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Abstract

With growing competition, loyal customers have become the key to the company's success. Brand loyalty has been a central structure for marketing for almost a century, yet this research topic is still modern and up to date. The aim of this contribution is to answer the research question of whether there are different segments of customers based on demographic and psychographic aspects that would differ in the level of brand loyalty in the company. In other words, do certain groups of company's customers (according to demographic or psychographic segmentation) have a higher degree of loyalty to the company's brand? To answer the research question, we have identified hypotheses expressing the existence of a statistical dependence between individual segmentation variable and the level of brand loyalty. Based on statistical testing of established hypotheses, we have confirmed the existence of certain company's segments that have a higher degree of loyalty.
Scientific Annals of Economics and Business
The Use of Demographic and Psychographic Segmentation to Creating
Marketing Strategy of Brand Loyalty
Lubica Gajanova*, Margareta Nadanyiova**, Dominika Moravcikova***
Abstract
With growing competition, loyal customers have become the key to the company's success. Brand
loyalty has been a central structure for marketing for almost a century, yet this research topic is still
modern and up to date. The aim of this contribution is to answer the research question of whether there
are different segments of customers based on demographic and psychographic aspects that would
differ in the level of brand loyalty in the company. In other words, do certain groups of company's
customers (according to demographic or psychographic segmentation) have a higher degree of loyalty
to the company's brand? To answer the research question, we have identified hypotheses expressing
the existence of a statistical dependence between individual segmentation variable and the level of
brand loyalty. Based on statistical testing of established hypotheses, we have confirmed the existence
of certain company's segments that have a higher degree of loyalty.
Keywords: brand; brand loyalty; customer segmentation; demographic segmentation; psychographic
segmentation.
JEL classification: M21; M31.
1. INTRODUCTION
Everyday practice has shown that loyal customers are a very valuable, but very
volatile, capital of each organization. It's much easier to keep a customer than to gain. While
on acquiring new customers, the company must spend on average six times more resources
than to maintain the current ones (Chlebovsky, 2005; Kotler and Keller, 2012; Krizanova,
2015). Simultaneously there are researches that show that the likelihood of selling to an
* Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of
Zilina, Slovakia; e-mail: lubica.gajanova@fpedas.uniza.sk (corresponding author).
** Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of
Zilina, Slovakia; e-mail: margareta.nadanyiova@fpedas.uniza.sk.
*** Department of Economics, Faculty of Operation and Economics of Transport and Communications, University
of Zilina, Slovakia; e-mail: dominika.moravcikova@fpedas.uniza.sk.
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Gajanova, L., Nadanyiova, M., Moravcikova, D.
existing customer is around 60-70%, while the likelihood of sales to a new customer varies
between just 5-20% (Stárová, 2003). Aaker (2003) states that companies often invest in
groups of passively loyal and dedicated customers. According to Karlicek (2013), firms tend
to regard loyal customers as a matter of course and focus more on gaining new customers,
but this approach can be very risky. Costs incurred to ensure and increase customer loyalty
should therefore be both a necessary but also highly efficient investment for the organization
that brings long-term benefits (Mece, 2017; Musova, 2016). Despite all these familiar
conclusions, most companies are currently investing more and more in acquiring new
customers rather than in keeping existing ones. Many companies have not yet accepted the
fact that customer loyalty becomes an economic necessity and a prerequisite for their further
and successful work in a competitive environment. Brand loyalty will ensure the business
impact, which is particularly important when introducing new product variants (Aaker,
2003). According to Kotler and Keller (2012), the loyalty of the brand provides the
company a predictable and certain demand. Brand loyalty also leads to certain marketing
advantages (Chaudhuri and Holbrook, 2001).
This paper focuses on possibility of creating the loyalty program within the surveyed
company according to use of demographic and psychographic customer segmentation. The
aim of the research was to verify if there is the statistically significant relationship between
categories of customer segmentation and brand loyalty level of customers. It was achieved
through the application of the One-way Anova test and Chi-square test of independence and
subsequent correspondence analysis. The analysis of the research results confirmed that the
company should focus on a certain customer segment when creating a loyalty program as a
next step in CRM strategy
2. BRAND
The word brand was first introduced in the world of advertising in the late 1950s, by
David Ogilvy who created brand-image advertising (Majerova and Kliestik, 2015). For a
basic demarcation of a brand it can be used these definition: a name, term, sign, symbol, or
design, or combination of them which is intended to identify the goods and services of one
seller or group of sellers and to differentiate them from those of competitors (Kotler and
Armstrong, 2004). Aaker (1995) defines brand a set of assets (or liabilities) linked to a
brand’s name and symbol that adds to (or subtracts from) the value provided by a product or
service. The brand identifies the manufacturer, service provider, or merchant. Branding is a
key tool for communicating with target groups. The goal of marketing strategies is to make
consumer brand perception as something specific, which can optimally satisfy his / her
needs (Stensova, 2006).
The concept of brand can also be an important phenomenon of the marketing mix.
Mostly, in this context, external brand features such as name or artistic expression that
distinguish goods or services in a competitive offer from competition are emphasized
(Lizbetinova, 2017; Starchon et al., 2017). The brand, however, is not only a rational
phenomenon that meets functional needs. It has strong emotional characteristics based on
the subjectivity of the perceptions of the brand and the feelings they produce. This is also a
concept of a brand that emphasizes the link of the brand to the needs of the customer, which
is created and changed in every encounter with the brand. The brand is above all the notion
of "brand placement" in the customer's head (Vysekalova, 2007; Weberova et al., 2016).
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 1, pp. 65-84
67
Some other authors further develop the idea and emphasize the role of associations in
the mind of the customer with the brand and also with emotional links to the brand. In the
view of many managers, the concept of the brand contains much more - they define it as
something that has already penetrated people into the consciousness, has its name and
important position in the commercial sphere (Keller, 2007). The brand according to
Adamson (2011) is a set of thought associations that are settled in people's heads. Kapferer
(2008) claims that the brand is a shared desirable and exclusive idea embedded in products,
services, places and / or experience. If more people share this idea, the brand becomes
stronger.
The brand can be almost everything from products, services, organizations and people
through places, regions and countries to sports, entertainment, art or thoughts. In addition to
this fact it allows to the product to escape from anonymity, it also carries certain
characteristics, culture and values, and the presence of functional and emotional values that
differ the trademark and brand (De Chernatony, 2009).
2.1 Brand loyalty
Strong and lasting relationships between the customer and the seller are the
cornerstones of long-term profitability. According to Mitchell (2004) only a customer-
oriented business can acquire its loyal customers over time and with it a higher profitability.
Many authors deal with loyalty in connection with the brand. Brand loyalty has been a
central structure for marketing for almost a century, yet this research topic is still modern
and up to date. Brand loyalty is a complex construct, which leads to many definitions, which
are often very different. Pelsmacker (2003) indicates loyalty as mental positive correlation
or relationship between the customer and the brand. Reichheld (1996) defines loyalty as the
long-term preference of the brand or firm based on maximum satisfaction with the provided
value and the positive expectations of the customer for the future. Jacoby and Kyner (1973)
quote six brand loyalty conditions: biased (i.e. nonrandom), behavioural response (i.e.
purchase), expressed over time, by some decision-making unit, with respect to one or more
alternative brands out of a set of such brands, and is a function of psychological (decision-
making, evaluative) processes. The American Marketing Association defines brand loyalty
the situation in which a consumer generally buys the same manufacturer originated product
or service repeatedly over time rather than buying from multiple suppliers within the
category or the degree to which a consumer consistently purchases the same brand within a
product class (Moisescu, 2006).
Aaker (1991) considers that brand loyalty reflects the likelihood that the customer will
go to another brand, especially if the brand makes a change in price, product function,
communication or distribution programs. Brand loyalty is also defined as a measure of
consumer loyalty to a particular brand expressed by repeat purchase regardless of the market
pressure generated by competing brands. When consumers commit to a brand, they make
repeated purchases over time. Brand loyalty is the result of consumer behaviour and is
influenced by personal preferences. Faithful customers consistently buy the products of their
preferred brands, regardless of convenience or price. Brand loyalty is a key goal and the
result of successful marketing programs, business initiatives, and product development
efforts. The core of every successful brand is the core of loyal customers. They understand
the brand better, buy more often and recommend the brand to others. Roy (2011) referring to
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Gajanova, L., Nadanyiova, M., Moravcikova, D.
Aaker, states that brand loyalty reflects the extent to which the customer is willing and able
to switch from one brand to another. Tuominen (1999) defines brand loyalty as a positive
attitude towards the brand, which leads to a consistent purchase of this brand over time. It is
the result of knowing the consumer that only a brand can satisfy its needs. One of the newest
definitions of brand loyalty comes from Chegini (2010) who describes it as theory and
guidance leadership and positive behaviour including, repurchase, support and offer to
purchase which may control a new potential customer.
2.2 Brand loyalty and customer segmentation
With growing competition, loyal customers have become the key to the company's
success. The loyalty of the brand may be different and its development over time clearly
predicts the market position of the brand (Pribova, 2003; Sebestova et al., 2017). Therefore,
it is important to identify different types of customer loyalty to the brand and to know the
impact of various marketing tools on these levels. According to Aaker (2003), the focus on
segmentation of customer loyalty provides a strategic and tactical insight into how to build a
strong brand.
Jones and Sasser (1995) distributes customers based on the satisfaction and size of
loyalty to four groups. The first, called Advocates, is made up of highly loyal and satisfied
customers who spread a good name. The second group, the Hostages, are loyal but
dissatisfied customers who cannot get rid of the company because of the excessive costs
associated with the change (switching costs) or because of the lack of substitution. The third
group is the Mercenaries. They are satisfied customers who are not loyal. The chance of
their transition to competition is great, so they need to offer something extra, which forces
them to stay. The last group is Terrorists, they are disloyal and very dissatisfied, and they
spread negative experiences and discourage potential customers (Gyalog et al., 2017).
Dick and Basu (1994) assess loyalty in terms of brand attitude (relative attitude) and
re-purchase frequency (repeat patronage). They distinguish two aspects of the attitude
towards the brand: the strength of the relation to the given brand and the degree of
differentiation, which means how many brands the customer prefers for the given category.
If he / she is loyal to one brand, the differentiation is high. Furthermore, on a basis of these
two categories, attitude towards the brand and re-purchase, they created a simple four-pane
table, dividing loyalty into four categories:
True loyalty (high relative attitude and high repeat patronage) the most preferred of
all four options, which is the result of strong branding and is supported by re-purchases at a
given brand.
Spurious loyalty (low relative attitude and high repeat patronage) this case of
loyalty is characterized by the fact that the attitude does not affect the behaviour of the
customer. The customer has low brand differentiation and the choice of brand is determined
by factors such as low cost, discounts, visible location, convenient placement of a branch.
Latent loyalty (high relative attitude and low repeat patronage) this case is
relatively significant for the brand. It may be a likely consequence of the fact, that the
factors in the market are of greater importance that exceed over the customer's own attitude.
No loyalty (low relative attitude and low repeat patronage) this situation can occur
under different circumstances. A low attitude may indicate that it is a new product or is
caused by a weak communication that cannot highlight the benefits of this product. The
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solution should be to increase brand awareness, along with customer re-purchase
orientation.
American Walker Company offers another customer segmentation approach based on
loyalty. Loyalty matrix is a framework that puts customers into 4 groups according to their
answers to a short battery of questions. The two axes in the matrix represent two key aspects
of loyalty - behavioural (what the customer plans to do) and attitude (how they feel in
relation to a particular brand). Based on these responses, we get a rank of 4 quadrants:
Truly loyal these customers are intent on continuing with a brand and have a
positive attitude towards the company. They like buying that brand and are more likely to
increase their brand purchases and recommend it to others.
Accessible customers feel good about buying the particular brand, but they do not
plan to continue with it. This is a very special combination. It also includes a small
percentage of customers. It often means that something has changed in their life situation /
lifestyle and they no longer need / use a brand / product. This kind of customers typically
speak highly of company.
Trapped these customers show all signs of continuing to buy the brand, but they are
not happy about it. They feel trapped in this relationship with the brand. This phenomenon is
common for customers who are bounded on a brand or service by contract, they do not
found a suitable substitute, or it seems that changing the brand would be a very difficult
process. Over time, this customer group will find a better opportunity and leave.
High risk as the name suggests, these customers are not faithful to the brand and do
not want to continue to buy / cooperate with the company. These are people who are
"halfway out the door" and not only do not want to be customers, they also speak badly
about the brand / company among other people (Gibbsons, 2012).
Aaker (2003) divides consumers into five levels of brand loyalty in the customer
pyramid: Switcher, Habitual, Satisfied, Likers and Committed. The first level is the most
basic level of loyalty. Customer of this level are not loyal buyers, who are indifferent to the
brand and buy product / brand based on the accepted price. The second level consists of
satisfied or at least not dissatisfied consumers who buy brands due to the habit factor. The
third level represents a category of buyers who are satisfied with the brand (Rodriques and
Alessandro, 2017; Rataj et al., 2017). However, the buyer can easily change brands because
of switching cost. The fourth level is the category of buyers who love the brand. There is a
sense of connection associated with the symbol, a set of use experience or a high perceived
quality. The last level is a category of loyal buyers. They are proud to use the brand and it
becomes even more important to them in terms of functionality and the expression of their
personality.
With regard to product engagement and perceived differences between brands, Kotler
and Keller (2012) identifies four consumer brand fidelity types:
Complex loyals: Consumers are looking for information about the product they want
to buy. Once the necessary data has been obtained, their attitudes towards the brand are
formed, on the basis of which they make the choice. These people try to behave rationally in
the buying process.
Dissonance loyals: These people make their purchasing decisions quickly and
without any worries. If they have the same price in one category, they consider them as the
same. Even in the case of expensive products or products reflecting their social status. Later,
consumers are looking for information that advocates their past choice.
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Gajanova, L., Nadanyiova, M., Moravcikova, D.
Habitual loyals: These consumers make their purchasing decisions based on brand
awareness and spontaneous brand knowledge. They are not influenced by advertising, and
their buying decisions are constantly being repeated over time.
Variety seeker: These consumers change their decisions more often than others. This is
not due to dissatisfaction with the existing brand or product, but because of a diverse
personality. They usually judge their decisions during the consumption or use of the product.
Reichheld (1996) argues that classical methods of measuring loyalty and subsequent
segmentation are unnecessarily complicated and he comes with a new idea that just one
question is enough to measure customer loyalty and that is a question of willingness to
recommend the brand to other people. Reichheld's loyalty is based on maximum satisfaction
with the provided value and the customer's positive expectations for the future. In order to
measure loyalty, it is enough to ask the question: "How likely are you to recommend to s
friend or colleague?" From the results of this one question, we can simply and efficiently
complete a single number (as the author says "The one number you need to grow."), from
which you derive the current state of brand loyalty. Reichheld (2003) argues that this is an
effective measurement that tests both the rational and emotional dimensions of the customer
as well. The basis for the measurement is that if we ask a statistically valid sample of
customers for that question, it will allow us to calculate that Net Promoter Score, which
expresses the ratio of the promoter versus the detractor. This ratio calculation is based on the
11-point scale, where respondents will indicate a number 0-10 (where 0 is not at all likely to
10, which is extremely likely). According to these values, respondents can be divided into 3
basic groups - promoters, passives and detractors. The respondent, who indicated the 9 or 10
option on the scale, is marked as promoter, so it is very likely that she / he will really
recommend you to other people. Respondents who have indicated their willingness to
recommend as 7-8 would be described as passives. Other critics, or those who have
indicated a willingness to recommend by a number of 0-6 on the scale, are unlikely to
recommend you to other people.
3. METHODOLOGY
Based on the delimitation of the theoretical backgrounds and the requirements of the
surveyed enterprise, we identified a research problem as follows: Does psychographic and
demographic segmentation make sense for creating a variety of marketing strategy of brand
loyalty?
In other words, do certain groups of company's customers (according to demographic
or psychographic segmentation) have a higher degree of loyalty to the company's brand?
After confirming the existence of these segments, the company plans to focus more on these
customer groups because of the fact that the management of the company is aware,
according to the secondary data, that the customer loyalty becomes an economic necessity
and a prerequisite for their further and successful work in a competitive environment. The
enterprise for which this analysis was conducted operates on the B2C market in the Slovak
Republic in the telecommunications sector. From the point of view of annual turnover, the
enterprise is classified as a medium-sized enterprise and is also classified as a medium-sized
enterprise from the point of view of number of employees. It has been operating since 2001.
The company has applied the CRM strategy for five years and regularly surveys the
satisfaction of its customers through a questionnaire survey.
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Based on the research problem identified in the first phase of the research, a method of
selecting a suitable sample, sample size, appropriate methods and survey tools was then
established. The choice of the target group of respondents is an important step for successful
marketing research. It is necessary to decide who will be the ultimate target entity. As the final
respondents, we defined the customers of the analysed enterprise, i.e. the stratified available
sample selection (Krizanova, 2014). The size of the research sample was determined on the
basis of a formula determined by Chráska in accordance with Nowak (2007):
n=(tα^2×p(1-p))/d^2 (1)
where n is the minimum number of respondents, tα is the critical value of the normal
distribution, p is the likely sample proportion, expressed as a decimal, and d is the
confidence interval, expressed as a decimal. The confidence level was set at 95%. The
critical value of the normal distribution at confidence level α = 0.05 was 1.96. This is based
on the fact, that 95% of the area of the normal distribution is within 1.96 standard deviations
of the mean. For those cases where the likely sample proportion was not known, p was set at
50%. The confidence interval was set at 5%.
The confidence interval determines the margin of error we tolerate within the
marketing research and the rate is based on current trends in marketing research (Kliestikova
et al., 2018). Therefore, the needed sample size is 384 customers of the monitored business.
In our case, we surveyed 500 respondents. The choice of method for data collection depends
on the information needs, as well as the budget, availability of resources and timetable. For
the purposes of this research, we chose the method of collecting data through a
questionnaire, even though we are aware of its shortcomings as time consuming and low
returns. The questionnaire contained 10 questions that we asked about brand loyalty and
segmentation variables.
3.1 Segmentation variables
There are many ways to sort and categorize individual segmentation variables.
Evidence may also be literature in which a variety of variants can be encountered, always
according to a particular author (Pelsmacker, 2003; Jobber, 2004; Kotler and Armstrong,
2004; Doyle and Stern, 2006; Drummond, 2008). However, they do not differ significantly
in the description of the individual segmentation criteria. For the needs of our research, we
are guided by the categorization developed by Foret and Stavkova (2003). They list three
categories for the significant segmentation variables: Geographic, Demographic, and
Psychographic Criteria.
Due to a small geographical area and the assumption that in the Slovak Republic it
does not envisage different purchasing behaviour due to geographic factors, the company
under investigation did not have an interest in geographically segmentation. Demographic
criteria are among the most commonly used segmentation variable. Drummond (2008) and
Jobber (2004) define key demographic criteria such as age, gender, and lifecycle. Age -
Customers want and expect a different offer with increasing age. Gender - This
segmentation criterion can be used where different product acceptance can be expected
depending on gender. Life cycle - the ideal way to indirectly combine the age, gender and
lifestyle criterion. This variable is based on the assumption that during the lifetime the
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consumer changes his / her requirements and preferences depending on the role he or she is
in the family, which is approximately related to age and gender (Jobber, 2004; Drummond,
2008). Pelsmacker (2003) illustrates these individual segments according to the life cycle:
young single human; young childless spouses; young spouses with children; young divorced
with children; middle-aged spouses with children; middle-aged spouses without children,
which are dependent; middle-aged divorced without children, which are dependent; older
spouses and older without partner. The first three questions in the questionnaire were
devoted to the above segmentation criteria (Kliestik et al., 2018).
Psychographic segmentation criteria divide consumers into different segments based
on belonging to particular social classes, based on different lifestyles or types of
personalities (Kotler and Armstrong, 2004). Their goal is to explain the differences in
market manner based on the psychological and social predispositions of consumers. It seeks
to uncover the reasons why some consumers with the same descriptive characteristics show
different buying behaviour.
Kotler and Armstrong (2004) define social class as society’s relatively permanent and
ordered divisions whose members share similar values, interests and behaviour. Affiliation
to the social class is not only determined by income but is influenced by employment,
education, or property. In terms of marketing, this classification is interesting because
members of a particular social class are characterized by similar shopping behaviour.
Different social classes have different preferences in consumer behaviour. Consumer
behaviour is to a large extent influenced by the lifestyle of customers, on the other hand
consumer behaviour reflects their lifestyle (Kotler and Armstrong, 2004).
One way to distinguish social classes is by using ABCDE classification by Nielsen
Admosphere (2016). It is based on the principle of the ABCDE index, which has been
compiled by ESOMAR for the purposes of dividing the socio-economic distribution of the
population, and consists of dimensions: the number of persons, the number of minors and
the number of economically active persons in the household, the education and status of the
head of the family, and property ownership. Other questions in the questionnaire were
intend to this factors. This classification is applied in the Slovak Republic because there are
no social classes in society in terms of more homogeneous and separate layers of society.
Terms such as "upper class", "middle class", etc. have no generally accepted meaningful
exact definition. ABCDE classification by Nielsen Admosphere has an ordinal character (a
fully structured system of categories) and strongly correlates with education, economic
activity, occupational status, facilities and income. The classification contains 8 categories
A, B, C1, C2, C3, D1, D2 and E. For the conditions of the Slovak market, it is sufficient to
use only the basic division into ABCDE, because this detailed breakdown from the
marketing perspective is applicable only in countries with a developed social structure. We
have constructed a methodological classification based on the ABECE socio-economic
classification by Nielsen Admosphere.
Lifestyle as one of the characteristics of market behaviour can be tracked and analysed
from many different viewpoints, often in combination with other segmentation factors. For
purpose of this research we used lifestyle generational market segmentation (Michman et
al., 2003). Understanding generation values and motivations has become essential because
each generations is driven by unique ideas about the lifestyle to which it aspires (Smith and
Clurman, 1997). Each generation represents a different set of unique expectations,
experiences, generational history, lifestyles, values, and demographics that influence their
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 1, pp. 65-84
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buying behaviours. This information empowers you to craft a relevant message that draws a
direct connection between individuals and how they relate to your brand. There are many
studies that identify and analyse differences in consumer behaviour according to the
customers generations. For the purposes of this contribution, respondents are segmented into
six classifications by their generational cohort: (Post-War Cohort - born: 1928-1945; The
Baby Boomers - born: 1946-1954; Generation Jones - born: 1955-1965; Generation X -
born: 1966-1976; Generation Y- born: 1977-1994 and Generation Z - born: 1995-2012).
The idea that consumer behaviour is also influenced by the type of personality or
character traits is very attractive in the light of the two preceding criteria. In part, it may
actually be, but for this criterion, its usefulness depends primarily on the product category.
The personality criterion can be used primarily where a consumer expresses attitude by
purchasing a particular good or service or the customer is defined within the society by this
purchase. Such a customer segmentation is not meaningful for the company under review.
(Kliestikova et al., 2017; Shvindina, 2016).
3.2 Brand loyalty variable
Based on the research problem, we had to identify the level of brand loyalty. Using
secondary data, we decided to find this variable by straightforward question. Through this
question, respondents themselves ranked in the category of their loyalty. For this reason, we
chose Walker’s brand loyalty segmentation as an alternative. The segmentation itself
consists of four groups (the most common level of brand loyalty), and the description of
each group is comprehensible by a general public. This should prevent the misclassification
of the respondent.
To confirm the accuracy of classification, i.e. rejecting the respondent's subjectivity we
also put a question by Reichheld (2003) in the questionnaire. Subsequently, we tested the
hypothesis that examined the existence of a statistical relationship between the levels of
brand loyalty gained in these ways. Statistical hypothesis testing is one of the most
important statistical inference procedures. The role of statistical inference is to decide on the
basis of information on the available choices whether to accept or reject certain hypotheses
with respect to the basic sample set. In order to do so, we proceeded in accordance with the
methodology of statistical hypothesis testing, which consists of the following steps:
Formulation of the null hypothesis (H0); Formulation of the alternative hypothesis (H1);
Determination of the level of significance (α); Calculation of test statistics and probability;
and Conclusion (Rimarčík, 2007).
The goal of most statistical tests is to evaluate the relationship between variables. The
null hypothesis expresses the independence of those variables. However, in the majority of
cases we want to prove the validity of the alternative hypothesis, which expresses the actual
relationship between the variables. The validity of the alternative hypothesis is proven
indirectly by showing that the null hypothesis is incorrect and the alternative (the only
remaining one) is correct. The null and alternative hypotheses must be mutually exclusive.
The level of significance (α) is the probability of the first type error, which occurs when the
null hypothesis is rejected even though it actually holds true, that is, we conclude that there
is and there is not a relationship between the variables. The significance level is traditionally
set at between 1% and 10%. For this research, the significance level was set at 0.05, which
corresponds to a confidence interval of 95%. The test statistic is calculated from the sample
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set. It has an appropriate probability distribution based on the validity of the null hypothesis.
Because the majority of primary data in this research is of a nominal nature, the test statistic
is a Chi-square test of independence. Only the variable of age is cardinal. We used the one-
way anova test (also known as a one-factor, single-factor, or single-classification anova), in
which there is one measurement variable and one nominal variable. The p-value is the
probability that the test statistic, assuming the veracity of the null hypothesis, achieves a
value that is at least equal to the value calculated from the sample set. The p-value is the
probability that the relationship found in the sample is only a consequence of chance. The p-
value is the lowest value of the significance level that leads to the rejection of the null
hypothesis. The smaller the p-value is, the more likely the null hypothesis is not true and
should be rejected. If the p-value is so low that the statement is valid (the null hypothesis is
rejected) at the significance level of 0.01 as well, it can be assumed that there is a
statistically significant relationship between the observed variables. To calculate the test
statistics for the hypothesis, IBM SPSS Statistics software was used. This software also
calculates the correlation coefficient. If the nominal variables are being examined in the
number of 3+ x 2+, it is appropriate to investigate the strength of the dependence between
the variables using Cramer's V measure of association. Cramer’s V is the most common
strength test with which to test data when a significant Chi-square result has been obtained.
While the correlation is a measure of the linear relationship between variable, the Eta
coefficient actually measures any relationship between the categorical variable and the
continuous variable. If the calculated p-value is less than the determined significance level,
the null hypothesis is rejected (thus the alternative hypothesis is accepted). The conclusion
is that the difference found in the sample set is too large to be reasonably considered as
random, so it is statistically significant. If the p-value is equal to or greater than the
established significance level, the difference found in the sample is not statistically
significant and the null hypothesis is not rejected (Rimarčík, 2007; Durica and Svabova,
2015; McHugh et al., 2013; Salking, 2010; Gravetter and Wallnau, 2016).
The hypothesis was: Between the level of brand loyalty according to Walker and the
level of brand loyalty according Reichheld there is no statistical dependence.
To calculate the test statistic, we used the IBM SPSS Statistics software and is shown
in Table no. 1.
Table no. 1 Chi-Square tests
Value
Df
Asymptotic Significance (2-sided)
Pearson Chi-Square
116.749a
6
0.000
Likelihood Ratio
118.341
6
0.000
N of Valid Cases
500
Note: a 0 cells (0.0%) have expected count less than 5. The minimum expected count is 11.91.
Source: self-processed
Based on the comparison of the significance level with the P-value (Asymptotic
Significance), the null hypothesis was rejected and we can confirm an alternative
hypothesis, so there is a statistical dependence between the variables examined (the level of
brand loyalty according to Walker and the level of brand loyalty according Reichheld). The
intensity of the interdependence of variables by the Cramer's V measure of association
(0.342) indicates a moderate dependence.
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 1, pp. 65-84
75
Based on the existence of dependence between variables it makes sense to examine the
internal structure of the contingency table. Therefore, we have subsequently used a
correspondence analysis that we can understand as the analogue of main components
method a factor analysis for qualitative characters in the contingency tables.
Correspondence analysis is a multivariate graphical technique designed to explore
relationships among categorical variables. When the study variables of interest are
categorical, correspondence analysis is an appropriate technique to explore the relationships
amongst variable response categories and can play a complementary role in analysing data
(Sourial et al., 2010). This method therefore lends itself well to marketing surveys that
explore customer preferences and attitudes, and which seek to assess brand attractiveness.
Examining the internal structure of the variables only makes sense if there is a dependency
between the observed characters (factors). The use of correspondence analysis have to
therefore be preceded by the testing of hypotheses on the independence of the observed
characters (Kráľ et al., 2009). We processed the result using the IBM SPSS Statistics
software and is shown in Figure no. 1.
Source: self-processed
Figure no. 1 Correspondence map
The essence of the correspondence analysis is the transformation of the points of the
multidimensional space, which represent the examined categories, into a space of lower
dimension, most often in the plane (2 dimensions). This transformation is unambiguous and
allows us to focus on revealing a certain type of relationship between categories (variables).
We evaluate the transformation quality based on the rates derived from the total inertia.
Interpretation of the correspondence map we have obtained in this way is relatively
simple. If the points are closer to each other, so the categories are more similar, they
correspond more to each other. There are three groups of similar categories of the level of
brand loyalty according to Walker and the level of brand loyalty according Reichheld.
76
Gajanova, L., Nadanyiova, M., Moravcikova, D.
Categorical variables Truly Loyal and Promoters form the first group, Trapped and Passives
form the second one and High Risk and Detractors form the last one. Brand loyalty level
"Accessible" is within the correspondence map remote because it is a very special group of
customers and also includes a small percentage of customers. From the correspondence
analysis we can conclude that the identification of the brand loyalty level was determined
correctly by the respondents without error based on a false of subjective self-assessment.
Based on the above, we were able to test statistical hypotheses resulting from research
questions.
4. RESULTS
To answer the research question, we have identified hypotheses expressing the
existence of a statistical dependence between individual segmentation variable and the level
of brand loyalty, see Table no. 2.
Table no. 2 Hypotheses
Variables
Hypothesis
Demographic
Age
Between the age and brand loyalty level there is no statistically
significant dependence.
Gender
Between the gender and brand loyalty level there is no
statistically significant dependence.
Life cycle
Between the life cycle and brand loyalty level there is no
statistically significant dependence.
Psychographic
Social class
Between the social class and brand loyalty level there is no
statistically significant dependence.
Generation
Between the generation and brand loyalty level there is no
statistically significant dependence.
Source: self-processed
To calculate the test statistic of the first hypothesis, we used the IBM SPSS Statistic
software and is shown in Table no. 3.
Table no. 3 One-way Anova tests results of the first hypothesis
Sum of Squares
Df
Mean Square
Significance
Between Groups
9,769.989
3
3,256.663
0.000
Within Groups
165,288.353
496
333.243
Total
175,058.342
499
Source: self-processed
A significance level was determined at 0.05 and corresponded to a 95% confidence
interval. Based on the comparison of the significance level with the P-value (Significance),
the null hypothesis was rejected and we can confirm an alternative hypothesis, so there is a
statistical dependence between the variables examined (age and brand loyalty level). The
intensity of the interdependence of variables by the Eta coefficient (0.236) indicates a low
dependence.
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 1, pp. 65-84
77
To calculate the test statistic of the second hypothesis, we used the IBM SPSS
Statistics software and is shown in Table no. 4.
Table no. 4 Chi-Square tests results of the second hypothesis
Value
Df
Asymptotic Significance (2-sided)
Pearson Chi-Square
6.914a
3
0.075
Likelihood Ratio
6.930
3
0.074
N of Valid Cases
500
Source: self-processed
A significance level was determined at 0.05 and corresponded to a 95% confidence
interval. Based on the comparison of the significance level with the P-value (Asymptotic
Significance), the null hypothesis was confirmed, so there is no statistical dependence bet
between the variables examined (gender and brand loyalty level).
To calculate the test statistic of the third hypothesis, we used the IBM SPSS Statistics
software and is shown in Table no. 5.
Table no. 5 Chi-Square tests results of the third hypothesis
Value
Df
Asymptotic Significance (2-sided)
Pearson Chi-Square
53.708a
24
0.000
Likelihood Ratio
60.158
24
0.000
N of Valid Cases
500
Note: a 11 cells (30.6%) have expected count less than 5. The minimum expected count is 0.37.
Source: self-processed
When testing the third hypothesis using the Chi-square test, the condition that at least
80% of the expected frequencies exceeds 5 and all the expected frequencies exceeds 1 was
not met. Consequently, we did not merge the characters of life cycle in the contingency
table, since the modified scaling was very similar to variable “age”, which we examined in
more detail. But we could merge the characters of brand loyalty level, specifically the
Accessible and Trapped. The logic of linking these variables is based on the fact that
accessible customers although feel good about buying the particular brand, they do not plan
to continue with it and also the trapped customers will leave the brand. Chi-Square Tests
Results of the third hypothesis after adjusted variables is shown in Table no. 6.
Table no. 6 Chi-Square tests results of the third hypothesis after adjusting of variables
Value
Df
Asymptotic Significance (2-sided)
Pearson Chi-Square
41.670a
16
0.000
Likelihood Ratio
42.752
16
0.000
N of Valid Cases
500
Note: a 4 cells (14.8%) have expected count less than 5. The minimum expected count is 1.28.
Source: self-processed
A significance level was determined at 0.05 and corresponded to a 95% confidence
interval. Based on the comparison of the significance level with the P-value (Asymptotic
Significance), the null hypothesis was rejected and we can confirm an alternative
78
Gajanova, L., Nadanyiova, M., Moravcikova, D.
hypothesis, so there is a statistical dependence between the variables examined (life cycle
and brand loyalty level).
The intensity of the interdependence of variables by the Cramer's V measure of
association (0.204) indicates a low dependence. Based on the existence of dependence
between variables it makes sense to examine the internal structure of the contingency table
by a correspondence analysis. Due to the big number of life cycle characters the
correspondence map is intricate, so we used bar chart to visualisation of internal structure of
contingency table. It is shown in Figure no. 2.
Source: self-processed
Figure no. 2 Bar chart
To calculate the test statistic of the fourth hypothesis, we used the IBM SPSS Statistics
software and is shown in Table no. 7.
Table no. 7 Chi-Square tests results of the fourth hypothesis
Value
Df
Asymptotic Significance (2-sided)
Pearson Chi-Square
73.138a
12
0.000
Likelihood Ratio
81.934
12
0.000
N of Valid Cases
500
Note: a 0 cells (0.0%) have expected count less than 5. The minimum expected count is 5.85.
Source: self-processed
A significance level was determined at 0.05 and corresponded to a 95% confidence
interval. Based on the comparison of the significance level with the P-value (Asymptotic
Significance), the null hypothesis was rejected and we can confirm an alternative
hypothesis, so there is a statistical dependence between the variables examined (social class
and brand loyalty level).
The intensity of the interdependence of variables by the Cramer's V measure of
association (0.221) indicates a low dependence. Based on the existence of dependence
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 1, pp. 65-84
79
between variables it makes sense to examine the internal structure of the contingency table
by a correspondence analysis. We processed the result using the IBM SPSS Statistics
software and is shown in Figure no. 3.
Source: self-processed
Figure no. 3 Correspondence map of Social class and Brand Loyalty
To calculate the test statistic of the fifth hypothesis, we used the IBM SPSS Statistic
software and is shown in Table no. 8.
Table no. 8 Chi-Square tests results of the first hypothesis
Value
Df
Asymptotic Significance (2-sided)
Pearson Chi-Square
103.793a
15
0.000
Likelihood Ratio
117.660
15
0.000
N of Valid Cases
500
Note: a cells (0.0%) have expected count less than 5. The minimum expected count is 5.03.
Source: self-processed
A significance level was determined at 0.05 and corresponded to a 95% confidence
interval. Based on the comparison of the significance level with the P-value (Asymptotic
Significance), the null hypothesis was rejected and we can confirm an alternative
hypothesis, so there is a statistical dependence between the variables examined (generation
and brand loyalty level).
The intensity of the interdependence of variables by the Cramer's V measure of
association (0.263) indicates a low dependence. Based on the existence of dependence
between variables it makes sense to examine the internal structure of the contingency table
by a correspondence analysis. We processed the result using the IBM SPSS Statistics
software and is shown in Figure no. 4.
80
Gajanova, L., Nadanyiova, M., Moravcikova, D.
Source: self-processed
Figure no. 4 Correspondence map of Generation and Brand Loyalty
5. CONCLUSIONS
Marketers spend more money on acquiring new customers than on keeping existing
ones who are loyal and persistent. Estimates in this area show that a portion of the budget
devoted to acquiring new customers is six times larger than the one dedicated to existing
ones. Similarly, efforts to gain new customers are far greater than keeping loyal ones.
Companies can even increase profits if they avoid losing their customers. The customer will
generate profits for as long as they are committed to a firm or brand. The result is declining
acquisition costs, lower operating costs and services per customer per year combined with
rising annual average purchases for a loyal client, declining sensitivity to benefits and, last
but not least, the transfer of positive information among people.
The managers of the surveyed company is aware of these facts and therefore they have
decided to apply its loyalty program as a next step in CRM strategy within its CRM. The
basis of this program is to create an emotional link between the company and the customer
by providing individual, special benefits. However, the existence of consumer loyalty need
to precede. For this reason, the company wanted to find out whether there are certain groups
of company's customers (according to demographic or psychographic segmentation) which
have a higher degree of loyalty to the company's brand. After confirming the existence of
these segments, the company plans to focus more on these customer groups and create a
variety of marketing strategies of brand loyalty. We have identified five hypotheses
expressing the existence of a statistical dependence between individual segmentation
variable and the level of brand loyalty.
The first surveyed demographic variable was the age. There is a statistical dependence
between the age and brand loyalty level. The intensity of the interdependence of variables
by the Eta coefficient is at the level 0.236. The second surveyed demographic variable was
the gender and there is no statistical dependence bet between the gender and brand loyalty
Scientific Annals of Economics and Business, 2019, Volume 66, Issue 1, pp. 65-84
81
level. The third surveyed demographic variable was life cycle. There is a statistical
dependence between the life cycle and brand loyalty level. The intensity of the
interdependence of variables by the Cramer's V measure of association is at the level 0.204.
Because these examined variables are of categorical nature, we used correspondence
analysis to gain information about which categories are more similar. It was essential to find
out which category of life cycle variable is the most similar to the truly loyal customers. The
young childless spouses, the middle-aged spouses with children and young single human
mostly correspondent to the truly loyal customers. The first psychographic variable was
social class. There is a statistical dependence between the social class and brand loyalty
level. The intensity of the interdependence of variables by the Cramer's V measure of
association is at the level 0.221. Based on the existence of dependence between these
variables we examined the internal structure of the contingency table by a correspondence
analysis. Once again, we had to find out which category of social class variable is the most
similar to the truly loyal customers and that are the categories A and B. The second
psychographic variable was generation. There is a statistical dependence between generation
and brand loyalty level. The intensity of the interdependence of variables by the Cramer's V
measure of association is at the level 0.263. Because these examined variables are of
categorical nature, we used correspondence analysis. The category generation Z mostly
correspondents to the truly loyal customers.
From the above-mentioned results of statistical hypothesis testing for an enterprise, it
is meaningful to focus on customer segmentation based on selected psychographic and
demographic aspects for development of brand loyalty program. It is the category of life
cycle from the demographic variables. The category of the age is not appropriate. Although
there is a statistical relationship between this category and brand loyalty level, but it is
impossible to find out which age most corresponds with truly loyal customers, because of
the variable character. We could use these values to create categorical variables by creating
groups born in a certain period, but any finer division, by example, intermittently after
several years, it leads to the division of the characteristic group. From the psychographics
variables, it is the category of generation, because there is the stronger value of Cramer's V
measure of association and this category more reflect the essence of psychographic
segmentation.
The methodology of this research can also serve as the basis for other businesses in
different sectors as well.
Acknowledgements
This contribution is an output of scientific project VEGA no. 1/0718/18: The impact of
psychographic aspects of pricing on the marketing strategy of companies across products and markets.
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Vanilla is a world-well-known spice product, especially as food and beverage flavoring; however, the availability of (natural) vanilla is too little (less than 1%) to meet the demand for vanilla either industry or household. So far, most Indonesian vanilla is traded as a commodity in the international (export) and domestic markets. For producers who are primarily smallholders, these conditions create business uncertainty and added value. This paper aims to review the strategy of product transformation from general commodities into certified products or branded products by utilizing digital marketing (e-commerce), which leads to the stability of the price of vanilla products (reduced uncertainty), and creates an additional value. The method used in this study is the literature review method. Product transformation requires product development, where certified products must follow particular procedures in production to obtain certification, and branded products require differentiating features in products that consumers appreciate. In addition to product development as a primary strategy is also needed supporting strategies such as proper pricing and promotion. This review implies that there is an opportunity to transform a vanilla product from a commodity to certified and branded products; hence it should become vanilla development agenda.
... Brand loyalty is still a topical issue in research areas and also for companies themselves. (Gajanova, Nadanyiova, Moravcikova, 2019) The trend where customers were the only authoritative element (often in retail) is slowly receding and attention is focused more on employees. (Gregova, Dengov, Tulyakova, Mustafaev, 2020) Today, employer branding is one of the most important aspects of acquiring a qualified and quality workforce in a company. ...
... These similar factors are also observed in relation to their customers, such as the demographic factors used for segmentation. For example, Gajanova et al. (2019) concluded that there is a statistical dependence between age and brand loyalty, but no statistical dependence between gender and brand loyalty. ...
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Purpose. The research study aims to evaluate the precision of the measurement system using Gauge R&R. Methodology. An experimental research design adopting a positivist empirical approach with deductive strategy was followed to assess the effectiveness of Crossed Gauge R&R technique for validating a measurement system using destructive testing. Results. Crossed Gauge R&R technique in Minitab was found to be highly effective in quantifying different components of measurement variation relative to process variation. The theoretical contribution. Clue generation from the Crossed Gauge R&R study combined with manufacturing and measurement process know-how helped in identifying and eliminating the root causes for measurement variation. Practical implications. Overall Crossed Gauge R&R proved successful in validating the burst strength test equipment. However, it should be noted that manufacturing and test equipment played an equally important part in developing and executing the gauge R&R study and accurately analysing the results. So, Crossed Gauge R&R should be used as an aid rather than the solution for measurement system validation.
... These similar factors are also observed in relation to their customers, such as the demographic factors used for segmentation. For example, Gajanova et al. (2019) concluded that there is a statistical dependence between age and brand loyalty, but no statistical dependence between gender and brand loyalty. ...
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Purpose. The research study aims to evaluate the precision of the measurement system using Gauge R&R. Methodology. An experimental research design adopting a positivist empirical approach with deductive strategy was followed to assess the effectiveness of Crossed Gauge R&R technique for validating a measurement system using destructive testing. Results. Crossed Gauge R&R technique in Minitab was found to be highly effective in quantifying different components of measurement variation relative to process variation. The theoretical contribution. Clue generation from the Crossed Gauge R&R study combined with manufacturing and measurement process know-how helped in identifying and eliminating the root causes for measurement variation. Practical implications. Overall Crossed Gauge R&R proved successful in validating the burst strength test equipment. However, it should be noted that manufacturing and test equipment played an equally important part in developing and executing the gauge R&R study and accurately analysing the results. So, Crossed Gauge R&R should be used as an aid rather than the solution for measurement system validation.
... Another paper is focused on global research trends in sustainable digital transformation in higher education [21]. In 2021, authors focus on the research of functionalities of free available learning digital platforms in scope of selected subjects [22,23]. However, the total amount of citations is still not enough reliable to state that this topic is the hot topic of the year in scope of digitalization of education. ...
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The process of population ageing has a global dimension and constitutes an important challenge for social policy, and in particular for housing policy. Demographic forecasts carried out by Eurostat, World Bank and the UN unanimously predict an increase in the number of the elderly, especially seniors aged 85+. The ageing of Polish society and the implications of the process for the housing sector is a challenge for social policy, which is responsible for creating an enabling environment to meet the needs of older people. Increasing expenditures on care for the elderly points to the need to find appropriate solutions for independent or partially independent seniors living in their own housing environment. The large percentage of Poland’s population aged 65 and older requires that housing policy seek solutions for the implementation of housing needs at a level appropriate to the needs of different groups. It is important to discuss the necessity to secure housing needs of the elderly, especially seniors with low level of independence. The development of senior housing, sheltered flats, residential homes and old people’s homes could bring a solution to the increasing demographic problem. The discussion concerns the European context and the implications that can be derived for Poland. The dynamic increase in the number of older people in the society is a complicated and relatively new phenomenon in Poland, therefore in the contemporary economic and sociological discourse there is a growing interest in the ways of increasing supply of housing units that meet the needs of the elderly. In the paper the authors describe management of the social housing stock (Lennartz et al., 2012; Czischke, 2012) that addresses the issue of securing housing needs of seniors. The discussion concerns the European context and the implications that can be derived for Poland.
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Every corporation has an economic and moral responsibility to its stockholders to perform well financially. However, the number of bankruptcies in Slovakia has been growing for several years without an apparent macroeconomic cause. To prevent a rapid denigration and to prevent the outflow of foreign capital, various efforts are being zealously implemented. Robust analysis using conventional bankruptcy prediction tools revealed that the existing models are adaptable to local conditions, particularly local legislation. Furthermore, it was confirmed that most of these outdated tools have sufficient capability to warn of impending financial problems several years in advance. A novel bankruptcy prediction tool that outperforms the conventional models was developed. However, it is increasingly challenging to predict bankruptcy risk as corporations have become more global and more complex and as they have developed sophisticated schemes to hide their actual situations under the guise of “optimization” for tax authorities. Nevertheless, scepticism remains because economic engineers have established bankruptcy as a strategy to limit the liability resulting from court-imposed penalties.
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Brand loyalty is first distinguished from simple repeat purchasing behavior and then conceptually defined in terms of six necessary and collectively sufficient conditions. An experiment designed to test this conceptualization provided strong empirical support for the distinction as conceptualized.
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Research background: Bankruptcy shouldn´t be considered only as negative phenomena although its impact is for companies in most cases more than devastating. This change of point of view is invoked by the needs of contemporary socio-economic evolution. If society wants to reach sustainable development, the bankruptcy should be perceived as an immanent part of normal cyclical economic development. Moreover, if the view of bankruptcy is changed in a positive way, it can be a stimulus for innovations, investment and global welfare. But it is not possible without an increase in the effectiveness of national and international bankruptcy law.Purpose of the article: The goal of this study is to analyse the position of a creditor in the case of a debtor´s bankruptcy on the basis of comparative law in the Slovak Republic de lege ferenda. It is because we assume that continuous attention should be given to the issue of the creditor’s position with regard to a debtor´s bankruptcy to achieve sustainable economic development.Methods: The potential consideration de lege ferenda should be based not only on performed legal analysis, but also on performed economic analysis. So, selected countries have been evaluated according to specific economic and legal indicators. We used the interdisciplinary approach based on selection analysis and legal comparative analysis applied to international comparison of the status of creditor and the effectiveness of bankruptcy law from his point of view.Findings & Value added: The applied approach has led us to the detection of the most important insolvency laws, specifically the insolvency laws of the United States and Austria. These legislations were further applied in the context of consideration de lege ferenda over the position of a creditor in the case of a debtor´s bankruptcy in the Slovak Republic.