cluster analysis

Mkt3004 Analytical techniques for marketing

Cluster Analysis

Segmentation for Student Food Shopper

Atiqah Ismail

Newcastle University Business School

2012

2012 Cluster Analysis

1. INTRODUCTION

The aim of this study is to employ cluster analysis to identify segments of student food

shoppers. Cluster analysis classifies objects into groups on the basis of the similarity of the

characteristics they possess. Two student segments were identified and profiled on the basis

of different importance factors for supermarket features and nominal measures of shopping

behaviour of students. The identification of segments will provide implications for marketers

that the student market should not be treated as a mass market, segments enable relevant

marketing strategies to be geared and targeted to each segments efficiently and effectively

based on segment profiles.

This essay has been organised in the following way. The following section, Section 2,

outlines the theory of cluster analysis. Section 3 reviews an application of cluster analysis by

Dobson and Ness (2009). Section 4 describes the clustering methods and processes employed

in this study. Subsequently, Section 5 will explain the results of cluster analysis. Section 6

will assess the marketing implications of the results. Finally, Section 7 will conclude with a

summary and evaluation of the study, and future research recommendations.

Atiqah Ismail Analytical Techniques for Marketing 2012


2. THEORY

2.1. Objectives of Cluster Analysis

Cluster analysis is a classification tool which aim is to assign objects into groups on the basis

of their numerical measures. It identifies and classifies objects into groups based on the

similarity of their characteristics, by minimizing within-group variance and maximizing

between-group variance, so that objects within a group are as similar as possible, and as

dissimilar as possible from objects belonging to other groups. Cluster analysis is used to

determine the number of clusters, and to identify the membership and profile characteristics

of each cluster.

2.2. Data Requirements

Cluster analysis can be applied to both metric and non-metric data. The measurement

properties of the data are important to determine transformation method for clustering.

Similarity measures are used for non-metric data, while distance measures are used for metric

data. However, this study focuses on distance measurement.

2.3. Data Measurement

Distance measurement is the measurement of distances. Cluster analysis requires distance

measurements between object-to-object and group-to-group. Object-to-object distance

measures the distance between objects. The most used object-to-object measurement is the

Euclidean distance, however there are also alternatives such as the city block metric distance

and the Mahalanobis distance. Group-to-group distance measures the distances between

different groups of objects. The group average method is regarded as the superior method of

group-to-group distances (Ness, 2011a), among other alternative techniques are, the nearest

neighbour, furthest neighbour and centroid method.

2.4. Main Theoretical Approaches

Two main theoretical approaches to cluster analysis are the hierarchical technique and the

optimisation technique.



The hierarchical technique agglomerates ungrouped objects into a sequentially smaller

number of clusters, based on objects that are most similar or nearest. The researcher will

decide on the appropriate number of cluster based on the researcher’s own judgement, with

the aid of summaries presented by the agglomeration schedule, dendogram or the Gower

diagram. The agglomeration schedule is judged to be the most reliable (Ness, 2011a), it can

be used alongside with the Gower diagram to decide the number of clusters.

The optimisation technique is a non-hierarchical clustering (K-means) technique, which

reassigns objects from an originally assigned cluster into another cluster. The relocation

criterion is based on the relationship between variances within group (W) and between

groups (B), so that total grouped data variance (T) is defined as:

T = W + B

Thus, the criteria can either minimise W or maximise B, given T is fixed.



3. APPLICATION TO MARKETING

This section will review an article by Dobson and Ness (2009) which applied cluster analysis

to their study to explore the existence of student segments. Their study aims to identify the

dimensions underlying students’ attitudes to food shopping and students’ attitudes to time,

and to subsequently use these dimensions to identify and profile student segments. The study

employed questionnaires on full-time Newcastle University undergraduates of 18-25 years.

The survey yielded 744 valid responses.

The questionnaire was designed to include nominal measures of food shopping behaviour, a

23-item five-point agreement scale relating to statements about students’ attitudes to food

shopping (1 = strongly disagree, 5 = strongly agree), a 27-item five-point agreement scale

relating to statements about students’ attitudes to time (1 = strongly disagree, 5 = strongly

agree) and a nominal measures of students’ characteristics (gender, accommodation type,

faculty of study and ethnic origin).

Cluster analysis was employed to group students into homogenous groups based on their

attitudes to shopping and attitudes to times. Factor analysis was initially performed to

produce factor scores to provide a basis for cluster analysis. Factor analysis produce six

factors for attitudes to shopping (enjoyment-fun, convenience, event-methodical, enjoyment-

value seeking, event-relaxed, apathy-impatience) and seven factors for attitude to time (time-

pressure, succession-planner, present-traditional, past-secure, future, past-traditional, present-

planner), and are used respectively as the target variables for cluster analysis.

Nominal cluster identity was used for cluster profile analysis. Dobson and Ness then used

average factor scores, shopping behaviour and demographic characteristics to establish

cluster profiles. Two-stage process was used to the factor scores for students’ attitudes to

shopping and attitudes to times. Stage 1 employed a hierarchical technique which suggested

2-4 clusters as an appropriate number of clusters from the agglomeration schedule. Stage 2

employed the K-means optimisation. However, consideration over cluster-size, the

descriptive ANOVA and desire for parsimony Dobson and Ness chose a three-cluster

solution. Cluster 1 comprising of 33%; Cluster 2, 31% and Cluster 3, 36% of the sample.

Cluster profiles were developed indicated by descriptive profiles of average factor scores for

attitudes to shopping and attitudes to times.



Cluster 1 was defined as ‘hedonistic, succession-planning females’, this cluster shops more

frequently, enjoys shopping and are not convenience-event value-seekers. They are relatively

financially-secure and may respond to offers linked to higher quality-premium food and

cross-selling offers. Cluster 2, ‘futuristic, spontaneous male convenience seekers’, do not

enjoy shopping, but prioritises convenience. They shop relatively less-frequent and appear to

be both present and future-oriented; they live for today but also plan daily. They may respond

to offers related to bulk purchase of convenience food. Cluster 3, ‘methodical, value-seeking,

apathetic, time-pressured females’, are apathetic-impatient, value-seeking shoppers and are

the most time-pressured. They emphasise regular, methodical shopping and are distinguished

by their emphasis on past-traditional values. They may be attracted to discounters and online

shopping.

The application of cluster analysis has contributed to the establishment of meaningful and

actionable student segments based on measures associated with scales for shopping behaviour

and students’ characteristics. The contribution creates a reliable foundation for segmentation,

targeting and positioning of students by food retailers.



4. METHOD

This study employed a questionnaire designed to include nominal measures of shopping

behaviour, a scale of students’ attitudes to the importance of supermarket features, and

nominal measures of students’ characteristics. The survey adopted face-to-face interviews

with full-time undergraduate Newcastle University students. A convenience quota sampling

method was used to approximate student representation by gender and faculty. Subsequently

the survey yielded 731 valid responses.

Factor analysis was previously applied to the data in the form of fourteen five-point scales

concerned with measuring the importance of supermarket store features (1 = Not at all

important, 5 = Very important) with 708 valid responses. Factor analysis employed principal

components with Varimax rotation using extraction criterion to derive factors with

eigenvalues greater than one. The analysis produced a 5-factor solution, defined as Economy,

Payment facilities, Range and quality product, Friendly staff, and Accessibility. For details of

the five factors see Appendix 2 (Ness, 2011b).

The five-factor scores are subsequently used as target variables for cluster analysis. Cluster

analysis employs transformation method of between-group linkage and squared Euclidean

distance. Cluster analysis involves a two-stage process. The first stage employs hierarchical

clustering to identify the appropriate number of clusters, where the agglomeration schedule

suggested a possibility of two-cluster and five-cluster solution (see Table 2, Appendix 3; in

Ness, 2011b). The second stage employs the K-means optimization method to obtain cluster

profiles. Descriptive ANOVA was employed to test the significant difference between final

cluster centres of each factors average score. The analysis was conducted using IBM SPSS

19.0 (SPSS, 2008).



5. RESULTS

Descriptive ANOVA test revealed significant difference between final cluster centres of each

factors average score, except that of ‘Range and quality products’ (see Appendix B) under

null hypothesis that the final cluster centre values are equal, against the alternative hypothesis

that they are not equal, at a 5% significance level. Hence, alternative solution was chosen

based on the consideration of feasible relative cluster size (Ness, 2011a, p. 27) and the desire

for parsimony; a two-cluster solution was chosen. The two-cluster solution comprises of 38%

(Cluster 1) and 62% (Cluster 2) of the student sample.

Sections 5.1 and 5.2 will explain profiling of the two clusters using factor scores and nominal

measures.

5.1. Profiling using Factor Scores: Importance of Supermarket Features

Iteration using cluster criterion provides the final cluster centres presented in Table 1. The

information represents the average score for each cluster on each factor. Cluster profiles are

established using the average factor scores obtained from the final cluster centres (Table 1).

The analysis will use descriptive profiles due to the nature of the ANOVA test produced in

the K-means procedure (see Appendix B).

Table 1 Final Cluster Centres

FactorCluster

1 2Economy -.93275 .56472Payment .16476 -.09975Range and Quality of Products

.07388 -.04473

Friendly Staff .17430 -.10553Accessibility .39555 -.23948

Cluster 1 places below average importance on economy and an above average importance on

accessibility, friendly staff and payments. Cluster 1 places most importance on accessibility

and least importance on economy.



Cluster 2 places above average importance on economy and below average importance on

range and quality of products, payment, friendly staff and accessibility. Hence, in terms of

importance, Cluster 2 places most emphasis on economy, and least emphasis on range and

quality of products.

5.2. Profiling using Nominal Measures: Shopping Behaviour

The cluster profiles are then extended using nominal measures of shopping behaviour. These

are established from chi-square contingency tests under the null hypothesis (H0) that the

(nominal) cluster identity and behavioural characteristics are independent. The test adopts a

5% significance level. The tests for cluster identity and behavioural characteristics are

summarised in Table 2.

Table 2 Summary of Tests for Cluster Identity and Behavioural Characteristics

Behavioural Characteristic Chi-square Statistic and Significance1

Null Hypothesis

Supermarket visits 2 (3)= 10.19, Sig = 0.018 Reject

Method of Travel 2 (3)= 70.045, Sig = 0.000 Reject

Shopping Group 2 (2)= 2.696, Sig = 0.260 Accept

Shop For 2 (2)= 8.225, Sig = 0.016 Reject

Storecard Ownership 2 (1)= 5.659, Sig = 0.011 Reject

Use of Budget 2 (1)= 32.562, Sig = 0.000 Reject

Weekly Expenditure 2 (2)= 36.602, Sig = 0.000 Reject

Note:

1. This information summarises Pearson Chi-square statistics from the Chi-Square Tests table

in SPSS output.

2. 2 (degrees of freedom (df)) = Chi-square value, Sig = Significance statistic

Cluster identity and behavioural characteristics are associated. Students generally visit the

supermarket once per week, with 72% of Cluster 1 visits the supermarket at least once per-



week, while 28.2% visits two to three times per month or less. Cluster 2 generally shops at

least two to three times per week, with 5% shop once per month or less often.

Cluster identity and method of travel are associated. Cluster 1 generally use own transport

with 55% use other methods of transportation (on foot, public transport, other) to visit the

supermarket. Cluster 2 generally travels to the supermarket on foot, with 37% use other

methods of transportation (public transport, own transport, other).

Cluster identity and shopping group are independent. Hence there is no distinction between

Clusters 1 and 2, where 50% of both Clusters 1and 2 go shopping with mates, with 50% shop

by themselves and with partner.

Cluster identity and shop for are associated. Cluster 1 generally shop for themselves, however

60% shop for others or depends. Cluster 2 generally shops for themselves with 51% shop for

others or depends.

Cluster identity and storecard ownership are associated. Cluster 1 shows an equally

proportionate number of students between those who own (53%) and does not own (47%)

storecards, however a slightly higher proportion owns storecards. Cluster 2 shows a

significantly higher proportion of storecard ownership with only 38% do not own storecards.

Cluster identity and use of budget are associated. Cluster 1 generally does not have a budget,

with 26% does. Cluster 2 shows a proportionate number of students with and without budget,

but a slightly higher proportion (52%) of students do not use budget.

Cluster identity and weekly expenditure are associated. Students generally spend £16-30 a

week, with 22% of Cluster 1 spends £0-15 a week, while 78% spend at least £16 per week.

Only 10% of Cluster 2 spend more than £31 per week, with 90% spend between £0-30 per

week.

Table 3 (see page 10) illustrates the summary of cluster profiles. For crosstab SPSS output for

cluster behavioural variables used for cluster profiles, see Appendix A.



Table 3 Summary of Cluster Profiles

Profile Cluster 1 Cluster 2Cluster composition: (38%) (62%)

Convenience-seeking, financially-stable, student shopper

Economy-seeking, personal student shopper

Importance factors:Economy Least MostPayment Some LessRange and Quality of Products Some LeastFriendly Staff Some LessAccessibility Most LessBehavioural measures:Supermarket visits

At least once per week

At least once per week, with a higher percentage more often than once a

weekMethod of Travel Own transport On footShopping Group With flatmates With flatmatesShop For Own self, with higher

percentage shop for others and depends

Own self

Storecard Ownership Own storecards. Almost proportionate, but only

slightly higher proportion owns storecard

Own storecard. Significantly low

percentage do not own storecards

Use of BudgetNo budget.

No budget. Almost proportionate, with only

slightly lower have budgetWeekly Expenditure £16-30. Significantly low

proportion spends £0-15 a week, while 78 spend at

least £16 per week.

£16-30. 90% spend between £0-30, with only 10% of Cluster 2 spend more than £31 per week



6. MARKETING IMPLICATIONS

Cluster analysis identified two student segments. Identification of segments enables

supermarkets to improve the effectiveness of targeting and positioning strategies, and to

differentiate their marketing strategies to the different student segments, such as offering

augmented supermarket services and promotions

Cluster 1 (convenience-seeking, financially-stable, student shopper) is distinctively least

concerned with economy and is relatively more concerned with range and quality product.

Hence supermarkets could augment offering to offer premium range of high-quality product.

The use of own transport to visit supermarket and their less-frequent shopping imply a higher

shopping volume benefited from convenience in transporting items home. Hence,

supermarkets can offer larger offer pack, slightly smaller than family-pack, to suit their

stocking behaviour.

Cluster 2 (economy-seeking, personal student shopper) are more financially-constrained and

view shopping as a financial concern. This segment may be attracted to cost-focused offers

such as discounts, price reductions, and loyalty-point card schemes. Generally, they shop for

themselves, consistent with their lower spending, whilst the trouble of transporting food

home by foot have resulted in more frequent smaller shopping trips. Hence, supermarkets

could offer small-bundled offering like, smaller packets of essentials such as pasta, cereals

and sauces, which are easier to carry considering them travelling on foot, convenient to store

as they shop more frequently, and suitable for a one-person consumption.

However, there are also some aspects that are common to all students. For example, students

generally shop with flatmates. Supermarket could offer products that could be purchased in

bulks to cater a group or a BOGOF strategy specifically targeted to students for main

household items like, dishwashing liquid, kitchen-paper or toilet-paper roll.



7. SUMMARY AND CONCLUSION

The aim of this study is to use cluster analysis to segment student food shoppers. Hierarchical

clustering and K-means optimisation were employed to derive cluster solution and cluster

profiles, respectively. Cluster analysis enables the extension of supermarket feature

importance factors with nominal students’ behavioural measures, allowing the identification

of student segments based on their behaviour to shopping associated with supermarket

features. The analysis generated two-cluster solution, ‘Convenience-seeking, financially-

stable, student shopper’ (Cluster 1) and ‘Economy-seeking, personal student shopper’

(Cluster 2). Most students however fall into Cluster 2 implying that Cluster 1 may be a niche

market; however this area could merit further research to explore whether it is a niche

segment worth exploiting.

The representativeness of the findings is limited to full-time undergraduate Newcastle

University students, hence it cannot be generalised to the UK student population. This

suggests the need for further research to ensure consistency of student-shopper segments by

reproducing this study in other universities.

There was no distinctively significant difference in terms of shopping behaviour. Crosstab

findings for differences in behavioural measures between the two groups does not show very

significant or distinctive differences in some shopping behaviour, such as storecard

ownership, frequency of visits and budgeting. Importance features seem to provide more

distinctive difference between the two groups than students shopping behaviour. This study

merits further research, for example, inclusion of more behavioural variables such as gender

or eating habit may perhaps contribute towards a more distinctive and actionable

segmentation. Richer segmentation and targeting strategy could also be achieved through

loyalty cards schemes, enabling supermarkets to track students shopping patterns, and use

these data to identify opportunities for more direct marketing strategies. Clusters may also be

restricted to this study as cluster interpretation is subjective and the selection of number of

clusters depends on researcher’s choice.


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