Benefit Hierarchy Analysis

The steps in the new product development process entail defining the product concept, identifying the

consumer needs and product benefits, and determining the target consumer demographics. Then, an

optimal product formulation (or several alternative formulations) is developed that can satisfy potential

consumer needs, at a manufacturing cost that is low enough to justify a reasonable price.

In every step of the new product development process, researchers are trying to determine what

product benefits, consumer or sensory attributes, ingredients (including their different levels and

combinations) drive product liking, purchase intent or preference. Hierarchy Analysis is a

relatively new data analysis technique that allows researchers to answer these questions by

organizing benefits, attributes or different ingredient levels into hierarchies according to their

relative impact on consumer choice and preference.

The most noticeable difference between Hierarchy Analysis and traditional approaches to product

optimization is the choice of optimization criterion. Let’s consider a typical study where each

respondent tastes several similar products sequentially and uses the following 9-Point Hedonic

Overall Liking Scale to evaluate each product:

9 - Like Extremely 8 - Like Very Much 7 - Like Moderately 6 - Like Slightly 5 - Neither Like nor Dislike 4 - Dislike Slightly 3 - Dislike Moderately 2 - Dislike Very Much 1 - Dislike Extremely Traditional data analysis methodologies will either calculate the mean Overall Liking score for

each product and use it as a criterion for decision making, thus implying that the best product is

the one with the highest mean Overall Liking score; or calculate for each product a percent of

respondents who rated the product as Like Extremely or Like Very Much, the so called Top 2 Box

score, and use it as a criterion for decision making, thus implying that the best product is the one

with the highest Top 2 Box Overall Liking score.

In contrast, Hierarchy Analysis uses the criterion that the best product is the most preferred

product. Let’s consider the example presented in Figure 1, which are the results from ten

respondents who rated two products using a 9-point Overall Liking scale.

Figure 1 – Product Ratings

Respondent

Product A

Rating

Product B

Rating

Preferred

Product

1 8 9 B

2 8 9 B

3 8 9 B

4 8 9 B

5 8 9 B

6 8 9 B

7 8 9 B

8 8 9 B

9 9 2 A

10 9 1 A

Mean Score 8.2 7.5

Top 2 Box Score 100% 80%

Preference 20% 80%

Using either the mean Overall Liking score or the Top 2 Box Overall Liking score, we would come

to the conclusion that product A is better than product B. However, when analyzing individual

preferences on a respondent by respondent basis, 80% of the respondents preferred product B

over product A. Thus, according to the criterion that the best product is the most preferred

product, we would infer that product B is better than product A.

The main source of discrepancies between the outcomes of different criteria usage comes from

the way that the three different methods use the original 9-Point Hedonic Overall Liking scale:

Mean Overall Liking score criterion treats the scale as an interval scale, presuming that all

differences between numeric tags assigned to each verbal statement are equidistant.

Top 2 Box Overall Liking score treats the 9-Point Hedonic Scale as binomial, recognizing

only the difference between a “good rating” (Like Extremely or Like Very Much) and a “bad

rating,” but neglecting all the other differences.

Preference criterion treats the 9-Point Hedonic Overall Liking scale as ordinal, assuming

that the rating 9 is better than the rating 8, that the rating 8 is better than the rating 7, etc.,

without any assumptions regarding distances between verbal statements and without any

loss of information resulting from aggregating the statements into a “good” and a “bad”

category.

From the measurement theory view point [1], preference criterion is the only correct criterion,

corresponding to the nature of the measurement scale used.

The theoretical behavior background of the technique is based on a model of consumer behavior

known as “bounded rationality.” The term and the concept were originally introduced by Herbert

A. Simon [2], who in 1978 was awarded the Nobel Prize in economics “for his pioneering research

into the decision-making process.” Ideas of bounded rationality were further expanded by Daniel

Kahneman [3], who in 2002 received the Nobel Prize in economics "for having integrated insights

from psychological research into economic science, especially concerning human judgment and

decision-making under uncertainty."

The main distinction of “bounded rationality” from “full rationality” (which is assumed is such

popular method as conjoint analysis) lies in the recognition that consumers have limited cognitive

abilities and limited time to make decision. Therefore, consumers are not able to evaluate all

product benefits, attributes or ingredients at once, than immediately construct a utility function and

maximize its expected value. There is overwhelming experimental evidence for substantial

deviation of actual consumer behavior from what is predicted by traditional rationality models [3].

Some authors call it “irrationality”, but, in our opinion, the problem is not that people behave

irrationally, but that elegant and beautiful mathematical rationality models do not adequately

explain the consumer’s decisions and choices. According to [4, p.9}, “The greatest weakness of

unbounded rationality is that it does not describe the way real people think.”

According to the bounded rationality concept, consumers employ the use of heuristics or schemas

to make decisions rather than strict rigid rules of decision optimization [5]. A schema is a mental

structure we use to organize and simplify our knowledge of the world around us. We have

schemas just about everything, including ourselves, other people, cars, phones, food, etc.

Schemas affect what we notice, how we interpret things and how we make decisions and act. We

use them to classify things, such as when we ‘pigeon-hole’ people. They also help us to forecast

and predict what will happen in the future. We even remember and recall things via schemas,

using them to ‘encode’ memories. Schemas are often shared within cultures and allow

communication to be shortened. Every word is, in fact, a schema, that we can interpret in our own

way. We tend to have favorite schemas which we use often. They act like filters, accentuating

and downplaying various aspects of the things surrounding us, including different product

attributes and benefits. Schemas are also self-sustaining, and persist even in the face of

disconfirming evidence. If something does not match the schema, such as evidence against it, the

contradictory evidence is often consciously or subconsciously ignored. Some schemas are easier

to change than others, and some people are more open to changing their schemas than others.

Schemas are also referred to in literature as mental models, mental concepts, mental

representations and knowledge structures. The basic proposition of the bounded rationality theory

applied to consumer behavior is that consumers are rational, and when they make choices or

preferences between products, they have some conscious or subconscious reasons for those

choices or preferences that are realized trough their individual schemas.

Hierarchy Analysis presumes that each consumer uses an individual schema for evaluating a

particular category of products and makes choices between products within the category based on

this schema. Hierarchy Analysis represents consumer schema in the form of a hierarchy of

benefits, attributes or ingredient levels arranged in the order of likelihood of their impact on

consumer decisions. By aggregating schemas among random probability sample of consumers,

Hierarchy Analysis allows us to determine the prevalent schema in a population. On other hand,

Hierarchy Analysis methodology allows us to group consumers into clusters based on similarities

or dissimilarities of their individual schemas to discover market segmentation based on consumer

schemas. In addition, Hierarchy Analysis methodology includes procedures for testing statistical

hypotheses related to consumer schemas, for example, if a particular product benefit is more

important than another benefit, or if a particular product benefit is more important for one

consumer group than for another consumer group, or if a particular product benefit is more

important for choice of one product than for choice of another product.

Bounded rationality concept assumes that consumers evaluate product in three steps [5]:

First they search for some familiar cues.

When consumers have found enough cues, they stop searching and start evaluating and

organizing these cues in some order of importance to them or the magnitude of the

differences between products.

Then they make judgments regarding “overall liking,” “purchase intent” and the choice of

product.

The Hierarchy Analysis model relies on the assumption that some of the cues recognized by

consumers are related directly or indirectly, consciously or subconsciously, to the set of product

benefits and attributes that we ask consumers to evaluate (or to the levels and the combinations of

the ingredients and the sensory attributes that are associated with the products, evaluated by

consumers).

Boundedly rational consumers do not necessarily make quantitative choices between alternative

options based on their perceived utilities. Instead, they rely on qualitative expectations regarding

directional changes. For each pair of products, one product could be evaluated by a consumer as

better than or worse than another, or the differences between two products could be negligible.

In this model of consumer behavior, the actual magnitude of the differences between products

does not affect the product choice, only the directional differences matter. On other hand, the

greater the magnitude of the differences between products, the more consumers will recognize the

differences as noticeable and express their preferences. Therefore, the strength of preferences is

measured, not in the magnitude of the differences between products or their utilities, as in the

case of conjoint analysis, but by the proportion of consumers who evaluated the product as

preferred over the alternatives. By considering only the directional differences between products

and benefits, this method essentially treats all scales of measurement used in consumer research

as ordinal, not interval. This corresponds to the actual nature of the scales and makes this

technique conceptually more valid in comparison with traditional statistical methods based on

means and correlations that treat all consumer research scales as if they were interval.

Another important advantage of this approach over traditional statistical techniques is an

acknowledgment of the fact that each respondent has an individual interpretation of the meanings

of different values on psycholinguistic scales. Traditional statistical methods compare ratings

given by an individual respondent to sample averages. This implies that all respondents interpret

scales in the same manner. But, individual interpretations of scales might differ between

respondents based on cultural background, education, age, gender, personal experiences, etc.

Hierarchy Analysis deals with data on a respondent by respondent basis, assuming that each

respondent interprets the scales in an individual manner but consistently across various products,

benefits, attributes, or concepts.

There are multitudes of articles in marketing research literature related to the affect of cross-

cultural differences on scale item interpretations. This issue taints inferences based on the

comparison of mean scores for the same product or benefit across different countries, languages

or cultures. By analyzing data on a respondent by respondent basis, Hierarchy Analysis is free

from this problem and allows the direct comparison of results across countries, languages and

cultures.

Traditional statistical methods usually assume the normal distribution of answers among

respondents for all attributes and criterion ratings. Even if this is not stated explicitly, the mere fact

that traditional statistical methods use only means and standard deviations to describe the

statistical distribution of answers, characterizes the distribution as normal. Moreover, assuming

normality implies that the distributions must be symmetric. In fact, we practically never observe

symmetrical normal distribution in marketing research studies; in many cases answers are skewed

toward high ratings, limited by range, and do not have a symmetrical normal distribution. Also, as

we stated above, a normal distribution could be applied only if we treat all scales as interval, which

actually contradicts the ordinal nature of the scales used. Hierarchy Analysis methodology does

not rely on any assumptions about distributions and accepts all actual distributions “as is”, which

makes it a robust statistical method by definition.

Most of the traditional statistical techniques are based on linear relationships between criterion

and factors (regression and correlation analysis) or linear additive models (conjoint analysis) or

polynomial models (response surface analysis). Hierarchy Analysis presumes only probabilistic

directional relationships between criterion and factors, which makes it independent from the

researcher’s assumptions regarding data.

The integral part of Hierarchy Analysis is the philosophy of Exploratory Data Analysis (EDA),

which was introduced by John W. Tukey [6]. The exploratory approach to data analysis calls for

the exploration of the data with an open mind. According to Tukey, the goal of EDA is to discover

patterns in data. He often likened EDA to detective work; Tukey suggested thinking of exploratory

analysis as the first step in a two-step process similar to that utilized in criminal investigations. In

the first step, the researcher searches for evidence using all of the investigative tools that are

available. In the second step, that of confirmatory data analysis, the researcher evaluates the

strength of the evidence and judges its merits and applicability.

In the classical analysis framework, the data collection is followed by the imposition of a model

(normality, linearity, etc.), and then the analysis that follows is focused on the parameters of that

model. For EDA, the data collection is followed immediately by an analysis that has the goal of

inferring which models are appropriate. Hence, the EDA approach allows the data to suggest

models that best fit the data. Following the spirit of EDA, Benefit Hierarchy Analysis evaluates all

the possible multimodal relationships between product preferences and benefits and estimates the

likelihood that each benefit has an impact on product preference. The result is a hierarchy of

benefits, arranged in the order of likelihood of their impact on product choice and preference.

Another cornerstone of Benefit Hierarchy Analysis is the concept of Probabilistic Causality [7]. A

probabilistic causality approach applied to the analysis of consumer choice and preference data

assumes the following:

The observed choices and preferences are not spontaneous, but are the results of the

conscious or subconscious use of schemas by consumers in their decision making

process.

The actual product characteristics, such as various ingredient levels or sensory attributes

could be related to cues discovered by consumers and used in their schemas.

The perceived product benefits and attributes could be related to cues discovered by

consumers and used in their schemas.

Consumer schemas represent reasons or causes for their choices.

Consumers do not use their schemas deterministically and always consistently.

Consumers do not use their schemas stochastically or completely randomly.

For each of the possible product benefits, attributes or ingredients, there is an objective

probability that consumers use this particular component in determining their choices and

preferences.

This causal probability could be estimated from the data.

The process of estimating causal probabilities from observed data starts with the assumption that

all attributes or benefits are mutually independent and a-priori each have an equal chance to be a

cause for the consumer’s choices or preferences. Then, by analyzing evidence of all pairwise

relationships between benefits from the data, and testing, for each pair of benefits, two alternative

hypotheses: (1) that benefit A is more likely to be a cause for the choice and preference between

products than benefit B, and (2) that benefit B is more likely to be a cause for the choice and

preference between products than benefit A, we can estimate for every benefit, the a-posteriori

likelihood that the benefit is a cause of choice and preference between products. The result is a

hierarchy of benefits, arranged in the order of this a-posteriori likelihood of the impact on product

choice and preference.

The following examples illustrate several practical uses of Hierarchy Analysis in consumer

research. The company wanted to develop a new kind of fresh baked bread to sell in stores

nationwide. Their product developers created nine prototypes for the bread using Taguchi

experimental design for four three-level (High-Medium-Low) design factors, as outlined below in

Figure 2.

Figure 2 – Design Factors

Product Factor 1 Factor 2 Factor 3 Factor 4

1 3 1 3 2

2 2 2 3 1

3 2 3 1 2

4 3 2 1 3

5 1 1 1 1

6 1 2 2 2

7 2 1 2 3

8 3 3 2 1

9 1 3 3 3

To identify which of the nine product prototypes is the most preferred by consumers, a nationally

representative sample of 450 consumers were interviewed in 25 locations. Each respondent

tasted 4 of the 9 samples of bread (incomplete block design). To avoid order bias, we

implemented a random balanced rotation algorithm. As a result of the random balanced rotations,

each respondent tasted a unique set of four products. Each product was tasted an equal number

of times in each position balanced by location and each pair of products was tasted an equal

number of times on each sequential position. For each product, respondents were asked Overall

Liking, using a 9-point scale, and 13 diagnostic attributes. The following Figure 3 shows the

results of the Hierarchy Analysis.

Figure 3: Hierarchy Analysis of Products

5.4

7.5

33.9 H

38.0 H

51.9 F

62.6 F

67.1 E

89.0 C

94.6 C

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

PRODUCT 1 (A)

PRODUCT 7 (B)

PRODUCT 8 (C)

PRODUCT 6 (D)

PRODUCT 2 (E)

PRODUCT 4 (F)

PRODUCT 9 (G)

PRODUCT 3 (H)

PRODUCT 5 (I)

In the Hierarchy Analysis, all products are arranged in the order of their preference and labeled

alphabetically, so “A” is a label for the most preferred or best product, while “I” is a label for the

least preferred or worst product. The bars for each product represent the likelihood that the

product is the most preferred by consumers in comparison to the other products being considered.

For PRODUCT 1, which is labeled with the letter “A,” the 94.6% denotes, that based on the

evidence in the data, we have a 94.6% confidence that PRODUCT 1 is the most preferred

product. The letter “C” after the confidence signifies that this product is more preferred than any

product labeled with the letter “C” or below, with at least 94.6% confidence. PRODUCT 7, which is

labeled with the letter “B,” is the second most preferred product. The likelihood that PRODUCT 7

is the most preferred product is equal to 89.0%, which is greater than all the products labeled with

the letter “C” or below. Statistically, PRODUCT 1 and PRODUCT 7 are at parity, despite the fact

that PRODUCT 1 has a numerically greater likelihood of being the most preferred product.

Now, when we know the hierarchy of product preference, we can define the optimal levels of four

Taguchi design factors using a procedure called Non-Parametric Response Surface Analysis. The

principal difference of this analysis from the traditional Response Surface Analysis is the fact that

we do not restrict a set of possible functions describing the relationships between the design

factors and the overall criterion to being the subset of polynomial regression functions, but we

build the response surface as a multitude of points of interest. The following Figure 4 shows the

results of the Non-Parametric Response Surface Analysis.

Figure 4– Non-Parametric Response Surface Analysis

PRODUCT Factor 1 Factor 2 Factor 3 Factor 4

1 3 1 3 2

2 2 2 3 1

3 2 3 1 2

4 3 2 1 3

5 1 1 1 1

6 1 2 2 2

7 2 1 2 3

8 3 3 2 1

9 1 3 3 3

Optimal Level 3 1 2 3

Confidence 98.2 91.1 88.4 76.5

Cells with the optimal levels of the corresponding factors are highlighted

PRODUCT 1 has the optimal levels for factors 1 and 2, while PRODUCT 7 has the optimal levels

for factors 2, 3, and 4. A product with a high level of factors 1 and 4, a low level of factor 2, and a

medium level of factor 3, which was not part of the original design, could potentially be the best

product.

Figures 5, 6, 7 and 8 represent the non-parametric response surfaces for the factors. The

numbers in the tables represent the likelihood that the corresponding level of the factor is

preferred by consumers over the other levels of the same factor.

Figure 5 - Non-Parametric Response Surface Analysis of Factor 1

5.1

46.7

98.2

0.0

25.0

50.0

75.0

100.0

Low Medium High

Figure 6 - Non-Parametric Response Surface Analysis of Factor 2

91.1

56.0

2.90.0

25.0

50.0

75.0

100.0

Low Medium High

Figure 7 - Non-Parametric Response Surface Analysis of Factor 3

0.0

88.4

61.6

0.0

25.0

50.0

75.0

100.0

Low Medium High

Figure 8 - Non-Parametric Response Surface Analysis of Factor 4

6.7

66.8

76.5

0.0

25.0

50.0

75.0

100.0

Low Medium High

As we can see from the results of the four main effect analyses for the four design factors above,

the gradient of differences between the optimal factor level and the second best factor level is

51.6% for factor 1; for factor 2 it is 35.2%, for factor 3 it is 26.8% and, for factor 4 it is 9.6%.

Therefore, by deviation from the optimal factor level, we would be exposed to the highest risk for

factor 1, followed by factor 2, and then factor 3, with factor 4 representing the lowest risk.

Another useful application of Hierarchy Analysis involves linking the consumer preferences to the

sensory attributes of the products. This methodology evaluates, for each sensory attribute, the

likelihood that consumers can recognize different levels of the attribute for different products and

make choices or express preferences between products based on this information. If consumers

do not express preferences between two products with different levels of a sensory attribute, then

we might conclude that the difference between these two levels of a sensory attribute is not

noticeable to the average consumer, but can be discriminated by a trained sensory panel.

In the bread optimization project described above, a sensory panel evaluated 55 various sensory

attributes for each bread sample;

32 attributes are related to the taste of the bread,

10 attributes are related to the texture of the bread,

13 attributes are related to the aroma of the bread.

As a result of applying the Hierarchy Analysis methodology to all 55 attributes, we discovered 11

sensory attributes that affect consumer choices with at least an 80% likelihood. Each of these 11

attributes has a larger impact on consumer preferences with at least a 95% confidence level than

any of remaining 44 attributes. The following Figure 9 illustrates the results of the application of

the Hierarchy Analysis methodology to the sensory attributes.

Figure 9 – Hierarchy Analysis of Sensory Attributes

77.7 oQ

80.9 L

81.6 L

85.5 J

86.0 J

89.0 H

91.0 G

93.5 F

95.5 F

95.5 F

95.5 F

99.2 B

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

FLAVOR27 (A)

FLAVOR6 (B)

TEXTURE5 (C)

TEXTURE9 (D)

FLAVOR26 (E)

FLAVOR9 (F)

FLAVOR23 (G)

AROMA8 (H)

AROMA2 (I)

AROMA7 (J)

TEXTURE7 (K)

FLAVOR4 (L)

The sensory flavor attribute FLAVOR27 has the singular highest impact on consumer choice, with

a 99.2% confidence level. The four attributes, FLAVOR6, TEXTURE5, TEXTURE9, and

FLAVOR26, are statistically at parity on their likelihood to impact consumer preferences, with

confidence levels ranging from 95.5% to 93.5%. Overall, flavor and texture sensory attributes

have a greater impact on consumer choices and preferences between the nine samples of bread

than the aroma related attributes, because the most impactful of the aroma attributes is ranked

only eighth in the hierarchy.

The Hierarchy Analysis for sensory attributes not only identifies which sensory attributes have an

impact on consumer choice, but defines the optimal range for each sensory attribute. The

following Figure 10 illustrates the optimal sensory attribute range for the most impactful sensory

attribute FLAVOR27.

Figure 10 – Hierarchy Analysis for the Most Impactful Sensory Attribute

PRODUCT 9

PRODUCT 8

PRODUCT 7

PRODUCT 6

PRODUCT 5

PRODUCT 4

PRODUCT 3

PRODUCT 2

PRODUCT 1

R2 = 0.4551

0.0

25.0

50.0

75.0

100.0

8 9 10 11 12 13 14 15 16

The optimal range for this attribute is below 9.5. Only two the most preferred products, PRODUCT

1 and PRODUCT 7, have this sensory attribute in the optimal range. As we can see from Figure

10, in this case, the application of the standard polynomial regression to the data would give a

similar conclusion: products with smaller levels of the sensory attribute are more preferred;

however, the Hierarchy Analysis reveals the two ranges of the attribute that are recognisable by

consumers. Products in the optimal range have relatively high average likelihood (91.8%) of being

the most preferred product, while products with sensory attribute levels higher than 9.5 have a low

average likelihood of being the most preferred product (only 38.1%).

During the product evaluation, respondents were asked the overall liking rating for each product

and the ratings of 13 diagnostic attributes, using the same 9-point scale mentioned above. The

following Figure 11 demonstrates the application of the Hierarchy Analysis to the ratings of the 13

bread diagnostic attributes.

Figure 11 – Hierarchy Analysis of Diagnostic Attributes

99.8 B

85.6 D

82.8 D

61.2 hI

60.2 hI

56.4 hJ

55.5 hJ

48.1 J

44.8 J

30.4 K

13.5 L

6.3

5.4

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

Taste of bread (A)

Texture of bread (B)

Crust of bread (C)

Appearance of bread (D)

Moistness of bread (E)

Aroma of bread (F)

Thickness/denseness of bread (G)

Crispiness/crunchiness of crust (H)

Color of bread crust (I)

Color of bread interior (J)

Liking of particulates (K)

Amount of crumbs from bread (L)

Amount of particulates within bread (M)

The liking of the taste of the bread is the singular best predictor of overall product preferences,

with 99.8% likelihood. The liking of the texture of the bread and the liking of the crust of the bread

are statistically at parity with an 85.6% and 82.8% likelihood, respectively. These results closely

match the sensory attribute Hierarchy Analysis, where the two sensory attributes with the highest

likelihood of impact were the flavor attributes and five out of the top seven attributes were the

flavor related sensory attributes, while the remaining two were the texture related attributes.

Figure 11 represents the prevalent schema in a population for choosing between samples of

bread. As mentioned above, while assessing the prevalent schema in a population, we calculated

the individual schema for each respondent. Now we can use these results to evaluate the

homogeneity of the consumer schemas. Applying traditional Ward’s algorithm of cluster analysis

to individual schemas, we discovered two different consumer segments with different schemas.

The following Figure 12 illustrates the statistical comparative analysis of two schemas.

Figure 12 – Comparative Schema Analysis

COMPARATIVE SCHEMA ANALYSIS

95% Significant Differences

Amount of particulates

within bread

Liking of particulates

Aroma of bread

Amount of crumbs from

bread

Moistness of bread

Thickness/ denseness of

bread

Crispiness/

crunchiness of crust

Crust of bread

Texture of bread

Color of bread interior

Color of bread crust

Appearance of bread

Taste of bread

0.0

25.0

50.0

75.0

100.0

0 25 50 75 100

SEGMENT 2 (48%)

SE

GM

EN

T 1

(5

2%

)

For all consumers, the most impactful product attribute is the taste of the bread. However for 52%

of consumers (SEGMENT 1), the second most impactful attribute is the aroma of the bread. For

the other 48% of consumers (SEGMENT 2), the aroma of the bread is ranked very low on the

schema hierarchy; this is why aroma was not placed high on an average consumer schema

presented on Figure 11. We can clearly see that the taste and the crust of the bread are equally

important for both consumer segments. However, the aroma of the bread is significantly more

impactful for SEGMENT 1, while the texture of the bread and the crispiness/crunchiness of the

crust are significantly more impactful for SEGMENT 2, with at least 95% confidence.

As result of applying two different schemas to the product evaluation, consumers belonging to the

different segments prefer different products. The following Figure 13 illustrates the results of the

statistical comparative product choice analysis.

Figure 13 – Comparative Choice Analysis

COMPARATIVE CHOICE ANALYSIS

95% Significant Differences

PRODUCT 1

PRODUCT 2

PRODUCT 3

PRODUCT 4

PRODUCT 5

PRODUCT 6

PRODUCT 7

PRODUCT 8

PRODUCT 9

0.0

25.0

50.0

75.0

100.0

0.0 25.0 50.0 75.0 100.0

SEGMENT 2 (48%)

SE

GM

EN

T 1

(5

2%

)

Consumers in SEGMENT 1 preferred PRODUCT 8, PRODUCT 4, and PRODUCT 9 with a

significantly greater likelihood than the consumers in SEGMENT 2, with PRODUCT 8 being the

most preferred product in SEGMENT 1, with 93.4% likelihood. Consumers in SEGMENT 2

preferred PRODUCT 1, PRODUCT 6, and PRODUCT 2 with a significantly greater likelihood than

consumers in SEGMENT 1, with PRODUCT 1 being the most proffered product in SEGMENT 2,

with 98.5% likelihood. Interestingly, PRODUCT 7 is the second most preferred choice for both

segments, and should be chosen if the manufacturer decides to introduce just one new product to

the market. Alternatively, the introduction of two new products corresponding to PRODUCT 1 and

PRODUCT 8 will better satisfy both segments. Product optimization, based on experimental

design and sensory attributes, illustrated above, could be performed for every segment for more

insight.

Hierarchy Analysis is a versatile and robust statistical methodology that helps to solve many tasks

of consumer research. It has more than 15 years of history of usage for hundreds of consumer

research projects by leading consumer packaged goods manufacturers. It is based on the

bounded rationality consumer behavior theory and treats all consumer research scales as ordinal.

The analyses are performed on a respondent by respondent basis, without unjustified

assumptions of interval scales, respondent uniformity, linearity and normality. It provides

quantifiable recommendations for choosing the best product prototype and the best levels of

design factors or sensory attributes. It reveals the reasons for consumer choice and preference

between products (known as consumer schemas), provides statistical tests for homogeneity of

schemas in the population and discovers consumer segments in the cases of heterogeneous

consumer schemas.

Bibliography 1. Coombs C. H. (1964) A Theory of Data. New York: John Wiley & Sons, Inc 2. Simon H. A. (1957) Models of Man: Social and Rational (Mathematical Essays on Rational

Human Behavior in a Social Setting). New York: John Wiley & Sons, Inc 3. Kahneman D., Slovic P., Tversky, A. (Eds.). (1982) Judgment Under Uncertainty:

Heuristics and Biases. New York: Cambridge University Press. 4. Gigerenzer, G., Todd, P.M., & the ABC Research Group. (1999). Simple Heuristics that

make us smart. New York: Oxford University Press. 5. Gigerenzer G. Selten, R. (Eds.). (2001) Bounded rationality: The adaptive toolbox.

Cambridge/MA: MIT Press. 6. Tukey J. W. (1977) Exploratory Data Analysis. Addison-Wesley 7. Pearl, J. (2000) Causality: Models, Reasoning, and Inference. New York: Cambridge

University Press.