table 1

The 28 coffee studies divided by category

Survey ‘end use’ Category Respondents% oftotal

Burger King Venue 393 18%Dunkin Donuts Venue 337Starbucks Venue 227

Coffee – teenagers Age 105 22%Coffee ages 21–30 Age 130Coffee ages 31–50 Age 495Coffee ages 51

+

Age 440

Folgers Brand 282 12%Maxwell House Brand 239Taster’s Choice Brand 108

Coffee General 312 6%

Coffee – tired Mood 109 8%Coffee – restless Mood 108Coffee – happy Mood 107Coffee – sad Mood 107

Energizing coffee Physiology 162 5%Relaxation coffee Physiology 137

Coffee – alone Social 222 10%Coffee – social Social 123Coffee – friends Social 110Coffee – family Social 109

Breakfast coffee Time 275 19%Before-breakfast

coffeeTime 205

Mid-morning coffee

Time 122

Early-evening coffee

Time 108

Late-evening coffee

Time 108

Lunchtime coffee Time 108

Afternoon coffee Time 107


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that across the 60 different combinations, eachelement appeared equally as often, and that eachelement appeared independently of every otherelement. The use of the particular experimentaldesign with so-called ‘zero conditions’, where asilo does not appear, ensured that the regressionmodelling could estimate the absolute contribu-tion of each concept element to the rating. Theexperimental designs were selected from 85 dif-ferent permutations of the basic designs, each

comprising all 36 elements in 60 different combi-nations. This permutation strategy reduced thebias that might have occurred because of theselection of a particular combination of conceptelements that performed unexpectedly well (syn-ergy) or unexpectedly poorly (suppression). With60 combinations, the presence of one or two suchcombinations could bias the results without theresearcher ever suspecting the existence of thisbias. The strategy of using different sets of com-

Table 2

The 36 concept elements for coffee, divided into four ‘silos’ or major categories

Silo 1 – Nature of the coffee (caffeine, organic, hot vs. iced)

E01 A lively

decaffeinated

coffee that will not weigh you downE02

A coffee that is guaranteed to wake you up

E03 100%

organic

coffee . . . healthy

for you and the planetE04 Dark fancy houseblend . . . an

extremely rich

cup of coffeeE05 A distinctive, well rounded cup of coffee . . . the

ideal way to start

a busy dayE06 A

slightly caffeinated iced

coffee drink . . . to help you get through your dayE07 A

jolt of caffeine

to awaken your sensesE08

Iced

to the max . . . for those hot summer daysE09 The mini-drink six pack . . .

iced coffee

drinks for people on the go

Silo 2 – Flavors

E10 Invigorate your senses with

Cinnamon Apple Spice and French Caramel

E11 A unique flavor, sure to delight . . . sweet and smooth rich cream complement this delectable treatE12 White

chocolate mousse

, and

wild raspberry . . .

a melt in your mouth dessert in a flavored coffeeE13 New classic combination . . .

pistachio and maple walnut . . .

unleash the nutty side in youE14

Chocolate

and

cognac

give this coffee a flair . . . try it once and you will come back for moreE15 Enjoy the taste of

toffee

in a

light cream . . .

a new summer favoriteE16

Vanilla

and

chocolate

fudge combined . . . a unique flavor that is sure to pleaseE17 Mocha and

spicy

Java create a one of a kind

chocolate

fantasyE18 Thrilling burst of

vanilla

flavor and sweet, crisp taste . . . gives you ‘more to go wild for’

Silo 3 – Sensory promises (aroma, taste, body)

E19 The

freshest

cup of coffee possibleE20 A masterful combination of

carefully chosen coffee

from each year’s harvestE21

Highly aromatic

,

rich in taste

with

smoky

overtonesE22 Wonderfully

smooth

with deep tonesE23 Its

unique aroma

will appeal to your sensesE24

Tangy taste, rich body and pleasing aroma

E25

Exceptional aroma

and a deep mellow bodyE26

Spicy aroma, medium body and clean flavor

make this coffee stand outE27

Aroma, body and flavor . . .

perfectly balanced

Silo 4 – Brew, quality, origin

E28 Made from exotic

Jamaican beans

, experience the magic of another worldE29

Premier

espresso

made from the finest beansE30 Made from a

select

combination of

African

and

Central American

beansE31

A dynamic blend of washed Arabian coffee

E32 A robust strong coffee blend . . . made from

dark roasted Brazilian

beansE33 What you always wanted . . .

café Americano

with all the worksE34

One of a kind coffee developed by top quality growers

E35

Dark exotic

taste . . . a superb Turkish brewE36 A robust strong coffee blend . . . made from

dark roasted Brazilian

beans

The key or defining text for the element is shown in bold.


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binations created on the same basic design struc-ture reduced but did not completely eliminate thispossibility.

The respondent rated each separate concept onthe following rating scale: how well does thisconcept fit (venue name, e.g. Burger King)?1

=

does not fit at all . . . 9

=

fits very well.It is important to note that this type of rating

scale requires that the respondent thinks about

the fit

of a concept to a specific end use, ratherthan thinking about the acceptance of a concept.Thus, by changing end uses or in this case threedifferent venues for drinking a coffee beverage, itshould be possible to determine whether thestated venue can influence the fit of a conceptelement.

Classification questionnaire

The respondents completed a large-scale classifi-cation questionnaire, which dealt with the follow-ing key issues involving coffee:

1

Demographics: gender, age, geographic area;

2

Frequency of drinking coffee;

3

Time of day for drinking coffee;

4

Factors influencing purchasing coffee;

5

Brand of coffee usually purchased.The respondent completed the classification

questionnaire after having been oriented in thestudy by a welcome page, and after having ratedthe 60 test concepts.

Field execution

The respondents were recruited through an e-mailinvitation that directed them towards a wall ofstudies. Open Venue Ltd. of Toronto, a strategicpartner of Moskowitz Jacobs, Inc., sent out theinvitations. Open Venue Ltd. specializes in inter-net fieldwork, using respondents who volunteerto participate in internet studies. That is, therespondent was invited to participate and wasgiven a link in the e-mail. The motivation forparticipation was the chance to win cash prizes.Eligibility for the cash prize resulted in a success-ful completion of the surveys. More than 5000people completed the study, of which 18% choseto participate in the ‘venue’ studies (namely, forBurger King, Starbucks or Dunkin Donuts).Figure 2 shows the welcome page for the coffeestudy, presenting the requirements for eligibilityand the sweepstakes prize.

Results

Modelling responses at the individual respondent level

Respondents evaluated their individual set of 60concepts on a nine-point scale with semanticanchors. The ratings on the nine-point scale weretransformed to a binary scale, 0 or 100. Ratingsof 1–6 were transformed to 0 to denote

does not

Figure 2

Screen shot for the wel-come page for the coffee study. The page shows the requirements for eli-gibility and the prize.


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fit venue

; ratings of 7–9 were transformed to 100to denote

fits venue

. This transformation fol-lowed the conventional market research interestin the proportion of respondents falling into aspecific category of behavior. The transformationcreated a binary response; either the respondentwas interested/felt the concept fits an end use, orwas not interested/did not feel the concept fits anend use.

Following the transformation, the ratings foreach individual respondent were analyzed bydummy variable regression analysis. For eachindividual, there were 36 independent variablescorresponding to the 36 elements, and 60 casescorresponding to the 60 test concepts.

Each respondent generated two models. Onemodel (called the persuasion model) used theoriginal nine-point ratings as the dependent vari-able. The other model (called the interest model)used the transformed ratings, so the dependentvariable was either 0 or 100. For most of thesubstantive analyses, this paper will use the ‘inter-est’ data, so that the results can be interpreted interms of ‘per cent of respondents’. For segmenta-tion, however, the analysis will use the persuasionmodel, which shows the magnitude of fit, ratherthan fit vs. no fit to an end use.

Both models can be expressed by the simplelinear regression model:

(1)

The additive constant,

k0, is an estimatedparameter. For the interest model (the one mostrelevant to the substantive results), the additiveconstant shows the estimated conditional proba-bility that the concept will fit the particular venue(Burger King, etc.) if no elements are present.Clearly, all the test concepts in the study com-prised elements, so that there was no conceptcomprising zero elements. However, the additiveconstant provided a good baseline, even if it wasa computed parameter rather than being anempirical parameter. Previous conjoint studies ofthis type, using zero conditions where a silo wasnot present in a concept, allowed for valid esti-mates of the absolute values of utilities from abaseline of 0. There are some norms for this addi-tive constant. Constants above 50 represent a

Rating Element #1 Element

Element #36

= + ( ) + ( )( )

k k k

k0 1 2

36

2#

. . .

high degree of fit; constants lower than 30 repre-sent a low degree of fit, and constants around 20or lower represent a very low degree of fit.

The coefficients k1 . . . k36 in the interest modelshow the contribution of the element to fit to theparticular venue used in the study. The coeffi-cients can be positive or negative. Positive coeffi-cients mean that the element when present in theconcept increases the probability that that ele-ment ‘fits the venue’. For example, a coefficientof +10 means that if the element is present in theconcept, then an additional 10% of the respon-dents will say that the concept fits the venue. Incontrast, a coefficient of −10 means that an addi-tional 10% of the respondents will say that theconcept ‘does not fit’ the venue, or that 10%fewer respondents say that the concept ‘fits’ thevenue.

From previous studies of this type, some rulesof thumb emerged:1 Utility >15 corresponds to extremely impactfuland important elements;2 Utility 10–15 corresponds to very impactfulelements;3 Utility 5–10 corresponds to impactful elements;4 Utility 0–5 means that the element adds littleto fit;5 Utility <0 means that the element detracts fromfit (should be avoided).

Analysis of respondent data by self-defined subgroups

The study wall provided some hints on the latentinterest of the respondents, just by the relativenumber of the percentage of total respondents.Despite the increasing popularity and success ofStarbucks coffee shops, the number of respon-dents for Burger King (393.0 ∼ 7.3% of total) andDunkin Donuts (337.0 ∼ 6.2%) was far largerthan the number of respondents choosing to par-ticipate in the Starbucks study (227.0 ∼ 4.2%).The study was run in 2002, so the latent interestmight change, however, over time, based upon themarketing efforts by Starbucks. This change inlatent interest cannot be assessed here.

The following additional results emerged fromthe classification of respondents, based upon thenumber of respondents, and how the respondentsclassified themselves.

Coffee beverage mindsets for three commercial venues H. Moskowitz et al.

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1 All three venues interested frequent drinkers, asmeasured by participation. Starbucks was oftenchosen as a study in which to participate by thosewho say that they drink a lot of coffee. In con-trast, Burger King was chosen by people who saythat they drink both a lot of coffee or a littlecoffee. Starbucks as a study topic appeals moreto the frequent coffee drinkers.2 Regarding time of coffee consumption, Star-bucks respondents consumed coffee during moreperiods of the day. These periods were early andmid-morning, as well as mid-afternoon. Earlyevening was another choice. This is consistentwith the fact that the Starbucks consumersdescribed themselves as heavy users. On the otherhand, Burger King and Dunkin Donuts respon-dents consumed coffee on premises during themorning hours. This coffee consumption is con-sistent with the stronger nature of Starbucks as acoffee drinking destination.3 Aroma and flavor were selected as the majorinfluence for purchasing coffee. Brand was notimportant at all.4 Finally, there were big and hard-to-understanddifferences in the brand purchased on a regularbasis. Burger King study respondents said thatthey usually bought Starbucks or Folgers coffees,and very few of them purchased Dunkin Donutscoffees. Dunkin Donuts study respondents wereequally divided between buying Dunkin Donuts,Folgers and Starbucks coffees respectively. A largemajority of Starbucks study respondents pur-chased Maxwell House coffee, but very few saidthat they purchased Dunkin Donuts coffee. It isclear, here, that venue participants differentiatedbetween venue (their interest) and the actualphysical product in a package.

Conjoint measurement models – the additive constant as a measure of readiness to accept a beverage

The additive constant provided information on fitof a new coffee beverage in a specific venue whenno concept elements were present. Table 3 showsthe additive constant, averaged across all of therespondents, for each venue. The constants rangefrom a low of 42 (Burger King) to a high of 65(Starbucks). This means that when a respondentchooses to participate in a study about coffee

beverage at Starbucks, the respondent feels thatthe concepts will fit Starbucks more than respon-dents who participate in a Burger King feel thatthe concepts fit Burger King. It is quite possiblethat the additive constant is a measure of readi-ness to accept a new coffee beverage, which notonly comes from the beverage itself, but also fromthe venue in which the beverage will be con-sumed. Such results agree with previous studiesthat emphasized both the product and its appro-priateness (Schutz 1989).

Although there are some differences in thevalue of the additive constant, for the most part,the constants are quite similar across subgroupswithin a particular study with a specific venue.This finding can be seen by looking down at thedifferent subgroups within a specific venue. Thus,it is the venue itself, rather than the subgroup,that drives basic interest. Thus, looking at age,there is a greater similarity in additive constantsfor a single venue across ages, than for a singleage across venues.

Drivers of concept interest

The key information from the conjoint studycomes from the utility values of the elements. Theutility values are computed as coefficients ofthe regression equation, on a respondent-by-respondent basis. Table 4 presents the results forthe total panel, and shows immediately what ele-ments drive ‘fit’ to the three different venues.From Table 4, it is clear that the distribution ofutility values is such that there are few elementsthat are positive, but quite a number of elementsthat are strongly negative. This finding immedi-ately signals the fact that the respondents will notsimply accept any product feature as appropriateto a coffee beverage for a specific venue. Respon-dents discriminate among the features, and mostof the features do not fit any of the venues, espe-cially the flavor features.

Elements that fit one venue may not fit another.As an example, a coffee beverage comprising ‘pre-mier espresso made from the finest beans’ isappropriate for Starbucks (utility = +3) but notfor Burger King (utility = −4), and virtually irrel-evant for Dunkin Donuts (utility = −1). However,Table 4 suggests that the three venues generatefairly similar patterns of utilities, with rather



15

minor exceptions to the similar patterns. Anexample of the exception is element E32 ‘an Ital-ian favorite . . . cappuccino with a flair’. This ele-ment does not fit the venues, on average. It doesnot fit Dunkin Donuts (−3), certainly does not fitBurger King (−5), but probably fits Starbucks,albeit modestly (+3).

Looking at inter-individual variability by signal/noise [analysis of variance (ANOVA)] analysis

The individual models created for the 957 respon-dents suggest a great deal of variability acrossindividuals, and a correspondingly low fit of allelements to the three venues. Could this be the

Table 3 Additive constant for total panel and key subgroups

Starbucksconstant

DunkinDonuts constant

BurgerKing constant

Total 65 57 42Gender

Male 62 51 37Female 66 60 44

Age18–21 65 20 1721–30 61 52 4831–40 65 57 4041–50 65 60 4451–60 66 62 3661+ 68 51 50

MarketSouthwest 72 49 48Mountain 69 55 29Northeast 69 64 44Southeast 65 59 36Northwest 63 72 46Midwest 59 38 44

Coffee consumptionDrink at every opportunity 62 67 60Frequently drink 70 64 47Drink now and then 62 52 43Drink on occasion 68 58 41Hardly ever drink 45 60 38

Time of day when coffee is consumedEarly morning 67 62 46Late morning 68 65 48Around lunchtime 65 66 51Mid-afternoon 64 63 50Early evening 68 65 49Late evening 64 64 50

What attributes influence coffee consumption?Brand 67 60 44Aroma 66 63 50Flavor 65 64 48

Regular brand consumedDunkin Donuts 67 66 60Starbucks 67 65 46Maxwell House 65 69 46Folgers 62 64 44

The constant is the predisposition to feel that the coffee beverage ‘fits the venue’.



16

Table 4 Utility values for the 36 elements for the three different venues

Weightedaverage

DunkinDonuts

BurgerKing Starbucks

Base size 337 393 227Additive constant – basic ‘fit’ to a venue 52 57 42 65

E05 A distinctive, well-rounded cup of coffee . . . the ideal way to start a busy day

3 3 5 1

E02 A coffee that is guaranteed to wake you up 2 3 3 0E19 The freshest cup of coffee possible 2 5 1 0E33 What you always wanted . . . café Americano with all the works 1 2 −1 3E04 Dark fancy houseblend . . . an extremely rich cup of coffee 0 −1 1 2E07 A jolt of caffeine to awaken your senses 0 1 1 −2E08 Iced to the max . . . for those hot summer days 0 0 1 1E11 A unique flavor, sure to delight . . . sweet and smooth rich cream

complement this delectable treat0 0 −2 4

E20 A masterful combination of carefully chosen coffee from each year’s harvest

0 0 1 0

E24 Tangy taste, rich body and pleasing aroma 0 0 −1 3E25 Exceptional aroma and a deep mellow body 0 2 0 1E27 Aroma, body and flavor . . . perfectly balanced 0 0 0 1E34 One of a kind coffee developed by top quality growers 0 1 −1 2E22 Wonderfully smooth with deep tones −1 −1 0 0E23 Its unique aroma will appeal to your senses −1 −1 −2 1E26 Spicy aroma, medium body and clean flavor make this coffee

stand out−2 −2 −2 1

E29 Premier espresso made from the finest beans −2 −1 −4 3E30 Made from a select combination of African and Central

American beans−2 −1 −4 0

E36 A robust strong coffee blend . . . made from dark roasted Brazilian beans

−2 −1 −4 2

E06 A slightly caffeinated iced coffee drink . . . to help you get through your day

−3 −3 −2 −3

E18 Thrilling burst of vanilla flavor and sweet, crisp taste . . . gives you ‘more to go wild for’

−3 −2 −6 1

E21 Highly aromatic, rich in taste with smoky overtones −3 −4 −3 0E28 Made from exotic Jamaican beans, experience the magic of

another world−3 −3 −4 0

E32 An Italian favorite . . . cappuccino with a flair −3 −3 −5 3E01 A lively decaffeinated coffee that will not weigh you down −4 −3 −1 −10E03 100% organic coffee . . . healthy for you and the planet −4 −4 −3 −2E15 Enjoy the taste of toffee in a light cream . . . a new summer

favorite−4 −4 −6 2

E16 Vanilla and chocolate fudge combined . . . a unique flavor that is sure to please

−5 −4 −9 2

E31 A dynamic blend of washed Arabian coffee −5 −4 −7 −3E17 Mocha and spicy Java create a chocolate fantasy −6 −7 −9 3E12 White chocolate mousse, and wild raspberry . . . a melt in your

mouth dessert in a flavored coffee−8 −9 −10 1

E35 Dark exotic taste . . . a superb Turkish brew −9 −9 −9 −5E09 The mini-drink six pack . . . iced coffee drinks for people on the

go−11 −11 −11 −10

E10 Invigorate your senses with Cinnamon Apple Spice and French Caramel

−12 −13 −14 −7

E14 Chocolate and cognac give this coffee its flair . . . try it once and you will come back for more

−15 −16 −18 −8

E13 New classic combination . . . pistachio and maple walnut . . . unleash the nutty side in you

−17 −21 −16 −10

Maximum utility (maximum fit) 3 5 5 4Minimum utility (minimum fit) −17 −21 −18 −10Range of utilities (range of fit, defined as maximum – minimum) 20 26 22 14

The utility is the conditional probability of a respondent saying that the element ‘fits the venue’.



17

result of different patterns of responses to theelements by subgroups within a specific classifica-tion, such as age or market? Dividing the respon-dents by different subgroups (e.g. gender, age,etc.) produced different arrays of 36 utility values,some of which were very different from eachother across groups, and some of which were verysimilar. What elements look like they hardlychange in utility value, no matter how we dividethe respondents? In contrast, what elements looklike they are very sensitive to the way we dividethe respondents? Is there any pattern and if so,how can this pattern be found and explicatedmost simply?

Looking at the elements that most fit a specificvenue by subgroup within a classification cate-gory such as age could produce patterns that werehard to interpret. A preliminary analysis of thissort by gender and age produced no clear pattern.A possibly more productive initial analysis is tolook at the the element as the source of a ‘signal’.Following this analysis, one can look at whether,for a specific element, the signal is strong when itcomes to venues, i.e. for a specific element, dorespondents see differences in the fit of that ele-ment across the three venues?

Following this logic, let us look at what hap-pens for two elements, E04 (’dark fancyhouseblend . . . an extremely rich cup of coffee’)and E11 (‘a unique flavor, sure to delight . . .sweet and smooth rich cream complement thisdelectable treat’). Both of these elements scored 0on average for their utility to fit to an end venue.Both scored as more appropriate for Starbucksthan for either Burger King or Dunkin Donutsrespectively. However, the dynamics differ.

To discover these dynamics, we used themethod of ANOVA, which partials out the vari-ability in the interest utility values across the 957respondents into the contribution from venue, thecontribution from classification subgroup (e.g.genders) and the error. ANOVA looks at thedegree to which the variation in venue or genderexceeds the variation in error noise. The ANOVApulls out the variability due to venue (V), togender (G), to their interaction (V × G) and thenerror.

We see the complexity of the situation by look-ing at the ‘signal’ generated by these two ele-ments. The summary data appear in Table 5.

With the P-value (probability that the effect israndom, because of noise) we can see whether thevariability across venues or genders is by chancealone, or whether there is some signal tryingto get through. For element E04 (’dark fancyhouseblend . . . an extremely rich cup of coffee’),the differences in venue are probably at chancebecause the P-value is 0.33. In contrast, the dif-ferences in gender are probably real signals thatcome through because the P-value is 0.02. Let uscontrast that with element E11 (‘a unique flavor,sure to delight . . . sweet and smooth rich creamcomplement this delectable treat). Here the signalis the same from both venue and gender, with P-values around 0.12–0.14. If we follow the samelogic, but look at venue and age rather than venueand gender, then we find a completely differentstory.

Thus, the bottom line is that the patterns in ‘fitto venue’ do not emerge by looking at element,venue and subgroup among a specific classifica-tion variable such as gender or age.

Concept-response segments

As the foregoing analyses revealed, conventionalmethods for dividing the respondents throughdemographic or psychographic defined subgroupsdo not ensure that the individuals in the differentsubgroups show different response patterns. An

Table 5 Signal to noise analysis for two elements (E04 and E11)

F Ratio

P-value that the result is from chance alone

E04 E11 E04 E11

Fit to venue 1.11 2.14 0.33 0.12From gender 5.06 2.19 0.02 0.14V × G 0.08 1.74 0.92 0.18Fit to venue 4.00 1.31 0.02 0.27From age 1.01 0.86 0.41 0.50V × A 0.82 1.10 0.61 0.36

The approach uses analysis of variance to partial outvariability in the interest utilities, looking at the venue,the self-profiled category and error. Low P-valuessuggest a strong signal and real differences in the utilityvalue. V, venue; G, gender; A, age.



18

analysis of the correlations between utility valuesfor complementary subgroups (e.g. males andfemales) shows high correlation, suggesting thatthe demographic division does not correspond toa real segmentation. This homogeneity in the‘mindset’ of consumers falling into conventionallydifferent segments reveals itself in rather lowutility values for winning elements. These lowutilities may result from competition of trueunderlying segment differences, which cannotemerge from the division of respondents into con-ventional subgroups based upon conventionalclassification measures such as gender, age, mar-ket, etc. The unhappy result is that the segmentscounteract each other, suppressing the high per-forming elements for the segments that wouldotherwise appear if the segment could be isolated.Figure 3A,B shows this high intercorrelation

among utilities of supposed different subgroups,defined by self-profiling. What appeals to onegroup also appeals to the other. Typical breakoutsof respondents into self-profiled subgroups, e.g.by gender or age, do not show similar patterns.There must be a clearer way to understand indi-vidual differences.

An answer to this problem is provided by theapplication of concept-response segmentation(Moskowitz 1996). Through this process, seg-ments are found using the similarities and dissim-ilarities between the pattern responses acrossrespondents. Segments are thus formed through aclustering algorithm using the distance or dissim-ilarity between pairs of respondents to dividerespondents. Respondents in the same segmentshow similar utility patterns, i.e. similar elementsthat fit the venue they selected. The number ofsegments is left to the researcher, but the extrac-tion of the segment is a statistical matter that isdetermined by the algorithm.

The segmentation was carried out on individualmodels, pooling together all 957 individual per-suasion models from the three separate studies,one per venue. The persuasion models, with thedependent variable being the rating rather thanthe binary, were the more appropriate numberson which to run the segmentation because thepersuasion models contained the metric informa-tion in the ratings (i.e. degree of fit). Such poolingof respondents in the three sets, with the threedifferent venues, may be justified on the basis ofvery similar average models shown in Table 4.

The segmentation followed these steps:1 List out the 957 rows of data, one per respon-dent, and the 36 columns of persuasion utilities.2 Perform a principal components factor analy-sis, extracting all roots with eigenvalues exceed-ing 1.5. This second step generated five factors,with each respondent located on the five factors,with five factor scores.3 Save the factor scores, which represent areduced set of variables on which the 957 respon-dents load.4 Cluster the respondents into two, three andfour groups, using k-means clustering. The mea-sure of distance between pairs of respondents isthe statistic 1-R, where R is the Pearson correla-tion between two respondents computed on thefive factor scores.

Figure 3 (A) Utility values for 36 elements for males vs. females. There were 682 females and 275 males. Each circle is the average utility for a single element. (B) Utility values for 36 elements for five ethnic groups. The largest group (Ethnic2) comprises Caucasians. Ethnic1 = African American (n = 43), Ethnic2 = Caucasian (n = 823), Ethnic3 = Hispanic (n = 33), Ethnic4 = Asian (n = 26) and Ethnic5 = other (n = 32). Despite the large differences in base size, the patterns of utility values are quite similar to each other.

A

B



19

5 Segmentation is a statistical procedure, but theselection of which segmentation to use for under-standing the consumer mind is more interpreta-tive than statistical.6 An analysis of the elements that do best in eachsegment showed that with a two-segment solutionand with a four-segment solution, the segmentswere not very clear, whereas the segments weremore interpretable with a three segment solution.

The winning (greatest fit for a venue) for eachsegment appears in Table 6. The losing elementsfor each segment are italicized. The segmentsshow almost the same additive constant, so thateach have the same predisposition to say that theconcept ‘fits the venue’. However, the segmentsdiffer dramatically in what they feel fits a venue.1 Segment 1 responds strongly to traditional orclassic messages such as ‘a distinctive, well-rounded cup of coffee . . . the ideal way to starta busy day’. Segment 1 may be called ‘classic’ or‘traditional’.2 Segment 2 respondents are more mixed. Theylike iced coffee, but they also like images talking

about situations such as ‘a slightly caffeinatediced coffee drink . . . to help you get through yourday’. Segment 2 may be called the imaginerbecause they respond to descriptions of situationsor implied situations (iced coffee is a summerdrink).3 Segment 3 is clearly a sensory seeker, respond-ing strongly to sensory descriptions, but not toelaborate descriptions. They like descriptors suchas ‘tangy taste, rich body and pleasing aroma’,and they do not like descriptors about ice coffee.4 The three concept-response segments show dif-ferent patterns of utilities, as Fig. 4 reveals.

The concept-response segments cut across allkey subgroups. However, it is clear from Table 7that Starbucks has a greater proportion ofElaborates than do either Burger King or DunkinDonuts. This finding makes sense, because Star-bucks features more coffee beverages. In fact,Starbucks has proportionally fewer Classicrespondents, i.e. those who we would expect todrink coffee because of its functional benefits suchas an early morning, wake-me-up beverage.

Table 6 Elements that most strongly drive ‘fit to venue’ for the three concept-response segments

Total

S1 S2 S3

Classic ImaginerSensoryseeker

Base size 273 406 278Additive constant 53 54 51 53S1 – Classic

E5 A distinctive, well-rounded cup of coffee . . . the ideal way to starta busy day

3 11 2 −3

E2 A coffee that is guaranteed to wake you up 2 8 2 −4E19 The freshest cup of coffee possible 2 7 −2 4E20 A masterful combination of carefully chosen coffee from each

year’s harvest0 7 −6 4

E25 Exceptional aroma and a deep mellow body 1 7 −6 5S2 – Imaginer

E8 Iced to the max . . . for those hot summer days 1 6 4 −10E16 Vanilla and chocolate fudge combined . . . a unique flavor that is

sure to please−5 −27 4 4

E6 A slightly caffeinated iced coffee drink . . . to help you get throughyour day

−3 0 3 −14

S3 – ElaborateE24 Tangy taste, rich body and pleasing aroma 0 2 −5 6E26 Spicy aroma, medium body and clean flavor make this coffee

stand out−1 0 −6 6

E25 Exceptional aroma and a deep mellow body 1 7 −6 5

S1, segment 1; S2, segment 2; S3, segment 3.



20

Using the segments and the venues to select features

The segments or mindsets, combined with thevenues (focus of attention), provide a way to startthe selection of features for the new coffee bever-age. The strategy is intuitive and straightforward.The analyst classifies each respondent by mem-bership in the segment (mindset), as well as thevenue of the study (focus of attention). This clas-sification generates nine combinations. For eachcombination, the researcher selects those particu-lar elements that show a utility value above a cut-off, which for argument’s sake here we make as+6 or higher. The utility +6 is the region wherethe element becomes impactful.

Following this approach, we see from Table 8that there are both different features for the samevenue from the three segments, but also differentfeatures desired by the same segment for each ofthe three venues.

Discussion and conclusions

It is clear from these results that respondents dis-criminate among the features of a coffee beverage,showing strong feelings of lack of appropriatenessfor many elements for the venues. However,respondents do not feel that the more conven-tional elements of a coffee product strongly fit onevenue more than they fit another, except for cer-tain flavor characteristics. Analysis of the data byvenue, by gender and by ethnic background sug-gests that these conventional breaks in the datalead to the same conclusion, with perhaps someelements doing a bit better in one subgroup vs.another.

A more instructive way to look at the resultscombines all respondents from the three studies,and then clusters them by the patterns of theirutilities. This analysis generates three segments,which we have labelled the Classic, the Imaginerand the Elaborate. Similar studies carried outwith different foods in the Crave It! Mega Studyrevealed three similar overarching segments infoods (Beckley & Moskowitz 2002; Moskowitzet al. 2005). The distribution of these three seg-ments in each study makes sense, with BurgerKing having a relatively larger proportion of Clas-sics, and Starbucks having a much larger propor-tion of Elaborates than either Burger King orDunkin Donuts. The distribution is consistentwith the brand heritage of the three outlets. It isimportant to note, further, that the concept-response segments all appear in every venue study,meaning that conventional methods for under-standing the underlying ‘rules’ of the consumermind regarding feature preferences may not besufficiently precise to show these more profounddifferences. Conventional methods cannot disen-tangle the underlying distribution of these three‘assumed primary mindsets’ that the segmenta-

Figure 4 Scatterplot matrix of the 36 utilities for the three concept-response segments. The plots suggest that the segments show different patterns, rather than being similar to each other, as were gender and ethnic group. Segment 1 (S1) = classic; Segment 2 (S2) = imaginer; Segment 3 (S3) = elaborate.

Table 7 Distribution of the three concept-response segments among respondents of the three different studies

S1 = Classic S2 = Imaginer S3 = Elaborate Total

Burger King 35 42 23 100Dunkin Donuts 29 41 31 100Starbucks 17 46 37 100

Participation in a study is assumed to act as a proxy for ‘interested in the outlet’.S1, segment 1; S2, segment 2; S3, segment 3.



21

Table 8 Features appropriate for each venue, based upon the responses of the three segments

S1 = Classic S2 = Imaginer S3 = Elaborate

Burger KingA distinctive, well rounded cup of

coffee . . . the ideal way to start a busy day

A slightly caffeinated iced coffee drink . . . to help you get through your day

Exceptional aroma and a deep mellow body


A masterful combination of carefully chosen coffee from each year’s harvest

Dark fancy houseblend . . . an extremely rich cup of coffee

A jolt of caffeine to awaken your senses

The freshest cup of coffee possible

Dunkin DonutsExceptional aroma and a deep

mellow bodyA coffee that is guaranteed to wake

you upThe freshest cup of coffee possible

A distinctive, well-rounded cup of coffee . . . the ideal way to start a busy day

Spicy aroma, medium body and clean flavor make this coffee stand out

The freshest cup of coffee possibleIced to the max . . . for those hot

summer daysA coffee that is guaranteed to

wake you upAroma, body and

flavor . . . perfectly balanced

StarbucksDark fancy houseblend . . . an

extremely rich cup of coffeeVanilla and chocolate fudge

combined . . . a unique flavor that is sure to please

New classic combination . . . pistachio & maple walnut . . . unleash the nutty side in you

A distinctive, well-rounded cup of coffee . . . the ideal way to start a busy day

Iced to the max . . . for those hot summer days

Vanilla and chocolate fudge combined . . . a unique flavor that is sure to please

Its unique aroma will appeal to your senses

Enjoy the taste of toffee in a light cream . . . a new summer favorite


Aroma, body and flavor . . . perfectly balanced

Mocha and spicy Java create a one of a kind chocolate fantasy

A jolt of caffeine to awaken your senses

A robust strong coffee blend . . . made from dark roasted Brazilian beans

Exceptional aroma and a deep mellow body

Spicy aroma, medium body and clean flavor make this coffee stand out

A masterful combination of carefully chosen coffee from each year’s harvest

A unique flavor, sure to delight . . . sweet and smooth rich cream complement this delectable treat

Iced to the max . . . for those hot summer days

A robust strong coffee blend . . . made from dark roasted Brazilian beans

Wonderfully smooth with deep tones

Thrilling burst of vanilla flavor and sweet, crisp taste . . . gives you ‘more to go wild for’





22

tion reveals. All three mindset segments appear inevery classification subgroup.

Research-driven development: using the data to create new products from ‘first principles’

Although this research was conducted with theaim to better understand the mind of the con-sumer respondent with respect to coffee beveragevenues, marketers working with conjoint mea-surement use it to create actual products. Thecreation of such products is not automatic,although it is fairly simple. The developer looksto the different silos, selects one element or noelement from each silo to create the concept andthen computes the expected response. Thecomputation itself is fairly straightforward. Theanalysis had deconstructed the concept intocomponents, and with ordinary least squares cre-ated a simple additive model. That model allowsthe researcher to combine the additive constantwith the utilities of the elements. The arithmeticsum is, in turn, an estimate of the proportion ofrespondents who would rate the concept 7–9 onthe attribute rating scale. Because the elements inthis study are all product features, the conceptwould be a product concept whose fit to venuecan be thus estimated.

Acknowledgement

The authors would like to acknowledge the helpof Ms. Margaret Mirabile, Ms. Suzanne Gabrioneand Ms. Joyce Mitchell in the preparation of thismanuscript.

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