A loyalty program: a connection between personalized marketing and the points pressure mechanism

Размещено на http://www.allbest.ru/

FEDERAL STATE EDUCATIONAL INSTITUTION

OF HIGHER EDUCATION

NATIONAL RESEARCH UNIVERSITY

HIGHER SCHOOL OF ECONOMICS

Saint Petersburg School of Economics and Management

Department of Management

Bachelor's thesis

Makarova Evangelina Sergeevna, Khudoley Ekaterina Yaroslavovna

A Loyalty program: a connection between personalized marketing and the points pressure mechanism

In the field 38.03.02 `Management'

Educational programme `Management'

Tyurina Karina Sergeevna

Saint Petersburg 2020

Abstract

While many studies have extensively investigated mechanisms influencing consumer behavior in terms of loyalty programs with thresholds, a better understanding of personalized marketing and points-pressure mechanism is lacking. In this regard, the purpose of this work is to study the impact of points-pressure mechanism, which is caused by time frame of points expiration, and personalized marketing, which is expressed in notifications about points expiration, on the participants' intention to make a purchase and redeem the accumulated points. Namely, this research is concentrated on examining customers attitude towards receiving notifications about reaching threshold, optimal time period between receiving such notification and points expiration and the channels of communication. To achieve stated goal, statistical method of data analysis is used, namely, frequency analysis, conditional logistic model, logistic regression and correlation analysis. The research is based on non-probability sampling method. Primary data was obtained by means of a survey. The study found that the way of communication and number of communication channels are significant factors that influence the intention to make a purchase. There also were identified additional significant factors that affect the intention to make a purchase such as the value of remuneration. As a result of this research recommendations on how companies can improve personalized marketing and points-pressure mechanism in their loyalty programs are provided.

Keywords: Loyalty program, personalized marketing, points-pressure mechanism, channels of communication, timeframe, threshold, notification.



Outline

Introduction

1. Literature review

1.1 Loyalty program definition

1.2 Types of loyalty programs

1.3 Frequency reward program

1.4 Effect Mechanisms of Loyalty Programs influencing customer behavior

2. Methodology

2.1 Research design

2.2 Data collection process

2.3 Sample

3. Results

3.1 Descriptive statistics

3.2 Frequency statistics

3.3 Regressions

Conclusion

Reference list

Appendix



Introduction

In modern conditions, companies need to increase their competitiveness in order to maintain and improve the efficiency and demand for the product or service provided. One of the most effective and efficient ways is to interact with customers by creating and implementing loyalty programs (Liu & Yang, 2009). The Loyalty program (LP) helps to maintain the relationship with already linked customers, and also attract new customers by offering them individual and better service conditions than their competitors (Breugelmans et al., 2015).

A well-designed and implemented program implies maintaining good relationships between customers and the company by providing material and non-material rewards, and in return companies receive loyal customers (McCall & Voorhees, 2010).

To confirm the effectiveness of using loyalty programs in the company, many studies have been conducted that have identified the advantages of loyalty programs over other marketing tools, such as: saving money on advertising moves through the introduction of a continuous remuneration system; thanks to such systems, it is possible to record and influence customer behavior in the long term perspective, as well as the ability to collect information about customers, which allows the company to become more customer-oriented (Bombaij & Dekimpe, 2020; Dorotic et al., 2012).

In the research written by American business strategist Joseph Turow (2017), aimed at studying the relationship between the quality of the loyalty program and the financial condition of the company, loyalty was considered as an indicator of its performance. The results of his research show that an increase in the share of loyal consumers in the company by 5% led to an increase in profits by 25-100%.

On average, every family in the United States can participate in more than 29 loyalty programs and increase the number of loyalty programs in which they participate every year (Eggert et al., 2015). In France, about 60% of customers can participate in a 3-10 loyalty programs (Eggert et al., 2015). A similar trend is observed in China, where the number of consumers participating in multiple loyalty programs at the same time is smaller compared to the examples above, but still significant. About 70% of Chinese consumers participate in at least one membership program, and 25% of consumers participate in three or more loyalty programs (Banik & Gao, 2020). There are no statistics on the number of loyalty programs cards in circulation in Russia. However, the scale of the market can be viewed by individual brands: for example, the retailer X5 Retail Group has more than 41 million cards, and Aeroflot has more than 18 million. According to ROMIR (2018), 84% of Russians have loyalty cards.

However, the loyalty program does not always work in a positive way (Michael McCall, Clay Voorhees 2010). According to French research, 96% of loyalty card holders have between 3 and 10 different loyalty cards, but only use 50% of the available cards (Bruneau et al., 2018). The situation with the use of only 50% of loyalty cards is also observed in the United States (Laszlo & Yit Sean, 2018). In Russia, the overall trend is about the same, with 40 to 56% of loyalty cards never used during 2016 (Demidova, 2017). Also, in confirmation, a representative of the group of hotels "Millennium", who said that his hotel uses a loyalty program, but nothing but spending on the loyalty program in return, he did not receive. (Michael McCall, Clay Voorhees 2010).

Therefore, an ill-conceived development and implementation of a loyalty program in a company may not only fail as a tool for attracting and retaining customers, but also cause financial damage to the company, which originally conceived the implementation of loyalty programs in order to create and strengthen long-term relationships with the client. (Fathy & Zidan, 2017; Lemon & Verhoef, 2016).

The path to success in using a loyalty program starts from the planning stage, after determining the main goals and ways to achieve these goals, taking into account the specifics of the company, it is necessary to calculate the profitability of such a loyalty program (Breugelmans & Liu-Thompkins, 2017). However, there are no universal recommendations for creating an ideal and effective loyalty program.

Despite the variety of loyalty programs, there are two main structures of loyalty programs: frequency reward programs (when the consumer receives bonus points for a purchase, remuneration), and customer tier programs (when with each purchase, the consumer accumulates bonus points, with which they reach a new LP level). There are also quite a large number of subtypes of loyalty programs, for example: cashback, charity and others (Kopalle et al. 2012).

According to Bijmolt & Verhoeff (2017), the frequency reward program is the most preferable loyalty program according to both companies and customers. In such loyalty programs, companies often implement the points expiration policy, a special time threshold in which customers have to redeem their accumulated points. Since it is believed that points expiration policy makes participants more susceptible to the expiring points and the intention to buy and redeem the points is higher than in a loyalty program without such policy (Breugelmans & Liu-Thompkins, 2017). Researchers of loyalty programs identify three mechanisms that affect participants in the frequency reward program with thresholds: personalized marketing, points-pressure mechanism, and rewarded behavior mechanism (Bijmolt & Verhoef, 2017; Dorotic et al., 2012).

Despite the fact that many researchers have studied these mechanisms, according to Breugelmans et al., (2015), there are still unclear some aspects that affect the customer's intention to make a purchase (e. g. what is the optimal time to send notifications about points burning, and which communication channels buyers are more susceptible to). Driven by scientific interest and taking into account existing research, this research paper will deepen knowledge about personalized marketing and the point-pressure mechanism. The points-pressure mechanism is likely to emerge in a loyalty program with a point expiration policy and refers to following: “The closer the moment of burning points, the higher the feeling of loss of accumulated points”, effective work of this mechanism leads to increased awareness of customers to use the accumulated points, which increases the likelihood of making a new purchase to get their reward (Dorotic et al., 2012). In this study, personalized marketing is defined as notifications with information about the threshold and expiration of points sent to members of the loyalty program. The research question of the article is as follows: how does personalized marketing, in terms of frequency-based reward programs with thresholds, affect a customer's purchase intent? Thus, the ultimate goal of this study is to determine the interaction effect between 2 mechanisms of loyalty programs based on threshold values: personalized marketing (sales promotion) and the mechanism of point pressure.

In order to answer the research question, the following tasks were set:

a. Determine the theoretical aspects of loyalty programs, types of loyalty programs, and effect Mechanisms of Loyalty Programs influencing customer behavior that will be studied;

b. Develop hypotheses that will answer the research question;

c. Develop a research method to measure the above concepts and evaluate the impact of personalized marketing (notification about reaching threshold) on the intention to spend accumulated points;

d. Collect and analyze data regarding the experience and preferences of loyalty program participants on various aspects of LP (types, timing, channels and time frames of notification);

e. Make descriptive statistics of results, relative to the aspects mentioned above;

f. Perform correlation and regression analysis to identify relationships and dependencies between variables;

g. Based on the results, create recommendations for companies that use or want to use personalized marketing as a mechanism that influences consumer behavior, create recommendations for future research to further explore this topic.

This paper is structured as follows: in the introductory part a brief description of the purpose of this work is presented, then, after the introductory part, there is a section which describes the theoretical details of the research topic and discusses previous researches. The third part considers the research design and methodology in this study. In this section survey as a method of data collection will be discussed. In the next, fourth part of this work, the results of the analyses (descriptive statistics, frequency analysis, correlation and regression analysis) are presented. The last section is devoted to the conclusion and recommendations for future researches, and they are followed by a list of references and appendices presented at the end of the document.

The significance of current research paper is considered from two sides. From academic perspective, the aim of the research is to deepen knowledge about effect mechanisms influencing customer behavior in loyalty programs and triggers influencing customer intention to make a purchase in terms of loyalty programs with points expiration policy. Considering the managerial output, the study argues how companies can improve and customize points-pressure mechanism and personalized marketing in order to make its loyalty programs more effective, what channels of communication should be used, in what timeframe notifications should be sent to customers.



1. Literature review

In this section the review of prior researches is presented. Firstly, the definition of loyalty program and main features are determined. Then 2 main types of loyalty programs are described. After that more detailed discussion of the frequency reward loyalty programs and their characteristic takes place. Then the literature review switches to the description of three main effect mechanisms of loyalty programs influencing customer behavior with representation of insights and results of recent researches devoted to these three mechanisms. In the end of the section the research question of current paper and its discussion are to be developed.

1.1 Loyalty program definition

Customer loyalty refers to the strength of the customer-firm relationship, which consists of two dimensions, namely the behavioral decision to repurchase a product over time - behavioral loyalty and the attitudinal attachment to the brand or firm - attitudinal loyalty (Kandampully et al., 2015). To maintain increase these two dimensions of loyalty, companies have developed so-called loyalty programs (Liu & Yang, 2009). The term loyalty program (LP) usually defines by academic researchers as specific relationship-building program offered by a firm which is aimed to increase behavioral and attitudinal customer loyalty, reward customer loyalty, stimulate customer repurchase behavior and cross-buying and thereby increase share of wallet (Dorotic et al., 2012; McCall & Voorhees, 2010). LPs may offer monetary benefits (e. g. discounts, rewards) as well as soft benefits which are focused on creating commitment to a company (e. g. giving special treatment to a customer). In the research paper written by Bijmolt, (2010) 5 criteria are developed in order to distinguish LPs from other marketing tools: LPs increase customer loyalty; LPs design require a customer to become formally a member to receive the rewards; LPs are long-term oriented; the rewards correspond with the purchase behavior of a customer; LPs are usually supplemented with marketing efforts (e. g. personalized offers). The influence of LPs on customer behaviors and attitudes is contingent on the LP design, this leads to the abundance of LP schemes as each company customize LP design to make it more effective. Thus, there occurs diverse understandings of LP design and the necessary components. However, along with the development of criteria to distinguish LPs from other marketing instruments, the researchers also have identified key design components of any LP. According to the extended literature review on the LPs made by Breugelmans et al. (2015), they are following:

program structure. when a company decide to introduce a LP, it has to decide on the frame of LP. There are defined two main types of loyalty program structures and both of them will be discussed in the next subsection;

membership requirements. Membership requirements refer to the steps a customer has to undertake to join a LP. The decisions on features of membership requirements involve the trade-offs, on the one hand, attracting a broader customer base due to decrease of the participation costs and development of customer convenience, on the other hand, increasing the quality and profitability of the customer base by being more selective;

program communication. Program communication refers to the channels the company decided to use to communicate with the LP members (e. g. e-mail, social networks);

points structure. In terms of point structure, company have to decide the way members will accumulate points or define the optimal number of tiers and requirements to reach each tier in a LP;

rewards structure. Developing the rewards structure company defines the type of rewards (e. g. monetary, non-monetary) and the timeframe within a member can receive a reward.

These 5 features are relevant to all types of LPs.



1.2 Types of loyalty programs

While there exists a huge variety of LPs with various expressions of the components Breugelmans et al. (2015) divide all LPs into two predominant types: customer tier programs (CTPs) and frequency reward programs (FRPs). In a customer tier program customer collect required amount of points and then qualify to a higher tier. They are also called hierarchical LPs (Eggert et al., 2015). Eggert et al. (2015) describe that such programs are more likely to be implemented by relationship-focused businesses (e. g. airlines, hotels). Sometimes the decision to implement particular type of LP structure is dictated by a market where company operates. For example, in airline's business customers prefer customer-tier structure more than reward frequency (Lemon & Verhoef, 2016). Once a company decided to introduce customer tier program it has to be cautious with tier determining and tier demotions. Banik & Gao, (2020) examine aspects of status demotion and reveal that tier demotion leads to a decrease of customers' loyalty intentions due to perceived unfairness. This effect directly influences loyalty program effectiveness thus, companies have to evaluate the risks and properly design the rules of status demotion in a customer tier program.

1.3 Frequency reward program

Since this research is aimed to extend knowledge about frequency reward programs in this subsection this type of LPs are to be discussed more detailed. In a frequency reward program, a participant collects X amount of points usually by buying products or services and then spends the collected points to get a reward (e. g. discount) (Breugelmans et al., 2015). Company have to choose in what way the points will be collected and how they may be spent Researchers assume that FRPs are more suitable for transaction-focused businesses that encourage frequent purchases (e. g. grocery stores) (Bijmolt & Verhoef, 2017).

One of the most detailed reviews of frequency loyalty programs is presented by Bijmolt (2010). According to the researcher there are 2 main variables which can vary in a FRP: type of reward and the time of receiving reward. Firstly, rewards in FRPs may relate or may not relate to the offering of a company. In the first case they are direct (e. g. `Buy 5 items get 1 item for free') and in the second - indirect (e. g. `rent a car 5 times and get a free meal coupon in a restaurant'). Also, rewards can be monetary or hard (e. g. discounts, coupons, cashback) and non-monetary or soft (e. g. preferential treatment, special events). The author argue that company-related monetary rewards are more preferred by a customer and exactly this type of reward more likely increase customer loyalty. However, there is empirical evidence that non-monetary rewards tend to create more sustainable loyalty effects due to enhancing attitudinal commitment (Melancon et al., 2011). There is evidence that monetary rewards are more likely to develop so-called spurious loyalty than non-monetary (Lemon & Verhoef, 2016). It means that sometimes a customer become more loyal to the LP instead of a company itself.

From the perspective of time horizon, rewards can be immediate and delayed (Bijmolt, 2010). In paper written by Dorotic et al. (2014) it is concluded that customers, who are not intrinsically motivated in building a relationship with a company, prefer immediate rewards even when they are less valuable. Also, according to time horizon, frequency reward programs can be infinite (participants can redeem points whenever they want) or finite (a program with a threshold of points expiration). Many researchers debate on the topic of comparison these two types of frequency reward programs which type is more effective and in what conditions. Some researches show that points expiry policy helps to induce points pressure mechanism (this mechanism will be discussed in the next section), which in turn increases purchase level. For example, Breugelmans & Liu-Thompkins, (2017) conducted research on expiration and non-expiration policies in FRPs and revealed that programs which implement expiration policy make customers more sensitive towards the number of points they can spend and stimulate customer to make purchases more frequently. When points do not expire and the participant of an LP decide when and how much points to redeem the mechanism of points-pressure is unlikely to occur because customers are not stimulated by timeframe.

1.4 Effect Mechanisms of Loyalty Programs influencing customer behavior

The effects of loyalty programs on participants behavior and attitudes can be managed through several processes. Previous studies identified that in frequency reward loyalty programs based on thresholds of accumulated purchases three mechanisms which influence behavior of a customer engaged in a loyalty program take place. These mechanisms are: point-pressure mechanism, rewarded-behavior mechanism and personalized marketing (Bijmolt, 2010; Dorotic et al., 2012). Specifically, LP design affects enrollment, behavioral and attitudinal responses, and the effectiveness of these mechanisms. Bijmolt & Verhoef (2017) illustrates these three mechanisms as follows:

Figure 1 Three effect mechanisms of frequency reward programs.

Source: Bijmolt, T. H. A., & Verhoef, P. C. (2017). Loyalty Programs: Current Insights, Research Challenges, and Emerging Trends.

Personalized marketing is an essential part of any loyalty program. While implementing a LP company gains a large database with information about its customers (e. g. purchases, responses to marketing communications). This information is essential in terms of creating personalized promotions to customers because it may include details on whether and when customer have received offers, through which communication channel, whether and when they have redeemed accumulated points and purchasing details (when and what they have bought) (Breugelmans et al., 2015). Analyzing received data helps to segment a market and increase value of targeted offers. In the area of LPs personalization usually is more traditional and includes selecting consumers according to the expected response behavior to offers or so-called look-a-like analysis (participant of an LP who buy product X will buy product Y) (Bijmolt & Verhoef, 2017). However, due to recent development of digitalization more and more companies implement more complex approach while analyzing the data.

Collecting LP currency or points and aiming to redeem them in a certain specified timeframe may affect participant behavior, known as points-pressure effect (Kopalle et al., 2012). Kopalle et al. (2012) states that this effect is manipulated by points-pressure mechanism which, in turn, expresses in the rules of points accumulation and points expiration of an LP. When loyalty programs are based on thresholds, points-pressure mechanism stimulate customers to redeem points and obtain a reward (McCall & Voorhees, 2010). Without any deadlines for reaching reward thresholds, effectiveness of points-pressure mechanism dissipates (Dorotic et al., 2014). Companies can customize points-pressure mechanism by changing thresholds volume (time period when a customer have to redeem their accumulated points). This mechanism can be effective only in case when a participant of a LP values the reward. Also, strength of this effect depends on the type of reward. Points-pressure mechanism is stronger in relation to hard rewards than attitudinal commitment (Melancon et al., 2011).

According to Dorotic et al. (2012), while points-pressure mechanism works only if a customer value the reward, the rewarded-behavior mechanism works only when a customer redeems points. When a customer redeems points and obtains a reward, he or she is likely to feel encouraged to keep on the reached level or increase purchase level mainly due to increase of emotional attachment (Dorotic et al., 2012). It was proven by Pez et al., (2017) that customers who are influenced by this mechanism with high probability learn a behavioral lesson “if I repurchase, I will get a reward”. The strength of this mechanism mostly relies on previous level of loyalty, intrinsic relationship motivation and like the points-pressure mechanism, the type of reward. According to findings of Bruneau et al. (2018), up to 35 % of accumulated points remain unredeemed. It occurs usually due to low level of customer engagement and unaware of the importance of reward redemption. Thus, to create an effective loyalty program a company need to understand what rewards are valuable for its customers and what are the most optimal timeframes for rewards redemption.

As the three effect mechanisms do not work independently from each other (Bijmolt & Verhoef, 2017) it is rational to examine them in an integrated framework. In the table 1 located in the appendix 1 most valuable studies concerning these three mechanisms are presented.

Considering the existing studies and pursuing our personal scientific interest this study is aimed to deepen knowledge about frequency reward programs based on direct hard rewards with thresholds and mechanisms influencing consumers behavior. As it is concluded by Bijmolt & Verhoef (2017) that the mechanisms are preferably to examine together not dividing it, in this research the connection between two of three mechanisms is to be examined: personalized marketing and points-pressure mechanism. In this research term personalized marketing is defined as personalized consumer notifications about reaching threshold and the number of expiring points. Thus, the research question is following: How personalized marketing, namely, customer notifications about reaching threshold influence customer intention to make a purchase? The goal of current research paper is to describe customers perception and attitude towards such notifications in terms of loyalty programs with points expiration policy. There will be examined such questions as: how do different communication frames influence consumer behavior; Should the communication of accumulated points status be delivered automatically or should it be self-initiated by the participants of an LP; what are the most effective way of delivering notifications; when the notifications have to be delivered.



2. Methodology

In this section there will be presented hypotheses, method of research, information about data collection and data analysis and description of sample. Since the research question which was stated earlier is “How personalized marketing, namely, customer notifications about reaching threshold influence customer intention to make a purchase?”, following hypotheses have been developed:

H 1.0: Receiving notifications about points expiration does not affect customer intention to make a purchase.

H 1.1: Receiving notifications about points expiration affect customer intention to make a purchase.

H 2.0: Channel through which notifications are received by a customer does not affect customer intention to make a purchase.

H 2.1: Channel through which notifications are received by a customer affect customer intention to make a purchase.

H 3.0: The time frame of sending notifications does not affect the customer intention to make a purchase.

H 3.1: The time frame of sending notifications affect the customer intention to make a purchase.

As the goal of this research is to examine influence of points expiration and notifications about points expiration on customer intention to make a purchase, by testing developed hypotheses this goal will be achieved. In addition to hypotheses testing there also will be developed descriptive statistics to examine customers perception and attitude towards loyalty programs with points expiration policy.

2.1 Research design

The design of this study is descriptive (Harkiolakis, 2017). As a result of this research, it will be possible to give recommendations to companies using loyalty programs on whether to send notifications, with what frequency, and through which communication channels.

The study is cross-sectional because data was collected at one-time point and creates a kind of “snapshot” of research.

The following variables will be examined. Dependent variable is intention to make a purchase. The independent variables are: channels of notification, timeframe of notification, the percent of a purchase which can be purchased with points. In the study quantitative method of the research is to be used. Also, to conduct analyses there will be used primary data. The data will be collected by means of a survey. The research is conducted in several steps. The first step (analysis of existing studies on loyalty programs with points expiration policy and its mechanisms affecting consumer behavior) is presented in the previous section. Next step is development of hypotheses based on literature review and stated research question. Then the strategy of research has been developed, survey have been chosen as the main tool of data collection. The survey is to be conducted in online format.

2.2 Data collection process

To collect quantitative data survey was developed. The survey consists of 5 sections with direct questions and situations with indirect questions created using projective techniques. All questions were formulated in a specific manner to avoid respondents to be forced to choose certain answers. The questioner is presented in attachments.

First section of the survey consists of 7 questions and is devoted to respondents' experience of participating in loyalty programs. In this section closed multiple choice questions are used. For example: Choose loyalty programs in which you are participating? (you can choose several answers)

Grocery stores (e. g. loyalty programs in Pyaterochka, Lenta, Perekrestok)

Cosmetics stores (e. g. Rive Gauche, Letoile, Gold Apple)

Clothing stores (e. g. Love Republic, H&M, Guess)

Restaurants (e. g. Marketplace, Tokio-City)

Hardware stores (e. g. Leroy Merlin, Castorama, Ikea)

Entertainment centers (e. g. cinema)

Pharmacies

Beauty salons

Transport

Other (write your answer)”

Second section is aimed to reveal respondents' experience of participation in specifically those loyalty programs which use tools of personalized marketing (notifications about points expiration date). The first question of this section is screening question (“Have you ever received notifications that your points accumulated for participating in a loyalty program will soon be expired?”). The aim of this question is to select only those respondents for answering further questions who have experience of receiving notifications about reaching threshold. There are 7 questions in this section and they are of following types: multiple choice questions and single choice questions. For example: “How often do you receive notifications about points expiration?

Daily

More than once a week, but not daily

More than once a month, but less than once a week

Less than once a month

Never”

In the third section answers from all respondents are collected. The questions of this section are designed to reveal the most convenient for respondents' ways of receiving notifications about reaching threshold. Furthermore, there are questions in this section which examine most relevant timeframe of sending notifications that allow respondents to realize points expiration and make a decision to make a purchase. The section consists of two multiple choice questions, for example: “What are the most convenient ways to get information about the burning of accumulated points? Please select all convenient options.

Email

Text message

Social networks

Notifications in the application

Other (write your answer)”

The next section contains simulated situations that put respondents in a particular circumstance. Data collected by means of this section will help to determine how notifications affect the decision to use expiring points, as well as it will help to find out what rewards members of loyalty programs consider valuable. To create the questions in this section projective techniques have been applied. Since the aim of this section is to understand consumers' perception towards rewards gained during participation in a loyalty program and their intention of redeeming points using such techniques is the most appropriate approach to reach stated goal. According to Mesнas & Escribano (2018) projective techniques help to get more honest answers than direct questions. It is believed that such techniques allow respondents to feel more relaxed and as a consequence reveal their true beliefs and attitudes. By asking a question not directly, but through a simulated situation, a respondent projects their true motives, attitudes, and beliefs onto the simulated situation (Mesнas & Escribano, 2018). There are presented three situations in this section. The questions to each situation are of a single choice. The example of the question in the section: “Elena visits coffee shop Z once in two weeks. The coffee shop has a loyalty program in the form of an app for your phone, which displays the accumulated points and the expiration date of points. Notifications about the status of points are not received. At the moment, she still has 100 points, which will expire in 5 days. 1 point is equal to 1 ruble, points can be used to pay 50% of the total cost of the order.

From your point of view, will Elena use the points?

Yes

No”

The last section contains questions that allow to create a socio-demographic portrait of respondents, so that future researchers have the opportunity to create a socio-demographic portrait of a potential buyer of a particular company to increase the company's customer orientation. The questions help to reveal gender, age, financial status, education level, family status, and etc. There 9 single choice questions in this section. The answers of this section are discussed in subsection “Sample”.

After the questions were developed and the survey was created on the Google platform, an additional pilot study was conducted to improve the quality of the survey and identify some potential problem areas that may mislead our respondents. 17 people took part in the pre-test. After the survey completion, each participant gave detailed feedback in unstructured format. The feedbacks were considered and the questioner was improved. For example, in the first question “Choose loyalty programs in which you are participating?” new answer option “transport” was added. In the second question “What type of loyalty program do you prefer? (you can select several)” definitions of types of loyalty programs were changed to more understandable once.

After correcting all the shortcomings, the next stage of the research began: distributing the survey by publishing this survey in social networks, such as Instagram, VKontakte, Telegram, WhatsApp, and collecting responses. At this stage snowball sampling method have been applied by asking our acquaintances to share the survey. Also, the survey was shared in special groups devoted to social surveys in Vkontakte and Telegram. The responses were collected in 10 days. The online method is the most suitable for this study, since it allows to collect data from as many people as possible. It is also a good solution in terms of limited resources such as money and time.



2.3 Sample

The general population is equal to the population of Russia over 18 years old, i.e. 124 million people according to the population census of 2017. This condition was established because participation in most loyalty programs is allowed only from the age of 18, as well as the fact that at this age most people already begin to earn independently and therefore independently manage their finances. Since the size of the general population for this survey exceeds 100,000 people, the sample size will be 308 respondents, with a confidence probability of 95% and a confidence interval of ±5.6%.

A simple sampling formula was chosen for the calculation:

,

Where: n- sample size;

z - selected critical value of desired confidence:

p - the estimated proportion of an attribute that is present in the population;

q equal to 1-p;

e - the desired level of precision.

The sample size in this case is calculated as follows. The confidence level is assumed to be 95%, then the normalized deviation z = 1.96. We accept the variation for 50%. Then p = 0.5, hence q = 1 - p = 1-0.5 = 0.5. The acceptable sampling error is assumed to be 5.6%, i.e. e = 0,056.

n = (1,962*0,5*0,5)/0,0562=308

The number of men who took part in the survey (figure 2) was 79 (26% of all respondents), and the number of women who took the survey was 228 (74% of all respondents).



Figure 2. Distribution of men and women who completed the survey.

The age distribution can be seen on the figure 3. The number of respondents aged 18 to 25 years was 228 people (74% of all respondents), aged 26 to 30 years was 37 people (12% of all respondents), aged 31 to 35 years was 17 people (5.5% of all respondents), aged 36 to 40 years was 4 people (1.3% of all respondents), aged 41 to 45 years was 6 people (1.9% of all respondents), aged 46 to 50 years was 12 people (3.9% of all respondents), between 51 and 55 years of age was 1 person (0.3% of all respondents), between 56 and 60 years of age was 1 person (0.3% of all respondents), from 61 to 65 years of age was 2 people (0.6% of all respondents).

Figure 3. Respondents age groups.

The figure 4 describes the respondents' level of education. According to the data obtained, it is clear that 122 respondents (40% of 308 respondents) have incomplete higher education, 117 respondents (38% of 308 respondents) have higher education, 24 respondents (8% of 308 respondents) have two or more degrees of higher education, 23 respondents (7% of 308 respondents) have secondary special education, 15 respondents (5% of 308 respondents) have completed secondary education, 7 respondents (2% of 308 respondents) have incomplete secondary education.

Figure 4. The level of education of the respondents.

The following figure 5 shows the financial situation of the respondents. From the data obtained, it can be seen that 145 respondents (47% of all respondents) can only afford to spend money on food and clothing, they do not have financial opportunities for something larger, 97 respondents (31% of all respondents) can afford to spend money on a TV or refrigerator, but they do not have financial opportunities for larger purchases, 33 respondents (11% of all respondents) can afford to spend money on buying a car, but they do not have financial opportunities for a larger purchase (for example, an apartment) , 16 respondents (5% of all respondents) can afford to spend money only on food and nothing else, 14 respondents (5% of all respondents) can afford to buy anything, 3 respondents (1% of all respondents) cannot buy food.



Figure 5. The financial situation of the respondents.

The respondents' marital status can be seen in the figure 6. According to the data obtained, 214 respondents (69% of all respondents) are single. This can be explained by the fact that the majority of respondents are in the age category from 18 to 25 years, and usually do not get married at this age. Further, we see that 48 respondents (16% of all respondents) are married, 37 respondents (12% of all respondents) are civilly married, 5 respondents (2% of all respondents) are widowed, and 4 respondents (1% of all respondents) are divorced.

Figure 6. Marital status of respondents.

Figure 7 shows whether the respondents have children. Thanks to the data obtained, it is clear that 275 respondents (89% of all respondents) do not have children, most likely this is due to the fact that the majority of respondents are in the age category from 18 to 25 years and have not yet had children. And 33 respondents (11% of all respondents) have children.

Figure 7. Children presence.

The sample geography is shown in the figure 7 and 8.

This research is aimed to examine respondents from any region of Russia. The largest number of respondents (50%) live in St. Petersburg. From Moscow, 27 people were interviewed, which is 9% of respondents. Also in the survey respondents from 34 other regions of Russia took part (Altai Krai, Belgorod oblast, Vladimir oblast, Volgograd oblast, Voronezh oblast, Zabaykalsky Krai, Kaluga oblast, Kemerovo oblast, Krasnodar Krai, Kursk oblast, Leningrad oblast, Moscow oblast , Nizhny Novgorod oblast, Novosibirsk oblast, Orenburg oblast, Penza oblast, Perm Krai, Primorsky Krai, Republic of Bashkortostan, Komi Republic, Tatarstan, Rostov oblast, Saratov oblast, Sverdlovsk oblast, Smolensk oblast, Tomsk oblast, Tula region, Ulyanovsk region, Khabarovsk territory, KHMAO-Yugra, Chelyabinsk region, Chuvash Republic, Yaroslavl region). The remaining 14 respondents (5%) skipped this column or incorrectly specified the region (indicated the Federal district of the Urals and far East).



Figure 8. Geography of respondents.

Figure 9. Map of respondents' distribution by regions.

Figure 10. Distribution of respondents' responses by the size of the population of the locality.

Figure 10 presents the estimated size of the population of the locality where the Respondent lives can be seen below. The most popular answer is the number of more than 1 million. This was the response of 76% of respondents. By a wide margin, the next most popular answer was with a city population of 500-1 million people - 37 respondents (12 %). It is worth noting that 5 (2%) people found it difficult to determine the size of their locality.

On the figure 11 there can be seen how often respondents buy discounted products, i.e. the responsiveness and attitude of respondents to discounts. 150 respondents (49% of all respondents) are willing to buy discounted products and try to do it as much as possible, 80 respondents (26% of all respondents) very often buy discounted products, 76 respondents (25% of all respondents) sometimes buy discounted products, when they find necessary product with a discount and 2 respondents (1% of all respondents) never buy discounted products.

Figure 11. Frequency of purchasing discounted products.



3. Results

Data from the survey is collected in an Excel table, where each response is later recoded into a numeric format for ease of further analysis. For the most part, binary variables are used, as well as nominal and ordinal variables. The transcoding process resulted in 63 variables out of 31 survey questions.

In this paper, 3 main methods of analysis were used: descriptive statistics, where all variables are described, correlation and regression analysis are also performed for the study to test the hypotheses.

3.1 Descriptive statistics

Experience of participants in LP.

First of all, descriptive statistics were conducted to identify the attitude of customers to loyalty programs, as well as to find out their experience of participating in such programs.

Next, in the figure 12 we consider the industries where respondents are members of loyalty programs. The most popular sector among respondents is loyalty programs in grocery stores, which involve 283 people (92% of the total number of respondents). In the second place by frequency, respondents participate in loyalty programs in cosmetics stores. This is 197 people (64% of the total number of respondents). On the 3rd place there are clothing stores and pharmacies, with 156 and 155 participants respectively. In the last place, there are only 45 participants in beauty loyalty programs.

There were also the following responses that did not fit the categories mentioned in the table: stores of children's things and toys - 3 people, fitness clubs, art materials, weapons stores, auto parts stores, online stores, jewelry stores, sex shops, sports and travel stores, electronics stores, hardware stores (for example, Podrygka), bonuses “Spasibo” and Tinkoff Bank were mentioned by 1 person each.

Figure 12. Participation in loyalty programs by industry.

Considering the frequency of participation in loyalty programs in various areas in the figure 13, on average, the respondent is a participant in 3.84 industries. The most common value is 3, meaning that respondents most often participate in loyalty programs in 3 industries. The median is - 4.

Figure 13. Frequency of participation in loyalty programs in various areas.

Figure 14 describes the experience of respondents participating in various types of loyalty programs, in order to find out how popular the bonus loyalty program is, since this study is aimed at this program. Thanks to the data obtained, a histogram was constructed, which shows that the bonus program, which is the subject of the research of the thesis, is in second place, since it is used by 200 respondents (64%). By a small margin from the most used type of loyalty programs - discount, it is used by 238 respondents (78%). The loyalty bonus program is in third place and is used by 200 respondents (64%). The smallest number of respondents uses charity as a loyalty program 59 people (19%).

Figure 14. Distribution of participation in loyalty programs by type

Based on the data obtained, the frequency of use of each type of program was revealed. The distribution of responses depending on the frequency of the number of choices of different LP types is shown in graph 15. On average, respondents chose 2.38 types of loyalty programs. Mode and median are the same - 2 types, it means that, this is the most frequent frequency of respondents' participation in various types of loyalty programs.

Figure 15. Distribution by the number of types of loyalty programs used.



In the figure 16 the degree of usability of each loyalty program on a scale from 1 to 5, where 1 is very inconvenient and 5 is very convenient, is presented. The data obtained shows that the most convenient loyalty programs for respondents are the discount and cashback programs. Since 161 respondents (52% of all respondents) voted for the discount program and 113 respondents (37% of all respondents) considered the most convenient type of loyalty programs - cashback. The lowest number of people rated the maximum level of convenience (5 points) in the multi-level loyalty program 38 respondents (12% of all respondents).

In charity, 143 respondents (46% of all respondents) chose an answer expressing uncertainty about convenience, which indicates that the respondents may not have used this type of loyalty program, or used it, but find it difficult to answer. The smallest number of respondents answered "Not sure" about the bonus type of loyalty programs - 12 respondents (4% of all respondents).

Among the types of loyalty programs rated at 1 (very inconvenient), the multi-level loyalty program is in the first place, 32 respondents (10% of all respondents) voted for it. All the others have approximately the same number of respondents (from 3% to 5%) who voted about the inconvenience of this type of LP: discount, bonus, cashback and charity systems.

Figure 16. Distribution of ratings based on the convenience of the types of loyalty programs used.



The next figures illustrate the convenience of each type of loyalty separately. There can be seen an increasing trend in the number of respondents voted for the convenience of a discount loyalty program (figure 17). Most of the respondents (77%) rated the convenience of this type at 4 and 5.

Figure 17. Evaluation of the convenience of discount loyalty programs.

As can be seen from figure 18, the bonus type of loyalty programs also shows an uptrend, but only 185 respondents (60% of all respondents) rated the convenience at 4 and 5 points. And 36% of respondents rated it as low as 1 to 3.

Figure 18. Evaluation of the convenience of bonus loyalty programs.

From the figure 19, when asked about the convenience of a multi-level loyalty system, the most common respondents (28% of all respondents) rate the convenience only 3 points out of 5. The next most frequent response is 4 points, with 61 respondents voting for it (20% of all respondents). In 3rd place, the answer "Not sure" is 52 respondents (17% of all respondents). It can be also seen that the minimum score was rated by about the same number of respondents as the maximum score, at 10% and 12%, respectively.

Figure 19. Evaluation of the convenience of multi-level loyalty programs.

Considering the convenience of the cashback system, the graph below (figure 20) shows that an equally large number of respondents (37% of all respondents) voted for the maximum convenience of this program, and answered "Not sure" (24% of all respondents).

Figure 20. Evaluation of the convenience of cashback loyalty programs.

Regarding the convenience of the charity loyalty program (figure 21), it is worth noting that only 59 respondents (19% of all respondents) rated the maximum score. But the largest part of respondents answered “not sure” -143 people (46%).



Figure 21. Evaluation of the convenience of charity loyalty programs.

Experience of getting notifications about threshold.

Next, we will review the data obtained regarding the experience of respondents' participation in bonus loyalty programs.

Bar chart below (figure 22) shows that the majority of 240 respondents (78% of all respondents) receive notifications that their points accumulated or accrued for participation in the loyalty program will soon be burned. 51 respondents (16.5% of all respondents) answered negatively, which means that they have never received such notifications, and 17 respondents (5.5% of all respondents) said that they are not sure that they received such notifications.

Figure 22. Experience of receiving notifications about reaching threshold.

Next figures are devoted to the experience of only those respondents who receive notifications that their accumulated points have an expiration threshold, namely 240 respondents.

Considering the experience of respondents regarding the frequency of receiving notifications, the following answers were provided: "Less than once a month", "More than once a month, but less than once a week", "Daily","More than once a week, but not daily".

...