Intensifying Use of Big Data for Emerging Markets in Society 5.0

Piotr Kulyk

1 a

, Viktoriia Hurochkina

1,2 b

, Bohdan Patsai

3 c

, Olena Voronkova

2 d

and Oksana Hordei

2 e

University of Zielona G

ora, 9 Licealna, Zielona G

ora, 65-417, Poland

State Tax University, 31 Universitetska Str., Irpin, 08200, Ukraine

Taras Shevchenko National University of Kyiv, 60 Volodymyrska Str., Kyiv, 01033, Ukraine

Keywords:

Emerging Markets, The Theory of Welfare, The Theory of Needs, Big Data Technology, Loyalty Programs,

Predictive Analysis.

Abstract:

The use of Big Data is of particular interest to emerging markets, especially to business owners of enterprises

selling goods and services. Big data is one of the opportunities to increase business results while meeting

the needs of each client. Use of Big Data is especially actual during economic crises and in conditions of

growing competition. Purpose – The main purpose of the article is to use Big Data technology for maximizing

customers‘ satisfaction and business proﬁts in Society 5.0 during crisis periods. Methodology – The study

was based on utility theory Jules Dupuit, ﬁrm theory by Dionysius Lardner, the economics of welfare, theory

Big Data. Findings – The low efﬁciency of discount loyalty programs is an incentive for the distribution of

personalized programs has been proven. Such programs allow you to monitor the impact of the enterprise mar-

keting policy in emerging markets on regular customers’ behaviors. They also allow you to study preferences,

purchasing power, and population migration. The impact of the rapid development of information technology

has been investigated; in particular, the impact of Big Data technology on loyalty programs in Society 5.0 has

been identiﬁed. Information component loyalty programs were classiﬁed. It will allow providing the most

effective processing of consumer beneﬁts information and increase sales, considering the features of crisis

periods in economic development. Signiﬁcance – The main competitive advantages of Big Data using have

been identiﬁed. The importance of this technology for businesses has been proven. It should be especially

used in the process of overcoming the crisis to form the optimal price for the product and maximize the results

of the activity.

1 INTRODUCTION

After overcoming the consequences of the Coron-

avirus disease (COVID-19) pandemic and the conse-

quences of the Russian-Ukrainian war there will be a

need to take advantage of the competitive advantages

of goods and services. It is necessary to be based

on the basic concepts of economic theory. Can the

concept of utility be used? Yes, the concept of util-

ity was proposed by Jules Dupuit (Numa, 2016) who

determined that the same product is sold at different

prices to different customers. Moreover, differences

in prices are irrelevant to the difference in costs. In ad-

https://orcid.org/0000-0003-2786-4020

https://orcid.org/0000-0001-8869-0189

https://orcid.org/0000-0001-5636-9219

https://orcid.org/0000-0002-7956-7768

https://orcid.org/0000-0001-6938-0548

dition, Dupuit partially discovers the conditions that

are necessary for its implementation. A merchant can

conduct such activities only if he is protected from

competition, if he is a monopolist. This condition is

necessary so that the seller can control the price. Ac-

cording to Dupuit’s concept rising depends not only

on the interests of the monopoly seller. Prices also

depend on how buyers evaluate one or another thing.

Based on utility theory the same product has different

utility and different price that consumers are willing

to pay for it. Since there are separate groups of buy-

ers (rich, wealthy and poor), the monopolist is able

to recognize these groups and consider the different

willingness to pay for the product. Dupuit considered

the theory of utility, primarily from the point of view

of the consumer.

The issue of maximizing satisfaction of the needs

and interests of individuals was studied by another

Kulyk, P., Hurochkina, V., Patsai, B., Voronkova, O. and Hordei, O.

Intensifying Use of Big Data for Emerging Markets in Society 5.0.

DOI: 10.5220/0011931300003432

In Proceedings of 10th International Conference on Monitoring, Modeling Management of Emergent Economy (M3E2 2022), pages 63-70

ISBN: 978-989-758-640-8; ISSN: 2975-9234

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

British engineer and economist Dionysius Lardner al-

most simultaneously with Dupuis. Lardner (Hooks,

1971) analysed the possibilities of maximizing in-

come from the perspective of the theory of the ﬁrm.

He argued that price competition can be used as a

means by which a ﬁrm is able to maximize proﬁts.

The analysis of railway tariffs allowed him to sum-

marize the practice of their differentiation according

to the distance and nature of the carried goods. He ex-

plained this differentiation by differences in elasticity:

the different demands for rail transport services and

the heterogeneity of the transported goods. Lardner’s

real contribution is to reveal the role of demand elas-

ticity in the process of meeting the end consumers’

needs.

Why will we use the theory of the economics of

welfare? Pigou (Pigou, 1920) formulated the gen-

eral conditions for price competition and identiﬁed

its three types in the work “The economics of wel-

fare”. According to Pigou’s concept general condi-

tions fully contribute to the implementation of price

competition. This happens when the demand price

for any unit of goods does not depend on the selling

price of any other unit of goods. This is possible only

when no unit of goods can replace any other unit of

the same product.

For example:

• no unit of goods sold in one market can be trans-

ferred to another market;

• not a single unit of demand presented in one mar-

ket can be transferred to another market.

However, to achieve such equilibrium states infor-

mation about the beneﬁts and the purchasing capacity

of buyers is necessary. This information must be ac-

cumulated considering domestic and foreign markets.

In practice, it is very difﬁcult because there are a lot

of analogy products. Therefore, after overcoming the

consequences of the Coronavirus disease (COVID-

19) pandemic and the consequences of the Russian-

Ukrainian war, the struggle for the end consumer will

gain unprecedented proportions in the world.

The basic element of success will be positive

emergent properties, which are focused on the emer-

gence and development of innovations, especially

when it comes to the struggle for the end consumer

and emerging economy (Dzhedzhula et al., 2022;

Hurochkina et al., 2021; Hordei et al., 2021). Drivers

of positive emergent properties are at a high level

of human capital development. Considerable atten-

tion is paid to the study of the features of the inno-

vative development of human capital in the condi-

tions of developing economies, and each direction and

sphere of functional implementation of this resource

is detailed (Czy

zewski et al., 2021; Czy

zewski et al.,

2022). The processes of realizing human capital in

a developing economy are complicated by the conse-

quences of the coronavirus disease (COVID-19) and

the Russian-Ukrainian war. Since there are problems

with the number of permanent persons and internally

displaced persons both in the middle of the country

and outside its borders.

2 LITERATURE REVIEW

Industry 4.0 will contribute to the emergence of a new

Society 5.0. Innovative technologies of Industry 4.0

will contribute to the rapid recovery and overcoming

the consequences of the Russian-Ukrainian war.

Fundamental provisions of formation Society 5.0

and implementation of innovative Industry 4.0 tech-

nologies are considered in a number of work. Kit-

suregawa (Kitsuregawa, 2018) highlight the questions

of how Japan is launching Society 5.0 and the vision

for a future smarter society. The work of Aquilani

et al. (Aquilani et al., 2020) is devoted to the ad-

vanced manufacturing solutions, augmented reality,

the cloud, and big data in the emergence of a new level

of social development. Rahmanto et al. (Rahmanto

et al., 2021) note the potential of huge advantages of

big data technology in the emergence of a new level of

social development and a breakthrough revolution in

people’s lives thanks to the use of technologies taking

into account the humanitarian aspect.

Foresti et al.; Hayashi and Nagahara (Foresti et al.,

2020; Hayashi and Nagahara, 2019) highlight the role

of artiﬁcial intelligence in the functioning of auto-

mated planning and data analysis with the help of

smart programs, smart infrastructure, smart systems,

and smart networks.

Ellitan (Ellitan, 2020) focuses on the lack of HR

(human resources) skills and the existing problem of

security of communication technologies, and the in-

ability of stakeholders to change, while in society 5.0

there is a clear priority due to the reliable and stable

operation of production machines, which in turn leads

to the negative consequences of worker losses places

through automation. for the rapid adaptation of hu-

man capital for the beneﬁt of improving public and

business services, achieving a high level of literacy

in working with data and its data analysis is an im-

portant condition. Simatupang (Simatupang, 2020)

noted that the slow progress of Society 5.0 can be

achieved through the development of integrated in-

formation technologies in universities and education.

De Felice et al. (De Felice et al., 2021) noted that

in order to achieve Society 5.0 it is important to man-

age the transition and identify the enabling factors that

M3E2 2022 - International Conference on Monitoring, Modeling Management of Emergent Economy

integrate Industry 4.0. According to

Onday (

Onday,

2019), digital transformation creates new values and

becomes a pillar of the industrial policy of many

countries. Therefore, in Society 5.0, the basis of qual-

ity functioning is the achievement of convergence be-

tween physical and cyberspace. But it should be noted

that the key drivers of the implementation of Industry

4.0 in Society 5.0 will contribute to rapid recovery in

the post-war period, new economies will emerge, the

only question will be the transfer of technologies for

recovery and adaptation at the fastest pace.

3 METHODOLOGY

If you determine the level of demand in various mar-

ket segments and in the markets of various countries,

you can set an individual price for each unit of a ho-

mogeneous product, which will be equal to the price

of its demand. This price is called the reserved price

of the buyer. In its pure form, such a pricing policy is

difﬁcult to implement. The company does not know

the reserved price of each buyer, but also cannot know

its level from the buyer, since it is in his interests to

reduce its value. It is the lack of information that does

not allow the full introduction of perfect price compe-

tition and the largest ﬁnancial effect.

The options (based on the collected data) for set-

ting different prices for certain consignments of goods

in accordance with the same demand function are

used today. In practice, it often takes the form of var-

ious kinds of discounts (depending on the size of pur-

chases, prepaid periods, etc.). In this case, the monop-

olist increases the volume of sales, and the consumer

can achieve certain economies of purchase volume.

Differentiation of buyers into groups with differ-

ent demand functions and subsequent pricing for each

such group occurs separately during market segmen-

tation. Segmentation is usually carried out by gender,

age, income level, social status. There is the practice

of setting different prices for students, senior citizens,

people with disabilities and people of working age.

Segmentation of end consumers is being made con-

sidering price and non-price ways of increase inﬂu-

ence on sales (ﬁgure 1), which are reﬂected in loyalty

programs.

However, the discount loyalty programs have

some disadvantages:

• the ability to saturation and, consequently, de-

crease the efﬁciency of use;

• the complexity of how to form a group of support-

ers as well as the completion of the closure of the

current program;

• the remoteness of non-regular customers and the

usual price overpricing.

Nowadays discount accumulators and bonus cards

are mostly used. Among the reasons that led to a

change in the accounting policies of many enterprises

there is a possibility of:

• the creation of various offers for various groups of

clients;

• provision of discounts in the form of a certiﬁcate

is an incentive for the client to return to the pur-

chase of well-known goods and services;

• tracking the movement of regular customers and

changing their preferences.

Introduction of such loyalty programs became

possible thanks to the rapid development of informa-

tion technologies that are capable to solve new prob-

lems. In addition, these cards can signiﬁcantly reduce

the turnover of small bills. But the main feature of

these changes is the personalization of discount pro-

grams.

Personalization of seller-buyer relationships, us-

ing data mining (OLAP technology), allows you to

analyze the dependencies of any values contained in

the database and respond to the situation quickly. Im-

portant information for the seller is not only attracting

new customers, but also controlling relationships with

regulars. Firstly, the sales increase may be a conse-

quence of a successful advertising company and, sec-

ondly, sales decrease for personalized discount cards

is a consequence of low level of service, which will

lead to a sharp decrease in sales in the medium and

long term.

Currently, in order to increase the effectiveness

of consumer segmentation the enterprise is trying to

group them according to the level of the product value

perception. In this case consumers are allocated:

• price-sensitive and thus easily change suppliers;

• sensitive to the quality of goods and services;

• are focused on creating long-term relationships

and, as a result, strive to establish long-term part-

nerships to improve the quality of goods and ser-

vices.

Internet trade has the greatest relevance during the

lockdown. It is devoid of such shortcomings that are

characteristic of the real sector of the economy:

• is not strictly connected with the territory of the

physical existence of the consumer;

• can be carried out without any territorial restric-

tions;

Intensifying Use of Big Data for Emerging Markets in Society 5.0

Figure 1: Classiﬁcation of loyalty programs.

• the rapid development of the information society

and information growth gave impetus to the de-

velopment of new methods of its implementation.

In particular the Big Data theory is rapidly devel-

oping (Market Research Future, 2022). The term “Big

Data” usually refers to a series of approaches, tools

and methods for processing of structured and unstruc-

tured large volumes and the different nature data to

obtain a consumer acceptable result. The introduc-

tion of the term “Big Data” is associated with Clif-

ford Lynch (Lynch, 2008) who was an editor of Na-

ture magazine and prepared a series of topical works.

Quite often the “triple V” criterion is used to describe

“Big Data”: volume, velocity, variety. Some leading

manufacturers of business intelligence software, such

as SAS (SAS Institute Inc, 2022), additionally use

two more: variability and complexity. In addition to

growing speeds and data varieties, data ﬂows can also

be characterized by periodic peaks. Such peak data

loads can be difﬁcult to manage. It is worth to note the

complexity factor as the most important factor when

you are working with Big Data. While increasing the

amount of data to variable n, the number of links be-

tween them grows in proportion to n! (n factorial). So

the problem is not limited only to the processing of

large amounts of data but also requires an additional

solution to the problem of analyzing connections’ n!.

To identify a consumer on the Internet data for

analysis is needed. The proﬁle of the network is

formed not only with the registration data on partic-

ular Internet resources but also activity in social net-

works, forums, blogs and the like. Thus, data reﬂect-

ing the user is unstructured.

4 RESULTS

Leading corporations have developed platforms for

big data business analytics (Market Research Future,

2022). In particular IBM, creating a full proﬁle from

social network data in the Big Data Analytical Sys-

tem, uses all the data that is more or less related to a

speciﬁc consumer (table 1). At the ﬁrst stage analysis

of the texts takes place, at the second the linking of

attributes takes place, at the third formation of statis-

tical models and at the fourth formation of business

logic take place.

Table 1: The data structure that is used to form a complete

social user proﬁle.

Full social customer proﬁle

Personal

characte-

ristics

Identiﬁers

Interests

Social status

Relation-

ships

Personal

Business

Chronolo-

gical

activity

Purchase intention

Current location

Feedback on products and services

Incident

Loyalty facts

Goods and

interests

Personal relation to goods

Shopping history

Recommendations

Politics

Attitude to power

Political views

Perception of reform

Life events

Personal

Reactions to events

Economic-mathematical modeling of the socio-

M3E2 2022 - International Conference on Monitoring, Modeling Management of Emergent Economy

economic system based on online Big Data algo-

rithms makes it possible to predict consumer behav-

ior based on the identiﬁcation of business logic and to

form a consumer proﬁle in the decision-making sys-

tem. This method is traditional, but the selection of

characteristic functional features for forecasting efﬁ-

ciency and optimization of Slick-Through-Rate fore-

casting processes is special in view of machine learn-

ing as a tool for economic and mathematical modeling

of the management decision-making system.

Taking into account the presented data structure of

the full proﬁle of a social network user and the model

of Big Data online algorithms, we have the possibil-

ity of ﬂexible targeting of the target audience, adap-

tation of advertising content in accordance with user

interests, the possibility of forecasting the effective-

ness of advertising and its impact on consumer be-

havior. In addition, when building a model of Big

Data algorithms, it is worth taking into account trafﬁc

segmentation and the Real-Time Bidding Exchange

RTB auction (corresponding to the business logic of

the consumer).

The use of Big Data in e-commerce provides such

competitive advantages:

1) customer service: Big Data helps to give the con-

sumer a sense of self-worth because his needs are

maximally met by creating a certain connection

between him and the brand. This cultivates con-

sumers’ loyalty and inﬂuence on their emotional

level;

2) dynamic and point pricing: analysis of market

data allows you to set an attractive price for each

speciﬁc consumer;

3) personalization: in the process of analyzing con-

sumers’ information, personalized solutions are

offered that become a competitive advantage for

the client;

4) predictive analysis: Big Data allows you to carry

out medium-term forecasting in the market and re-

spond accordingly to possible changes in the mar-

ket environment.

An example of this approach can be an application

developed for the clothing brand Free People which

provided sales growth of 38 percent (Dishman, 2013).

The application allows users to discuss the latest col-

lections, share their photos on Pinterest and Instagram

social resources and vote for the best photos. This in-

teraction is an example of the monetization of accu-

mulated data by retailers using social platforms. Point

discounts of Internet commerce can be divided by

analogy with traditional commerce into two types de-

pending on the technology that is used. The ﬁrst type

is personalized which provides for mandatory regis-

tration on a web resource, the second is not person-

alized (does not require registration). The ﬁrst option

of a point discount is for a price offer based on cus-

tomer data, a history of web surﬁng (viewing products

on a store page) and purchase history. Retailers often

use social media accounts to register. It simpliﬁes the

registration procedure and gains access to user data.

This signiﬁcantly increases the amount of data to be

analyzed.

Based on the data (table 1) on using Big Data, a

consumer proﬁle is formed and its segment afﬁliation

is determined. In the future the client is offered an

individual price offer. The price that is offered is min-

imal in order for the fact of purchase. In addition,

goods are offered in accordance with the target audi-

ence. In other words, an individual approach to pro-

posals is formed based on the analytical processing of

unstructured data.

For convenience we have built EPC diagram

(Software AG, 2022), which is often used to describe

the workﬂow in ArisExpress environment (ﬁgure 2).

If the visitor is not a consumer of goods and ser-

vices, HTTP-cookie analysis of the web page is car-

ried out that allow carrying out authentication, storage

of personal user preferences and settings, session state

tracking of user access, maintain user statistics.

It is also possible when there is not enough data

to determine the proﬁle of the visitor. This may be

due to both the low activity of the Internet user and

his conscious reluctance to “external tracking”. One

such way is to use an anonymous session. In this case

the basic offers are determined by the system.

For machine learning target audience targeting,

we use the Datch approach, taking into account the

social network user proﬁle, to build a model of online

Big Data algorithms. The Datch approach is based

on two-level testing of Big Data algorithms: training

dataset and test dataset. The condition of the model is

the constancy of the data of the decision-making sys-

tem over time. At the same time, the dynamism of the

system and the resonance of news on the website can

become an emergent property of the socio-economic

system, which will contribute to a further change in

the trend. The model of Big Data algorithms for the

task of predicting CTR is based on the systematiza-

tion of the modeling process by stages and on a certain

set of parameters of the data structure of the complete

proﬁle of a social network user.

p+1

= argmin

t−1

∑

i=1

v(w

, w

pol

, w

)

+ R(w

, w

pol

, w

)

(1)

Intensifying Use of Big Data for Emerging Markets in Society 5.0

Figure 2: Structurally Logical Pricing Scheme in an EPC Chart.

where:

p+1

– function social customer proﬁle;

v(w

) – loss function for optimization Personal

characteristics (Identiﬁers, Interests, Social status);

v(w

) – loss function for optimization Relation-

ships (Personal, Business);

v(w

) – loss function for optimization Chrono-

logical activity (Purchase intention, Current location,

Feedback on products and services, Incident, Loyalty

Facts);

v(w

) – loss function for optimization Goods and

interests (Personal relation to goods, Shopping his-

tory, Recommendations);

v(w

pol

) – loss function for optimization Politics

(Attitude to power, Political views, Perception of re-

form);

v(w

) – loss function for optimization Life events

(Personal, Reactions to events).

R(w

) – regularization function Personal charac-

teristics (Identiﬁers, Interests, Social status);

R(w

) – regularization function Relationships

(Personal, Business);

R(w

) – regularization function Chronological

activity (Purchase intention, Current location, Feed-

back on products and services, Incident, Loyalty

Facts);

R(w

) – regularization function Goods and in-

terests (Personal relation to goods, Shopping history,

Recommendations);

R(w

pol

) – regularization function Politics (Atti-

tude to power, Political views, Perception of reform);

R(w

) – regularization function Life events (Per-

sonal, Reactions to events).

The loss function for optimizing the proﬁle char-

acteristics of a social network user will have the fol-

lowing form:



, w

pol

, w



∥

w −x

∥

(2)

Under the conditions of a linear loss function in

order to optimize the characteristics of the social net-

M3E2 2022 - International Conference on Monitoring, Modeling Management of Emergent Economy

work user proﬁle, the formula will have the following

form:

, w

pol

, w

) = ⟨w, x

⟩ (3)

Under conditions of activation of emergent prop-

erties in the socio-economic system, such as dynamic

system changes or trend changes under the inﬂuence

of high-proﬁle news on the site, which contribute to

the manifestation of binary dependence at the bifurca-

tion point, the function will have the following form:

, w

pol

, w

) =

(σ



, w

pol

, w



−y

(4)

σ – sigmoidal function:

σ(α) =

1 + e

(5)

With the activation of emergent properties in the

socio-economic system, the regularization function

will have the following form:

R(w

, w

pol

, w

) =

∥w∥

(6)

Under the conditions of if η > 0, then the iteration

of the machine learning algorithm will include a step-

wise gradient descent algorithm and will look like:

p+1

= −η

∑

i=1

= W sc

−ηz

W sc

−∇v

, w

pol

, w

)

(7)

The resulting formula for optimizing management

decisions, taking into account the parameters of the

data structure of the full proﬁle of a social network

user, will look like this:

p,i

(

≤ ε

−



β+

√



−sign(x

)ε

)

> ε

(8)

where x and n iteration parameters, ε

, ε

are regular-

ization intensity parameters according to the selected

type and α, β – are input parameters characterizing

the learning rate.

Since, based on the above, in order to achieve the

optimum at each step of the algorithm execution, the

optimal decision is made and the previous ones are

not foreseen, then this model belongs to the Greedy

algorithm. A characteristic feature of these algo-

rithms is relative simplicity and speed of execution.

This technique of point discount has been actively

developing over the past three years. One of the

ﬁrst companies that offered this service was Fresh-

plum whose founder was Sam Odai. Later Fresh-

plum joined the TellApart company (SAS Institute

Inc, 2022), which operates in the market of services

for online stores. Moreover, the algorithm for po-

tential customers’ selection of this company uses a

number of “non-standard” indicators such as: place

of residence (city center or outskirts), weather, etc.

This allows you to increase the likelihood of making

a purchase up to 36 percent (Tanner, 2014).

For the ﬁrst time the analysis of differential

pricing in online stores was conducted by the The

Wall Street Journal. The editors conducted a study

(Valentino-DeVries et al., 2012) of pricing in 200 on-

line stores.

The economic situation in the world is extremely

dependent on the geopolitical risks that can now be

observed (for example the corona virus pandemic

and the consequences of the Russian-Ukrainian war).

Therefore, the widespread use of Big Data concept

may increase the proﬁtability of enterprises. The use

of Big Data methods will become an additional source

of budget revenues after taxation. This will maxi-

mally satisfy the needs of consumers whose incomes

have recently been declining due to devaluation and

inﬂationary processes. In order to increase competi-

tiveness of European goods and services markets the

use of big data is a mandatory requirement of our

time.

5 CONCLUSIONS

The economic situation of all countries of the world

is extremely dependent on the geopolitical risks that

can now be observed (for example, a COVID-19 pan-

demic and the consequences of the Russian-Ukrainian

war). Therefore, the widespread use of Big Data ba-

sics will increase the proﬁtability of enterprises. The

use of Big Data methods will become an additional

source of budget revenues, after taxation. This will

maximally satisfy the needs of consumers whose in-

comes have recently been declining due to devalua-

tion and inﬂationary processes. In addition, in order

to increase competitiveness in European markets for

goods and services, the use of big data is a mandatory

requirement of our time. The use of the presented

economic-mathematical model will make it possible

to reach the optimum at each step of the algorithm

execution, the optimal decision is made according to

the Greedy algorithm type, which is characterized by

a fairly simple feature and speed of execution.

Intensifying Use of Big Data for Emerging Markets in Society 5.0

ACKNOWLEDGEMENTS

The work was carried out within the framework of

the program to support scientists from Ukraine dur-

ing the implementation of the project “Mechanism to

strengthen the social responsibility of refugees and

people ﬂeeing the Russian armed conﬂict on the terri-

tory of Ukraine” on funding from the Polish Academy

of Sciences and the National Academy of Sciences of

the United States and University of Zielona G

ora.

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