LOYALTY, BRAND CHOICE AND COMPETITION. AN

EMPIRICAL APPLICATION

Sandra Cavero & Javier Cebollada1

ABSTRACT2

An understanding of market structure is needed for decision making. Market

structure is defined in terms of competition among brands that are sold in the

market. Competition can be viewed as a choice problem among discrete

alternatives, which are the brands in the market competing to attract consumers.

Regarding consumer behavior as probabilistic, a brand choice model is

developed in order to classify consumers as Loyal (consumers who do not consider

brands other than the one they usually buy) and Shoppers (consumers who consider

all the brands in the market before making a buying decision).

The model is applied to the laundry detergent for delicate clothes market

in Barcelona city and its metropolitan area. Some relationships among loyalty,

variability of prices, type of retailing, consumption intensity of households, and

structure and intensity of competition are observed.

The standard errors of the parameters are estimated using bootstrap. A

more complex model, which is a generalization of the applied model is also

estimated and compared to the simple one.

1Correspondence to the first author. Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Navarra, Spain. Email: cebollada@upna.es, tel: +34 48 169736, fax: +34 48 169404.

2The authors acknowledge the comments of V. Salas and the permission to use the data from Dympanel, S. A..

2

INTRODUCTION

Information about the competitive structure of markets is needed in marketing to

design strategies and to take decisions. In particular, decisions such as launching

and repositioning products, designing advertising and promotion campaigns, etc.,

need information about which products are close competitors, and which are not.

An common objective of firms is to differentiate their products, in order to

make them to be perceived as different from the rest, and unique by consumers so

they are protected from direct competition. One way of obtaining such a privilege

is to create loyalty to their brands.

In a market with brand loyalty (or with brand switching costs), the current

market share of a brand determines its future profitability in the mid and long-term.

Moreover, loyalty protects brands from competitive actions taken by rivals. Brands

with a high degree of loyalty enjoy a certain degree of monopoly over their current

consumers. The existence of brand loyalty also helps to explain why managers are

sometimes more interested in increasing market share than in short run profits. It

also explains some strategic actions such as anticipating the entrance in a market

with the objective of attracting consumers and deterring other firms from entering

the market.

Due to these and other reasons, firms try to create and maintain brand

loyalty or brand switching costs. They develop strategies in order to improve the

product, invest in post-purchase services, and create costs for customers associated

with ending the relationship with the firm3.

Not only is it interesting to know the existence and degree of loyalty in the

market, but also the distribution of this loyalty among groups of consumers. The

existence of segments of loyal and non-loyal consumers has consequences for

3Examples ot these costs are the penalties when cancelling some bank accounts, frequent-flyer programs or discount-for-next -purchase coupons given together with some products.

3

strategies and actions. It would not make much sense, for example, to target a

promotional campaign to a group of consumers loyal to other brand, but rather

would be more efficient to target it to the non-loyal segment. When a market is

mainly made of non-loyal, price and promotion strategies are usually important.

Besides the number and relative size of competitors, the degree of loyalty

and its distribution among different brands in the market is also important when

determining competitive intensity. Let us consider a market where there is a high

degree of loyalty to just one brand. If this brand also has a high market share, it has

a double advantage. If its market share is low, although this brand will not be a

threat to the rest, it has a sure source of profitability. In a market with many small

but high loyalty brands, the degree of competition will be low, since competitive

actions will not affect, at least in the short-term.

There are many causes for loyalty and they can be found in any marketing

or consumer behavior textbook. Post-purchase satisfaction stimulates loyalty,

whereas dissatisfaction stimulates switching. Consumers enjoy some economies

when repurchasing, such as a decrease in risk and a time reduction in searching and

evaluating alternatives. Also, as consumers purchase their evaluation and decision

criteria become more precise and defined, so they become more loyal. Frequent

promotions, the increase in the number of brands, the availability of information

and the accessibility to brands can decrease loyalty. Brands with high price

variability (that can be synonymous to frequent promotions), tend to have a lower

degree of loyalty since they are bought by switcher consumers only when their

prices are low; these consumers switch from these brands when their prices return

to regular. As we mentioned before, the existence of brand switching costs make

consumers repurchase (this is a kind of "forced" loyalty). These costs can be

artificially created by firms or due to market or product category characteristics.

Examples are the incompatibility between the main product and other brands'

4

components (i.e. cameras-lenses), high learning costs (i.e. when switching between

software programs), etc.

It should be reminded that loyalty is different from repetition: all loyal

consumers tend to repeat but not all consumers who repeat are loyal. Some of them

may repeat just by chance or because the brand is now on sale.

In this work we try to analyze loyalty by estimating a model previously

used in the marketing literature (the Colombo-Morrison model, Colombo and

Morrison, 1989). This model can also be derived in a Random Utility framework

context (Bordley, 1989). Using panel data of Spanish laundry detergent for delicate

clothes, and estimating the model for different groups of consumers, some

relationships among loyalty and (i) intensity of consumption, (ii) type of store

where the purchase takes place, and (iii) variability of retail prices are found.

In the next section we briefly mention part of the most relevant literature

on this topic, and the model we will estimate. In Section §3 the goodness of fit, and

the estimates precision measures (making a special mention of the bootstrap method

used to estimate the standard error of the estimates) are explained. In Section § 4

the data, the empirical application and the results of the estimation are presented. In

Section § 5 the limitations of the article and future research directions, and in

Section § 6 the main conclusions of the study and applications to brand

management are mentioned.

THE PREVIOUS LITERATURE AND THE MODEL

Brand loyalty has been a topic deeply studied in marketing. Jacoby and Chesnut

(1978) did an extensive study of the conceptual and operational aspects of brand

loyalty. They distinguished attitude and behavioral studies of brand loyalty and

classified a huge amount of previous articles. In the line of behavioral literature,

Colombo and Morrison applied a Mover-Stayer model previously developed by

5

Blumen, Kogan and McCarthy (1955), to study loyalty in the American automobile

market. They classified consumers as completely loyal to one of the brands in the

market, completely non-loyal or shoppers. Shoppers could repurchase the same

brand they purchased in a previous occasion or switch to a different brand.

Although they found some limitations in the model, they concluded that it was

appropriate to estimate loyalty and switching in that market. Bordley (1989) linked

this model with the Multinomial Logit and relaxed some assumptions made by

Colombo and Morrison. Bayus applied this model to study loyalty in the home

appliances market and concluded that loyalty was influenced by the duration of the

previous appliance. McCarthy et al. (1992), extended the Colombo and Morrison

model to admit information about three purchases and allowed purchase feedback

among shoppers. McCarthy et al. (1993) estimated a version of the DOGIT model,

called the PLC (Parametrized Captivity Model) for the automobile market in the

United States. This model allows for captivity of consumers by one of the brands in

the market.

In this article we use the Colombo and Morrison model to estimate the

degrees of loyalty and switching in a packaged product market in Spain. To briefly

describe the model, if i and j are two brands in a market, and Ui,j is the utility for an

individual when buying i in the previous purchase occasion and j in the current

occasion, the probability that this individual chooses i and j respectively is

Pi,j=exp Ui,j / k

N

??

1

exp Ui,k (1)

where i,j,k are brands in the market. We assume that there are two kind of

consumers, loyal and shoppers. Loyal always purchase the same brand and

shoppers can either repeat or switch. We also assume that consumers who switch

follow a zero-order behavior (i. e., the brand they choose in the previous occasion

does not influence the brand they choose in the current occasion). We classify

consumers who repurchase brand i in two segments, ###* and ###** where U###

6

*i,i=Ui and U###**i,i######, and we call ###i the degree of loyalty of the individual

to brand i, and ###j the probability that, if the consumer is a shopper, he or she

purchases brand j in the current occasion. If we also assume that individuals are

homogeneous and integrate across the population, (see Bordley (1989)), we can

express the probability of repurchasing brand i and the probability of switching

from brand i to brand j, as

Pi,i=###i+(1-###i)### ### i (3)

Pi,j= (1-###i) ### ###j (4)

The parameters ###i and ###j can be estimated by maximum likelihood.

An important measure, the degree of loyalty of the market, is LP = i

N

??

1

###

###i###Si, where Si is the market share of brand i.

GOODNESS OF FIT AND PRECISION OF THE ESTIMATES

In order to know the degree in which predictions adjust to the data, we make use of

the Chi-squared test. If nei,j is the estimated value of ni,j, (nei,j = ni+###pei,j , where ni+ is the number of consumers who bought in the previous occasion i and

pei,j are the estimated probabilities), the formula is

X2 = i

N

??

1

(nij - neij)2/ne

ij .

When the model fits to the data and the sample size is large enough, X2 is

distributed as a ###2 with [(number of columns -1) x (number of rows) - number of

free parameters of the model] degrees of freedom . However, besides the degree of

adjustment of the predictions to the data, we need to test the precision of the

estimates. The kind of data and estimation method we are using, does not allow us

to directly build the co-variance matrix. Therefore, we need to estimate it. It would

not be difficult to do it if we had many samples of the population and could make

7

an estimation for each of them. Then, we would have the sample distribution of the

estimators and could estimate their mean and co-variance matrix. Since we just

have one sample available and we are using it all in the estimation process, we need

another methodology to estimate the covariance matrix.

The method we use is called bootstrap4. If we generate M samples and ###

m is the vector of the estimated parameters for the sample m, m = 1, ..., M, then the

average vector of the estimated parameters can be expressed by:

###*=(1/M) ### m

M

??

1

###m (4)

and the co-variance matrix5:

SM=[1 / (M-1)] ###m

M

??

1

(###m-###*) ### (###m-###*)' (5)

DATA AND EMPIRICAL STUDY

In this part we develop the empirical application of the model on a real market. We

will mainly be interested in investigating the competitive structure of the market in

those aspects which are related with brand loyalty. In this sense, we will also be

able to propose some recommendations for management.

We will first describe the market and the data base. Then, we will explain

the partitions we make in the market and finally, we will present and comment on

the results of the estimations (for the whole market and for the partitions) .

4Bootstrap was introduced in 1979 by Efron as a computational method to estimate the standard error of the estimated parameters. It starts by considering the sample as if it were the whole population. Then it makes a random sampling with replacement on it to obtain the sample distribution of the estimator.

5Readers interested in more details about bootstrap can contact the first author of this paper.

8

The Market and the Data Base.

The market chosen for this empirical study corresponds to the delicate clothes

detergents market in Barcelona and its metropolitan area (Spain).

The domestic detergent market in Spain is very dynamic. There are a few

manufacturers, and 50% of the market share belongs to just three companies.

Within the category of domestic detergents we can find many kinds, varieties, and

sizes. The reason we chose detergents for delicate clothes is that it is a well defined

product category. We chose Barcelona and its Metropolitan Area due to availability

of data. This area, is 11% of the total laundry detergent market in Spain and the

15% of it corresponds to delicate clothes detergents.

From now on, when speaking about the market, we will be referring to the

delicate clothes detergent market in metropolitan Barcelona.

The data comes from a panel of Spanish households and was kindly given

to us by a market research company named DYMPANEL6. The double selection

made on this data base (kind of product and geographical area) gives us two

purchases for each of the 159 households who bought the product at least twice

during 1991. Thus we will have 318 observations to make the study.

For each purchase, among the information available, we know the kind of

store where it took place, the brand chosen and the price paid, together with the

number of members of the family who made the purchase.

There are twenty brands in the market. The first one enjoys 21% of the

market share, the second one has17% and the third one holds 16,7% of it. These

three brands together have more than one half of the market (54,7%). The rest of

6The panelists (householders) fill up a bulletin which is weekly sent to the market research company for its treatment. They are all members of a national panel of consumers which gives information on households purchases from a number of products. The sample is representative of the population in Spain, and it also guarantees the representativeness in each geografical area. The sample is renoved a 25% each year.

9

the brands have market shares between 0,5% and 8%. In order to make the study

more tractable, we will group these twenty brands into four alternatives. The first

three categories will correspond to the first three brands in market share7 (which

are supposed to be available to all the households); the fourth will be formed by the

rest of the brands, grouped under the name 'other brands'.

The kind of stores which can be found in Spain are the following ones:

hypermarkets, supermarkets and traditional stores. We would like to pay special

attention to traditional stores, since they are not that common in other countries.

Those are small shops, non-self-service ones, where at most three or four brands of

each product can be found. There are many of them and they are very conveniently

placed in most of the corners of the city centers. They enjoy 33% of the market

share. The remaining 67% corresponds to supermarkets and hypermarkets. It is

interesting to remark that 85% of the households who bought the first time in

supermarkets or hypermarkets, choose the same kind of store for the second

purchase. This rate of repetition is 75% among people who bought in traditional

stores.

The initial sample ("Whole Sample") is formed by the 318 observations

mentioned before (2 for each of the 159 households). With these observations we

can build the so called brand switching matrix, whose ni,j elements are the number

of consumers who bought brand i in the previous purchase and brand j in current.

These elements are used in the likelihood function, whose maximization will give

the ### and ### parameters (Colombo and Morrison 1989).

Market Partitions.

One of our goals is to investigate some of the factors that influence loyalty. It is

usually considered that loyalty is something inherent to consumers. Then, firms

7For reasons of confidenciality, we call these brands Brand 1, 2 and 3.

10

take the degree of loyalty as given, and develop marketing strategies based on that

information. However, we consider that these strategies and the way brands are

commercialized also have effects on loyalty. Therefore, we can say that there is

cause-effect relation in both senses. We will not analyze these relationships in an

explicit way, but will observe the degrees of loyalty in different groups of

consumers, stores, and brands.

In particular, we want to observe the relation between loyalty and: (i) the

rate of consumption, (ii) the type of store where the brands are sold and (iii) the

variability of retail prices.

As we mentioned at the beginning of the article, one of the causes of

loyalty is the experience the consumer has with the product. As consumer

experience increases, their evaluation and decision criteria become more defined

and, therefore, loyalty should be higher. The group of consumers with more

experience are the heavy users, which are larger families in this product category8.

Then we hypothesize that large families are more loyal than small families.

On the other hand, hypermarkets and supermarkets have some interesting

common characteristics that are not shared by small and traditional stores: they sell

a large number of different brands, these brands are exposed and accessible to

consumers on shelves, consumers feel free to look and compare different brands,

there is no shop assistant who can influence consumers' decisions, and a large

number of promotional and merchandising activities take place. We think that these

characteristics make it easy for consumers to make more rational decisions based on

real attributes, such as price, and can decrease loyalty9. We hypothesize that among

8While families with 3 or less members (from now on "small families") purchase on average 3.5 times per year, families with 4 or more members (from now on "large families") purchase 5.95 times.

9An article published in 1989 in The Wall Street Journal mentioned a research conducted by Peter D. Hart Research Associates in the United States among 2000 consumers. It was found that loyalty was decreasing, due mainly to the increase of promotions.

11

the purchases made in these stores the degree of loyalty is lower than in traditional

stores.

Related to the variability of retail prices, we think that a proportion of the

consumers of brands with higher price variability, purchase these brands only when

their prices are lower. On the other hand, brands with stable prices are not attractive

to switchers, since one of the main reasons for switching to a brand is a price

reduction. Therefore, we hypothesize that brands with more price variability have a

lower degree of loyalty.

The same kind of arguments, but in an inverse direction, can be used to

make hypothesis about switchers. Therefore, we also hypothesize that brands with a

higher degree of loyalty have a lower degree of attraction to switchers and vice

versa.

One can think that in hypermarkets and supermarkets the variability of

retail prices is also higher, and that we are measuring the same effect by both, type

of store and price variability. However, these stores have other important

characteristics besides price variability.

In order to test these hypotheses, we will first estimate the model for the

whole sample and then we will divide it into groups based on:

(i) the size of the family: small families (3 or less members) and large

families (4 or more); and

(ii) the place where the second purchase took place: traditional store, on

one hand, and hyper and supers on the other.

In Table 1 we can see that, besides the average price and market share,

there are differences in variability of retail prices by brands. Brand 2 has the lowest

variability in all the market partitions, Brand 1 the highest in 3 of 5, and Brand 3

has the highest in the other 2. Given our hypothesis about loyalty and price, and

12

about loyalty and switching, Brand 2 should have the highest degree of loyalty and

the lowest attraction over switchers.

Empirical Analysis.

Table 2 shows the estimated coefficients, for equations (2) and (3), of the loyalty

and switching parameters for all brands (except the composite alternative10) in

each one of the market partitions mentioned above. Also shown are the proportion

of loyal and switchers in the partition, (LP and 1-LP respectively), and the market

share of each brand in the partitions, Si.

The results obtained using bootstrap show that of the 45 parameters

estimated (20 ### 's, 20 ### 's and 5 LP's), 38 are significant at the 5% level of

significance, 6 at the 10% level and there is only one, ### of Brand 3 in traditional

stores , which is not.

The ###2 tests show that for all the market partitions the fit of the model is

good at the 5% level of significance.

Comments about the results

In the whole sample, the level of competitiveness is not very high, since LP=0,59.

This means that 59% of the households are loyal to the brand bought in their

previous occasion and they "automatically" purchase the same brand again. In other

words, on average, 59% of brand's consumers are fixed. In the market partitions,

similar comments can be done.

10The alternative 'other brands' is not included in the results, since the interpretation of its coefficients seems to be difficult. This is due to the variety of brands grouped under it. It is important to say that, although the LP's have been calculated using the loyalty parameter and the market share of this alternative, LP is not affected by the number of alternatives aggregated under this composite. That is, in analysis done over the same data with 4 brand names and the rest as composite brand, 5 brand names and a composite brand, etc, LP does not change.

13

It is observed that LP is smaller in the large households partition than in

the small households partition (0.66 and 0.54 respectively). This confirms our first

hypothesis and we conclude that large households are more loyal than small ones.

As we said before, the size of the household in this market is a good proxy of the

intensity of consumption and, therefore, of the inverse of the interpurchase time.

We conclude that these two variables are positively related with loyalty.

It is also observed that LP is smaller among the purchases made in

supermarkets and hypermarkets than in small stores. This also confirms our second

hypothesis and we conclude that the commercialization in these kind of stores is

negatively related with loyalty for the reasons we argued before.

Trying to find relations between the variability of brands' prices and their

degree of loyalty seems more difficult. If we look at the estimations within each

partition, we observe that Brand 2 always has the highest ###, the lowest ###, and

also the lowest price variation. Something similar happens with the other two

brands: Brand 1 has the highest price variation in the whole sample, small

households, and super and hypermarkets. It also has the smallest ### and the highest

### in these partitions. Brand 3 has the highest price variation in large households

and in small stores. It has the smallest ### in large households, but it is not

significant in small stores. Brand 3 also has the highest ### in these two partition. If

we look at the coefficients of the brands across partitions, we find a positive

relationship between the ###'s and price variability for Brand 1 and Brand 3 across

size of households, and for Brand 2 across the type of stores. We find a negative

relationship between price variability and the ###'s for Brand 1 and Brand 2 across

all the partitions, and for brand 3 across size of households.

Looking at the ###'s and the ###'s, we observe that they are negatively

related when comparing brands within partitions (except Brand 3 across stores, and

Brand 2 across households) and across partitions for all brands.

14

Although these results appear to show a positive relationship between price

variability and attraction over switchers, and a negative relationship between the

former and loyalty, we are not sure that this relation is more than brand specific,

since the results do not all go in the same direction. Moreover, analyzing just 3

brands could be too few to build conclusions. The same could be said about the

negative relationship between loyalty and attraction for switchers.

All these results can be shown in Table 3. In this table we have cross-

tabulated the following variables: size of the family, type of store, brand bought,

loyalty, and repetition. The number of consumers in each cell is computed taking

into account the degree of loyalty (or repetition) in each of the market partitions.

For example, the number of loyal in traditional stores is 57 x 0.75 ### 42. We see

that these relations are significant, in the sense that the chi-squared tests of

independence between rows and columns are rejected for loyalty and family size,

loyalty and type of store, and loyalty and brand purchased. We also see that loyalty

and repetition are clearly correlated, and that loyalty segments better than repetition

the variables mentioned above (the chi-squared tests of independence are not

rejected with repetition). We also see that size of the family and type of store are

independent. In order to show these relations more clearly we have performed a

multiple correspondence analysis (Greenacre (1993)) with this table (see Picture 1).

In this map we see that traditional stores, large families and Brand 2 are closer to

loyalty. Hyper and super, small families and Brands 1 and 3 are closer to non-

loyalty. In this map, closer points mean more related categories.

LIMITATIONS AND FUTURE RESEARCH

A clear limitation of the model is to consider that there are only two kind of

consumers. Although this limitation is overcome in part when considering the

population instead of the individuals, more degrees of loyalty should be

incorporated. The ideal would be a continuous measure of brand loyalty.

15

Another limitation is to consider that shoppers have a zero order behavior.

A model which considers that shoppers are influenced by the brand they bought

before has also been estimated11. This model considers that there is a ###i,j for each

pair of (i,j) brands in the market. Although the goodness of fit of the model

increases, the estimates of the ###'s are very similar to those obtained by the model

presented here, so the conclusions do not change. The ###i,j move around the ###j's

presented here. Moreover, the large number of parameters of the model makes it

difficult to interpret the results. So we conclude that the model presented here

explains well enough the data in this market.

We have inferred relationships among loyalty and other variables

comparing estimates between groups of consumers and purchases. It will be

interesting to explicitly model loyalty as a function of explanatory variables.

MAIN CONCLUSIONS AND APPLICATIONS TO BRAND

MANAGEMENT

The main conclusions from the empirical analysis are the following. First, is has

been shown that heavy users (represented by large families) are more loyal than

light users. Second, among purchases made in hyper and supermarkets, the degree

of loyalty is lower. Third, although the brand with less price variability has been

identified as the brand with higher loyalty and the brand with higher price

variability as the brand with generally less loyalty, we are not sure that these

relationships are more than pure coincidence. Fourth, it has been observed a

negative relationship between loyalty and switching.

Some applications to brand management can be derived. First, it seems

that it is difficult for a brand to have high loyalty and be the brand preferred by

11See McCarthy et al. (1989). The estimation procedure is sequential and imposes restrictions of equality among parameters in order to increase the degrees of freedom.

16

switchers at the same time. Hence, a firm must decide to position its brands as one

of these two brand types. Second, given that there is clear tendency towards the

commercialization of products in large surface stores, such as hypermarkets, loyalty

will tend to decrease in the products sold in these stores. Therefore, brand managers

should develop strategies which take this phenomenon into account. Third, different

strategies for loyal and non-loyal consumers should be developed. In this market,

given that large families are most loyal, brands could promote smaller packages

rather than large ones, for example.

As a final note, it is important to say that loyalty is an important topic in

marketing and that more interest should be placed in it. Although many studies use

loyalty as an explanatory variable of consumer behavior, it is not easy to find

articles with emphasis in determining the factors that influence it. It is necessary to

develop conceptual and empirical models to better understand brand loyalty.

17

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20

Table 1. Market Share, Average Price and Sample Deviation of Prices12,13 by

Brand and by Market Partition

MARKET PARTITION MARKET

SHARE AVERAGE

PRICE SAMPLE

DEVIATION OF PRICE

WHOLE SAMPLE (n=159) 1 310 216 m1 .24 364 106 m2 .14 256 21 m3 .13 421 56 SMALL HOUSEHOLDS (n=90) .56 312 278 m1 .24 389 144 m2 .14 258 30 m3 .12 423 45 LARGE HOUSEHOLDS (n=69) .44 307 156 m1 .23 330 35 m2 .14 255 19 m3 .14 420 62 SMALL STORES (n=57) .36 324 197 m1 .23 380 36 m2 .12 265 26 m3 .05 435 38 SUPER AND HYPER (n=102) .64 306 293 m1 .24 355 133 m2 .16 247 19 m3 .17 420 66

12Average Price and Sample Deviation of Price calculated over the previous and current purchase data.

13n=number of househols

21

Table 2. Estimated parameters.

WHOLE SAMPLE

SMALL HOUSEHOL

DS

LARGE HOUSEHOL

DS

SUPER AND

HYPER

TRAD. STORES

? BRAND 1 0.41* 0.33** 0.49** 0.37** 0.49** BRAND 2 0.75** 0.74** 0.77** 0.79** 0.67** BRAND 3 0.40** 0.47* 0.26* 0.46** 0.06 ? BRAND 1 0.34** 0.46* 0.14* 0.30** 0.40** BRAND 2 0.12* 0.11** 0.15* 0.11** 0.14* BRAND 3 0.17** 0.14** 0.25** 0.13** 0.29** LP 0.59 0.54 0.66 0.53 0.75 1-LP 0.41 0.46 0.34 0.47 0.25 ? 29 9.65** 5.22** 6.89** 7.54** 1.33** ** : significant at 5%. *: significant at 10%.

22

Table 3. Cross-tabulation and independence ? 2 tests among variables of interest. LOYALTY

TYPE OF STORE

FAMILY SIZE

BRAND BOUGHT IN

THE PREVIOUS OCASION

MARG.

TOTAL

LOYALS NON-LOYALS

TRADIT.STORES

HYPER AND SUPER

SMALL LARGE 1 2 3 4

REPETITION YES 94 20 45 69 62 52 23 18 10 63 114 NO 0 45 12 33 28 17 15 5 10 15 45 X2

1=90.7 p<<.001 X21=2.30 p=.129 X2

1=.8 p=.37 X23=10.6 p=.01

LOYALTY LOYALS 42 53 48 46 16 17 8 54 94 NON-LOYALS 15 49 42 23 22 6 12 24 65 X2

1=7.7 p=.0074 X21=2.87 p=.09 X2

3=12.9 p=.0046 TYPE OF STORE TRAD. 31 26 13 7 3 34 57 H. AND S. 59 43 25 16 17 44 102 X21=.18 p=.67 X23=6.15 p=.1 FAMILY SIZE SMALL 22 13 11 44 90 LARGE 16 10 9 34 69 X2

3=.04 p=.99

MARG. TOTAL 94 65 57 102 90 69 38 23 20 78 159

23

Picture 1. Multiple Correspondence Analysis among Loyalty, Family Size, Type of Store, and Brand Bought, and Repetition.

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

-0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6Loyals

Brand 2

Non-Loyals

Hyper and Super

Trad. StoresSmall Families

Large Families

Brand 1

Brand 3

First Principal Axis (33.42%)

Seco

nd P

rinci

pal A

xis

(18.

4%)