Handbook of research on intelligent techniques and modeling applications in marketing analytics:
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| 245 | 1 | 0 | |a Handbook of research on intelligent techniques and modeling applications in marketing analytics |c Anil Kumar (BML Munjal University, India), Manoj Kumar Dash (ABV-Indian Institute of Information Technology and Management, India), Shrawan Kumar Trivedi (BML Munjal University, India), Tapan Kumar Panda (BML Munjal University, India) |
| 246 | 1 | 3 | |a Intelligent techniques and modeling applications in marketing analytics |
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Datensatz im Suchindex
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| adam_text | Titel: Handbook of research on intelligent techniques and modeling applications in marketing analytics
Autor: Kumar, Anil
Jahr: 2017
Detailed Table of Contents
Preface..................................................................................................................................................xx
Acknowledgment.............................................................................................................................xxvii
Section 1
Consumer Analytics: Fuzzy Applications
Chapter 1
A New Perspective on RFM Analysis.....................................................................................................1
Mohammad Hasan Aghdaie, Shomal University, Iran
Parham Fami Tafreshi, Shomal University, Iran
The aim of this chapter is proposing a novel integrated Fuzzy Group Multiple Attribute Decision Making
(FGMADM) and Fuzzy C-Means Clustering (FCM) as a DM tool for segmentation of customers (retailers),
based on an updated RFM model. For this purpose, the most important criteria to evaluate retailers from
the in depth literature survey and experts opinion are added to the traditional RFM model. In addition,
in order to make the model more efficient, FGMADM approach, in this paper, Fuzzy Group Step-wise
Weight Assessment Ratio Analysis (FGS WARA) is used to weight KPIs. Then, FCM is applied to segment
customers, based on their purchase behavior (RFM scores). A case study in one of the most famous Fast
Moving Consumer Goods (FMCG) companies in Iran illustrated the applicability of the proposed model.
Chapter 2
A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques......21
Ba§a.r Oztaysi, Istanbul Technical University, Turkey
Ugur Gokdere, Blesh Incorporated, Turkey
Esra Nur Simsek, Blesh Incorporated, Turkey
Ceren Salkin Oner, Istanbul Technical University, Turkey
Customer segmentation has been one of hottest topics of marketing efforts. The traditional sources of
data used for segmentation are demographics, monetary value of transactions, types of product/service
selected. Today, data gathered by location based services can also be used for customer segmentation. In
this chapter a real world case study is summarized and the initial segmentation results are presented. As the
application, data gathered from beacons sited in 4000 locations and Fuzzy c-means clustering algorithm
are used. The steps of the application are as follows: (1) Categorization of the shops, (2) Summarization
of the location data, (3) Applying fuzzy clustering technique, (4) Analyzing the results and profiling.
Results show that customers location data can provide a new perspective to customer segmentation.
Chapter 3
Fuzzy Clustering: An Analysis of Service Quality in the Mobile Phone Industry...............................40
Mashhour H. Baeshen, Cardiff University, UK
Malcolm J. Beynon, Cardiff University, UK
Kate L. Daunt, Cardiff University, UK
This chapter presents a study of the development of the clustering methodology to data analysis, with
particular attention to the analysis from a crisp environment to a fuzzy environment. An applied problem
concerning service quality (using SERYQUAL) of mobile phone users, and subsequent loyalty and
satisfaction forms the data set to demonstrate the clustering issue. Following details on both the crisp
k-means and fuzzy c-means clustering techniques, comparable results from their analysis are shown, on
a subset of data, to enable both graphical and statistical elucidation. Fuzzy c-means is then employed
on the full SERYQUAL dimensions, and the established results interpreted before tested on external
variables, namely the level of loyalty and satisfaction across the different clusters established.
Chapter 4
An Analysis of the Interactions among the Enablers of Information Communication Technology
in Humanitarian Supply Chain Management: A Fuzzy-Based Relationship Modelling Approach.....62
Gaurav Kabra, Indian Institute of Technology Roorkee, India
A Ramesh, Indian Institute of Technology Roorkee, India
The rise in the occurrence of disasters has hampered the development of many countries. Practitioners and
academicians are making continuous demands to enhance the utilization of information communication
technologies (ICTs) in humanitarian supply chain management (HSCM) in order to continue or enhance
the pace of economic growth and development of countries, as well as to reduce the impact of disaster
on society. Identifying and analysing key decision variables improving the utilization of ICT in HSCM
is essential in trying to improve overall performance. Therefore, to assist the organizations involved in
HSCM, this study explores eleven enablers to enhancing the utilization of ICTs, with a focus on the
mutual relationship among them using an integrated interpretive structural modeling (ISM) and fuzzy
cross-impact matrix multiplication applied to classification (F-MICMAC) analysis. This study seeks to
advance the understanding on enablers of ICTs in HSCM and to classify them, on the basis of driving
and dependence power.
Section 2
Computational Intelligence: Business Analytics
Chapter 5
Auto Associative Extreme Learning Machine Based Hybrids for Data Imputation.............................75
Chandan Gautam, Institute for Development and Research in Banking Technology, India
Vadlamani Ravi, Institute for Development and Research in Banking Technology, India
This chapter presents three novel hybrid techniques for data imputation viz., (1) Auto-associative Extreme
Learning Machine (AAELM) with Principal Component Analysis (PCA) (PCA-AAELM), (2) Gray
system theory (GST) + AAELM with PCA (Gray+PCA-AAELM), (3) AAELM with Evolving Clustering
Method (ECM) (ECM-AAELM). Our prime concern is to remove the randomness in AAELM caused
by the random weights with the help of ECM and PCA. This chapter also proposes local learning by
invoking ECM as a preprocessor for AAELM. The proposed methods are tested on several regression,
classification and bank datasets using 10 fold cross validation. The results, in terms of Mean Absolute
Percentage Error (MAPE,) are compared with that of K-Means+Multilayer perceptron (MLP) imputation,
K-Medoids+MLP, K-Means+GRNN, K-Medoids+GRNN, PSO_Covariance imputation, and ECM-
Imputation. It is concluded that the proposed methods achieved better imputation in most of the datasets
as evidenced by the Wilcoxon signed rank test.
Chapter 6
Multi-Criteria Decision Making in Marketing by Using Fuzzy Rough Set........................................100
Tapan Kumar Das, VIT University, India
Most of the marketing problems are complex and unstructured due to the business dynamics and
considerable uncertainty involved in the operating environments. Hence decision making in marketing
involves evaluation of several parameters and thus multi criteria decision makings are a good choice in
most of the decision-making tasks like supplier selection; market places selection; target marketing; etc.
This chapter begins with a brief introduction of the theory of rough set which is an intelligent technique
for handling uncertainty aspect in the data. However, the notions of fuzzy rough set and intuitionistic
fuzzy rough (IFR) sets are defined, and its properties are studied. Thereafter rough set on two universal
sets has been studied. In addition, intuitionistic fuzzy rough set on two universal sets has been extensively
studied. Furthermore, this chapter shows that intuitionistic fuzzy rough set can be successfully practiced
in decision making problems.
Chapter 7
Fuzzy Multi-Objective Association Rule Mining Using Evolutionary Computation.........................119
Ganghishetti Pradeep, IDRBT, India
Vadlamani Ravi, Institute for Development and Research in Banking Technology, India
In this chapter, we model association rule mining as a Fuzzy multi-objective global optimization problem
by considering several measures of strength such as support, confidence, coverage, comprehensibility,
leverage, interestingness, lift and conviction by utilizing various fuzzy aggregator operators. In this,
pdel, each measure has its own level of significance. Three fuzzy multi-objective association rule mining
techniques viz., Fuzzy Multi-objective Binary Particle Swarm Optimization based association rule
miner (FMO-BPSO), a hybridized Fuzzy Multi-objective Binary Firefly Optimization and Threshold
Accepting based association rule miner (FMO-BFFOTA), hybridized Fuzzy Multi-objective Binary
Particle Swarm Optimization and Threshold Accepting based association rule miner (FMO-BPSOTA)
have been proposed. These three algorithms have been tested on various datasets such as book, food,
bank, grocery, click stream and bakery datasets along with three fuzzy aggregate operators. From these
experiments, we can conclude that Fuzzy-And outperforms all the other operators.
Chapter 8
Improved Seating Plans for Movie Theatre to Improve Revenue: An Integrated Best Worst
Method with EMSR-B........................................................................................................................149
Kedar Pandurang Joshi, T. A. Pai Management Institute, India
Nikhil Lohiya, T. A. Pai Management Institute, India
Bollywood is not only one of the biggest film producers in India but also one of the largest centers of
film production in the world. Seat occupancy rate and pricing of each seat are important parameters that
determines the revenue of a cinema business. The objective of the chapter is to enable theater managers
to determine the prices at the time of booking according to the occupancy rate so that the revenue is
improved based on preferred demand for the respective seats. A multi criteria analysis is applied with
seat occupancy rate as dependent variable and other factors as independent variables like Show time,
Poster Size, Day of week and Timing of Release. Further, a predictive analysis can be carried out to
determine the occupancy rate for the upcoming movies. Based on the occupancy rate, the managers at
theater can adopt variable pricing concept to improve the revenue. This work shows an integrated method
to develop a seating plan based on occupancy rate to improve the revenue using EMSR-b heuristic with
an illustrated example for a theater.
Section 3
Consumer Analytics: Multi-Criteria (MCDM) Applications and Sentiment Analysis
Chapter 9
Applications of the Stochastic Multicriteria Acceptability Analysis Method for Consumer
Preference Study.................................................................................................................................160
Tadeusz Trzaskalik, University of Economics in Katowice, Poland
Piotr Namiecinski, University of Lodz, Poland
Andrzej Bajdak, University of Economics in Katowice, Poland
Slawomir Jarek, University of Economics in Katowice, Poland
Introducing a new product to the market is a complex, costly and time-consuming process which
requires research on consumer preferences. On the basis of information on the characteristics of the new
product and its competitors, as well as on the competitors and their market shares, the company has to
estimate future market shares and to determine the profile of potential consumers inclined to purchase
the new product. The purpose of our paper is to present a method of consumer preference research when
introducing a new product, using a multiple criteria method called Stochastic Multicriteria Acceptability
Analysis (SMAA). To apply this method, no information requiring tedious research is needed. SMAA
allows to obtain essential information on the potential market power of the new product already at an
early stage of its preparation. Furthermore, the flexibility of the SMAA method allows to easily expand
the scope of the analysis by including additional information and various techniques of the modeling of
the consumer selection process.
Chapter 10
Modeling Consumer Opinion Using R1DIT and Grey Relational Analysis.......................................185
Rohit Vishal Kumar, Xavier Institute of Social Service, India
Subhajit Bhattacharyya, Xavier Institute of Social Service, India
In order to understand consumers, researchers are forced to gather primary data on Likert scale. Such
data is usually considered as ordinal or at best interval scaled data. One key requirement in research is to
identify components which have high individual contribution to understanding the research problem. Hence
the concept of ranking of the components comes under consideration. Most of the ranking techniques are
based on simplistic mean ranks or overtly complicated methods. In this chapter the authors highlight two
techniques - Grey Relational Analysis (GRA) and RIDIT - for the purpose. In this chapter the authors
explain the techniques of the two methods and then try to show the simplicity and efficiency of GRA
and RIDIT algorithms in analyzing a commonly available dataset. The outcome of the GRA and RIDIT
analysis is also compared with the commonly used techniques and the authors would examine if GRA
and RIDIT does a better job at ranking data than the commonly used techniques.
Chapter 11
Sentiment Analysis as a Tool to Understand the Cultural Relationship between Consumer and
Brand...................................................................................................................................................202
Nicola Capolupo, University of Kent, UK
Gianpaolo Basile, University of Salerno, Italy
Giancarlo Scozzese, University of Perugia, Italy
One of the most relevant issues that companies, offices and marketing experts, sociologists and scholars
must address studying a new brand or product launch is without any doubt the impact - in terms of
feedback - on the consumer sentiment. The study of users opinions on a specific product or brand has
changed with the advent of Web 2.0, which has overcome the old surveys model leading consumers in
a too complex and not genuine area, reaching more sophisticated research or even better tracking their
opinions directly on the field , i.e. in the community where this exchange of views and information
happens naturally and not artificially. The analysis of consumers opinions on social media provides
enormous opportunities for the public and the private spheres. Concerning the last on the reputation
of a certain product/brand or firm is strongly influenced by the voices and negative opinions published
and shared by users on social networks. Indeed, companies need to adapt their behaviour monitoring
public opinion.
Chapter 12
Improving Customer Experience Using Sentiment Analysis in E-Commerce...................................216
Vinay Kumar Jain, Jaypee University of Engineering and Technology, India
Shishir Kumar, Jaypee University of Engineering and Technology, India
In today s world, millions of online users post their opinions on product features, services, quality,
benefits and other values of the products. These opinions or sentiment data generated via different
communication mediums often include vital data points that can be fruitful for businesses in understanding
customer experiences, products quality and services. The E-commerce companies considered social
media platform for new product launch, promotion of products and features or establishing a successful
business to customer relationship which produces great results. Analytics on this Social media data helps
in identifying the customers in the right demographic, psychographic and lifestyle group. This chapter
identifying important characteristics of customer reviews which help businesses houses to improve their
marketing strategies.
Section 4
Marketing Analytics: Digital Market Place
Chapter 13
Adoption of Online Marketing for Service SMEs with Multi-Criteria Decision-Making
Approach.............................................................................................................................................226
Lanndon Ocampo, University the Philippines Cebu, Philippines
Rosalin Merry Berdin Alarde, University of San Jose-Recoletos, Philippines
Dennis Anthony Kilongkilong, University of San Jose-Recoletos, Philippines
Antonio Esmero, University of San Jose-Recoletos, Philippines
This chapter attempts to fill in the gap of evaluating the viability of adopting online marketing for
small and medium enterprises (SMEs) in service industries. As SMEs are generally characterized by
shortage of resources, the use of online marketing strategies is apparently difficult. However, the current
landscape of competition among SMEs in a global market economy prompts the necessity of adopting
online marketing. With these, the decision-making process of SMEs in this area becomes complex and
the decisions must integrate complex and interrelating criteria and constructs in order to provide a more
holistic solution. Thus, this work adopts a multi-criteria decision-making (MCDM) method particularly
the analytic network process (ANP) in order to evaluate the practicability of using online marketing for
service SMEs. It becomes highly relevant as it provides significant insights to decision-makers in SMEs
regarding the use of online marketing strategy. The contribution of this chapter lies in the application of
MCDM in evaluating viability of online marketing in service SMEs.
Chapter 14
E-Retailing from Past to Future: Definitions, Analysis, Problems, and Perspectives.........................244
Zehra Kamisli Ozturk, Anadolu University, Turkey
Mehmet Alegoz, Anadolu University, Turkey
In this chapter, first, the definition, the advantages and disadvantages of e-retailing are given and the
related literature about e-retailing is briefly explained in order to give a background to unfamiliar readers.
Then, the qualitative and quantitative criteria which affect the e-retailer selection are determined and some
e-retailers are evaluated by using a multi-phase, integrated Multi Criteria Decision Making (MCDM)
approach. In first phase of proposed MCDM approach, the weight of each criterion is determined. In
second phase, a pre-evaluation is made and some of the e-retailers are eliminated. In last phase, the
remaining retailers are evaluated and the best one is determined. Finally, the study is concluded by
discussions, inferences and recommendations for future work.
Chapter 15
Fuzzy Time Series: An Application in E-Commerce.........................................................................258
Ali Karasan, Yddiz Technical University, Turkey
Ismail Sevim, Yildiz Technical University, Turkey
Melih ginar, Yildiz Technical University, Turkey
In this chapter, we are planning to make a comparison between conventional Time Series Models and
Fuzzy Time Series Models by an application in an e-commerce company. Future sales of furniture will
be predicted. The performance of different models and forecasting periods are going to be analyzed to
discuss advantages and disadvantages of each method. MAE is chosen as performance indicators of
each model and forecasting period combination. As a conclusion to this chapter, generic strategies for
prediction in an e-commerce company will be formulated in consideration of these indicators.
Chapter 16
Understand the Frequency of Application Usage by Smartphone Users: Door Is Open, but Closes
Quickly................................................................................................................................................291
Geetika Jain, UP Technical University, India
Sapna Rakesh, IMS Ghaziabad, India
Smartphone users download the apps after the enormous popularity in this mobile world and then
eventually delete those apps. There are various factors like frequency, relevance and space it consumes
in the phone, which decide a user s preference for an app. All the app provider companies are trying hard
to fit into right place, so that they can increase the engagement with the users. Companies are upgrading
their technology to make an app convenient and relevant based on user s requirement. This study is
trying to understand the frequency of application usage and the importance of various factors like time
to complete transaction, relevance, space it consumes, features, User Index, and ease of use for a user
which leads to purchase intention. The study has found that UX/UI is the most important factor followed
by other factors. The output of the study has the practical implication for online retailer.
Section 5
Advanced Modelling Applications: Business Analytics
Chapter 17
Car Safety: A Statistical Analysis for Marketing Management..........................................................305
Antonio Carrizo Moreira, University ofAveiro, Portugal
Monica Gouveia, University ofAveiro, Portugal
Pedro Macedo, University ofAveiro, Portugal
Car safety is an essential feature of marketing strategies for automobile companies. In this work, a
statistical analysis on crash tests is conducted based on data available from European New Car Assessment
Programme (Euro NCAP). The research work developed in this chapter presents a statistical analysis of
the information produced by Euro NCAP, using the SPSS and MATLAB software, and seeks to answer
the following research questions: - are there statistically significant differences on adult occupant safety
in the six years under study? - are there statistically significant differences among the best-selling car
classes regarding safety in frontal collisions? - are electric and hybrid automobiles less secure than their
traditional counterparts with respect to frontal collisions?
Chapter 18
Banking Credit Scoring Assessment Using Predictive K-Nearest Neighbour (PKNN) Classifier.....332
Saroj Kanta Jena, BML Munjal University, India
Anil Kumar, BML Munjal University, India
Maheshwar Dwivedy, BML Munjal University, India
Credit scoring models is a scientific methodology adopted by credit providers to assess the credit
worthiness of applicants. The primary objective of such models has been to predict the potentiality of
the loan applicant. A proper evaluation of the credit can help the service provider to determine whether
to grant or to reject credit. Therefore, the objective of the study is to predict banking credit scoring
assessment using Predictive K-Nearest Neighbour (PKNN) classifier. For the purpose of analysis two
different credit approval datasets: Australian credit and German credit have been used. The results from
the study show that the proposed model used for classification works better on credit dataset. Here, the
study firstly attempted to find the optimal K value of the neighbourhood so that the classifier is tuned
to forecast the credit worthiness and secondly, validated our proposed model on two credit approval
datasets by checking the performance of the proposed models on the basis of classification accuracy.
Chapter 19
Prediction of the Quality of Fresh Water in a Basin...........................................................................351
Sira M. Allende, Universidad de La Habana, Cuba
Daniel C. Chen, Smith and King College, UK
Carlos N. Bouza, Universidad de La Habana, Cuba
Agustin Santiago, Universidad Autonoma de Guerrero, Mexico
Jose Maclovio Sautto, Universidad Autonoma de Guerrero, Mexico
Derivatives play an important role in social and economic studies. They describe the behavior of
conditional expectations. Once a phenomena is characterized by parametric specifications, the conditional
expectation m(x) may be modeled by a regression function. Then, derivatives may be computed by fitting
the regression function. In applications, parametric estimators are commonly used, because of the un-
knowledge of other more effective methods. The validity of a regression fitting approach depends on the
knowledge of certain aspects related with the true functional form. In this paper, we develop a study on
the usage of soft computing methods for providing an alternative to the use of non-parametric regression.
We develop our modeling including neural networks and rough sets approaches. The studied problem is
the eutrophication due to the growth of the population of algae. Real life data is provided by a study on
a fresh water basin. They are used for developing a comparison of different approaches. A methodology
is recommended for implementing a monitoring system of the water quality.
Chapter 20
Operating Commodities Market by Automated Traders.....................................................................368
Fodil Laib, CEVITAL Group, Algeria
Mohammed Said Radjef, Unit Research LaMOS, Algeria
This is an introductory work to the field of automatizing futures markets, related to commodities, so far
operated by human traders. First, we build a mathematical framework for a futures market with many
producers and consumers represented by automated traders in the market platform. Then we suggest an
automatic trading strategy for the automatons. This strategy takes into account the forecasts of supply
and demand streams as well as the evolution of nominal price. Later, we recall a set of analytical criteria
used to measure the performance of a trading strategy. Next, we illustrate our approach by showing a
price pattern generated by the automatic strategy and calculate its performances. Finally, we exhibit a
heuristic based on simulation allowing to compute a quasi-optimal parameters matrix for this automatic
trading system.
Compilation of References...............................................................................................................381
About the Contributors....................................................................................................................418
Index 426
|
| any_adam_object | 1 |
| author2 | Kumar, Anil 1980- Dash, Manoj Kumar Trivedi, Shrawan Kumar 1985- Panda, Tapan Kumar |
| author2_role | edt edt edt edt |
| author2_variant | a k ak m k d mk mkd s k t sk skt t k p tk tkp |
| author_GND | (DE-588)1073611299 (DE-588)1120711606 |
| author_facet | Kumar, Anil 1980- Dash, Manoj Kumar Trivedi, Shrawan Kumar 1985- Panda, Tapan Kumar |
| building | Verbundindex |
| bvnumber | BV044219362 |
| collection | ZDB-98-IGB ZDB-1-IGE |
| ctrlnum | (OCoLC)961374559 (DE-599)BVBBV044219362 |
| format | Electronic eBook |
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| id | DE-604.BV044219362 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:57:07Z |
| institution | BVB |
| isbn | 9781522509981 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029625372 |
| oclc_num | 961374559 |
| open_access_boolean | |
| owner | DE-1049 DE-706 DE-91 DE-BY-TUM DE-573 DE-1050 DE-20 DE-898 DE-BY-UBR DE-83 |
| owner_facet | DE-1049 DE-706 DE-91 DE-BY-TUM DE-573 DE-1050 DE-20 DE-898 DE-BY-UBR DE-83 |
| physical | 1 Online-Ressource (xxvii, 427 Seiten) |
| psigel | ZDB-98-IGB ZDB-1-IGE ZDB-98-IGB FHD01_IGB_Kauf ZDB-98-IGB TUB_EBS_IGB |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | IGI Global, Disseminator of Knowledge |
| record_format | marc |
| series2 | Advances in business information systems and analytics (ABISA) book series |
| spellingShingle | Handbook of research on intelligent techniques and modeling applications in marketing analytics |
| title | Handbook of research on intelligent techniques and modeling applications in marketing analytics |
| title_alt | Intelligent techniques and modeling applications in marketing analytics |
| title_auth | Handbook of research on intelligent techniques and modeling applications in marketing analytics |
| title_exact_search | Handbook of research on intelligent techniques and modeling applications in marketing analytics |
| title_full | Handbook of research on intelligent techniques and modeling applications in marketing analytics Anil Kumar (BML Munjal University, India), Manoj Kumar Dash (ABV-Indian Institute of Information Technology and Management, India), Shrawan Kumar Trivedi (BML Munjal University, India), Tapan Kumar Panda (BML Munjal University, India) |
| title_fullStr | Handbook of research on intelligent techniques and modeling applications in marketing analytics Anil Kumar (BML Munjal University, India), Manoj Kumar Dash (ABV-Indian Institute of Information Technology and Management, India), Shrawan Kumar Trivedi (BML Munjal University, India), Tapan Kumar Panda (BML Munjal University, India) |
| title_full_unstemmed | Handbook of research on intelligent techniques and modeling applications in marketing analytics Anil Kumar (BML Munjal University, India), Manoj Kumar Dash (ABV-Indian Institute of Information Technology and Management, India), Shrawan Kumar Trivedi (BML Munjal University, India), Tapan Kumar Panda (BML Munjal University, India) |
| title_short | Handbook of research on intelligent techniques and modeling applications in marketing analytics |
| title_sort | handbook of research on intelligent techniques and modeling applications in marketing analytics |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-0997-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029625372&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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