Markovian application to brand switching behaviour

European Journal of Business and Management

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ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online)
Vol.5, No.22, 2013

Markovian Application to Brand Switching Behaviour: A Survey
of Toothpaste
Godwin I. Umoh, Ph.D
Department of Management, University of Port Harcourt
Hart O. Awa, Ph.D
Department of Marketing, University of Port Harcourt
Prince T. Ebitu, Ph.D
Department of Marketing, University of Calabar
Abstract
Every industry and economy is idiosyncratic especially in terms of what drives consumer loyalty and so,
extrapolating knowledge may not always provide the right basis for competitive advantage. This paper borrows
transition matrix as a forecasting instrument for determining the market environment in the future, and sets out to
unveil the potential of Markov chains in sequencing family brand choice and in determining intensive
transitional probabilities for toothpaste. Data were purposefully drawn from 785 students of universities of
Abuja, Lagos, and Port Harcourt. The data generated were cast into a Markov matrix to permit meaningful
observation of the households’ behaviour toward five brands of toothpaste. Further, the data were analyzed using
multiple regressions and Pearson correlation co-efficient; and the findings showed that households exhibit
AAAAA, AAAAB, and AAABC not because of trade deals, out-of-stock of favourites, and in-store stimuli;
rather because of such product-delivery attributes as flavour and functionality, brand availability and awareness,
and brand name and image. While the paper concludes that subjects exhibit brand loyalty based on brand’s
selling points, the paper advised on creating corporate framework that allows for more strategic and perhaps
tactical actions.
Key words: Markov chain, toothpaste, switching behaviour, brand loyalty.

Introduction
Building sustainable competitive advantage (SCA) has long made the phenomenon of consumer brand switching
a central issue underlying market dynamics (Dick and Basu, 1994; Parker, 1979). Amidst the prevailing stiff
competition and informed consumers, scholars and practitioners recognize that steady throughput lies on
grasping the processes that underlie brand switching/loyalty and to cast it into a framework suitable for
evaluating market factors and for predicting their influences on sales behaviour (Farley and Kuehn, 1965; Parker,
1979). This is worthwhile because loyal behaviour attracts product-support information and advocacy behaviour
(Schoenbachler, 2004; Kuenzel, 2009; McGrath, 2011), less price sensitivity, and willingness to spend more
resources to maintain undisrupted behaviour (Paswan et al., 2007). Aside loyal consumers refusing to switch in
order to show cohesiveness to the symbols of their social class (Nia and Zaichkowsky, 2000); their positive
word-of-mouth represents the mechanism for saving marketing cost (Rundle-Thiele and Mackay, 2001). Boone
and Kurtz (2007) observe that generating a new customer costs 5 to 7 times as much as keeping a current one, so
firms pay steep prices when customers stray to other brands. Depending on the industry, a 10 percent reduction
in value-chain cost structure may yield 40 to 50 percent improvement in pre-tax profit (Eisingerich and Bell,
2007; Alrubaiee and Al-Nazer, 2010; Shaker and Basem, 2010) or as little as a 5 percent improvement in

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customer retention can upsurge profitability to about 25 to 85 or 95 percent (in terms of NPV) (Reichheld and
Sasser, 1990). Ha et al. (2008) found strong correlation between loyalty and the mean number of different stores
visited per shopping time.

Consumers make brand choice as a result of many influences; habits, in response to merchandising cues, or with
explicit consideration of alternatives. The economic sense of such behaviour is guided by correctly predicted
marketing influences leading to it (Parker, 1979; Farley and Kuehn, 1965; Kuehn, 1956). Literally, consumers
prefer loyal behaviour to brands perceived to be satisfactory to purchasing untried ones (Kalu and Awa, 2008).
The surveys of acceptance rate of a new food product (see Arndt, 1967) and headache remedies (see
Cunningham, 1967) found that high-risk perceivers were more likely to be loyal to old brands than low-risk
perceivers. Even at the exploratory phase of purchase when consumers make trial behaviour, Shoemaker and
Shoaf (1975) opine that they tend to buy smaller quantities than usual in order to minimize risk associated with
disrupted behaviour. However, people love familiar stimuli; the unfamiliar is perceived discomforting but with
aggressive acquisition of more knowledge, the unfamiliar becomes familiar (Venkatesan, 1973). The behavioural
scientists and instrumental conditioning theorists suggest that consumers make two types of purchase- trial and
repeat (Schiffman and Kanuk, 2009); trial behaviour permits firsthand experience of novel brands and reinforces
repeat behaviour if perceived more satisfactory than familiar behaviours.

Modelling brand loyalty seems an uphill task perhaps because of the intricacies of human behaviour. With the
advent of management sciences and mathematical models as well as availability of software, the erstwhile
complication in transforming marketing problems into mathematical equations is now primordial as explicit
attempts are made to model and test various theories of brand loyalty and/or brand switching. Attempts to
understand the dynamics of consumer brand choice necessitated the investigation of frequency of occurrences of
various configurations in short purchase sequences. Kuehn (1962) surveyed 2 to 5 purchase sequences for
several products while Harary and Lipstein (1960) focused on consumer buying behaviour in terms of twopurchase sequences. These studies examined consumer brand choice as stochastic or probabilistic processes
(Farley and Kuehn, 1965). The two-purchase sequence analysis underlies the concept of first-order purchase to
purchase Markov brand shifting and the longer purchase sequence of Kuehn (1962) led to brand shifting learning
model. The basic concepts of Markov Chain as marshalled out by a Russian mathematician, Andrey
Andreyevich Markov, span predicting income distribution, immigration as a geographic structure, brand
switching and brand loyalty, occupational mobility, and long-term market share, especially in oligopolistic
environment (Frydman, 1984; Geweke et al., 1986; Simper and Spilerman, 1977).

This paper borrows transition matrix as a forecasting instrument for determining the market environment in the
future, and sets out to unveil the potential of Markov chains in sequencing family brand choice and in
determining intensive transitional probabilities for toothpaste. The research question is: given the last purchase
of toothpaste, what is the probability that the consumer purchases same brand (say close-up, X1) or switches to
another (say Aqua-fresh, X3) next time? The study fills gap in knowledge at the instance of two points; (1)
toothpaste seems a fascinating area of inquiry perhaps because of huge competition and brand extension as well

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as its neglect by previous scholars; and (2) where similar studies (Cheng et al., 2012; Parker, 1979; Brown,
1953) exist, they seem somewhat alien to toothpaste and to developing economies such as Nigeria. For instance,
Cheng San and Yee (2012) focused on counterfeit clothing and footwear in Malaysia; Parker (1979) on
consumer grocery shopping in Dublin; and Brown (1953) on nine product classes in Chicago, one of which was
toothpaste. Although Brown took a qualitative approach and sets the pace for similar scholarly inquiries, his
findings and those of Cheng San and Yee (2012) and Parker (1979) can rarely be wholly extrapolated to
specifically solve problems in today’s changing Nigeria; hence, the need for this study.

Theoretical Development
a.

Brand Loyalty and Measurement

Defining, operationalizing, and measuring brand loyalty vary amongst scholars and practitioners. One of the
thorniest ordeals of marketing management is how to obtain objective and general measurement metric(s) of
brand loyalty (Kahn et al., 1986) and to correlate such metrics with consumer characteristics in order to segment
markets. Studies (Phau et al., 2009) and the theory of reasoned action (TRA) suggest that purchase intention is
the surrogate measure of actual purchase (Summers and Belleau, 2006); the best predictor of behaviour is
intention to perform (Fishbein and Ajzen, 1980; Young et al., 1998). Brand loyalty may mean high customer
lifetime value (Kalu and Awa, 2008); it measures the length of time a customer is predisposed to resist
competitive offers (Hasouneh and Ayed Alqeed, 2010) or a deeply held accord to repurchase preferred product
versions in future despite the potential influences of perhaps ugly experiences and marketing efforts (Yim et al.,
2008; Gommans et al., 2001). The loyal consumer does not only repurchase and cause me-too but also shuns
switching even when better offers exist elsewhere (Oliver, 1997; Oh and Fioritio, 2002). Scholars (de Rio-Lanza
et al., 2009; Michel et al., 2009) suggest that committed customers assume that ugly experiences may be settled
out overtime and turn them even more satisfied. Loyalty may mean a passive improvement of situation; a
measure of tolerance amidst voiced out inconvenient situations (Alrubaiee and Al-Nazer, 2010), a function of
satisfaction and switching barriers (Fornell, 1992), and a process whose end product affects perceived quality
and ultimately behavioural intentions (Bitner, 1990).

The theory of brand loyalty is premised on the fact that the longer a relationship lasts the more profitable it turns
and so, the core of relationship marketing is to win and keep customers by creating relational value chain (Peng
and Wang, 2006) in a manner that culminates more contacts and deeper accords, frequency marketing and
efficient operations, cannibalization/cross selling, making disruptive behaviour costly, and detailed probing into
customer complaints and competitive benchmarking (Hasouneh and Ayed Alqeed, 2010). Early scholarly
inquiries (Brown, 1953; Cunningham, 1956; Kuehn, 1962; Harary and Liptein, 1962) and recent ones (Dick and
Basu, 1994; Taylor et al., 2004; Gil et al., 2007) on customer brand loyalty principally emphasized behavioural
and attitudinal loyalty. Further studies (Schiffman and Kanuk, 2009; Asseal, 1992; Wilkie, 1994) emphasized
cognition and consistency in attitudinal loyalty and behaviour. Behaviour explains consumer loyalty in terms of
purchase habits or evidence of consistency; whereas, attitudinal sees loyalty from favourable predisposition to a
brand. Conceptually, Jacobs and Chestnut (1978) provided that brand loyalty is biased (i.e. non-random); a

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purchase behaviour expressed overtime by some decision making units with respect to one or more brands out of
a set of brands, and shaped by psychological (decision-making, evaluative) processes. Charnatony and
McDonald (1992) opined that behavioural measures define loyalty by sequence of purchases and/or by the
proportion of purchases; an indication that a satisfied consumer repeats behaviour in a relatively short time.
Lyong (1998) served operational definition when he viewed brand loyalty as a function of brands relative
frequency of purchase in both time independent and time dependent situations.

Brown’s (1953) study represents the earliest inquiry on behavioural measures of brand loyalty. The study
compared brands across product classes using The Chicago Tribune Consumer Purchase Panel Data and
attempted to model sequences of purchase patterns of 100 families. The findings led Brown to categorized
family sequences of brand choice into four- undivided and stable loyalty (AAAAAA), divided but stable loyalty
(ABABAB), unstable loyalty (AAABBB), and no loyalty (ABCDEF). Farley and Kuehn (1965) accused Brown
(1953) of having ambiguous classification that makes replication of the survey almost difficult. Cunningham
(1956) recognized Brown’s (1953) ordeals in defining pattern of behaviours and avoided them with a crosssectional share of purchase statistic a household would make of specific brand(s). He showed various aggregates
of this statistic for seven product classes with particular focus on each family’s loyalty to its most frequently
purchased brands. Though Cunningham’s (1956) work permitted stable comparison of behaviour over the
various product classes, it lent very little to the understanding of consumer behaviour dynamics and thus extends
and/or complemented Brown’s. Individuals who exhibit ABABAB sequences were categorized as breaking
purchase vicious between two brands in the semi-period of the study. Brown’s and Cunningham’s classifications
were supported by other scholars (e.g., Tucker, 1964; Finn, 1984). Finn proposed that a consumer is considered
brand loyal if he makes three successive purchases of same brands of a product (AAABBB); whereas Tucker
measured brand loyalty by the proportion of total product purchases a household devotes to a brand most
frequently purchased (AAAAABB).

Although, behavioural loyalty is applauded for focusing on customers’ value towards a brand (Schultz and
Bailey, 2000), the cognitive learning theorists assert that the classifications lack precision since they do not
distinguish between true brand faithful and the spurious loyalists, who repeat brands in response to cues/ in-store
stimuli and/or out-of-stock of favourites (Schiffman and Kanuk, 2009; Shapiro, 1977; Schoenbachler et al.,
2004); thus, there is a sufficient difference between brand loyalty and repeat buying behaviour. Loyalty is
measured by attitude rather by purchase consistency (Schiffman and Kanuk, 2009) though scholars (see Sharp et
al., 2002) note that attitudinal loyalty is not sufficiently relevant in determining brand loyalty in a short-run.
Therefore, Baloglu (2002) and Ha et al. (2008) emphasized the need to extend brand loyalty definition by
synchronizing attitudinal and behavioural influences. The operant conditioning and cognitive learning theorists
provide knowledge that complemented the existing literature to enlarge the understanding of the dynamics of
brand loyalty. The first views behaviour consistency as signal of loyalty based on the theories of Thorndike’s
law of effects, reinforcement, and stimulus response (S-R); and the second signifies that favourable
predisposition to a brand may not be reflected by just measuring continuous behaviour rather by following
Edward Tolman’s proposition of thinking about a problem and developing experiences that could serve to reduce

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perceived risk in similar situations in future (left hemisphere). Other theorists (Schiffman and Kanuk, 2009;
Hansen, 1981) suggest that brand loyalty is measured by the degree of involvement/hierarchy of effects; the high
involvement theorists emphasize on extensive information search leading to attitude change and attitude
formation, purchase behaviour, and ultimately brand loyalty; while low involvement involves brand awareness
leading to purchase behaviour, attitude change and attitude formation, and then brand habit.

b. Markovian Models
Markov chain is a dynamic quantitative model with Markov property; the probability of moving to state (X) at
time (n+1) depends only on current state (immediate past purchase behaviour) and not on the sequence of entire
previous states. Thus, it is a system where discrete random variables {X1, X2, X3, ......, Xn} form a stochastic
process of transition from one state to another in a chain-like manner. Given the chronological times {to,
t1,........,tn}, the family of random variables {X1, X2, X3, .......,Xn} is said to be a Markov process if it possesses
the following property.
P {Xtn =xn/Xtn-1 =xn-1,............,Xto =xo} = P{Xtn =xn/Xtn-1 =xn-1}, where n=0,1,... and the possible values
of Xi form finite set S referred to as state space of the chain.
Usually, a Markov chain is characterized by a set of states and transition probabilities (Trivedi and Sahner, 2009)
and defines finite set of times (i.e. a discrete-time Markov chain) though same terminology applies to situations
where time takes continuous values (Nummelin, 2004; Parthasarathy and Vinoth, 2010). However, Markov chain
Monte Carlo methodology covers cases of the two extremes (discrete algorithm steps and continuous state
space). The first-order purchase-to-purchase Markov process was the first application of a stochastic model to
describe brand switching. The model assumes that each purchase is affected in a stochastic sense only by
preceding purchase and is statistically independent of prior purchases (Taha, 2007; Loomba, 1978; Farley and
Kuehn, 1965; Law and Kelton, 2000; Damodaran and Gopal, 2009); knowledge of the immediate past behaviour
of a system helps to predict its future behaviour. The conditional probabilities
P {Xn+1 =Xn+1/Xt =xn}; where n= 0, 1, 2,........
of the next state (and in fact all future states) given its current state depends on the system’s current state and not
additionally on the state of the system’s previous steps. Given that the data on market share behaviour of
different brands of a product (say toothpaste, Xt) is known, Markov analysis provides insight into how a system
(e.g., high frequency, medium frequency, and low frequency purchasers) transits to the next or previous integer
or how consumers shift behaviour (rejection operator) from one brand (say close-up, X1) to another (purchase
operator) (say Aqua fresh, X3) in a trial. The assumption is that a system or a customer at a specified time can be
in one of the nth state; either buying close-up (X1), McCleans (X2), Aqua-fresh (X3) or some other brands (Xn)
in the choice set. The probability of moving from state i in nth time step is given as
(n)
Pij = P {Xn =j/X0 = i} and for single-step transition Pij = Pr {X1 = j/X0 = i}
Since systems change (transition) randomly often in response to managerial cues or something else to a point of
inflicting a change in loyalty (say from X1 to X2 or to X3), it is difficult to make exact prediction about their
future behaviour though their future statistical properties may be predicted. The probabilities of state-changes
termed transition probabilities (Pij) representing the probability of a customer transiting from brand i to brand j

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may change with time (dependent or non-stationary Markov chains) or remain constant overtime (timedependent or stationary Markov chains). Formally for all (n) and probability of transition independent of (n), the
equation below holds.
P {Xn+1 =x/X1 =y} = P {Xn = x/Xn-1 =y}, where n =0, 1, 2.....
The probability of transiting from states i to j in nth time steps
(n)
Pij = P {Xk +n = j/Xk = i} and Pij = Pr {Xk +n = j/Xk = i}
Research Plan
This study investigates and models brand loyal behaviour of toothpaste consumers using the Markov Chains.
Data were drawn purposefully from 785 students of universities of Abuja, Lagos, and Port Harcourt. These
universities are located in choice cities, they cover the six geo-political zones of Nigeria, and they assure federal
character in students’ enrolment. Questionnaire was developed and the questions therein bordered on five brands
of toothpaste {Close-up X1, McCleans X2, Colgate X3, Dabur X4, and Aqua-fresh X5}. The decision to arrive
at this five was informed by preliminary inquiries. To qualify for the study, subjects must at present use
toothpaste and by implication such households/persons expect replacement in due course either in favour of
current brand or another. The questionnaire was made up of three sections and of structural disguised and
structural undisguised questions. The first section dealt with demographic matters; and the second relates to the
brands that form the foundations of Markov matrix, where the subjects ranked brand currently used and the
likely preference(s) in the next purchase. The unwieldiness of the data precipitated casting them in transition
matrix. To generate meaning from the data, we
•

analyzed sets of summary statistics like the share of total purchases represented by favourite brand ;

•

categorized families in loyalty classes using purchase shares or similar measures; and

•

traced sources and destination- that is given the last purchase, what brand is likely to be purchased
next?

The third borders on marketing variables that informed the choice. Responses to this last section was measured
on 5 point scale from strongly agree (1) through strongly disagree (5).
Data and Measures
Brand loyalty measures consumption consistency in a product class. Given the absence of trade deals, out-ofstock of favourites, and in-store stimuli, this paper draws from Finn’s (1984) model (AAABBB); and Tucker’s
(1964) model (AAAAABB) to measure brand loyalty as the most frequently purchased brand(s) within a stated
sequence. In the 25 events within 5 states, each household/student was categorized in terms of the number of
times it uses a particular brand of toothpaste, and the responses were cast into transitional matrix to generate
meaning. Any household that makes consistent behaviour at least three times {AAAAA, AAAAB, AAABC}
was considered exhibiting brand loyalty. Further, measures of the independent variables that informed brand
loyalty were reported. Flavour was measured by herbal content, mint, menthol, fluoride, strawberry, and fresh;
functionality by tooth brightness, decay prevention, and protection of cavity. Brand awareness was measured by
brand’s diffusion via mass media and epidemic approach (word-of-mouth) (see Fourt and Woodlock, 1960;

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Mansfield, 1961; Bass, 1969) whereas brand availability measures ease of search and access. Brand name and
image measures affect transfer (see Wright, 1975) and deals measure sales-inducing incentives.

Validity and Reliability
The validity and reliability of the instruments were tested for accuracy of their measurement power. Though
validity was confirmed in previous studies (Dick and Basu, 1994; Kahn et al., 1986), we reconfirmed in twofold. First, the instruments were subjected to face validity involving the scrutiny of colleagues and other
informed persons in order to ensure that the statements raised adequately represent the property to measure.
Second, a pilot study to pre-test the scale measurement on a purposeful sample of 58 respondents in order correct
inconsistencies and/or ambiguities before the actual survey. For reliability and empiricism, Cronbach test was
conducted and the result slightly surpassed Nunnally’s (1978) benchmark of 0.7.

Switching Behaviour and Transition Probabilities
Probability models in general and transition probability matrices in particular have considerable appeal for
organizing sequences of panel data. See the household purchase sequence of toothpaste in a time period
summarized below. They were 5 states and 25 state spaces/events; thus,

S = { Close-up X1, McCleans X2, Colgate X3, Dabur X4, Aqua-fresh X5}.
Group A Households

{X1 X4 X4 X4 X3 X3 X3 X2 X2 X5 X3 X3}

Group B Households

{X1 X1 X4 x5 X3 X1 X2 X1 X1 X4 X4 X4}

Group C Households

{X2 X2 X4 X3 X4 X4 X1 X4 X1 X4 X4 X5}

Group D Households

{X2 X2 X2 X2 X5 X5 X5 X1 X4 X4 X4 X4}

Group E Households

{X1 X1 X1 X1 X1 X1 X1 X2 X2 X2 X3 X4}

From learning model theories, these states define the probability vectors representing the probability of a
consumer buying each brand on the next trial; whereas events or state spaces provide a single purchase act that
modifies consumer’s probability vector and alters the state the consumer finds himself. The following data cast
in a transition matrix summarizes that data shown above as fractions of purchases of a given brand going to all
brands (including itself) at the next stage. Each element in the array is divided by the sum of elements in the row
in which it occurs with a resulting matrix, which describes the likelihood of a consumer purchasing any given
brand in the next purchase act given information on the immediate preceding purchase.

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Table 1: Probability distribution
Brand Purchased at Trial t+1

X1

X3

0.083

Brand Purchased at trial t X1

X2
0.167

0.417

X4
0.250

X5
0.083

X2

0.417

0.083

0.083

0.333

0.083

X3

0.167

0.167

0.083

0.500

0.083

X4

0.333

0.083

0.083

0.250

0.250

X5

0.583

0.250

0.083

0.083

0

The matrix shows the states of the process; the existing and the next brand preference of the subjects. This array
of data in a probability form (each entry ≥ 0 and each row exhaustively sums to 1) offers insight into the process
of brand loyalty and brand switching behaviours. Observe that the probability of subsequent purchases increases
as a result of continual purchase of Say X1 or X2, and decreases when X3 or X4 is chosen. If the process (refer
to table 1) remains stable in a long-run and if the 785 households represent the entire buying population, brand
X1 (Close-up) with a probability of 0.583 is the most likely preferred when compared with other brands. The
next most preferred brand is X4 (Dabur) with a probability of 0.5; X1 and X4, each loses customers almost
equally to other brands and X1 is most likely to lose to X4 than to X2, X3, and X5. These insights have
managerial usefulness, especially in identifying competitive behaviour but this is only critical when there is a
long-run stability in transition matrix. Ehrenberg (1965) posits that instability turns up almost all empirical tests;
marketing activities, especially deals, disrupt the stability of transition probabilities. Therefore, there is a gap in
knowledge in using Markov chain to make long run forecasts amidst instability in transition probabilities.
Simple Markov process rarely provides adequate description that underpins brand switching behaviour.
Therefore, the variables that influence existing brand preferences are reported below. Variables such as quality in
terms of flavour and functionality (Xa), asking price (Xb), brand availability and awareness (Xc), brand name
and image (Xd), deals (Xe), and packaging (Xf) were surveyed to unveil how paramount each is in maintaining
the observed customer loyalty. SPSS (version 17) was used to analyze the data. A progressive enter method of
variables was used; for each variable, a separate regression coefficient describes its relationship with loyal
behaviour while Pearson’s Correlation Coefficient measured their interactive relationships.
The regression equation is thus
Yi = a + b(Xa) + b2(Xb) + b3(Xc) + b4(Xd)-----------------------+ei
Table 2: Summary of Regression Analysis
Model

R

0.78

R2

Adjusted R

0.608

0.512

SE

1.225

The study model summary in table (1) shows a value of R = 0.608, P<0.01; indicating that a large portion of
overall variance to explain brand loyalty. In other words, the control variables in the above equation explained
about 61 per cent variations of customer loyalty to toothpaste.

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Table 3: Pearson Correlation Coefficients of the Control Variables
Correlation Coefficients
Loyalty
Loyalty

Xa

Xb

Xc

Xd

Xe

Xf

1

Xa

0.35*

1

Xb

-0.23**

-0.12

1

Xc

0.32*

0.01

0.14

Xd

0.22*

0.03

0.10** 0.05

1

Xe

0.03*

-0.14

0.07

0.08

0.17

Xf

-0.12**

-0.06

-0. 04

0.20

-0.05 0.18** 1

1

1

Note: Correlation is significant at *0.05 and **0.01 levels (two-tailed test)
Discussion
This paper surveyed purchase sequences in five states (brands of toothpaste) with the probability of choosing one
state at a time depending only on immediate past purchase behaviour. The probability distribution shows that
close-up and dabur are the most likely preferred brand; thus, as long as no other brands are chosen, the
probability of subsequent purchases of close-up or dabur increases. However, on recognition that simple Markov
process lacks adequate explanation to such choice; brand availability and awareness, quality in terms of flavour
and functionality, packaging, asking price, brand name and image, and deals were investigated to provide insight
into such purchase behaviour. All these control variables differ in their relationships with customer loyalty either
at p<0.05 or at p<0.01. Quality in terms of flavour and functionality, brand availability and awareness, brand and
name image, and deals explained significant positive correlations with such loyal behaviour whereas packaging
and asking price explained weak correlations.

Table (2) shows that flavour and functionality (Xa) attracts significant positive correlation coefficient (0.35,
p<0.05) and the direction of such relationship explained the most significant weighted average impact (β =0.321)
(see also table 4) on the observed loyalty. Previous (Haley, 1968; Fiske, 1982) and recent (Puligadda and Ross,
2010; Datamonitor, 2007; Topping, 2007) studies lend support to this finding. Fiske (1982) inferred evaluation
of functionality by brand attribute beliefs and their relative importance as well as some overall attitude towards
the parent brand(s). While Puligadda and Ross (2010) revealed that increasing number of flavours increases
perceived variety, Haley’s (1968) study on segmenting toothpaste markets based on benefit sought, found that
children look for flavour and product appearance, teens and young married look for tooth brightness, large
families for decay prevention, and men for good price deals. Further support came from Topping (2007), who
found that children's flavour preferences are genetically influenced, with preferences for sweet flavours often
prevalent; and adult’s flavour preferences are predominantly guided by social and cultural norms. A Datamonitor
survey in the U.S. and Europe revealed that 80% of men and women agreed that their flavour preferences are
guided by three mega-trends- health and wellness, sensory indulgence, and homing/comfort. This explains why
herbal favour ranks first in most consumer decisions.

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Table 4: Regression Analysis
β

Control Variable

Correlation Coefficient

t-test

Xa

0.321

0.526

4.16

Xb

-0.286

-0.240

-3.10

Xc

0.301

0.489

3.91

Xd

0.280

0.312

3.64

Xe

0.212

-0.265

3.11

Xf

-0.171

-0.210

-2.25

Negative correlation coefficients occurred when asking price (-0.23, p<0.05) and packaging (-0.03, p<0.05)
entered the equation with resulting inverse weighted average impacts of β = -0.286 for asking price and β = 0.171 for packaging. The result for packaging contrasts the previous studies (e.g., Strecker et al., 1990; Yamoah,
2005), which reported that apart from a well-designed package differentiating and ensuring wholesomeness of its
contents, it influences consumer preference (through promotional messages) especially in a competitive scene.
For asking price, studies (Choi, 1991, Draganska and Jain, 2005) contradict our finding. The explanation to this
stems from the fact that Port Harcourt city is an oil rich zone where even the sellers understand that as long as
price differential is not too far-off for low involvement products and the product in question delivers the ideal
satisfaction, consumers rarely complain aggressively. When brand availability and awareness (Xc) and brand
name and image (Xd) entered the equation, positive correlation coefficients resulted; (0.32, p<0.05) for Xc and
(0.22, p<0.05) for Xd. A weighted average impact of β= 0.301 was made by Xc and β= 0.280 by Xd.
This finding is consistent with stimulus generalization of Professor Ivan Pavlov (see Schiffman and Kanuk,
2009; Kotler and Keller, 2009), cognitive consistency (see Heider, 1958; Osgood and Tannenbaum, 1955;
Festinger, 1957), affect transfer (see Wright, 1975), categorization theory (see Fiske, 1982), Thorndike’s law of
effects (see Thorndike, 1947), and technology cluster (Cook, 2009) since all suggest that brand equity expressed
in terms of strong reputations and psychological attributes have assurance of awareness, quality, affect,
dependability, performance, and service (Aaker and Keller, 1990). Firms maximize the synergy of parent brand’s
heightened awareness, availability, and good reputation/brand image to launch successful extensions (Randall,
2000) on the grounds that transfer of cognitive and affect processes permit instant communication of salient
image as well as higher product acceptance (Salciuviene et al., 2010; Ambler and Styles, 1997; Martin et al.,
2005; Kohli and Harich, 2005). Yamoah (2005) studied the availability factor of rice in Ghana and found that
brands that experience seasonal shortages suffer customer loyalty.
Finally, deals (Xe) attracted a positive correlation coefficient of 0.03, p<0.05 and a weighted average impact of
β= 0.202. This finding may be explained on the grounds that toothpaste is a low involvement product with many
versions; thus, deals add distinctive values, retain loyalty, and steer up trial behaviours which are rarely preceded
by aggressive search and evaluation efforts. Previous studies (e.g., Haley, 1968; Murphy and Sohi, 1995; Strang,
1976; Shapiro, 1977; Keller, 2001) support this finding. Haley’s (1968) study on toothpaste found that men look
for good price deals. Going by this stepwise approach, flavour and functionality is the most significant

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determinant of the observed customer loyalty, followed by brand availability and awareness, brand name and
image, and deals.

Limitations and further studies
Rarely would every product class find the finding(s) of this study wholly useful, after-all toothpaste is just one of
low involvement products. That our sample was drawn from users of frontline brands of toothpaste in the low
density area of Port Harcourt limits the power of generalization against those areas, other brands, and other
product classes not investigated bearing in mind that every locale, brand, and product class/category is, to a large
extent, idiosyncratic. Extended data and measures are required to build external validity; future scholars may
replicate the survey in other locales and product classes for cross learning and building of guiding laws. Further,
caution need be exercised in the measures used since they seem subjective and prone to common method bias
even though concrete steps were taken to minimize their effect on results. Finally, factors such as in-store
stimuli, store attendant’s advice, product’s country of origin, situational influences, who makes choice decisions,
organization’s size (for producers), and others were not measured and thus, scholars are challenged to take them
up.
Conclusions and Contributions
Marketing studies are thought discrete from time and place viewpoints, thus finite Markov chains apply. This
paper surveyed brand switching and brand loyalty of toothpaste users in the low density areas of Port Harcourt.
The data gathered were transformed into a Markov matrix and the switching behaviour amongst the five brands
was observed. Because the matrix was a systematic one, the paper concludes that subjects exhibit brand loyalty
(AAAAA; BBBBB), and Close-up and Dabur were the most preferred brands. Markov matrix shows strength in
predicting long-run brand loyalty but it rarely provides lenses to understand which variables are responsible for
such loyalty. Therefore, this paper steps further to unveil such lenses and further concluded that flavour and
functionality were the most significant determinants of this observed loyalty, followed by brand availability and
awareness, brand name and image, and deals.
The dearth of local studies that have specifically identified the most preferred brands and ‘’why’’ carves a
strength for this study. Further, though there is a general assumption that price sensitivity is a common
phenomenon amongst people of low income groups, this paper distinguishes itself by reporting that price does
not hold top-notch position in consumer decision-making especially when there is a ruling price range and the
product itself delivers its functional, psychological, and social promises to the audiences. Finally, familiarity
with the various brands of toothpaste makes packaging a mere container with little or no promotional values.
Managerial Implications
Amidst the existing stiff competition, the implication is to key strategic and tactical actions along the strengths of
the factors identified by the study in order to build competitive advantage that retains AAAAAA sequence of
purchase via clear grasp of consumers’ changing expectations. First, packaging must be made more aesthetic and
appealing to cause consumers to guess, and/or actually recognize, the salient selling points embedded in its
positioning and repositioning message contents. Second, price need be de-emphasized as a major decision
determinant in this product category, rather flavour (in terms of herbal content), brand availability and
awareness, brand name and image, and deals.

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