Behaviours Versus Demographics as Predictors of Segment Membership

Christopher Riquier, Rachel Kennedy and Byron Sharp

Marketing Science Centre

University of South Australia

Riquier, Christopher;Kennedy, Rachel; Sharp, Byron (1998) "Behaviours Versus

Demographics as Identifiers of CHAID Splits: Implications for Segment Formation",

Journal of Segmentation in Marketing, Vol.2, No.1, p.111-129.

Demographic variables have traditionally been adopted as bases and descriptors for segmentation

studies due to their inherent convenience and their conformance to the golden rules of

measurability, substantiality, accessability and actionability. Despite significant research in the

field of segmentation, little has examined the power of demographics versus other, more

behavioural variables, in predicting segment membership.

This paper uses data from a large empirical study to investigate the predictive power of a range of

segmentation variables. Specifically, the ability to predict segment membership using descriptive

demographics is compared to the predictive ability of other behaviours. Segments are determined

by propensity to react to particular marketing stimuli. Propensity to react is measured using the

Juster scale based verbal probability scale.

For the product category examined, results highlight a stronger ability of behaviours in predicting

segment membership. Results as such highlight the additional wealth that behavioural

descriptors can provide to segmentation studies in addition to having significant implications for

the facilitation of marketing and communication strategies for segments.

Keywords: segmentation, demographic, behavioural, probabilistic segmentation

Christopher Riquier (B.Bus., M.Bus. research) and Rachel Kennedy (B.Bus. hons.) are Senior

Research Associates at the Marketing Science Centre. Byron Sharp (B.Bus., M.Bus. research) is

Director of the Marketing Science Centre.

Introduction

Segmentation, a well-known concept to readers of this journal, involves the process of dividing a

market into groups of buyers or potential buyers who are similar to each other but different from

other buyer and potential buyer groups. The aim and practical significance of the segmentation

exercise being that payoffs are likely to be greater if companies match their products and

marketing offerings to particular segments . Wind and Beane and Ennis provide more details

for those new to the area.

While it is not necessary or indeed often possible to identify the members of a segment,

traditional views of segmentation assume that this is a requirement and certainly there are a

number of targeting advantages of knowing who constitutes the core segments in a market. This

is one of the reasons that descriptive, identifiable and hence actionable demographic variables

have been so popular as segmentation bases. Other bases for segmentation have also been used

but less regularly, including psychographics, geographics and different behaviours (including

other buying behaviours).

Demographic segmentation appears to be the most prevalent form of market segmentation as

measurement is easily understood, interpreted and transferable between studies . For certain

categories such as infant products which change with the childs age and femine hygiene products

demographic variables are an obvious choice, however, in many cases they will produce no clear

or actionable segments.

Behaviour, the alterative segmentation variable that is compared in this paper, can take many

forms including, purchase occasion, benefits sought, degree of usage and degree of loyalty .

Specific examples of the types of demographic variables and other behaviours used and

compared in this study include;

Demographics Other Behaviours Age Attendance at sporting events Gender Cinema attendance Occupation Eating Out Education Going to the beach

Segmentation & the Role of Identifying Variables

Segmentation is the process of dividing a market up, with the intention of targeting marketing

activities toward specific segments based on a relevant underlying factor such as propensity to

purchase or react to marketing stimuli. Segments must consist of different buying demand. The

buying behaviour within each segment will be relatively homogeneous and different from that of

other segments. It therefore follows that segments may or may not consist of different

buyers/individuals (or types of buyers). Thus there is:

1. Segmentation where membership of each segment is identified, or at least, membership of the

core segment(s) is identified.

2. Segmentation where membership is not identified because buyers continuously move

between segments. While a particular interaction of, say, situation and person may identify

membership, such identification is still extremely complex and often impractical. Thus you

may know that there is a segment of the market (say 20%) which buys the flavour strawberry.

So you might want no more than 20% of your (say ice cream) brands' flavour combinations to

be strawberry, and you know that your strawberry brand is unlikely to gain share above 20%.

But you probably don't know who these strawberry buyers are. Most people buy strawberry

at some time. The demographic (etc) profile of strawberry buyers may be slighly different

but overall pretty much the same as vanilla buyers (largely because they are the same people).

Textbooks and market research studies tend to infer that the first type of segmentation is most

common. Marketing practitioners the world around have upheld this view probably due only to

its simplicity and hence attractiveness. In fact the opposite is true. As (at least in the area of

FMCG’s) consumers hold a repertoire of brands from which they regularly select, consumers

also, by current segmentation practices, belong to a repertoire of segments for an individual

product category. Using the ice cream example, the same individual will likely fit into a segment

profile for strawberry and vanilla flavour consumers, but possibly also into the chocolate and all

other flavours of ice cream segments. This segment cross-pollenation reduces signficantly the

utility of the segmentation process and makes for a difficult targetting process for marketing

activities. A common fallacy leading to such circumstance is the attempt to achieve total

segmentation. Total segmentation is the attempt to segment the entire market using one of the

many bases for segmentation currently available. Total segmentation is most easily facilitated

using demographic bases but has rarely led to marketing success .

The fact that segments do not appear to consist of easily identifiable members has and will not in

the near future diminish the desirability of being able to identify which individuals (&/or when

these individuals) belong to which segment and which of these segments exhibit higher

propensities to purchase or adopt a particular marketing offerring. Today, more than ever,

marketers have at their fingertips the ability to target individuals, thanks to technologies such as

database mailings, the internet, smart cards, and a more fragmented “mass” media. However, this

ability can not be exploited without being able to identify who belongs, and does not belong, to

the segment(s) which the marketer is aiming for.

The researchers postulate that it is the reliance on traditional demographics as segmentation bases

and descriptors that has led to the decline in the utility of the segmentation process. A key

objective of any segmentation exercise is to identify groups of individuals with greater propensity

to react to a particular marketing stimuli. Still to this day, marketers have placed great emphasis

on apparently inefficient demographics as identifying variables despite their (often) lack of a

relationship with purchase propensity. This is largely due to the widespread availability and

convenience of demographic statistics rather than their effectiveness at predicting segment

membership. In addition, demographics have been upheld by practitioners due to their

conformance to Kotler’s segmentation criteria of measurability, substantiality, accessability and

actionability. What is most often overlooked is the criteria added by Mitchman that

segmentation studies should be “meaningful”. Meaningful segments will be identified by the

products they currently use or need.

Traditional segmentation studies tend to utilise the firms perspective rather than the customers

needs when assigning a basis for segmentation. Ultimately this has resulted in segmentation

bases which are easily measurable, easily understood, easily interpreted, easily gathered, and

easily compatible with external data sources (ie. census data). On the downside, however, such

segmentation bases (often demographic) have exhibited little if any correlation with actual

purchase behaviour of individuals within the segments.

Demographics have typically performed reasonably well in predicting market membership (eg.

women tend to buy dresses) but have performed less well in predicting segment membership (eg.

which women buy brand x rather than brand y and which women are very price sensitive and

which women aren’t?) and worst of all in predicting future buying behaviour. This has lead to

marketers experimenting with other potential identifying variables, including complex

multivariate combinations, such as intentions, preferences, attitudes, personality profiles, values,

and psychographics.

The search for other identifying variables has very much reflected the impact of cognitive

psychology on marketing, and as a result behavioural variables have been somewhat overlooked.

Yet intuitively since segments themselves reflect different buying behaviours it is reasonable to

expect that these particular behaviours might be correlated to other behaviours, particularly other

buying behaviours. This line of thinking is very much in line with modern behaviourist theories.

Mitchell suggests but provides little support for the notion that the usefulness of demographic

segmentation bases is related to the extent with which they can be correlated with behavioural

tendancies. The true test of geographics and demographics is how well they perform against

other behaviours as segment descriptors using a propensity to react (behavioural) base for

segmentation.

In this paper we examine the effectiveness of such behavioural variables at predicting segment

membership based on propensitiy to react to a particular stimuli. We segment a market in terms

of propensity of individuals to react to various potential future marketing scenarios, and we then

compare the ability of demographic variables to behavioural variables to identify segment

membership.

Methodology

Traditional segmentation models involve the selection of a basis for segmentation (dependent

variable) in addition to a series of descriptors (independent variables) used to define the identified

(objective) or specified (subjective) segments . Wind’s extensive review of the segmentation

literature reveals that all possible variables appear to have been used as bases and/or descriptors

for segmentation studies. These variables include general customer characteristics:

• demographic and socio economic characteristics

• personality and lifestyle characteristics

• attitudes and behaviour toward mass media and distribution outlets

and situation specific customer characteristics including:

• product usage and purchase patterns

• attitudes toward the product and its consumption

• benefits sought in a product category

• responses to specific marketing variables including new product concepts and advertising

In this segmentation study which aimed to objectively evaluate the use of behavioural versus

demographic descriptors of segmentation, a segmentation base independent of such variables was

required. To meet this need a sophisticated form of probabilistic market segmentation using a

segmentation base of propensity to react to marketing stimuli was developed and adopted as the

methodology.

Probabilistic Market Segmentation

The probabilistic market segmentation device incorporated two major elements; the verbal

probability scale and chi-square automatic interaction detection (CHAID). Probabilistic market

segmentation incorporates the verbal probability scale as the dependent variable in the survey

instrument. CHAID is used to identify the most significant predictors (descriptors) of the

dependent variable. The range of predictor variables utilised is limited only by the length and

complexity of the survey instrument. Each element of the probabilistic market segmentation

device is discussed in following sections.

The Verbal Probability Scale

The verbal probability scale is the verbal equivalent of the Juster scale of purchase probability.

Juster originally proposed the use of an eleven point probability scale to overcome inaccuracies

in earlier purchase intention scales. Such purchase intention scales predominantly utilised a

range of verbal intention descriptors ranging from three to nine points . The shorter scales,

particularly yes-unsure-no, placed obvious limitations on respondents while larger versions

suffered from the varying interpretations of respondents . These issues were highlighted in a US

census which found that 70% of automobile purchases were made by respondents stating no

purchase intention . Of those respondents who intended purchasing, only 40% reflected this

intention in their behaviour.

Juster’s probability scale (table 1) was based on the argument that verbal intentions are really

disguised probability statements. He believed respondents to be familiar with the concept of

chances or odds out of ten and that such familiarity would both facilitate questionnaire

administration and increase the accuracy of response.

Table 1 - Juster Probability Scale

Scale Point Verbal Equivalent

10 Certain, practically certain (99 chances in 100)

9 Almost sure (9 chances in 10)

8 Very probable (8 chances in 10)

7 Probable (7 chances in 10)

6 Good possibility (6 chances in 10)

5 Fairly good possibility (5 chances in 10)

4 Fair possibility (4 chances in 10)

3 Some possibility (3 chances in 10)

2 Slight possibility (2 chances in 10)

1 Very slight possibility (1 chance in 10)

0 No chance, almost no chance (1 chance in 100)

Juster’s original testing of the scale extended to 6, 12 and 24 month purchase probabilities for

cars and other durables for 451 respondents. Results showed no tendency toward the scale

midpoint. When compared to intentions scale data collected on the same group of respondents,

purchase probabilities explained twice as much of the variance in actual purchase rates as buying

intentions data . In addition, 5% of respondents indicating zero purchase probability actually

purchased compared to 11% as indicated by the intentions scale.

The Juster scale has since been used for the prediction of purchase rates and adoption for many

different product categories, for high and low involvement products and over varying periods in

time .

The biggest limitation associated with the Juster scale was that almost all executions used

personal or mail survey methodologies. This is not surprising, given the limited ability of

respondents in remembering more than four or five response categories . In an attempt to

overcome this limitation, Brennan, Hini and Esslemont tested three potential telephone survey

techniques; an eleven category verbal probability scale, a branched eleven category verbal scale

and a pre posted Juster scale.

The branched scale was abandoned due to its laborious nature. The key finding resulting from

testing of the remaining two techniques was that they both produced relatively comparable results

in addition to their ease of use. Testing of the two techniques was replicated by Brennan et al

involving significantly greater sample sizes. Again results suggested that both techniques are

appropriate for this form of data collection, with similar response rates, refusal rates, levels of

accuracy and ease of administration.

The verbal probability scale is an eleven point scale from 0 to 10. The higher the score recorded

represents a higher probability of adoption. A score of 10 would mean the respondent is

practically certain they would adopt the product. A score of zero would mean there is no chance

or almost no chance of adopting the product. The Juster scale (table 1) is basically the written

equivalent of the verbal probability scale.

The verbal probability scale was adopted in preference to the Juster scale in the empirical study

conducted for this research project due to the practicalities of cost and the time of administration

of a telephone based survey instrument.

Chi-Square Automatic Interaction Detection (CHAID)

The second component of probabilistic segmentation is the analysis technique, chi-square

automatic interaction detection (CHAID). CHAID was used for all statistical analysis in this

research project. Although statistically complex, CHAID is relatively straightforward in terms of

application. A full description of the workings of CHAID is beyond the scope of this paper,

however, a brief overview follows.

CHAID uses a semi-hierarchical sequential procedure for partitioning a contingency table .

CHAID analysis consists of three key elements. Firstly, the chi-square significance test. This is

used to identify the most significant predictors within the data. A key strength of the chi-square

test is that few assumptions need to be made of the data. Secondly, the Bonferroni correction

adjusts for the problem of when the numbers of degrees of freedom for the chi-square test are too

small . The third element is an algorithm used to combine categories of predictor variables (ie

scale points on the verbal probability scale).

Predictors that contain three or more categories are pre defined as either monotonic, free or float.

These definitions affect the CHAID category merging algorithm . Categories of a predictor

variable may be merged where there is no statistically significant difference between categories.

Predictors defined as monotonic, generally ordinal variables, may only have adjacent categories

combined. Float variables are identical except that the last category may be combined with any

other category, to account for missing value categories. Nominal variables are defined as free

such that their categories may be combined in any order or grouping. As verbal probability

scales were the predictor variable in all analyses conducted for this research, predictors were

classified as monotonic.

CHAID for SPSS which was used in this research, first selects the smallest number of splits of

the categories of a predictor using pairwise merging and splitting of the response levels of each

predictor variable . Once the new categories have been determined, the original sample response

is split using the most significant predictor. Each of the sub-groups is then regarded as a new

parent subgroup and is split in a similar fashion. The analysis proceeds down additional levels

until the size of the group becomes too small or there are no further significant splits .

The Segmentation Study: Government Cycling Initiative

A specific government body was established in the relevant market with the sole purpose of

doubling cycling by the year 2000. This body formed part of the larger government transport

department which controlled all roads, public transport and related facilities. Hence, the body

had influence over the construction of cycling related facilities, over the implementation and

administration of the relevant legislation and over the provision of community education which

could potentially encourage more people to cycle and existing cyclists to cycle more often.

The first stage of the study was qualitative, with six focus groups being conducted to determine

the key issues, attitudes and behaviours of the various groups of cyclists and non-cyclists and

specifically to explore avenues that would lead to a doubling of cycling. Six specific segments

were identified a priori and were made the focus of the study. Specifically the segments were;

commuter cyclists, recreational cyclists, children who cycle to school, children who do not cycle

to school, non-cyclist commuters and potential recreational cyclists. These segments were

upheld by the government cycling body and correlated well with their previously implemented

segmentation strategy.

Quantitative research was then conducted to ascertain the potential increase in cycling that would

result from changes to cycling facilities, regulations, parking, education and information

provision. Thirty seven scenarios encompassing such changes were devised by the researchers

and the government body and the potentials of these scenarios to increase cycling were tested

using the verbal probability scale. Respondents were asked what was their likelihood of

increasing their cycling (or adopting cycling for non-cyclists) if the scenario were implemented.

The survey instrument was administered by telephone to a random sample of 600 respondents

from all 6 segments. It encompassed the following 40 demographic and behavioural variables

(table 2) as well as the 37 verbal probability scale items relating to the proposed cycling related

changes (scenarios). In addition to the demographic and behavioural variables included as

potential segment descriptors, the a priori segment classifications (mentioned above) were

included as potential descriptors (in the probabilistic segmentation analysis) to ascertain if they

held any relationship to the propensity to react to the proposed cycling changes (scenarios).

Table 2 - Cycling Study Demographic and Behavioural Variables

Demographic Variables Explanation

Age Of respondent in years

Education Highest level achieved by respondent

Employment status Using standard classifications (professional, clerical,

unemployed)

Industry of employment Australian Standard Industry Coding

Household income Combined household income level

Household size Number of persons in household

Home to employment Distance in kilometres

Home to shops Distance in kilometres

Home to own education Distance in kilometres

Home to downtown Distance in kilometres

Home to child’s school Distance in kilometres

Home languages Main languages spoken at home

Home address Postcode

Work address Postcode

Gender Male/Female

Behavioural Variables

Main commuting method To/from work, education, etc.

Currently cycle Respondent is currently a cyclist

Learn to cycle age Age when first learnt to cycle

First cycle age Age when first received/bought cycle

Cycle collision Accident with motor vehicle while cycling

Cycle near miss Near miss with motor vehicle while cycling

Multi mode transport Use multi mode transport (combination of car/train etc.)

Cycling organisation Member of cycling organisation

Outdoor activity Recently participated in outdoor activity

Library Visited library in past month

Beach Visited beach in past month

Bike store Visited bike store in past month

Fast food Visited fast food outlet in past month

Theatre Visited theatre (live) in past month

Cinema Visited cinema in past month

Cafe Visited cafe in past month

Sports ground Visited sports ground/stadium in past month

Promo aware Awareness of cycling promotions

Drivers licence Whether respondent holds drivers licence

Own/access car Ownership or access to a motor vehicle

Hit cyclist, driving Collided with cyclist while driving a motor vehicle

Frequency of cycling Daily, weekly, monthly

Time - cycle Time of day that respondent currently cycles (cyclists only)

Cycling classification From a priori classifications ie commuter cyclist, recreational

cyclist, etc

Results

Individual CHAID analyses were conducted for each of the 37 scenarios. A basic example of

such an analysis follows.

Figure One - CHAID Analysis for Cycling Scenario 24

Own/have access to carLow - 32%Med - 16%High - 53%

Don't have a carLow - 9%Med - 30%High - 61%

Use multimodeLow - 28%Med - 18%High - 54%

Don't use multimodeLow - 49%Med - 14%High - 36%

Cycling ScenarioTwenty Four

Most significant predictor

(use of multimode transport)

2nd most significant predictor

(whether respondent owns or

has access to a car)

This particular scenario related to trains being fitted with ample, easily accessed spaces with

anchor mechanisms that secure bikes in transit.

The most significant predictor, that is, the demographic or behavioural variable which best

described those most likely to respond to the scenario (eg those who felt that they would be

likely to double their cycling behaviour as a result of the scenario) is provided at the first level of

the tree. In the example, whether respondents used multi mode transport (or not) was the most

significant predictor of their reaction to the scenario. Multi mode transport involves more than

one mode of transport in a trip (eg cycling to the train station and then commuting by train for the

balance of the journey).

Respondents who currently used multi mode transport were more likely to respond positively to

the scenario (high = 54%). In the multi mode box, three figures are given, refering to low

medium and high probabilities of respondents increasing cycling as a result of implementation of

the scenario. To determine these three levels the likelihood of adoption was rescaled from the 11

point verbal probability scale to three levels. Three levels are used to simplify and aid in the

interpretation of findings. Various rescaling combinations were tested and cross checked against

earlier qualitative research. The three levels created were deemed to hold the greatest face

validity. Levels are outlined below (table 3).

Table 3 - Adoption Levels for Probabilistic Segmentation Analysis

Level Adoption Probability for Level Equivalent Verbal

Probability Scale Levels

Level One (1) - Low Very low probability of adoption 0 to 3

Level Two (2) - Medium Moderate to low probability of adoption 4 to 7

Level Three (3) - High Higher probability of adoption 8 to 10

The second level predictors define the sample ever further. In the example, of those people who

use multi mode transport currently, those who do not own or have access to a car are most likely

to respond positively to the scenario (high = 61%).

This particular scenario only involved two significant predictors on two levels. Other analyses

included between 1 and 7 predictors on up to three levels of a CHAID segmentation tree while

still maintaining statistical significance at the 0.05 level.

Individual CHAID analyses for the 37 cycling scenarios are not presented in this paper in the

interests of conciseness. Rather, the results were collated to indicate the number of times each

demographic or behavioural variable appeared as a statistically significant predictor in the

individual CHAID analyses. In addition, the number of times each variable appeared as the most

significant predictor was recorded. These results are presented in table 4.

Table 4 - Demographic and Behavioural Predictors

Demographic Variables Most Significant Predictor Significant Predictor

Age

Education

Employment status

Industry of employment

Household income

Household size

Home to employment

Home to shops

Home to own education

Home to downtown

Home to child’s school

Home languages

Home address

Work address

Gender

TOTAL 18 38

Table 4 - Demographic and Behavioural Predictors (cont...)

Behavioural Variables Most Significant Predictor Significant Predictor

Main commuting method

Currently cycle

Cycling reason

Learn to cycle age

First cycle age

Cycle collision

Cycle near miss

Multi mode transport

Cycling organisation

Outdoor activity

Library

Beach

Bike store

Fast food

Theatre

Cinema

Cafe

Sports ground

Drivers licence

Own/access car

Hit cyclist, driving

Cyclists in household

Frequency of cycling

Time - cycle

Cycling classification

(a priori segmentation)

TOTAL 19 56

Discussion

Expectations of the practitioners1 involved in this project were that demographic variables would

emerge from the CHAID analyses as the most significant predictors of responsiveness to the

proposed changes (scenarios). In addition, the practitioners were confident that the a priori

segment classification would be the most significant predictors of propensity to react to the

scenarios. On the second point, it should be noted that cycling classification only emerged twice

as a significant predictor (once as most significant predictor for a scenario) in the CHAID

analysis, of a total of 94 significant predictors. Clearly, the a priori segmentation performed

poorly and would have been detrimental to the objectives of the government cycling body.

Table four presents the results for the thirty seven scenarios from the cycling study. Each

scenario was tested using the verbal probability scale to ascertain whether it would increase

cycling (if respondent was currently a cyclist) or encourage uptake of cycling (if respondent was

not currently a cyclist).

The results presented in table four demonstrate that demographic variables are by no means the

most appropriate means of targeting those individuals most likely to respond to the initiatives

implemented by this government body. Of the 37 scenarios tested using the probabilistic market

segmentation tool, 18 of the most significant predictors of increased cycling were demographic,

while 19 of the most significant predictors were behavioural.

Although demographics showed a fair degree of predictive power the results still highlight the

inappropriateness of solely using demographic variables for segmentation and targeting. The

point is strengthened when all significant predictors that emerged from the 37 cycling CHAID

analyses are examined.

As CHAID analysis continues until all statistically significant predictors emerge (at the 0.05

level), many other variables were incorporated into the final CHAID analyses. At face value,

these predictors display a similar pattern to the “most” significant predictors.

A total of 94 significant predictors emerged from the 37 CHAID analyses. Of these, 38 (40%)

were classified as demographic variables while 56 (60%) were classified as behavioural

variables.

1 Practitioners from the government cycling body were consulted at all stages of this research project.

Conclusion

While Beane and Ennis suggest that many different ways exist to segment a market and that one

must remain creative when conducting segmentation research, it still remains important to

validate and compare the usefulness of the different techniques available. It is hoped that this

article helps build the knowledge pool that will guide market segmentation studies in the future.

A paper on segmentation would not be complete without some note of the long standing

criticisms of segmentation. Wright in listing the key criticims of “segmentation” includes it

being confused with variety marketing or product differentiation, that the process to identify

segments is frequently flawed, that there is no logical reason for payoffs to be greater under target

mareting, that segment membership is not stable over time and that empirical results fail to

demonstrate the patterns of purchase which are expected to arise from target marketing. While

these criticisms are accepted, the empirical evidence does not yet negate segmentation from

marketing altogether and thus the criticisms support the need to test alternative approaches to

segmentation for stability and usefulness.

Implications

It is the analysis of research projects such as the one presented in this paper that will help to

dispel the myth that demographic variables alone are suitable for segmenting markets and

targeting particular groups of consumers. This too is only an initial step in the larger

investigation into the value and role of segmentation in marketing.

Overall the study has highlighted that the use of behavioural variables can add additional wealth

to studies that currently use only demographics to understand a particular group of respondents.

In addition to just understanding who comprises a segment, the use of behavioural variables has

the practical benefit of facilitating communication with a segment. In the cycling study for

instance the fact that cyclists had a high propensity to visit cafes and sporting events but a low

propensity to go to the cinema guided the marketers of the cycling facilities in their promotions

plan for the new changes.

The results also highlight the benefits of conducting probabilistic segmentation research prior to

implementation of segmentation strategies. Even in new product categories where little

knowledge of demand exists, such probabilistic research can provide accurate estimations of

segment size and propensity to react to the offerring, in addition to providing appropriate segment

descriptors.

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