Clustering and Profiling Consumer Buying Behavior

2021 ◽  
Vol 11 (1) ◽  
pp. 50-66
Author(s):  
Lambros Nikolaos Tsourgiannis ◽  
Stavros Ioannis Valsamidis
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Nonparametric Tests ◽  
Demographic Characteristics ◽  
Behavior Patterns ◽  
Purchasing Behavior ◽  
Buying Behavior ◽  
Product Features ◽  
Main Factors ◽  
Shopping Basket

This paper aims to identify the factors that affect consumers' buying behavior towards goods of consumers' shopping basket to classify them into groups according to their similar buying behavior patterns and to profile each group of consumers. A primary survey conducted to 242 consumers in Greece. Principal component analysis (PCA) conducted to identify the main factors that affect consumers purchasing behavior. Cluster analysis performed to classify consumers into groups with similar purchasing behavior whilst discriminant analysis conducted to check cluster predictability. Nonparametric tests are performed to profile each group of consumers according to their demographic characteristics and other factors. PCA identified six main factors: (1) price, (2) entertainment during shopping, (3) advertisement, (4) public relationships, (5) product features, (6) promotion activities. Cluster analysis classified consumers into three groups: (1) advertisement-orientated consumers, (2) promotion-orientated consumers, and (3) entertainment-orientated consumers.

scholarly journals COVID-19 Pandemic and Adaptive Shopping Patterns: An Insight from Indonesian Consumers

2021 ◽  
pp. 097215092110135
Author(s):  
Arif Hartono ◽  
Asma'i Ishak ◽  
Agus Abdurrahman ◽  
Budi Astuti ◽  
Endy Gunanto Marsasi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Demographic Characteristics ◽  
Depth Information ◽  
Factor Scores ◽  
Consumer Spending ◽  
Consumer Responses ◽  
Buying Behaviour ◽  
Manova Analysis

Although existing studies on consumers typology are extensively conducted, insights on consumers typology in adapting their shopping attitude and behaviour during the COVID-19 pandemic remain unexplored. Current studies on consumer responses to the COVID-19 pandemic tend to focus on the following themes: panic buying behaviour, consumer spending and consumer consumption. This study explores a typology of adaptive shopping patterns in response to the COVID-19 pandemic. The study involved a survey of 465 Indonesian consumers. Principal component analysis is used to identify the variables related to adaptive shopping patterns. Cluster analysis of the factor scores obtained on the adaptive shopping attitude and behaviour revealed the typology of Indonesian shoppers’ adaptive patterns. Multivariate Analysis of Variance (MANOVA) analysis is used to profile the identified clusters based on attitude, behaviour and demographic characteristics. Results revealed five adaptive shopping patterns with substantial differences among them. This study provides in-depth information about the profile of Indonesian shoppers’ adaptive patterns that would help retailers in understanding consumers and choosing their target group. The major contribution of this study is providing segmentation on shopping adaptive patterns in the context of the COVID-19 pandemic which presents interesting differences compared with previous studies. This study reveals new insights on shoppers’ adaptive attitude and behaviour as consumers coped with the pandemic.

Analyzing Consumer Impulse Purchasing Behaviour Using Observational Data

2018 ◽  
Vol 3 (2) ◽  
pp. 16-28 ◽  
Author(s):  
Yuliia Kyrdoda ◽  
George Baltas ◽  
A.Malek Hammami
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression Model ◽  
Explanatory Power ◽  
Age Groups ◽  
Principal Component ◽  
Demographic Characteristics ◽  
Marketing Strategies ◽  
Purchasing Behavior ◽  
Purchasing Behaviour ◽  
Categorical Principal Component Analysis

This article identifies consumers' impulse purchasing behavior in supermarkets. The study includes an interpretation of the impulse decision relationship with the final purchase and an analysis of the distribution of impulse purchasers' demographic characteristics (age and shoppers' company). SPSS was used to analyze the observed data at a national retail supermarket chain. The logistic regression model was developed in order to identify the explanatory power of the variables. Categorical principal component analysis was employed to analyze the distribution of the variables. Empirical findings indicated that “impulsive decision” has a stronger intensity on “purchase” than “gender” does. Impulsive customers are split into three age groups and two company categories. These results could be used to design marketing strategies in order to increase sales. However, a few limitations occurred during the study such as: observation timing, unicity of location and observers' subjectivity.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Quality evaluation of tugaksheeree samples by ATR-FTIR spectroscopy using multicomponent analysis

2020 ◽  
Vol 10 ◽  
Author(s):  
Nikunj D. Patel ◽  
Niranjan S. Kanaki
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Quality Evaluation ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Attenuated Total Reflectance ◽  
Chemometric Methods ◽  
Total Reflectance ◽  
Fingerprint Region ◽  
Ftir Technique

Background: Numerous Ayurvedic formulations contains tugaksheeree as key ingredient. Tugaksheereeis the starch gained from the rhizomes of two plants, Curcuma angustifoliaRoxb. (Zingiberaceae) and Marantaarundinacea (MA) Linn. (Marantaceae). Objective: The primary concerns in quality assessment of Tugaksheeree occur due to adulteration or substitution. Method: In current study, Fourier transform infrared (FTIR) technique with attenuated total reflectance (ATR) facility was used to evaluate tugaksheeree samples. Total 10 different samples were studied and transmittance mode was kept to record the spectra devoid of pellets of KBR. Further treatment was given with multi component tools by considering fingerprint region of the spectra. Multivariate analysis was performed by various chemometric methods. Result: Multi component methods like Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)were used to discriminate the tugaksheeree samples using Minitab software. Conclusion: This method can be used as a tool to differentiate samples of tugaksheeree from its adulterants and substitutes.

Impact of the divorce experience on the marital and family mindsets

2018 ◽  
pp. 88-96
Author(s):  
Елена Лактюхина ◽  
Elena Laktyukhina ◽  
Георгий Антонов ◽  
Georgy Antonov
Keyword(s):  
Comparative Analysis ◽  
Demographic Characteristics ◽  
Representative Sampling ◽  
First Marriage ◽  
Active Population ◽  
Volgograd Region ◽  
The Social ◽  
Marriage Stability ◽  
Main Factors ◽  
The Individual

The article presents a comparative analysis of marital and family mindsets of two categories of the demographically active population of modern Russia: (1) individuals that have no experience of a divorce and (2) those who have already experienced one or more official termination of a marriage. The empirical base of the analysis is the data of the author’s questionnaire survey conducted by representative sampling in Volgograd and Volgograd Region in 2015–2016. The analysis was made on the following basic empiric indicators: optimal (from the viewpoint of the respondents) age for the first marriage, frequency of mentioning marital and family statuses as the respondents describe their own social and demographic “portrait”, legitimate causes of a divorce and a number of others. It is found that, in the case of sufficiently strong traditional marital and family mindsets, perception of marital norms is adjusted, if an “abnormal” event (such as a divorce) occurs in the individual’s life course. At the same time, perception of the marriage stability is less variable and does not depend on the social and demographic characteristics of the respondents, including the presence/absence of a marriage termination experience. The “strongest” factor that affects the change of the marital and family mindsets is age. With age (and, consequently, experience accumulation), importance of the majority of main factors capable of preventing the individual from a divorce decreases and, therefore, the risk of such event increases.

scholarly journals Chemical Fingerprinting of Cryptic Species and Genetic Lineages of Aneura pinguis (L.) Dumort. (Marchantiophyta, Metzgeriidae)

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1180
Author(s):  
Rafał Wawrzyniak ◽  
Wiesław Wasiak ◽  
Beata Jasiewicz ◽  
Alina Bączkiewicz ◽  
Katarzyna Buczkowska
Keyword(s):  
Cluster Analysis ◽  
Cryptic Species ◽  
Plant Material ◽  
Chemical Constituents ◽  
Principal Component ◽  
Molecular Data ◽  
Rapid Identification ◽  
Multivariate Statistical ◽  
Genetic Lineages ◽  
Marker Compounds

Aneura pinguis (L.) Dumort. is a representative of the simple thalloid liverworts, one of the three main types of liverwort gametophytes. According to classical taxonomy, A. pinguis represents one morphologically variable species; however, genetic data reveal that this species is a complex consisting of 10 cryptic species (named by letters from A to J), of which four are further subdivided into two or three evolutionary lineages. The objective of this work was to develop an efficient method for the characterisation of plant material using marker compounds. The volatile chemical constituents of cryptic species within the liverwort A. pinguis were analysed by GC-MS. The compounds were isolated from plant material using the HS-SPME technique. Of the 66 compounds examined, 40 were identified. Of these 40 compounds, nine were selected for use as marker compounds of individual cryptic species of A. pinguis. A guide was then developed that clarified how these markers could be used for the rapid identification of the genetic lineages of A. pinguis. Multivariate statistical analyses (principal component and cluster analysis) revealed that the chemical compounds in A. pinguis made it possible to distinguish individual cryptic species (including genetic lineages), with the exception of cryptic species G and H. The classification of samples based on the volatile compounds by cluster analysis reflected phylogenetic relationships between cryptic species and genetic lineages of A. pinguis revealed based on molecular data.

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