Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail

Why Retailers Should Care about Net Neutrality: The Impact of Website Performance on Online Retail

Online shopping is gaining popularity. Traditional retailers with physical stores adjust to this trend by allowing their customers to shop online as well as offline, in-store. Increasingly, customers can browse and purchase products across multiple shopping channels. Understanding how customer behavior relates to the availability of multiple shopping channels is an important prerequisite for many downstream machine learning tasks, such as recommendation and purchase prediction. However, previous work in this domain is limited to analyzing single-channel behavior only. In this paper, we provide the first insights into multi-channel customer behavior in retail based on a large sample of 2.8 million transactions originating from 300,000 customers of a food retailer in Europe. Our analysis reveals significant differences in customer behavior across online and offline channels, for example with respect to the repeat ratio of item purchases and basket size. Based on these findings, we investigate the performance of a next basket recommendation model under multi-channel settings. We find that the recommendation performance differs significantly for customers based on their choice of shopping channel, which strongly indicates that future research on recommenders in this area should take into account the particular characteristics of multi-channel retail shopping.

From aspects scarcely studied, such as the online store ambiance and display of the product in the online store, to more prevalent issues related to purchasing behaviour, such as online customer service, the ethics of online stores, and online technologies, this article develops and tests a conceptual model of the factors that significantly impact consumer interactions, experience, behaviour and their willingness to revisit online stores. Data collected from 272 Romanian online shoppers during 2021–2022 is analysed by structural equation modelling with SmartPLS. The results highlight a positive link between online customer service, consumer behaviour, and store ambiance. Ethics of online sales positively influence consumer behaviour within the online store. Online store technology is significantly associated with consumer behaviour and online store interaction. The paper extends the theory of Planned Behaviour, highlighting significant links between online consumer service and other factors and their implications on online purchasing behaviour and on the willingness to return to the online store.

The exponential expansion of e-commerce in America has redefined the retail landscape, presenting both opportunities and challenges for online retailers. This research aims to apply machine learning techniques to develop a strategic online sales strategy through deep consumer behavior analysis. This research paper focuses on a consumer behavior analysis based on U.S.-based datasets underlining American consumers' unique characteristics and preferences. The consumer behavior dataset contained complete data on various aspects of the customer's behavior in online retail. The dataset consisted of transaction records for customer purchase history, items purchased, frequency of purchases, and values of transactions. It also contained browsing history data that would point out user interaction patterns, such as visited pages, time spent on each page, and views of different products to draw fine-grained inferences on consumer interest and preference. The analyst implemented accredited and credible models, such as Random Forests, Logistic Regression, and Gradient Boosting Classifiers, that are useful in various analyses of the dataset related to customer behavior. Random Forest turned in a strong performance, having relatively high accuracy, reflecting that it is efficient in picking up complex patterns in the data. Machine learning can revolutionize the way online retailing is approached in the U.S., as it has the potential to make full use of consumer data on a large scale for more nuanced decision-making. While integrating machine learning algorithms, retailers can develop highly personalized shopping experiences that best meet the preferences and behaviors of individual customers.

User Intent Estimation from Access logs with Topic Model

As the Internet is widespread and there are many online shops in the Internet, many persons buy products in the online shops. Customer's behavior in the online shops is a sequence of customer driven activities intrinsically because his/her movement in an online shop occurs according to only his/her decision. Hence, to achieve satisfactory purchase experiments it is important how the shop supports them. Online shops have to predict customer's intents correctly to support them effectively. One of information resources the shops can use is an access log including information on customer's movement in the online shop. If they are histories of customer's behaviors in online shops and the behaviors depend on customer's intents, we can extract knowledge on them from the access logs. Speaking concretely, we can predict customers' intents from the access logs since their internal intents affect their activities. We can realized more appropriate recommendation service by changing recommendation strategy depending on customer's intents. In this paper, we propose a method to predict customer's intents from access logs in a real online shop. We adopt a Topic Tracking Model (TTM) to analyze the access logs.

Online retailers worldwide invest beyond their core business of retailing to offer their own delivery services (ODS) to deliver products to customers’ homes through their own logistics network. How does this shift to ODS affect customers’ behaviors and sales performance? When and why do retailers venture beyond their core competence to offer ODS? To explore these questions, the authors analyze 250,055 customer transactions over 10 years across 416 cities and 49 product categories from JD, a major online retailer in China. Using difference-in-differences models, causal mediation analysis, and the synthetic control method, they find that ODS increases customers’ monthly spending by 7.8% and grows city-level sales by 11.9%. This study is the first to quantify the sales impact of ODS and shed light on when and why it works. The findings reveal that ODS has greater value for markets with lower trust levels, infrequent customers, high-risk product categories, and consumers who prefer the focal retailer (versus that of third-party sellers). Causal mediation analysis further reveals that ODS not only improves delivery quality but also builds customer trust, which together increase customers’ monthly spending, purchase frequency, and the number of items ordered.

Online shopping or internet shopping is increasing day by day. With the advancement of modern technology, the online market is growing in a vast way. People nowadays prefer online shopping because it saves time, energy, and money. Because of the blessing of the internet that online shopping has made its debut which also affects the common citizens for online shopping. So, for the emerging growth of the online market, it is necessary to find out the behavior of online shopping and customer satisfaction. Safety, trust, product quality plays an important role in customer satisfaction. In this study, we examined customer online shopping satisfaction its impact. The quality of the product, the price of the product compared to the local market, the policy of return, timely delivery of the product are also essential elements of online shopping. By analyzing all these factors we have tried to find the customer behavior and satisfaction with online shopping. In our study, we used a machine learning method to search for the result. We used 40 thousand data to find out the accuracy of our work and to analyze customer shopping satisfaction. We use Naïve Bayes, Apiorir, Decision Tree, and Random Forest classification algorithms for this analysis. We have got our best result by using Apiorir algorithm (88% accuracy) and Naïve Bayes algorithm (87% accuracy). We have also focused on customer behavior and interest in online shopping. Our study can help to develop the business intelligence and satisfaction enhancement about E-commerce.

Abstract: Online purchasing is the new process in the marketing system. The International electronic marketing is a grand this revolution of epoch. It is used to the computer, mobile phone and tap based shopping in India. The main objectives of this research paper is the factors influence the buying behaviour of the online shoppers and identify the most favorable online sites in Nagercoil city. Online shopping provides many choices to consumers than the traditional bricks-and-mortar retail stores. Online shopping is shopping while online or while on the internet. A lot of shoppers are using the internet now-a-days as internet provides a lot of advantage to shoppers. Online shopping includes flexibility, measurability and affordability. Moreover, online shopping has depended on the customer's attitude and their buying behaviour. Hence, an attempt has been made to study the buying behaviour of customers towards online shopping. The researcher suggested that Secured online payments, better to Electronic Stores, return policies and exciting discounts could help the perception of shopping benefits and Online marketers should reduce the delivery charges. The researcher concluded that concluded that Amazon. in is the most favorable online shopping sites and also it depicts that majority of the respondents preferred to buy through online because of accessibility. Keyword: Online Shopping, Electronic marketing, Customer Behaviour.

Businesses are leveraging digital technology through e-commerce platforms as a strategy to continue operating following the movement control order (MCO). Online shopping, a subset of e-commerce platforms, allows consumers to browse, select, and purchase products using digital interfaces. Unfortunately, there is a lack of understanding of how this platform can impact customer behavior. Therefore, this study aims to develop new knowledge of customer behavior and assess the relationship between consumer behavior and e-service quality in online shopping to better understand the e-service quality provided by the vendor in Malaysia. A novel model of e-service quality and consumer behavior has been developed and investigated. Survey questionnaires were distributed and tested using structural equation modeling (SEM) to 200 customers who had experienced online shopping during COVID-19. The finding revealed that website design, security/privacy, and fulfillment are statistically correlated with e-service quality. However, customer service is not significantly correlated with e-service quality. Meanwhile, the quality of e-services is statistically significantly related to customer behavior. This study shows that e-service quality can provide good customer behavior post-COVID-19. Online shopping is therefore predicted to boost the economy of the country, but consumers also want high-quality e-services to continue. Retailers can improve their storefronts, engage customers, and promote responsible consumption.

Online purchase is one of the big changes to the retail marketing. As the lifestyle changed, the people are not going to shop for purchasing required items like gifts, accessories and any electronic items. Everyone started to use online and saving their time and money by getting a good offer through online shopping. Online shopping helps the customer to know the price of the item in advance and able to compare the price with different vendors. It helps the customer to buy the item from the vendor who offers the item with low-cost and good quality. The customer behaviour analysis always depends upon the usage of the internet and service provided by the multi vendor for the various products. Customer behaviour analysis is very much needed to help the vendors to define their strategy for online shopping, advertising, market segmentation and so on. The idea behind this work is to predict the customer behaviour based on their internet usage for various online shopping activities. Multi process prediction model is proposed to analyse customer behaviour using logistic regression method. The proposed model result is validated and compared with many existing online shopping customer models.

Online purchase is one of the big changes to the retail marketing. As the lifestyle changed, the people are not going to shop for purchasing required items like gifts, accessories and any electronic items. Everyone started to use online and saving their time and money by getting a good offer through online shopping. Online shopping helps the customer to know the price of the item in advance and able to compare the price with different vendors. It helps the customer to buy the item from the vendor who offers the item with low-cost and good quality. The customer behaviour analysis always depends upon the usage of the internet and service provided by the multi vendor for the various products. Customer behaviour analysis is very much needed to help the vendors to define their strategy for online shopping, advertising, market segmentation and so on. The idea behind this work is to predict the customer behaviour based on their internet usage for various online shopping activities. Multi process prediction model is proposed to analyse customer behaviour using logistic regression method. The proposed model result is validated and compared with many existing online shopping customer models.

This study explored Istanbul internet buyers' COVID-19 concerns. Epidemic, financial, and internet shopper futures were analysed. The pandemic has increased online shopping. Evaluated consumer spending. The study acknowledges financial, time, and audience limits. Istanbul's internet shoppers were evaluated before, during, and after an outbreak. The study affects governments, e-commerce corporations, and others. It emphasizes how corporations must accommodate client preferences and attitudes. Customer behaviour research builds trust and adapts to internet purchase patterns. COVID-19 affected online buyers. Health and safety have increased online shopping. Post-pandemic consumers are cost-conscious and value-oriented. Predict online customer behaviour. However, the pandemic promoted e-commerce. Consumers choose simplicity, digital platforms, and economic growth. TPB, EBM, influential purchasing attitudes, and COVID-19 internet shopping examined customer behaviour. The study found COVID-19 fear affects online shopping. TPB claims subjective norms, attitudes, and perceived behavioural control affect customer intentions and behaviour during and after the pandemic. Price and quality matter during outbreaks. They want cheap, good solutions. Data reveal firms, organizations, and stakeholders must comprehend and adapt to customer attitudes and behaviours. These data help e-commerce enterprises satisfy customers. The findings can help authorities promote and regulate e-commerce for customer safety and convenience.

Over the past decade, there has been an enormous change in the way consumers shop online, despite continued buying from retails or physical stores. Now-a-days consumers have several options to buy either from retails stores, wholesale suppliers and Online stores. Online shopping offers benefits to both customers and businesses that lead industries switching to Online than retail outlets. Online buying is the solution to the busy lives of consumers to save time for modern individuals. Consumers wanted to explore the convenience and comfort than willing to spend more time in shopping. Does it mean, Individuals are becoming lazier with the penetration of Online shopping into their everyday life? whether anyone accepts or not, everyone likes to spend more time Online than physical stores. Why is it so attracting? Every organization also focused on the utilizing the power of Online and devising their own strategy for selling and marketing of products or services. Are there situations any Online only stores opened up the physical stores as well? To discover answers for these specific questions, we thought of understanding the literature on online buying in India. This paper begins with introduction to the online buying in providing the best experience to the consumers and explores further on growth in India in utilizing the online websites or mobile apps, depicts the role of online buying in influencing the services offered to the public and approaches business takes towards going online. KEYWORDS: Online buying, Online Marketing, Online Sales, Online stores, Mobile shopping

The COVID-19 pandemic has changed online shopping behaviors, according to a survey of about 3,700 consumers in nine emerging and developed economies. As people embraced social distancing, they turned to online shopping more than ever before. The fluctuation of the COVID-19 pandemic and the ways it influences and modifies our shopping habits will likely continue into the foreseeable future. Without a doubt, the COVID-19 pandemic forced everyone to change the way they shop. Whether you were a fan of online shopping or in-store browsing, the pandemic altered routines in many obvious, and some not so obvious. Therefore to understand what now actually affects consumer purchase behavior when it comes to online shopping is key to success for many e-tailers especially for those online fashion retailers which is the main focus of this study.The purpose of this research is to study factors influencing online shopping behavior of online fashion retailers (apparel, fashion accessories, shoes). These factors include eight independent variables: Web design(WD), Reputation(RP), Web contend(WC), Brand effect(BR), Product(PD), Service(SV), Price(PR), and Promotion (PM), and one dependent variable: Online shopping behavior (OB). 437 sample were collected using electronic questionnaire through social media. We used Structural Equation Models (SEM) for data analysis. The result shows that the RMSEA, which is an absolute fit index that assesses how far our hypothesized model is from a perfect model, for this model is .027(.90), the model seems to fit well according to the descriptive measures of fit. On the contrary, CFI and TLI, which are incremental fit indices that compare the fit of our hypothesized model with that of a baseline model (i.e., a model with the worst fit), whose values are both greater than .90 (CFI = .981, TLI = .977) indicating an acceptable fit. More importantly almost all factors included in the model except Web contend (WC) i.e. Reputation(RP), Web design(WD), Brand effect(BR), Product(PD), Service(SV), Price(PR), and Promotion(PM) seem to significantly affect online shopping behavior of online fashion retailers due to their p-values are all less than .05.