Shopping Behavior Of Customers Research Articles

Towards visual sentiment summary to understand customers’ satisfaction

Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected and is being shifted towards online shopping. Understanding the customers’ opinions, attitudes, and emotions in feedback and comments plays an essential role in making decisions for organizations and individuals (e.g., companies and customers). In this study, we propose sentiment summaries from the customer knowledgebase (SSoCK) framework that analyses customer feedback and improve a mechanism for sentiment summarization by using text analysis including sentiment analysis. In the experiments, various domains from customer reviews (e.g., computer and Canon) are used to conduct. The results show that the proposed SSoCK framework has the high performance of sentiment classification in terms of its accuracy when compared to the other approaches. Moreover, the proposed framework generates various kinds of sentiment summaries that can support managers/potential customers understand trending/interesting aspects of the product with customer satisfaction and can be easily updated with new reviews within the same domain without storing any original data.

Digital Marketing Effect on the Shopping Behavior of Customers: An Analysis

Digital Marketing Effect on the Shopping Behavior of Customers: An Analysis

Digital Marketers for Attitudes and Purchase Intentions Towards Modern Personalized Cosmetics, Impact of Manufacturing and Engineering Technology Management

Thailand's e-commerce cosmetics and personal beauty care market has grown by double digits, spawning a new field of competition that has migrated from offline in-store to online platforms. Despite its rapid growth, Thailand is still in the early stages of internet commerce. Not everyone in Thailand is comfortable or passionate about internet shopping. Given this market potential, the study's results will assist luxury cosmetic and skincare firms in better understanding the behaviors and expectations of Thai women, since best practices from other countries may not be applicable to the Thai market and clients. This study looks at the relationship between perceived value and trust in order to predict whether people will buy customized cosmetics online. To assess clients' desire to buy cosmetics online, a critical relationship between brand image, customer trust, and digital marketing must be established. Differences in customer demographics, behaviors, and expectations across physical locations will also be investigated, as will offline and online sales of counter-brand cosmetics and skincare. The study will analyze the elements that influence Thai women's online cosmetics and skincare purchases by using secondary data and observation to obtain information from 500 female participants. The participants were female internet consumers who had used products from the top three brands in the previous year: L'OREAL (LOR), Lancome (LAN), and Maybelline (MBL), and the respondents had "some" comprehension of customized cosmetics. Furthermore, participants must be a Facebook user for more than a year and a member of any Facebook cosmetics fan sites. Customers' demographic and psychographic characteristics, as well as their purchase behaviors, digital marketing strategies, and marketing methods, are among the key elements investigated. The findings of this study are intended to provide a more in-depth understanding of customer shopping behavior and expectations, as well as to identify the critical success factors that brands should prioritize when developing digital marketing strategies to influence female consumers' purchasing decisions through online shopping channels in Thailand.

The optimal capacity decision of the catering merchant in omnichannel – service, production and delivery capacity

PurposeOmnichannel sales have provided new impetus for the development of catering merchants. The authors thus focus on how catering merchants should manage capacities at the ordering, production and delivery stages to meet customers’ needs in different channels under third-party platform delivery and merchant self-delivery. This is of great significance for the development of the omnichannel catering industry.Design/methodology/approachThis paper formulates the capacity decisions of omnichannel catering merchants under the third-party platform delivery and merchant self-delivery mode. The authors mainly use queuing theory to analyze the queuing behavior of online and offline customers, and the impact of waiting time on customer shopping behavior. In addition, the authors also characterize the merchant’s capacity by the rate in queuing model.FindingsThe authors find that capacities at ordering stage and food production stage are composed of base capacities and safety capacities, but the delivery capacities only have the latter. And in the self-delivery mode, merchants can develop higher safety capacities by charging delivery fees. The authors prove that regardless of the delivery mode, omnichannel sales can bring higher profits to merchants by integrating demand.Originality/valueThe authors focus on analyzing the capacity management of omnichannel catering merchants at the ordering, production and delivery stages. And the authors also add the delivery process into the omnichannel for analysis, so as to solve the problem of capacity decision-making under different delivery modes. The management of delivery capacity and its impact on other stages’ capacities are not covered in other literature studies, which is one of the main innovations of this paper.

Equilibria on a circular market when consumers do not always buy from the closest firm

We study spatial competition by firms which is often studied in the context of linear markets where customers always shop at the nearest firm. Here, customer behavior is determined by a probability vector mathbf{p }=(p_1,ldots ,p_n) where p_i is the probability that a customer visits the ith closest firm. At the same time, the market is circular a là Salop (Bell J Econ 10(1):141–156, 1979), which has the advantage of isolating the impact of customer shopping behavior from market boundary effects. We show that non-convergent Nash equilibria, in which firms cluster at distinct positions on the market, always exist for convex probability vectors as well as probability vectors exhibiting a certain symmetry. For concave probability vectors, on the other hand, we show that non-convergent Nash equilibria are unlikely to exist.

Leveraging loyalty programs using competitor based targeting

Loyalty programs (LPs) are widely used by firms but not well understood. These programs provide discounts and perks to loyal customers and are costly to administer, but produce uncertain changes in spending patterns. We use a large and detailed dataset on customer shopping behavior at one of the largest U.S. retailers before and after joining a loyalty program to evaluate how behavior changes. We combine this with detailed spatial data on customer and store locations, including the locations of competing firms. We find significant changes in behavior associated with joining the LP with a large amount of heterogeneity across customers. We find that location relative to competitors is the factor most strongly associated with increases in spending following joining the LP, suggesting that the LP’s quantity discounts work primarily through business stealing and not through other demand expansion. We next estimate a model of what variables determine how spending will change after joining the LP. We use high-dimensional data on spatial relationships between customers, the focal firm’s stores, and competing stores as well as customers’ historical spending patterns. This model is used to test whether past sales data reflecting customer’s vertical value to the firm or spatial data reflecting customer’s horizontal vulnerability are more important determinants of post-LP spending increases. We show how LASSO regularization estimated on complex spatial relationships are more effective than are models using past sales data or simpler spatial models. Finally, we show how firms can use customer and competitor location data to substantially increase LP performance through spatially driven segmentation.

A Qualitative Investigation of Shopping Behavior of Customers at Bottom of the Pyramid

In recent years, the “Bottom of the Pyramid” (BOP) market has become an attraction for researchers, business managers, and policymakers. Numerous researchers have contributed theoretically and empirically to the BOP literature, however, little focus is accorded to BOP customers’ buying behavior. It is interesting to study the shopping behavior of BOP customers characterized by low disposable income, poor standard of living, and geographical isolation. Therefore, the present study aims to investigate the shopping behavior of BOP customers to identify the drivers and hindrances in the buying decisions. The study follows a qualitative approach by conducting in-depth personal interviews of 100 BOP customers visiting Vishal Mega Marts (VMMs) in a northern Indian city. The results indicate that BOP customers aspire for a better shopping experience like affluent customers, however, income constraint restricts them to buy superior brands. Indeed, BOP customers are persuaded by excessive discounts, gifts, coupons, peer influence, and seasonal offers by VMMs. The study provides implications for researchers, retailers, and policymakers.

The impact of the COVID-19 pandemic on the shopping behaviour of customers of selected global brand marketing strategy

Research background:Research will highlight the impact of the COVID-19 pandemic on Mc Donald’s brand marketing strategy. In the research, we will analyze the internal and external environment of the global brand. We will find out how the current pandemic situation has affected the customer shopping behavior of the global brand Mc Donald’s.Purpose of the article:In this article, we point out the importance of analyzing customers’ shopping behavior before and during a pandemic. We identify differences in shopping behavior before and during a pandemic. We will confirm whether our research identifies differences in the behavioral characteristics of the global brand’s shopping behavior in terms of differences and penetration.Methods:We used the methods of analysis, synthesis, comparison and generalization to assess the theoretical assumptions of purchasing behavior and marketing strategy. By comparison and generalization, we compare the financial performance of the brand during the COVID-19 pandemic. We will evaluate its significance in relation to shopping behavior. We will also use an analysis of the internal and external environment to identify the right marketing strategy for the global Mc Donald’s brand. Subsequently, we compare the results and identify the possibilities of adapting the marketing strategy with respect to purchasing behavior before and during the COVID-19 pandemic.Findings & Value added:We will point out the importance of the shopping behavior of Mc Donald’s global brand customers before and during the pandemic. We will find out how customers’ shopping behavior has changed and we will identify this change to the marketing strategy.

The Importance of Irrational Purchasing Behaviour in Applying the Marketing Strategy of Selected Global Brands

Research background:The progress of business activity depends on current trends in customer shopping behavior in a global environment. The current trend is also becoming a factor of irrationality, which enters the process of purchasing behavior. It is supported by the emotions and desires of consumers. In this sense, it is important to understand the irrational behavior of customers and respect the individual behavioral characteristics in the sales process. By respecting these characteristics, a company can strengthen its brand and marketing strategy with a view to gaining a global competitive advantage. Research will highlight the importance of irrational customer behaviour between the global Lindt and Hershey brands. We will find out whether the pricing policy and the discount are important for influencing shopping behaviour.Purpose of the article:In the article, we pointed out the importance of behavioral characteristics of irrational shopping behaviour in the above-mentioned global brands. We will confirm whether the research of Dr. Ariely and despite the current trends, irrationality prevails in shopping behaviour in the environment of Slovak customers.Methods:In order to assess the theoretical assumptions of behavioral economics and irrational shopping behavior, we used methods of analysis, synthesis. In the analytical part of the article, we also used the method of sociological questioning in order to ensure the credibility of the entire research. In the discussion part of the article, we used the method of induction.Findings & Value added:Pointing out the importance and connection of irrational purchasing behavior in the application of the company’s marketing strategy in a global market environment.

Diamonds in the Rough: Leveraging Click Data to Spotlight Underrated Products

Problem Definition. Inspired by a real data set from the Chinese retailer JD.com, we study the click and purchase behavior of customers in an online retail setting by employing a structural estimation approach. In particular, we aim to understand what characteristics of products drive the click versus purchase decisions of customers.With the boom in e-commerce, which has been further fueled due to the current COVID-19 pandemic, an ever increasing number of customers are shopping online. Such a rapid growth in online shopping provides retailers with abundance of data to study customer shopping behavior. For example, online retailers can benefit from a better understanding of the customer search (click) and purchase behavior to improve their operational decisions such as assortment planning. From an academic standpoint, even though there is an extensive body of work on consumer search models, most prior work do not incorporate search sequences and purchase decisions of customers jointly in their analysis. We, however, combine the click and order data to estimate both the click and purchase behavior of customers.Methodology/Results. Using a large data set from JD.com, we propose a structural model to estimate the click and purchase behavior of customers according to a dynamic discrete choice model. In particular, we assume that the customer's utility from each product has an observed and (pre-click) unobserved part (in addition to a random shock). The observed part of the utility is known to the customer prior to the click; however, the unobserved part of the utility can only be learned after the customer clicks on the product. We assume that the customer solves a dynamic program in order to find the optimal search strategy. Due to the curse of dimensionality, we propose a novel value function approximation scheme inspired by the Conditional Choice Probability approach. This, in turn, reduces the estimation into a computationally tractable two-stage process. By combining the click and order data, our proposed structural framework allows us to disentangle and estimate the observed and unobserved parts of product utilities. Our estimation results show that the value of click for customers can be quite significant. This is evidenced by the fact that the unobserved utilities of products vary significantly across products. Most importantly, we are able to identify underrated products which we call diamonds in the rough: these are products with low “face” values (i.e., low observed utilities), but high total utilities due to their high unobserved utilities. Thus, even though such products have a low chance of being clicked (due to their low observed utilities), they have a high chance of being purchased, if clicked.Managerial Implications. Our structural framework provides an online retailer with new tools and insights to better manage the product assortment based on customer click and purchase behavior. In particular, our structural model allows the retailer to disentangle the observed and unobserved parts of product utilities and identify underrated diamond-in-the-rough products. The online retailer can increase her revenue by bringing such products into the spotlight by promoting them on the search page or using tags such as “spotlight product” (similar to “Amazon's Choice” tags on Amazon.com) to entice customers to click on them.

A Behavioral Study on Leisure Shopping Purchasing in Supermarket Setup

Asian country retail sector blooming fast. Sri Lankan retail association expected a revolution in the retail sector. Recently Sri Lanka retail sector has been facing huge challenges with technology changes. Customers are more engaging with technology so in this situation, they are more informed toward their purchasing.
 Retailers use many strategies and tactics to attract customers and enhancing market shares. Most of the retailers have used 4ps strategies (product, price, place, promotions). Apart from those traditional retail strategies, retailers adopt more innovative strategies for getting customers. Recently they have engaged with enhanced retailers display settings, in-store initiatives, online listening strategies with digital marketing, build retail navigation with buzz marketing, enhancing merchandising windows display, and recently most of the retailers using curbside also to explore new retail features to customers.in addition to that retailers maintain good sales staff, using leverage social media like Facebook, Instagram, SMS marketing, email marketing, and online contest and referral retailer marketing and influence retailers' strategies.
 Sri Lanka retail sector accounts for approx. 34% of the country’s GDP,14% of the employments, and over 250 million. Footfall per annum thus contributing positively toward the national economy. Sri Lankan was ranked 12th in the global retail development index (GRDI).
 The supermarkets' service providers face competition to attract customers and enhance their shopping behavior, to increase retail market share. Therefore, supermarket service providers should be conscious of the leisure shopping factors affecting shopping behavior.
 This study aims to investigate the behavioral study of leisure shopping factors (wide choice, Atmosphere, Convenience, Salespeople, Location, promotional activity, merchandising policies) on customers' shopping behavior with regards to the retail industry. The objective of this study is to identify the level, relationship, and impact of leisure shopping factors on customer shopping behavior. This study was based on the customer's perspectives and their experiences with retail service. Data were obtained from a sample of 150 customers, and convenience sampling was used. The descriptive statistics, correlation, and regression analysis were used to analyses the data by using SPSS.
 The results revealed that leisure shopping factors have a significant impact enhance customer purchasing behavior. Same as, leisure shopping factors (wide choice, Atmosphere, Convenience, Salespeople, Location, promotional activity, merchandising policies) impact on customer buying behavior significantly through customer leisure shopping factors. The study contributes to existing
 Theoretical and practical knowledge by providing evidence regarding the relationship and impact between leisure shopping factors and customer buying behavior. The modern retail service providers, in Sri Lanka; it is growing rapidly with high competition between the service providers. Hence, this research has the potential to contribute to the overall modern retail service providers in terms of how to maintain long-term relationships between the service providers and their customers through the strategies of leisure shopping factors.

Comparing Customer Attitude towards Amazon and Flipkart in Patna

After the outbreak of Covid-19 pandemic there had been huge changes in the shopping behaviour of customers across world. From bulk buying to online shopping there is a major change in what, when and how people are buying. As people are avoiding going to public places, online shopping has become even more prominent now. This paves the way for the online shopping stores giving them competitive advantage over traditional shopping stores. Amazon and Flipkart, which are the two most leading online shopping stores have also been beneficially affected by this shift of customer purchasing behaviour. Both Amazon and Flipkart are seeing accelerated growth in online sales fuelled by first time buyers and sellers from small towns. In this study the overall customer attitude towards the two sites is measured and compared with the help of Fishbein Multi-Attribute Model. An analysis is done to determine the important attributes that matters more to the customers ,on which these online sites need to focus on and also the perception of customers on performance of the two sites on those attributes is assessed.

HABITUAL MOBILE SHOPPING BEHAVIOR: COMPARISON OF CHINESE AND VIETNAMESE CUSTOMERS

Consumers are increasingly using their smartphones for making purchases, with the other modes being traditional offline and online channels (Kim et al., 2017). In light of the growing number of smartphone users worldwide, many e-commerce companies have responded by launching mobile apps that offer more convenient and flexible shopping experiences to smartphone users (Bang et al., 2013). A number of e-commerce related studies have identified crucial differences between mobile shopping (m-shopping) and online shopping. They find that m-shopping provides greater flexibility in terms of time and place as well as multi-tasking capabilities (for instance, a mobile shopper can communicate with others and even post a picture of an item to social media asking for others’ opinions and comments, all while shopping via a mobile app). These types of apps simultaneously cater to consumers’ shopping and social affiliation needs (Yang et al., 2012). Current research has shown that this type of shopping has gradually become habitual or automatic (Shankar et al., 2016). This new type of e-commerce has brought about a change in the shopping behavior of customers, thereby challenging companies to develop specific strategies to attract this new breed of shoppers.

In-store Customer Shopping Behavior Analysis by Utilizing RFID-enabled Shelf and Multilayer Perceptron Model

Understanding customer shopping behavior in retail store is important to improve the customers’ relationship with the retailer, which can help to lift the revenue of the business. However, compared to online store, the customer browsing activities in the retail store is difficult to be analysed. Therefore, in this study the customer shopping behavior analysis (i.e., browsing activity) in retail store by utilizing radio frequency identification (RFID)-enabled shelf and machine learning model is proposed. First, the RFID technology is installed in the store shelf to monitor the movement tagged products. The dataset was gathered from receive signal strength (RSS) of the tags for different customer behavior scenario. The statistical features were extracted from RSS of tags. Finally, machine learning models were utilized to classify different customer shopping activities. The experiment result showed that the proposed model based on Multilayer Perceptron (MLP) outperformed other models by as much as 97.00%, 96.67%, 97.50%, and 96.57% for accuracy, precision, recall, and f-score, respectively. The proposed model can help the managers better understand what products customer interested in, so that can be utilized for product placement, promotion as well as relevant product recommendations to the customers.

Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.

Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values > 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.

Buying Intention through User Interface Design

Purpose: Internet has made many things easier, cheaper and faster, especially in the retail industry. An example is customer shopping behavior, starting from advertising and ending up to the final transaction. One of the essential things in online selling is how to attract as many consumers as possible to visit the website so as to buy the products offered. Design/methodology/approach: This study aims to learn about the impact of e-impulse buying, user interface design and user experience of consumer buying behaviour relating to sports fashion. By implemeting the partial least square analysis method to measure the correlation between variables, the result of the research found that e-impulse buying and user interface design have a significant influence on consumer buying interest in website. Findings: Whereas, user experience has no effect at all on buying interest. On the other hand, the user interface design and user experience proved to have an impact on e-impulse buying on consumers while shopping online. Practical Implications: The proposed analysis can be used in customer shopping behavior by respective entities to improve sales. Originality/Value: It is a unique approach in a subject with a lot of different results and moderating variables.

Recommendation Product Based on Customer Categorization with K-Means Clustering Method

Nowadays, web shopping is more than selling product. Many web shopping have basic analytics system to analyze customer data, transaction data including demographics, age and gender. They add many features in web shopping to maintain customer loyalty. In this research, we made a web shopping that can analyze customer shopping behavior. We used FM (Frequency and monetary) analysis based on a “transaction” data set. We categorize the customer based on how often they buy, how much they buy and how much the value of purchased item. We use K-Means algorithm to cluster the customer based on their transaction. Analyzing and understanding customers’ buying behavior can help the store to know what they are looking for. Therefore, at every customer web page that is in the same cluster will appear recommended products accordance with the transaction that has been done. Recommendation Products are presented is the prediction of the type of goods that may be chosen by the customer. So that the recommendation products that appear on web pages between customers will be different. The data used for this test is “Istana Accessories” store data from January to June 2014. The results show that recommendation product from K-Means algorithm successfully obtained and displayed on the customer page.

Leveraging Loyalty Programs Using Competitor Based Targeting

Loyalty programs are widely used by firms but not well understood. These programs provide discounts and perks to loyal customers and are costly to administer, but produce uncertain changes in spending patterns. We use a large and detailed dataset on customer shopping behavior at one of the largest U.S. retailers before and after joining a loyalty program to evaluate how behavior changes. We combine this with detailed spatial data on customer and store locations, including the locations of competing firms. We find significant changes in behavior associated with joining the LP with a large amount of heterogeneity across customers. We find that location relative to competitors is the factor most strongly associated with increases in spending following joining the LP, suggesting that the LP's quantity discounts work primarily through business stealing and not through other demand expansion. We next estimate a set of predictive models to test what variables best predict how spending will change after joining the LP. We use high-dimensional data on spatial relationships between customers, the focal firm's stores, and competing stores as well as customers' historical spending patterns. These models are used to test whether past sales data reflecting customer's vertical value to the firm or spatial data reflecting customer's horizontal vulnerability are more predictive of post-LP spending increases. We show how LASSO regularization estimated on complex spatial relationships are more predictive than are models using past sales data or other spatial models including gravity models. Finally, we show how firms can use this type of model to leverage customer and competitor location data to substantially increase the performance of their LP through spatially driven segmentation rules.

Pricing and coordination strategies of a dual-channel supply chain considering green quality and sales effort

Due to the rapid growth of the internet and smartphone users, the shopping behavior of customers is changing towards online purchasing. Hence, a manufacturer would like to include an online (direct) channel along with offline (indirect) channel. This study investigates the pricing strategies and coordination mechanism between the members in a dual-channel supply chain (DCSC). A manufacturer offers a new substitutable green (eco-friendly) product through a direct channel and a non-green (traditional) product through an offline retail channel. The demand is expressed as a linear function of selling (online/offline) prices, green quality level, and sales effort level. In this study, three models (centralized, decentralized, and collaboration) have been examined in the DCSC. A Stackelberg game-theoretic approach is applied to analyze the decentralized model, and the channel coordination is achieved through the surplus profit-sharing mechanism to obtain a win-win situation for each member of the supply chain. The optimal results are compared, and a sensitivity analysis is carried out to study the effect of some key parameters. It is revealed that the green quality level is high in the collaboration model, which is beneficial from the environmental perspective.