Customer Purchase Behavior Research Articles

Visual Merchandising And Impulsive Purchase Tendency: An Empirical Study Of Young Consumers In Apparel Sector

PURPOSE – This study intends to investigate the impact of four elements of visual merchandising namely window display, mannequin display as well as floor merchandising and promotional signage on impulse buying behaviour of customers in apparel sector. METHODOLOGY- The respondents of the given study constitute 200 customers from Varanasi. The responses were collected online via Google forms since offline collection was not suitable due to Covid 19 norms. FINDINGS- The study revealed that all four elements of visual merchandising exert a significant influence on the impulse buying behaviour of customers in apparel sector. Promotional signage has the highest impact followed by window display, floor merchandising and mannequin display. VALUE- Visual merchandising influence closely impulse buying. And given the importance of impulse purchase in retailers’ sales revenue, it becomes relevant to understand the impact of visual merchandising on impulse buying tendency.

A Study of Buyer Motivations at the Bottom of Pyramid in Urban India

The Indian bottom of pyramid (BoP) segment contributes around 85% of the total national household market. This study attempts to ascertain the purchase behaviour of customers at the Indian urban BoP. It endeavours to appreciate the viewpoint of the urban BoP consumers in the purchase process with reference to their purchase basket comprising of products mainly across categories such as grocery, perishables and basic consumer durables. The study starts with qualitative grounded theory followed by quantitative survey-based approach. It presents and validates emergent themes to give insights about purchase behaviour of consumers at urban BoP. The empirical findings of the study discovered five consumer motivations through factor analysis. The subsequent result of the cluster analysis showed that the urban BOP market is heterogeneous. Since the size of cluster is substantial, companies must make marketing efforts to target them on a priority basis. The study proposes a conceptual model of consumer motivation supported by the self-determination Theory for the urban BoP market in India.

IDENTIFYING CUSTOMER BUYING PATTERNS USING MARKET BASKET ANALYSIS

Market Basket Analysis (MBA) is an approach that finds the strength of association between pairs of products that customers buy and can determine patterns of co-occurrence. The main aim of MBA is to determine customer buying behavior and predict next purchase. It can help companies to increase cross-selling.To generate association rules, the Apriori algorithm employs frequently purchased item-sets. It is based on the idea that a frequently purchased item’s subset is also a frequently purchased item. If the support value of a frequently purchased item-set exceeds a minimum threshold, the item-set is chosen. This paper observes the advantages of implementing MBA, algorithms that applies in this technique and ways to identify customer buying patterns.

Facebook Marketing and Its Influence on Consumer Purchase Behaviour in the Context of Bangladesh

Facebook is a widely used social media platform where people usually spend a lot of time. Presently, it is considered a great way to outspread information globally through its artificial intelligence-based targeted control. As a result, Facebook also plays a vital role in the expansion of businesses due to its huge number of audiences around the universe. Nowadays to reach these customers very quickly and easily, businessmen around the world are utilizing Facebook as one of the best tools for marketing purposes. In Bangladesh, almost every person who possesses a smartphone or has internet accessibility uses Facebook as their primary social networking website. This frequent use of Facebook should have an influence on digital marketing. In this paper, we have tried to evaluate the influence of Facebook Marketing on customers’ purchase behavior in the context of Bangladesh through an online survey and some face-to-face interviews. Secondary data are also collected from existing research, journals papers, online reports, and websites. After the analyses, it can be stated that Facebook Marketing, having a list of positive customer feedback, has a great influence on customer purchase behavior in Bangladesh. From the positive reviews of existing customers, it can also be said that the influence rate of new customers is very high as well. Keywords: Facebook Marketing, Customers Buying Behaviour, Influence of Facebook Marketing, Digital Marketing DOI: 10.7176/EJBM/13-21-03 Publication date: November 30 th 2021

BASKET MARKET ANALYSIS USING R-BASED APRIORI ALGORITHM TO FIND INFORMATION FROM SALES DATA

Market Basket Analysis is a data mining technique that is used to determine which products a customer will buy simultaneously by analyzing a list of customer transactions. By knowing these products, an e-commerce system can create or develop a customer profile system and can determine its own customer catalog layout. This journal discusses data mining techniques, with association rules that can help check customer buying behavior and increase sales. The result can provide reference prices for cross selling, designing promotions and placing merchandise in stores increasing sales

Discovering Customer Purchase Patterns in Product Communities: An Empirical Study on Co-Purchase Behavior in an Online Marketplace

Marketplace platforms gather and store data on each activity of their users to analyze their customer purchase behavior helping to improve marketing activities such as product placement, cross-selling, or customer retention. Market basket analysis (MBA) has remained a valuable data mining technique for decades for marketers and researchers. It discovers the relationship between two products that are frequently purchased together using association rules. One of the issues with this method is its strict focus on binary relationships, which prevents it from examining the product relationships from a broader perspective. The researchers presented several methods to address this issue by building a network of products (co-purchase networks) and analyzing them with network analysis techniques for purposes such as product recommendation and customer segmentation. This research aims at segmenting products based on customers’ purchase patterns. We discover the patterns using the Stochastic Block Modeling (SBM) community detection technique. This statistically principled method groups the products into communities based on their connection patterns. Examining the discovered communities, we segment the products and label them according to their roles in the network by calculating the network characteristics. The SBM results showed that the network exhibits a community structure having a total of 309 product communities, 17 of which have high betweenness values indicating that the member products play a bridge role in the network. Additionally, the algorithm discovers communities enclosing products with high eigenvector centralities signaling that they are a focal point in the network topology. In terms of business implications, segmenting products according to their role in the system helps managers with their marketing efforts for cross-selling, product placement, and product recommendation.

Customer Choice Models vs. Machine Learning: Finding Optimal Product Displays on Alibaba

We compare the performance of two approaches for finding the optimal set of products to display to customers landing on Alibaba's two online marketplaces, Tmall and Taobao. Both approaches were placed online simultaneously and tested on real customers for one week. The first approach we test is Alibaba's current practice. This procedure embeds thousands of product and customer features within a sophisticated machine-learning algorithm that is used to estimate the purchase probabilities of each product for the customer at hand. Our second approach uses a featurized multinomial logit (MNL) model to predict purchase probabilities for each arriving customer. In this way, we use less sophisticated machinery to estimate purchase probabilities, but we employ a model that was built to capture customer purchasing behavior and, more specifically, substitution patterns. Our experiments show that despite the lower prediction power of our MNL-based approach, it generates significantly higher revenue per visit compared with the current machine-learning algorithm with the same set of features.

A Novel Approach of Product Recommendation Using Utility-Based Association Rules

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.

How to understand “salable fake goods” and “unsalable quality goods”? An insight from the view of quality responsibility and consumers’ brand loyalty

A body of theoretical and practical research suggests technology innovation is the driving force for enterprise survival and development and that maintaining business ethics is an important guarantee in sustaining these enterprises developments. Fulfilling the “hard responsibility” of product quality based on technical standards and the “soft responsibility” of product quality based on business ethics is an important principle stimulating the benign interaction and continuous progress of technological innovation and business ethics, and realizing a balanced development of enterprises and society, which could promote positive brand and enterprise image to both stakeholders and consumers, helping consumers choose high-quality products in the market and social competition. However, the strange marketing phenomena of “salable fake goods” and “unsalable quality goods” shows that no systematic marketing strategies have been identified to date, that enterprises could adopt to effectively promote the recognition of consumers and turn them into a loyal customer group and have a stable market share. Based on customer buying behavior theory, cognitive consistency theory, signal theory, and social identity theory, this study explored the influencing mechanism of quality responsibility on brand loyalty of consumers and the mediating role of corporate reputation and brand identification, using 224 valid questionnaires from 16 communities (residents) in Shanxi and Henan provinces of China tested by SPSS 20.0 and AMOS 25.0. Results show that quality responsibility has a significant positive influence on brand behavior loyalty and brand attitude loyalty, and both corporate emotional reputation and brand identification play mediating roles in the relationship between quality responsibility and brand behavior loyalty, and quality responsibility and brand attitude loyalty. Additionally, corporate cognitive reputation only plays a mediating role in the relationship between quality responsibility and brand behavior loyalty. Obtained conclusions in the study provide references for enterprise managers and government decision-makers in exploring management strategies of expediting future technological development and next-generation upgrades & transformations, improving quality and technology benchmarks, optimizing product quality responsibility system and promoting sustainable social development.

Customers’ Awareness and Attitude in Purchasing Herbal Products in Kuala Terengganu

The growth of the herbal industry in Malaysia has sparked the economy and it is identified as one of the new incomes to the country. This industry is expected to bring more job opportunities to the citizen in Malaysia. Demand for herbal products also has been increasing for the past few years. This study aims to contribute the general understanding of customer awareness and attitude in purchasing herbal products and show the relationship between demographic profile and customer awareness and attitude in purchasing herbal products. This study was conducted in Kuala Terengganu and the online Google form was used as study instrument for data collection. There were around 150 respondents that answered the questionnaire. The questionnaire was divided into 2 sections. The first section which is section A, discusses the socio-demographic profile of the respondents. The second part which is section B were in Likert-scale design to obtain the customers’ awareness and attitude in purchasing herbal products. Data that were collected in this study were analysed using a statistical tool which is Statistical Package for Social Science (SPSS). The Descriptive analysis was used to provide simple summaries of the respondents while the Cross-tab with Chi-Square analysis was chosen to be used to test the statistical independence of the variables. From this study, the variable Age is the only demographic factor that shows a significant relationship towards the customers’ awareness and attitude in purchasing herbal products. Other demographic factors such as Gender, Education, Occupation, and Income shows independent and no relationship towards the customers’ awareness and attitude in purchasing herbal products. This study could be beneficial to researchers and marketers in understanding customer buying behaviour.

Factorizing Historical User Actions for Next-Day Purchase Prediction

It is common practice for many large e-commerce operators to analyze daily logged transaction data to predict customer purchase behavior, which may potentially lead to more effective recommendations and increased sales. Traditional recommendation techniques based on collaborative filtering, although having gained success in video and music recommendation, are not sufficient to fully leverage the diverse information contained in the implicit user behavior on e-commerce platforms. In this article, we analyze user action records in the Alibaba Mobile Recommendation dataset from the Alibaba Tianchi Data Lab, as well as the Retailrocket recommender system dataset from the Retail Rocket website. To estimate the probability that a user will purchase a certain item tomorrow, we propose a new model called Time-decayed Multifaceted Factorizing Personalized Markov Chains (Time-decayed Multifaceted-FPMC), taking into account multiple types of user historical actions not only limited to past purchases but also including various behaviors such as clicks, collects and add-to-carts. Our model also considers the time-decay effect of the influence of past actions. To learn the parameters in the proposed model, we further propose a unified framework named Bayesian Sparse Factorization Machines. It generalizes the theory of traditional Factorization Machines to a more flexible learning structure and trains the Time-decayed Multifaceted-FPMC with the Markov Chain Monte Carlo method. Extensive evaluations based on multiple real-world datasets demonstrate that our proposed approaches significantly outperform various existing purchase recommendation algorithms.

A Latent Topic Analysis and Visualization Framework for Category-Level Target Promotion in the Supermarket

Deciding when and which products to recommend to whom is always an essential issue for retailers. In this study, we propose a mixed framework with two components to capture customer buying behavior and its changes over time and visualize these results to better help retailers choose and target products strategically for marketing. In this framework, a topic model is first used to extract customer’s purchase behavior instead of association rules or K-means as mainly used in market field. To automatically choose the optimal number of topics, we implement an approach proposed by Koltcov et al. on point-of-sale (POS) data in the supermarket. Meanwhile, to grasp the change of topics over time, we divided monthly POS data in half and applied the topic model with Renyi entropy separately. The results suggest that splitting data might be a better way to understand customer behavior. Second, we consider how to develop an effective way to visualize the results of the topic model, which is essential, because in a supermarket context, simply knowing which product categories are included under which topics is not enough to support how a supermarket promotes their products. To address this, we design a three-layer visualization approach to better interpret the topic model results and to help retailers design target promotion strategies. The design of visualization was overlooked by studies related to the use of topic models on supermarket data. Finally, to demonstrate the usefulness of our proposed framework, we conduct a simple scenario-based analysis between our framework and other models, such as Latent Dirichlet Allocation (LDA) and the Dynamic Topic Model (DTM). The results show that for most periods, our proposed framework outperforms LDA and DTM.

I read, therefore I buy? Analyzing the impact of flyer distribution and readership on purchase behaviour

Store flyers are one of the most important media used to feature retail and manufacturer promotions. Yet the body of research on their effectiveness on customer behaviour demonstrates significant gaps. We addressed this by conducting a field experiment with over 9000 members of a retailer's loyalty programme to identify what drives customers to read flyers and understand how flyer distribution and readership impact upon customer purchase behaviour. The results show that flyer distribution and flyer readership do not seem to change purchase behaviour. However, they confirm that flyer proneness, loyalty to the retailer, subscription to its newsletter, and household size all positively contribute to the likelihood of flyer readership. Our findings thus call upon retailers to significantly revise their flyer distribution strategy and reconsider the role of store flyers within their communication and marketing strategies.

Click & Collect Retailing: A Study on Its Influence on the Purchase Intention of Customers

The retail sector, over the years, has evolved dramatically to provide better service to its customers. With the superior convenience of online shopping and tangible experience of in-store shopping, retail industries are looking forward to integrating both modes, thus embracing omni channel to provide better service to their customers. The prime objective of the research is to investigate the level of influence that using the Click & Collect online shopping mode can have on customer purchase intention and to ascertain the effects that online and offline shopping attributes have on this intention. The study emphasizes the usefulness of integrating both the shopping modes, thus embracing omni channel in the retail sector to provide a better shopping experience to the customers. The primary data were collected from 356 respondents. Secondary data were collected by reviewing articles, research papers, extant studies and newspaper articles. In the analysis, the buying behaviour through an e-commerce platform and customers’ purchase intentions are taken as the dependent variable. Product risk, online trust, website quality, offline experience and perceived usefulness are identified as the independent variables. The data thus collected were processed for regression tests using IBM SPSS 25 software to analyse the results. The Stimulus-Organism-Response model was deployed as the proposed model for the research. The results obtained from the research will allow retailers to understand the customer's buying behaviour towards the new Click & Collect system better by identifying the key variables that influence their purchase intention.

THE IMPACT OF CUSTOMER BUYING BEHAVIOR TOWARDS SMALL AND MEDIUM ENTERPRISES (SMEs) PERCEPTION DURING PANDEMIC (COVID-19) IN JOHOR

The objective of this study is to identify the conceptual framework of customer buying behavior and Small Medium Enterprises perception during Pandemic Covid-19 in Johor. The variables consist of Customer Buying Behavior (panic buying, herd mentality, role of the media in influencing consumer behavior) as an independent variable and Small and Medium Enterprises (SMEs) perception during Pandemic Covid-19 as the dependent variable. This study adopted a quantitative approach using IBM SPSS version 26. The survey was conducted on Johor SMEs firms. The conceptual framework has been developed in this study. The expected findings show there is a positive relationship between herd mentality, panic buying, and the role of the media have significant relationship toward SMEs enterprises during Pandemic Covid-19 in Johor.

The effects of cruise attributes on impulse buying behavior: the mediating role of impulsive buying tendency

PurposeThis study aims to examine the effects of cruise attributes on impulse buying of service customers through impulsive buying tendency. Specifically, this study investigates whether service attributes (variety of selection and price) influence the impulsive buying behavior of customers by stimulating their impulsive buying tendency.Design/methodology/approachPartial least squares-structural equation modeling was used to test the hypothesis using a sample of 143 cruise customers. Mediating analysis was conducted to examine whether customer personal traits (impulsivity) mediated the relationship between service attributes and impulsive buying behavior.FindingsThe findings showed that price attributions had a significant positive direct impact on impulsive buying tendency and behavior, while a variety of selections had a significant direct effect on impulse buying behavior. The results revealed a significant mediating role of impulsive buying tendency in the relationship between price attributions and impulse buying.Originality/valueAlthough studies on the decision-making style of service customers have been widely discussed, a limited number of studies has examined customers’ impulsive buying behavior in the service sector. Considering the importance of impulsive purchasing as a valuable marketing tool, the results of this study can help service providers and researchers enhance their knowledge of the mechanism of impulse buying behavior.

The Influence of Electronic Personalization on Performance of Three to Five-Star Hotels in Kenya

Electronic Personalization plays a vital role in the growth and development of the hotel industry, whether in a developed or a developing economy. The hotel industry in Kenya has grown enormously since the year 2000, and despite this growth, the hotel industry in Kenya has experienced slow growth compared to other countries. Electronic Personalization has been identified as an electronic customer relationship management strategy that has had a significant impact on hotel performance. The aim of this study is therefore to seek the influence of Electronic Personalization on the performance of Three to Five-Star Hotels in Kenya. The study adopted a descriptive design for the research. In addition, a quantitative and qualitative research approach was utilized. The target population for the study was one hundred and twelve Three to Five-Star Hotels in Kenya. The study examined one hundred and twelve Three to Five-Star Hotels because they represent the major market share of Kenya’s hotel industry. A survey was conducted on the target population. Simple Regression was utilized to draw inference from the data collected. Descriptive findings revealed a mean of 4.524 with a standard deviation of 0.5654 for electronic personalization. In addition, findings revealed an R square value of 0.366 between Electronic Personalization and performance. This suggests that Electronic Personalization influences the performance of Three to Five-Star Hotels. Consequently, hotel managers should develop and implement effective Electronic Personalization strategies in their hotel’s operations. For instance, they can electronically personalize their hotel information by assigning the right marketing effort to the right customer, hence increasing customer buying behaviour which shall in turn increase performance.

Predicting Customer Behavior Using Prophet Algorithm In A Real Time Series Dataset

Customer Relationship Management is important in analyzing business performance. Predicting customer buying behavior enables the business to better address their customers and enhance service level and overall profit. This paper focuses on proposing a model that predicts future period sales in a real retail department store with low prediction error rate, and it also discovers the main sales trends over time. A model based on the Prophet algorithm is implemented and modified according to different parameters in order to lower the prediction error. The modifications consisted of the insertion of a new seasonality pattern, changes in the Fourier order of the existing and the new seasonality pattern, inclusion of the holiday data, and parameterizing its impact. The performance of the standard and modified model is evaluated in terms of the MAE (mean absolute error) and MAPE (mean absolute percentage error). The standard and the modified model were tested on a real dataset consisting of the sales between 2011-2019 in a department store of a shopping center in Albania. Implementation results show that the MAE in sales prediction for the modified model is reduced, while the MAPE in sales prediction for the modified model was measured for prediction periods. The implementation results indicate a comparable or evenbetter performance than the standard model. different

Impact of Visual Merchandising on Impulsive Buying Behavior of Sri Lankan Modern Trade Customers

Impact of Visual Merchandising on Impulsive Buying Behavior of Sri Lankan Modern Trade Customers

Post-epidemic factors influencing customer's booking intent for a hotel or leisure spot: an empirical study

PurposeThe new coronavirus is a highly infectious disease with mutating variants leading to pervasive risk around geographies and public health system. The economy has been suffering due to the strategic lockdown adopted by the local administrative bodies, and in most of the countries, it is further leading to a major wave of unemployment with millions of job and business losses affecting the hotels, travel and tourism industry widely. To attain a sustainable business in the post-pandemic situations, the industry now must think of information system approaches to convince tourists to feel safe with the most hygienic hospitality and services to be offered in any property. The key aspect of the study is to provide the impact of new-age AI-driven technology solutions that will dominate the future direction of the modernized hospitality industry promising robust health-safety measures in a hotel, and further help create sustainable business and leisure travel facilities to cope with post-epidemic scenarios.Design/methodology/approachThe study emphasizes to provide a robust technology-oriented framework based on a mixed research method that would help hotels to adopt and implement new-age AI-driven solution within the hotel premise to serve customers with at most hygiene, contactless service and thereafter, aiming for faster recovery of businesses and regaining customer trust to fuel booking intent in the post-epidemic scenario.FindingsThe paper provides a technology-focused solution that would impact hotel industries' post-pandemic scenario. The study contributes to helping boost the tourism industry using information management solutions such as biosensors, robotic room services and contactless hosting. The findings show the adoption of robots/RPA solutions and Biosensors by the industry will be a disruptive paradigm shift.Originality/valueThe study expands the scope of research in information technology and management with a focus on the hospitality industry while contributing to new factors impacting customer buying behavior in the industry.