Customer's View to Provide Information for Loyalty
Programs in the South Bohemia
Lenka Ližbetinová
Faculty of Corporate Strategy
The Institute of Technology and
Business
České Budějovice, Czech Republic
lizbetinova@mail.vstecb.cz
Jaroslav Stuchlý
Faculty of Corporate Strategy
The Institute of Technology and
Business
České Budějovice, Czech Republic
stuchly@mail.vstecb.cz
Tsolmon Jambal
Faculty of Corporate Strategy
The Institute of Technology and
Business
České Budějovice, Czech Republic
tsolmon@mail.vstecb.cz
Abstract—The aim of the article is to determine the
attitudes of South Bohemian customers to providing personal
data for the loyalty program needs. The article is based on the
partial output of research focusing on customer loyalty to
loyalty programs. The research was conducted in 2018 by a
questionnaire survey with 267 respondents. Respondents
interviewed were customers who live in the South Bohemian
Region with an emphasis on the younger generation. The
results presented in the article are based on the part of the
questionnaire, where the respondents answered, which
personal data are willing to provide for loyalty programs.
Consequently, it is tested dependencies of overall evaluation in
relation to provide of personal data in terms of identification
variables: gender, age, education and the size of the
respondent's place of residence. Two-choice statistical tests and
the Kruskal-Wallis test are used to determine dependencies.
The statistical program R. was used to perform these tests.
Keywords—loyalty program, customers, customer view,
marketing
I. INTRODUCTION
Turbulent conditions of globalized trade creates
demanding requirements for companies in various sectors
[1]–[3]. The fight to gain loyal customers is realized also
through the creation of various forms of loyalty programs.
Loyalty programs have had their beginnings in the field of
transport services. The American Airline is one of the first
companies to develop and launch a loyalty program. In the
early 1920s, American Airlines decided to reward their
customers for free flights for a certain number of miles with
their company. Then, there has evolved a system with several
levels of membership where rewards correspond exactly to
those loyalty categories. Over time, loyalty programs have
quickly spread to the following airlines and beyond the
aviation industry. In the first place, the loyalty program has
been taken over by hotels, rental cars and other businesses in
other sectors. At present, loyalty programs can be found in all
modes of transport, in the hotel industry, in financial
institutions, in electronics and retail chains [4].
Loyalty programs are an overwhelming trend today. By
joining the company's loyalty program, customers can get
various discounts on purchases, gifts, or any extra service as
a reward for loyalty. Karlíček and Král [5] state that "loyalty
programs can be an effective way to motivate customers to
purchase and how to effectively build a customer contact
database through them." Due to the existence of a customer
information database, problems are reduced [6], [7] in
processing business processes in different areas of the
company. Detailed customer databases enable companies to
build individual relationships with customers that lead to the
exact satisfaction of their requirements. The transformation
of anonymous mass contacts into individual relationships is
made possible by various CRM tools such as call center, emarketing
or individual website [8], [9]. Customer
relationship management ensures a more efficient and
smoother process of business processes, allowing the
company more time to add to improving customer
relationships [10], [11]. An important fact here is the
collection of personal data and data on buying habits, sorting,
processing and storing them in a database that must be in
accordance with the law. Knowledge of this information is an
essential element for gaining a competitive advantage.
Hrachovec [12], Vilcekova and Starchon [13] says
processing data is not easy at all, and most companies do not
work with these data at all. The reason is the high demands
on human resources that can not only effectively evaluate
these data but also use them [14-18]. In fact, marketers
should use mathematical and statistical models [19]–[21] to
recognize which data are useful [22], [23] and which are not.
Modern technologies then organize customer data into
groups [24]–[27]. Unfortunately, without this information,
we do not know the customer and we cannot fully exploit the
potential of loyalty programs. When building these
databases, the customer is exposed to personal information
requirements [28], [29]. The nature and extent of these
information requirements may not always be positively
perceived despite the obligation to comply with the laws
governing the handling of personal data (GDPR regulation).
On the other hand, these databases are creating many entities
that the customer comes into contact with [30]–[32]. For this
reason, the customer is often harassed by various forms in
order to find out his personal details. The article focuses on
the attitude of customers to provide creditors for loyalty
programs.
II. MATERIAL AND METHODS
The aim of the article is to determine the attitudes of
South Bohemian customers to providing personal data for
loyalty programs. The article is based on the partial outputs
of research conducted in 2018 and the emphasis on the
attitude of young customers to loyalty programs. The results
in the article are based on data obtained through a
questionnaire survey on a sample of 267 respondents. The
interviewees were customers who have their permanent
11th International Scientific Conference "Economics, Management and Technology in Enterprises 2019" (EMT 2019)
Copyright © 2019, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Advances in Economics, Business and Management Research, volume 78
173
residence in the South Bohemian Region. The presented
results in the article are based on the question of providing
personal data for loyalty programs, and respondents could
choose their answer from two options - yes (0) or no (1). In
the processing of the results, the basic statistical
characteristics were used - relative and absolute frequency,
weighted arithmetic mean. In addition to the basic
information on the provision of information, attention was
also focused on the assessment of the overall rating
dependencies “share of the information provided” (SIP), on
the following identification variables: gender, age, education
and the size of the respondent's municipality size. We
determine the overall rating variable (SIP) as the ratio of
positive numerical scores of all variants of information by
individual respondents. In particular, we will be interested in
whether this evaluation variable depends significantly on the
identification variables. We will use the two-choice statistical
tests and the Kruskal-Wallis test to be performed using the
statistical program R.
III. RESULTS AND DISCUSSION
To evaluate the basic knowledge of the information that
respondents are willing to provide when registering a loyalty
program, we processed the data using the basic descriptive
statistics (frequency and percentage). We offered respondents
a "yes" (1) or "no" (0) from 12 variants of information: title,
surname, e-mail, postal address, date of birth, education, size
of clothing, number of household members and average
monthly household income (Fig. 1.). Fig. 1. shows the
structure of the distribution of absolute response rates.
Fig. 1. Frequency distribution for variants of individual claims. (source:
authors)
The largest customer consent (Fig. 1.) with the provision
of personal data is expressed with e-mail variants, 237
(88.8%), and surname 237 (88.8%) and age (82.8%).
Responding customers are the least willing to provide
information about category of household income 52 (14.2%)
number of household members 130 (35.4%) and permanent
address 131 (49.1%) for loyalty programs. Most of the
respondents are also willing to provide personal information
like: contact address (up to 66.7% of respondents), academic
title (62.3%), telephone number (58.4%), birthday (56.9%
education (55.07%) and dress size (54.3%).
The dependence of willingness to provide personal
information for loyalty programs is addressed through the
creation of a share of the information provided (SIP). We
use the ratio of positive numerical evaluations of all variants
of information by individual respondents to express the
overall SIP variable. This value was tested in terms of
dependence on the following identification variables:
gender, age, education and the size of the respondent's
municipality size. We use statistical two-choice tests and the
Kruskal-Wallis test. Table 1 lists the calculated basic
numerical characteristics of the SIP variable, and Fig. 2.
shows its flow using the box diagram and the absolute
frequency histogram.
TABLE I. NUMERICAL CHARACTERISTICS OF THE SIP
Arithmetic
mean
Standard
derivation
Coefficient
of variance
25%
quantile
Median
75%
quantile
n
0.6177 0.218508 0.35377 0.5 0.667 0.75 267
Source: Authors
Fig. 2. Distribution of evaluation parameter SIP. (source: authors)
We test the Shapiro-Wilk test for the normality of the
SIP data variable, and the result obtained is W = 0.9677, pvalue
= 1.026x10-5. Normality was rejected at 5%
significance level. Therefore, dependency testing will be
performed using nonparametric tests.
TABLE II. CONDITIONAL NUMERICAL CHARACTERISTICS OF THE SIP BY
GENDER
Gender
Arithmetic
mean
Standard
derivation
Coefficient
ofvariance
Quantiles
n
0% 25% 50% 75% 100%
female 0.6374 0.2085 0.3271 0.083 0.500 0.667 0.75 1 151
male 0.5919 0.2293 0.3873 0.000 0.417 0.583 0.76 1 116
Source: Authors
The two-choice Wilcoxon test will be used to test the
independence of the SIP variable on the gender (Table 2 and
Fig. 3.). We get W = 9801, p-value = 0.09326. The test
failed to demonstrate the dependence on the 5% level of
materiality. At 10% significance level, dependence is
proven. It follows from the above that at the significance
level of 5% a zero hypothesis is confirmed, indicating that
there is no statistically significant dependency between the
SIP variable and gender of customers. On the contrary, such
Advances in Economics, Business and Management Research, volume 78
174
dependence is demonstrated at a 10% level of significance
and a zero hypothesis reversed in favour of an alternative
hypothesis.
Fig. 3. Conditional distribution of the assessment variable SIP by gender.
(source: authors)
Consequently, the dependence on respondents' age is
examined here (Table 3, Fig. 4.). Independence testing of the
SIP variable in age is performed using the Kruskal-Wallis
test. The obtained output of this test is: chi-squared = 4.3547,
df = 4, p-value = 0.3601. The test did not confirm statistically
significant dependence on the 5% significance level.
TABLE III. CONDITIONAL NUMERICAL CHARACTERISTICS OF THE SIP BY
AGE
Age
Arithmetic
mean
Standard
derivation
Coefficient
ofvariance
Quantiles
n
0% 25% 50% 75% 100%
15-29 0.6307 0.206 0.326 0.000 0.500 0.667 0.750 1 123
30-39 0.6140 0.217 0.353 0.083 0.417 0.583 0.750 1 68
40-49 0.5637 0.245 0.435 0.083 0.417 0.583 0.750 1 51
50-59 0.6853 0.230 0.335 0.333 0.500 0.667 0.917 1 18
60and_
more
0.6429 0.191 0.297 0.333 0.542 0.750 0.750 0.833 7
Source: Authors
Fig. 4. Conditional distribution of the assessment variable SIP by age.
(source: authors)
Dependence of the evaluating variable on the customer's
education is tested again using the Kruskal-Wallis test
(Table 4, Fig. 5.). The resulting values are chi-squared =
0.7034, df = 3, p-value = 0.8724. This dependency was not
proven by a 5% level of significance test, and therefore a
zero hypothesis was confirmed that the willingness to
provide personal data for loyalty programs is independent of
the age of customers.
TABLE IV. CONDITIONAL NUMERICAL CHARACTERISTICS OF THE
ASSESSMENT VARIABLE SIP BY EDUCATION
Education
Arithmetic
mean
Standard
derivation
Coefficient
ofvariance
quantiles
n
0% 25% 50% 75% 100%
primary 0.6000 0.245 0.408 0.33 0.35 0.63 0.73 1 10
lower
secondary
0.6459 0.184 0.284 0.25 0.50 0.67 0.75 1 40
upper
secondary
0.6134 0.215 0.351 0.00 0.50 0.58 0.75 1 144
university 0.6130 0.241 0.393 0.00 0.42 0.58 0.75 1 73
Source: Authors
Fig. 5. The conditional distribution of the assessment variable SIP by
education. (source: authors)
Testing the SIP dependence on the size of the
respondent's municipality size (in thousands of inhabitants)
was performed by the Kruskal-Wallis test. Conditional
numerical characteristics are shown in Table 5 and the
conditional distribution is shown in Fig. 6.
TABLE V. CONDITIONAL NUMERICAL CHARACTERISTICS OF THE
ASSESSMENT VARIABLE SIP BY RESPONDENTS' MUNICIPALITY SIZE
Municipality
size(in
thousandsof
inhabitants)
Arithmetic
mean
Standard
derivation
Coefficientof
variance
25%quantile
Median
75%
quantile
n
(0;1] 0.582 0.215 0.370 0.417 0.583 0.667 53
(1;10] 0.673 0.204 0.303 0.500 0.709 0.833 100
(10;50] 0.640 0.188 0.293 0.500 0.667 0.750 60
(50;1200] 0.526 0.248 0.471 0.417 0.583 0.667 54
Source: Authors
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The output values of the Kruskal-Wallis test are: chisquared
= 16.5007, df = 3, p-value = 0.0008951. The test
determined a significant statistical dependence on the 5%
level of significance from the size of the respondent's
municipality. It follows that there was a reversed zero
hypothesis in favour of an alternative and it can be stated that
the degree of willingness of customers to provide personal
data for the needs of loyalty programs depends on the size of
the city.
Fig. 6. Conditional distribution of the assessment variable SIP by size of
the respondent’s municipality. (source: authors)
IV. CONCLUSION
The article presents partial research results that focus on
the South Bohemia customer attitude to providing loyalty
information. The dependence of their attitude is tested in
terms of age, gender, education, and municipality size.
When creating loyalty programs and thus creating customer
databases, it is necessary to consider how it works but also
the practical setting. The extent of requested personal data
from customers should be considered in advance as they are
sensitive data that may not be willing to provide. This may
discourage or deter them from registration to the loyalty
program. Based on the outputs we have presented in the
article, the data - name and surname, e-mail and age are data
that customers do not have a problem to provide under
loyalty programs (82.8 – 88.8%). The loyalty program
creator should consider the necessity of the data and justify
its provision: as a contact address, academic title, telephone
number, date of birth, education and clothing size. However,
customers of the overwhelming majority are willing to
provide this information, but there are also some of the
customers who provide this information to the unwanted. On
the other hand, customers are strongly reluctant to provide
data on average household incomes, the number of
household members.
It is also clear from the results that there is a statistically
significant difference at 10% level of significance, when
women are more positively inclined to provide personal data
than men. At the same time, the willingness to provide data
for the needs of loyalty programs is different from the size
of the municipality to the significance level of 5%.
Customers from the smallest municipalities up to 1000
people are least accessible to data provision. By contrast,
customers in municipalities with a population of between
1,000 and 10,000 and between 10,000 and 50,000 are most
accessible to providing personal data. This may be related to
a lesser knowledge of loyalty programs within small
municipalities and thus more distrust of them.
ACKNOWLEDGEMENTS
This paper is an output of the science projects IGS
8110IGS201813, TL02000559 Secure cities for pedestrians
and seniors and TL02000017 Intergenerational management
to support digitization in construction.
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