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International Journal of Innovation in Marketing Elements, Vol 1, No 1, (2021), 25-36
Fuzzy Clustering Approach for Marketing Recycled Products of
Tabriz Municipality Waste Management Organization
Vahid Saeid Nahaei
1
, Farzad Naziri-Oskuei2
1,2 Center Municipality Building, Tabriz Municipality, Tabriz, Iran
A R T I C L E I N F O
A B S T R A C T
Received: 08 August 2021
Reviewed: 18 August 2021
Revised: 10 September 2021
Accepted: 20 September 2021
The main concern of municipalities is the realization of sustainable revenues.
Organizations affiliated with municipalities should play a role in generating
revenue by defining specialized tasks while assisting municipal tasks. Tabriz
Municipality Waste Management Organization seeks to achieve this by defining
its strategies and goals. The organization has implemented various projects to
generate revenue from recycled products. Poor planning and failure to fully
outsource are among the obstacles of this organization. Therefore, marketing of
recycled products is an important project. Lack of careful planning in this regard,
marketing costs and weakness of private sector investment projects are the most
important obstacles facing the organization. This article has determined the degree
of homogeneity of waste organization projects in the marketing of recycled
products with a fuzzy clustering approach and according to the opinions of
experts. The results show that some of the organization's projects lack value.
Instead, some projects, such as the construction of a recycling town with a variety
of recycled products, renewable energy recycling, and plastic recycling with a
variety of products, have similar features in the product mix marketing element,
and this can reduce marketing costs and Focus on such projects.
Keywords:
Fuzzy Clustering, Marketing,
Tabriz Municipality, Waste
Management Organization.
1
Corresponding Author: v.nahaie@tabriz.ir
https://doi.org/10.59615/ijime.1.1.25
International Journal of Innovation in Marketing Elements
Journal homepage: www.ijime.ir
26
1. Introduction
The COVID-19 crisis has affected many issues in the world, including marketing. The COVID-19 crisis
is affecting consumer behavior and thus the way in which marketing can be used. The use of marketing
during (and after) the COVID-19 crisis shows (and will continue to show) similarities with the way that
marketing is carried out during economic downturns (He, & Harris, 2020). In this context, urban
management must seek innovative solutions in all areas from tourism to service to citizens (Parviznejad,
& Akhavan, 2021). The stable periods of the economy are more suitable for the implementation of
marketing mix elements. In such periods, as the financial situations of consumers are stable, it is
relatively easier to make marketing decisions (Sigindi, 2018). Municipalities face the challenge to
grow – with the “right” mix of businesses, services, and infrastructure to support the needs and wants
of all stakeholders. Sales is one of the most important topics in marketing. For services firms in the
market, regardless of the ownership issue of prices is very important. A community’s strategic growth
is the product of short and long term goals. Municipalities that naim to grow and differentiate from
neighboring communities must understand their audience and craft a message that is unique and
positions them for strategic growth. Marketing segmentation is key for municipalities to share the right
message with the right people at the right time through the right channels (Akcay, & Okkay, 2017).
Historically, municipalities have focused on investing in economical development for the region rather
than marketing designated to attract people to the municipality. Several researchers have pointed out the
importance of the politicians being able to provide statistical evidence that the cost for marketing the
place will be surpassed by the economical gains from new businesses, new residents or tourists attracted
to the place (Lundmark, 2006). The issue of waste management (waste management, collection
management, transportation, processing, recycling or disposal of waste) is very important in terms of
environmental, cultural and economic issues and is considered by researchers and capital in various
aspects (Sengupta, & Agrahari, 2017). The global waste management market size was valued at $1,612.0
billion in 2020, and is expected to reach $2,483.0 billion by 2030, registering a CAGR of 3.4% from
2021 to 2030. Waste management is the collection, transportation and disposable of garbage, sewage,
and other waste products. It involves treating solid waste and disposing unwanted products and
substances in a safe and efficient manner. Waste management includes all types of waste including solid,
liquid, or gas. Waste management deals with municipal, industrial, and hazardous waste. Municipal
waste generally refers to residential waste and non-hazardous waste generated in towns and cities.
Industrial waste refers to waste generated in industries while production and manufacturing processes.
Hazardous waste refers to waste generated in pharmaceuticals, medicals, chemicals, and paint
manufacturing industries. However, high cost of procuring and operating waste management solutions
is expected to hamper the market growth. In addition, waste management is labor intensive and can
consume a lot of amount as their wages. Similarly, costs of transportation of waste can take up a lot of
allocated amount as the cost includes collection cost and further transportation to landfills or recycling
facilities. Cost recovery for waste services differ largely from income levels. Thus, making the waste
management market a little rigid, considering the investments related to its process, which, in turn,
hampers the market growth (Alliedmarketresearch, 2021).
Theories related to fuzzy sets are important approaches in urban management projects (Youssefi, et al.,
2011). Investigating the uncertainty in the components of urban management is one of the factors that
can lead to the development of sustainable incomes (Parviznejad, & Bahrami, 2021). Due to the
uncertainty in the data related to municipal waste and the diversity of recycled products in municipal
waste management, the fuzzy clustering method developed has been used for this research. Considering
27
the fields of sales and export of recycled products of Tabriz Municipal Waste Management
Organization, fuzzy clustering of these products has been determined according to the sales priorities
and a marketing mix approach has been considered for these clusters.
2. Literature Review
Today's businesses, especially in Iran, face many factors and challenges, one of which is uncertainty in
inputs and laws and regulations. Especially in the current situation and with the development of e-
commerce on the one hand and on the other hand despite critical conditions such as COVID-19, the
purpose of a paper is a comparison between businesses with the study of hypermarkets and net markets.
This research is a descriptive-analytical type that after explaining the goals and components of
organizational business using library resources and Internet search, interviews and questionnaires, from
a multi-criteria decision approach and fuzzy logic for effective analysis. The implications of
organizational business are exploited. Two areas of physical retail businesses such as hypermarkets and
virtual ones such as net markets have been compared and analyzed. The result of the research has been
that due to the capabilities of the development of net markets such as the effective use of information
technology and experts, their comprehensive development and growth in the future is more realistic that
the ability to extend this to other areas of virtual business. Especially in spite of critical conditions such
as the spread of pandemics, the popularity of using net markets has increased (Nahaei, & Bahrami,
2021). In any study of market segmentation, researchers often use clustering analysis as a tool. The
analysis often is in a crisp partition form. But in practice, the sample are usually not well distributed,
therefore the form may not be precisely defined. That is, one sample can belong to two or more groups.
But, due to the fact that the requirements on the consumers and on the market are very high and the
many real-market problems are fuzzy by nature and not random, the probability applications have not
been very satisfactory in a lot of case. In a study, the authors adopt the fuzzy cluster method and attempt
to combine a new compactness and separation validity function to build market segmentation in order
to address the fuzziness among the group boundaries. Then they could use membership grade to describe
each group. Therefore, the real market situation is clearly presented. Through membership grade, they
depict the reality of the market, which lies between integers and real number. Buyers' mindsets are both
rational and complicated, so their purchasing decisions are not predictable and are affected by many
factors. The structural stability of the market can be tested by the loyalty of buyers who pertain to
different clusters. Marketing strategies will also have effects on the movement of group for housing
buyer (Hsu, et al., 2000). Segmentation has several strategic and tactical implications in marketing
products and services. Despite hard clustering methods having several weaknesses, they remain widely
applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used
to understand consumer behaviour. In another study, the authors propose a strategy of analysis, by
combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data
(FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method
inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The
method is applied on a sample of 328 Chinese consumers revealing the existence of four segments
(Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a
tourist destination. The results highlight the heterogeneity in Chinese consumers’ place preferences and
implications for place marketing are offered (D’Urso, et al., 2015). It has long been argued that the
housing market is spatially subdivided within an urban area. The argument has important implications
for explaining how the housing market works and describing the distinctiveness of each housing
submarkets, having determined, a priori, its segmentation. The most commonly used method for
28
identifying housing submarkets is based on cluster analysis, although hedonic analysis has been
extensively used. The hedonic analysis is used to derive dimensionality of the housing market by
estimating what attributes are significant factors influencing housing price. Those attributes or variables
can then be used for cluster analysis. A paper proposes an analysis of the real estate market in San
Cristoforo, Catania, trying to integrate two different clustering analysis approaches to defining its
possible submarkets articulation. The first one is a hard clustering approach using the K-means method
and hypothesizing different numbers of clusters. The second one can be considered a verification of the
previous results: a fuzzy algorithm is applied to obtain the fuzzy set membership degree of each data
point to housing submarkets defined within the examined urban area. The comparison between the
results coming from the two different approaches suggests some reflections about the use of these
powerful techniques for integrating the knowledge of the complex and multi-layered real estate markets
in the urban recovery policies (Gabrielli, et al., 2017). The purpose of a paper is to propose a data mining
approach for mining valuable markets for online customer relationship management (CRM) marketing
strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research.
Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm
to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems.
The research found three hard markets and one fuzzy market. Furthermore, the study discovered two
association rules and two fuzzy association rules (Chiang, 2018). Another study evaluates the
performance of different data clustering approaches for searching the profitable consumer segments in
the UK hospitality industry. This paper focuses on three aspects of datasets including the ordinal nature
of data, high dimensionality and outliers. Data collected from 513 sample points are analysed in this
paper using four clustering approaches: Hierarchical clustering, K-Medoids, fuzzy clustering, and Self-
Organising Maps (SOM). The findings suggest that Fuzzy and SOM based clustering techniques are
comparatively more efficient than traditional approaches in revealing the hidden structure in the data
set. The segments derived from SOM has more capability to provide interesting insights for data-driven
decision making in practice. This study makes a significant contribution to literature by comparing
different clustering approaches and addressing misconceptions of using these for market segmentation
to support data-driven decision making in business practices (Arunachalam, & Kumar, 2018).
A paper analyses the voice of customers (VoCs) using a hybrid clustering multi-criteria decision-making
(MCDM) approach. The proposed method serves as an efficient tool for how to approach multiple
decision-making involving a large set of countrywide customer complaints in the Iranian automotive
sector.The countrywide data comprising 3,342 customer complaints (VoCs) were gathered. A total of
seven determinant complaint criteria were identified in brainstorming sessions with three groups (six
each) of experts employing the fuzzy Delphi method. The weights of these criteria were assigned by
applying the fuzzy best–worst method (FBWM) to identify the severity of the complaints. Subsequently,
the complaints were clustered into five categories with respective customer locations (province), car
type and manufacturer using the K-mean method and further prioritised and ranked employing the fuzzy
complex proportional assessment of alternatives (FCOPRAS) method. The results indicated that the
majority of complaints (1,027) from the various regions of the country belonged to one specific model
of car made by a particular producer. The analyses revealed that only a few complaints were related to
product quality, with the majority related to service and financial processes including delays in
automobile delivery, delays in calculating monthly instalments, price variation, failure to provide a
registration (licence) and failure to supply the agreed product. The proposed method is an efficient way
to solve large-scale multidimensional problems and provide a robust and reliable set of results
(Mahdiraji, et al., 2020). The increasing interest in fuzzy-set Qualitative Comparative Analysis (fsQCA)
29
in Information Systems and marketing raises the need for a tutorial paper that discusses the basic
concepts and principles of the method, provide answers to typical questions that editors, reviewers, and
authors would have when dealing with a new tool of analysis, and practically guide researchers on how
to employ fsQCA. This article helps the reader to gain richer information from their data and understand
the importance of avoiding shallow informationfromdata reporting. To this end, it proposes a different
research paradigm that includes asymmetric, configurationalfocused caseoutcome theory construction
and somewhat precise outcome testing. This article offers a detailed step-by-step guide on how to
employ fsQCA by using as an example an already published study. We analyze the same dataset and
present all the details in each step of the analysis to guide the reader onto how to employ fsQCA. The
article discusses differences between fsQCA and variance-based approaches and compares fsQCA with
those from structured equation modelling. Finally, the article offers a summary of thresholds and
guidelines for practice, along with a discussion on how existing papers that employ variance-based
methods are extendable and complemented through fsQCA (Pappas, & Woodside, 2021). The
population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure
upgrades to provide telecommunication services is of importance. New antennas can be installed at hot
spots of user demand, which will require an investment, and/or the clientele expansion can be carried
out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical
zones. In this paper, the authors explore the second alternative. Informally speaking, the term
Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to
produce service failures, if further loaded. They have studied the Infrastructure-Stressing population in
the light of their correlation with geo-demographic segments. This is motivated by the fact that specific
geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create
an interface between big data, numeric methods for its processing, and a manager who wants a
comprehensible summary (Podapati, et al., 2017). In else paper, the writers investigate the multiple
attribute decision making problems with picture fuzzy information. Then, they utilize induced OWA
(IOWA) operator to develop picture fuzzy induced OWA (PFIOWA) operators. The prominent
characteristic of this proposed operator are studied. Then, they have utilized the PFIOWA to develop an
approach to solve the picture fuzzy multiple attribute decision making problems. Finally, a practical
example for evaluating the enterprise marketing capability is given to verify the developed approach
and to demonstrate its practicality and effectiveness (Li, et al., 2017). Nowadays, a huge amount of data
is generated due to rapid Information and Communication Technology development. In a paper, a digital
banking strategy has been suggested applying these big data for Iranian banking industry. This strategy
would guide Iranian banks to analyse and distinguish customers’ needs to offer services proportionate
to their manner. In this research, the balances of more than 2,600,000 accounts over 400 weeks are
computed in a bank. These accounts are clustered based on justified RFM parameters containing
maximum balances, the most number of maximum balances and the last week number with the
maximum balance using k-means method. Subsequently, the clusters are prioritised employing Best
Worst Method- COmplex PRoportional ASsessment methods considering the diverse inner value of
each cluster. The accounts are classified into six clusters. The experts named the clusters as special,
loyal, silver- high interaction, silverlow interaction, bronze, averted- low interaction. Silver-low
interaction cluster and loyal cluster are picked in order by experts and BWM-COPRAS as the most
influential clusters and the digital banking strategy is developed for them. RFM parameters are modelled
for customers’ accounts singly. The aggregation of the separate accounts of a customer should be
considered (Mahdiraji, et al., 2019). Today all business organizations are adopting data driven strategies
to generate more revenue out of their business. Growing startups are investing a lot of money in data
economy to maximize profits of business organizations by developing intelligent tools backed by
machine learning and artificial intelligence. The nature of BI tool depends on factor like business goals,
30
size, model, technology etc. In a paper architecture of business intelligence tool and decision process
has been discussed with a focus on market segmentation, based on user behavior analysis using k-mode
clustering algorithm and user geographical distributions. The proposed toolkit also incorporates
interactive visualizations and maps (Kamthania, et al., 2018). Data mining and big data analytic
techniques are playing an important role in many application fields, including the financial markets.
However, only few studies have focused on predicting daily stock market returns, and among these
studies, the data mining procedures utilized are either incomplete or inefficient. A paper presents a
comprehensive data mining process to forecast the daily direction of the S&P 500 Index ETF (SPY)
return based on 60 financial and economical features. The fuzzy c-means method (FCM) is initially used
to cluster the preprocessed data. A principal component analysis (PCA) is applied next to the entire data
set and each of seven clusters. The dimension of the entire cleaned data set is then reduced according to
the combining results from the entire data set and each cluster (Zhong, & Enke, 2017). In the current era
of big data, high volumes of a wide variety of valuable data of different veracity are generated or
collected at a high velocity. A rich source of these big data is the stock market. Since the inception of
the stock market, people have been trying to "beat" it for the purpose of monetary gain. A stock market
is an exchange where people trade shares of companies, also called stocks. The purpose of the exchange
is to make it easy to match buyers and sellers together to make transactions. The usual goal of someone
participating in the stock market it to generate profit through the buying and selling of stocks. The main
way people accomplish this is by buying a stock, waiting anywhere from seconds to decades, and then
hopefully selling for more than they bought it for. This is where the common term "buy low, sell high"
comes from. There are many factors (e.g., hurricanes) that may affect the stock price. In a paper, the
writers present a computational intelligent tool that applies fuzzy logic-based data analytics to predict
the effect of hurricanes on the stock market (Camara, et al., 2018). Marketing analytics is a diverse field,
with both academic researchers and practitioners coming from a range of backgrounds including
marketing, expert systems, statistics, and operations research. A paper provides an integrative review at
the boundary of these areas. The aim is to give researchers in the intelligent and expert systems
community the opportunity to gain a broad view of the marketing analytics area and provide a starting
point for future interdisciplinary collaboration (France, & Ghose, 2019). Prediction of stock market
trends is considered as an important task and is of great attention as predicting stock prices successfully
may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge
owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among
the investors to invest the money for making profits (Gandhmal, & Kumar, 2019). Stock market is
basically nonlinear in nature and the research on stock market is one of the most important issues in
recent years. People invest in stock market based on some prediction. For predict, the stock market
prices people search such methods and tools which will increase their profits, while minimize their risks.
Prediction plays a very important role in stock market business which is very complicated and
challenging process. Employing traditional methods like fundamental and technical analysis may not
ensure the reliability of the prediction. To make predictions regression analysis is used mostly. In the
paper in relation this topics, the authors survey of well-known efficient regression approach to predict
the stock market price from stock market data based. In future, the results of multiple regression
approach could be improve using more number of variables (Sharma, et al., 2017).
31
3. Data and Methodology
The data of this research are obtained from the paper (Nahaei, & Novin, & Khaligh, 2021) and the
methodology is in accordance with the method of the paper (Nahaei, et al., 2021). In this research, the
library and field methods have been used to collect information. Also, the tool used is the 7-point Likert
scale for evaluating factors and also interviews, and the fuzzy clustering method (FCM) has been used
for data analysis. In this study, the fuzzy clustering method by programming in software environment
MATLAB was implemented. The FCM algorithm has been proposed by Bezdak and has been widely
used for regional frequency analysis. In order to express the FCM fuzzy clustering method, a set of data
in form of is considered. The purpose of fuzzy clustering is to classify data into C
clusters in the form of a matrix in which is the degree of membership and belonging
k to the C cluster is modeled as follows: (Bezdek, et al., 1984)
In the above relations, i is the number of clusters and k is the number of data. On the other hand, it can
be shown that by minimizing the following objective function, the data in each cluster will be more
similar than the data in other clusters.
(4)
In the above relation, m is a number greater than one that controls the degree of membership, is the
data vector, and is the center of the i cluster, as well as is the Euclidean distance between
the data and the center of the clusters, which are often based on cluster centers. To minimize (4), (5) and
(6) must always be updated in different iterations.
(5)
(6)
In the above relations,
is the degree of membership of k is from the category of c in repetition (t +
1). The implementation of the proposed algorithm has the following steps:
1. Consider the value of t to be zero and create an initial code p (0).
2. In each iteration, the centers of the clusters were calculated using Equation (5) and a value for m was
selected.
3. Calculate
using (6) and update the initial code in the (t + 1) iteration.
Therefore, proper clustering of projects is a crucial decision for organizations, investors and
stakeholders, and due to the many influential factors and variables, it is not easy and requires a model
(1)
(2)
(3)
32
by which appropriate projects can be found for investment due to multiple goals and limitations. For
this purpose and consider marketing recycled products of waste management organization of Tabriz
Municipality by obtaining the opinion of experts regarding the review and prioritization of investment
and participatory projects in this article, FCM fuzzy clustering method was used.
Recycled products of important investment projects (10 projects) in the Waste Management
Organization of Tabriz Municipality through interviews and field research among experts as follows:
A- Energy extraction project from waste (electricity and heat): 28 product.
B- Project for organizing informal recyclers: 7 product.
C - Construction waste recycling project: 10 product.
D- Construction project of a specialized recycling town: 14 product.
E- Project to replace gasoline motorcycles with electric ones: 4 product.
F- Project of scrapping used cars: 32 product.
G- Recycled tire recycling project: 10 product.
H- Glass recycling project: 4 product.
I - Electronic waste recycling project: 48 product.
J - Plastic recycling project: 96 product.
4. Results and discussion
The purpose of clustering is to divide the data into a set of categories in which each category is more
similar and closer to each other than the data of other categories. In this study, for marketing of recycled
products, the fuzzy clustering method by programming in software environment MATLAB was
implemented. The survey of experts with the Likert scale (1-7) is as shown in Table 1 below. Due to the
heterogeneity of marketing methods and low diversity of recycled products, by analyzing the opinions
of experts, projects B, E and H were left out of the research.
Table 1. Expert Survey on Projects
J
I
G
F
D
C
A
Project /
Expert
7
2
3
2
2
4
4
1
6
2
7
7
4
7
7
2
7
2
6
7
7
3
5
3
7
4
5
7
5
4
6
4
7
5
4
7
6
2
7
5
5
6
3
6
5
4
6
6
2
6
7
4
4
7
7
7
5
3
4
5
6
6
4
8
33
Using MATLAB software, Inputs: number of data points (D): 8 and number of clusters (N): 3 and m: 2
and number of iterations: 1000 and stop threshold value: 0.00001, centers of clusters are shown in Table
2 below.
Table 2. Cluster centers (each row corresponds to a cluster)
Degree of membership of each project to each of the clusters (each row related to each cluster, each
column related to the projects) means the degree to which each project belongs to each cluster. Selecting
the highest membership level and place the project in that cluster, projects related to each cluster in the
specified table will have a higher priority due to the 1-7 Likert range of numbers close to 7. In fact,
cluster I has the highest priority and cluster П has the lower priority and cluster Ш has the lowest priority.
The projects were divided into three clusters, and membership rates, cluster centers, and other details
are listed in the table 3.
Table 3. Degree of membership of each project to each of the clusters
The assignment of projects to clusters in Table 3 is obtained based on the maximum membership rate.
The results are amazing. According to marketing methods and companies involved in selling and
exporting marketing products, recycled electronic products and auto parts are in a cluster, both of which
are marketed from one source of the organization. On the other hand, the recycling of municipal and
plastic products, which have the highest investment in the organization, are in the highest cluster. The
second cluster is justified by the most important element of the marketing mix, the product. All three
energy recovery products, construction waste and tire waste are products that the private sector plays a
key role in recycling. In other words, this cluster represents the outsourcing of municipal duties and its
oversight of the marketing of recycled products.
5. Concluding remarks
Tabriz Municipal Waste Management Organization is one of the important organizations in sustainable
urban revenue generation. This organization processes more than one hundred types of recycled products
with the investment of municipalities and private companies. One of the important problems of this
organization is the marketing of these products in the country and more importantly the ground for the
export of these products. Some recycled materials are used in many industrial areas of the country and
can pave the way for business opportunities. Unfortunately, in the organization, there is no scientific
and research-oriented approach to organizing recycled products and marketing these products. One of
the important tools of marketing management is paying attention to the marketing mix and the most
important element of the product. In this study, we focused on this element and recycled products from
the most important projects of Tabriz Municipality Waste Management Organization.
J
I
G
F
D
C
A
Project /
Cluster
2305.9
2.3254
4.2356
6.2568
6.2315
6.2534
4.2354
Cluster I
42035.
3.1125
5.0124
6325.9
5.1218
5.2478
4.2358
Cluster II
99924.
2.9514
5.2148
6.2145
4.2325
6.1452
5.4125
Cluster III
J
I
G
F
D
C
A
Project /
Cluster
0.7562
0.0142
0.2456
0.2251
0.4256
0.4541
0.2540
Cluster I
0.2936
0.1256
0.6324
0.4215
0.3215
0.6532
0.4562
Cluster II
0.0111
0.9852
0.1715
0.4253
0.1025
0.1247
0.2145
Cluster III
I
III
II
III
I
II
II
Preferred
34
In previous studies, the most important projects of this organization and investment priorities in these
projects on recycled products were identified. In this paper, by studying different methods to achieve a
single plan for the organization in order to introduce their products with an export-oriented approach,
the fuzzy clustering method was used. Through this method, both priority recycled products and related
clusters were graded in order to take advantage of the best product-based marketing practices. The
results and analysis of the proposed model show that the project of organizing informal recyclers, the
project of replacing gasoline motorcycles with electric, and the project of recycling glass did not have
products that could be offered to the market and were therefore removed from the analysis. On the other
hand, the plastic recycling project and the construction project of a specialized recycling town with
various recycled products that are intended for it, have a higher priority for export. The realization that
most of the town's products are somehow related to recycled plastic products ensures that both projects
are in the same cluster. Future research focusing on other marketing elements such as price and
distribution methods of recycled products can draw appropriate strategies for the Waste Management
Organization of Tabriz Municipality.
Conflicts of Interest
No potential conflict of interest was reported by the authors.
References
• Akcay, D., & Okkay, I. (2017). REAL TIME MARKETING IMPLEMENTATIONS: EXAMPLE OF
KADIKÖY MUNICIPALITY. Turkish journal of design, art and communication, 7(1), 99-109, doi:
10.7456/10701100/009.
• Alliedmarketresearch. (2021), Waste Management Market by Type (Municipal Waste, Industrial Waste
and Hazardous Waste) and Service (Collection and Disposable): Global Opportunity Analysis and
Industry Forecast, 2021–2030, from https://www.alliedmarketresearch.com/waste-management-market.
• Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation
of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications,
111, 11-34. https://doi.org/10.1016/j.eswa.2018.03.007
• Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers &
geosciences, 10(2-3), 191-203. https://doi.org/10.1016/0098-3004(84)90020-7.
• Camara, R. C., Cuzzocrea, A., Grasso, G. M., Leung, C. K., Powell, S. B., Souza, J., & Tang, B. (2018, July).
Fuzzy logic-based data analytics on predicting the effect of hurricanes on the stock market. In 2018 IEEE
International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE, doi: 10.1109/FUZZ-
IEEE.2018.8491523.
• Chiang, W. Y. (2018). Applying data mining for online CRM marketing strategy: An empirical case of
coffee shop industry in Taiwan. British Food Journal, doi: https://doi.org/10.1108/BFJ-02-2017-0075.
• D’Urso, P., Disegna, M., Massari, R., & Prayag, G. (2015). Bagged fuzzy clustering for fuzzy data: An
application to a tourism market. Knowledge-Based Systems, 73, 335-346.
https://doi.org/10.1016/j.knosys.2014.10.015.
• France, S. L., & Ghose, S. (2019). Marketing analytics: Methods, practice, implementation, and links to
other fields. Expert Systems with Applications, 119, 456-475. https://doi.org/10.1016/j.eswa.2018.11.002.
• Gabrielli, L., Giuffrida, S., & Trovato, M. R. (2017). Gaps and overlaps of urban housing sub-market: hard
clustering and fuzzy clustering approaches. In Appraisal: from theory to practice (pp. 203-219). Springer,
Cham, doi: 10.1007/978-3-319-49676-4_15.
35
• Gandhmal, D. P., & Kumar, K. (2019). Systematic analysis and review of stock market prediction
techniques. Computer Science Review, 34, 100190. https://doi.org/10.1016/j.cosrev.2019.08.001.
• He, H., & Harris, L. (2020). The impact of Covid-19 pandemic on corporate social responsibility and
marketing philosophy. Journal of business research, 116, 176-182. https://doi.org/10.1007/s43039-020-
00016-3.
• Hsu, T. H., Chu, K. M., & Chan, H. C. (2000, May). The fuzzy clustering on market segment. In Ninth IEEE
International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No. 00CH37063) (Vol. 2, pp. 621-626).
IEEE, doi: 10.1109/FUZZY.2000.839064.
• Kamthania, D., Pawa, A., & Madhavan, S. S. (2018). Market segmentation analysis and visualization using
K-mode clustering algorithm for e-commerce business. Journal of computing and information technology,
26(1), 57-68. https://doi.org/10.20532/cit.2018.1003863.
• Li, D. X., Dong, H., & Jin, X. (2017). Model for evaluating the enterprise marketing capability with picture
fuzzy information. Journal of Intelligent & Fuzzy Systems, 33(6), 3255-3263, doi: 10.3233/JIFS-161741.
• Lundmark, L. (2006). Mobility, migration and seasonal tourism employment: Evidence from Swedish
mountain municipalities. Scandinavian Journal of Hospitality and Tourism, 6(3), 197-213,
https://doi.org/10.1080/15022250600866282.
• Mahdiraji, H. A., Hafeez, K., Kord, H., & Kamardi, A. A. (2020). Analysing the voice of customers by a
hybrid fuzzy decision-making approach in a developing country's automotive market. Management
Decision, https://doi.org/10.1108/MD-12-2019-1732.
• Mahdiraji, H. A., Kazimieras Zavadskas, E., Kazeminia, A., & Abbasi Kamardi, A. (2019). Marketing
strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach. Economic research-
Ekonomska istraživanja, 32(1), 2882-2892, https://doi.org/10.1080/1331677X.2019.1658534.
• Nahaei, V. S., & Bahrami, M. (2021). Uncertainty analysis of business components in Iran with fuzzy
systems: By comparing hypermarkets and Net markets. International Journal of Innovation in
Management, Economics and Social Sciences, 1(1), 45-55, https://doi.org/10.52547/ijimes.1.1.45.
• Nahaei, V. S., Novin, M. H., & Khaligh, M. A. (2021). Fuzzy clustering of investment projects in Tabriz
Municipality Waste Management Organization with ecological approach. International Journal of
Innovation in Management, Economics and Social Sciences, 1(2), 28-42,
https://doi.org/10.52547/ijimes.1.2.28.
• Nahaei, V. S., Novin, M. H., & Khaligh, M. A. (2021). Review and prioritization of investment projects in
the Waste Management organization of Tabriz Municipality with a Rough Sets Theory approach.
International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 46-57,
https://doi.org/10.52547/ijimes.1.3.46.
• Pappas, I. O., & Woodside, A. G. (2021). Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines
for research practice in Information Systems and marketing. International Journal of Information
Management, 58, 102310, https://doi.org/10.1016/j.ijinfomgt.2021.102310.
• Parviznejad, P. S., & Akhavan, A. N. (2021). Impact of the Tourism Industry Scenarios in Urban
Economy:(Case Study Tabriz). International Journal of Innovation in Management, Economics and Social
Sciences, 1(1), 1-15, https://doi.org/10.52547/ijimes.1.1.1.
• Parviznejad, P. S., & Bahrami, M. (2021). Uncertainty analysis of tourism components in Tabriz.
International Journal of Innovation in Management, Economics and Social Sciences, 1(3), 1-14,
https://doi.org/10.52547/ijimes.1.3.1.
36
• Podapati, S., Lundberg, L., Skold, L., Rosander, O., & Sidorova, J. (2017, September). Fuzzy
recommendations in marketing campaigns. In European Conference on Advances in Databases and
Information Systems (pp. 246-256). Springer, Cham, doi: 10.1007/978-3-319-67162-8_24.
• Sengupta, D., & Agrahari, S. (Eds.). (2017). Modelling trends in solid and hazardous waste management.
Springer Singapore, doi: 10.1007/978-981-10-2410-8_2.
• Sharma, A., Bhuriya, D., & Singh, U. (2017, April). Survey of stock market prediction using machine
learning approach. In 2017 international conference of electronics, communication and aerospace
technology (ICECA) (Vol. 2, pp. 506-509). IEEE, doi: 10.1109/ICECA.2017.8212715.
• Sigindi, T. (2018). Consumers, Businesses, and Governments During an Economic Crisis: A Marketing
Perspective. In Managerial Strategies for Business Sustainability During Turbulent Times (pp. 208-222).
IGI Global, doi: 10.4018/978-1-5225-2716-9.ch011.
• Youssefi, H., Nahaei, V., & Nematian, J. (2011). A new method for modeling system dynamics by fuzzy
logic: Modeling of research and development in the national system of innovation. Journal of Mathematics
and Computer Science, 2(1), 88-99, http://dx.doi.org/10.22436/jmcs.002.01.10.
• Zhong, X., & Enke, D. (2017). A comprehensive cluster and classification mining procedure for daily stock
market return forecasting. Neurocomputing, 267, 152-168, https://doi.org/10.1016/j.neucom.2017.06.010.
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