E-commerce Retailers Research Articles

Defining The Decisive Factors on Purchase and Comparing Feature Importance Methods

Online retail companies focus on two activities for getting revenue in their businesses and to survive in the market. First activity is increasing traffic of the online shopping platform and second activity is converting this traffic to revenue for the company. Marketing facilities try to attract customers to the online shopping platforms at great costs. Because of the costs of getting traffic, it is crucial to make customers order. Online shopping platforms need to understand which factors are decisive on customer purchase decision. In this study, which factors are decisive on consumer purchase decisions will be studied on an e-commerce retail platform from Turkey, Hepsiburada. Which of these factors are most decisive try to be defined: traffic source type (google, campaign, direct etc.) of the customer, customer persona or segment, which types of page or page components has been seen, product position on the page, does the customer benefited from campaign or discount, product review scores and counts, has the product recommended or not. In this study, data will be gathered from Hepsiburada transactions stored in google's big query environment. Performance problems will be solved via SQL optimization and other methods. Data quality issues will be fixed to get consistent results. Then statistical methods, supervised machine learning and deep learning methods will be applied to data for getting feature importances. Importance value of the features will show which factor decisive on customer purchase decision. Feature importance values will be compared and evaluated according to method, model results. Hyperparameter tunings is applied to the methods. Also, the model performances will be compared and evaluated. This study uses and compares 7 methods and there is no comprehensive study in literature in terms of method variety.

Analyzing the Influence of COVID-19 on the E-Commerce Customer’s Retail Experience in the Supermarket Industry: Insights from Brazil

Background: E-commerce’s convenience, speed, and ability to overcome geographical barriers have made it increasingly popular across industries. This study analyzed the impact of the COVID-19 pandemic on customer experience and satisfaction in supermarket retail e-commerce in the Brazilian market. The objective was to understand how the pandemic influenced satisfaction in this sector. Methods: A survey research method was employed, utilizing the Critical Incident Technique to identify key quality dimensions important to customers. A total of 133 valid responses underwent exploratory factor analysis. Results: Data analysis identified three dimensions significantly affecting overall customer satisfaction. “Presentation of Products in the Application (app)” had the highest correlation, while “Delivery Logistics” had the lowest. However, when considering the COVID-19 factor, “Delivery Logistics” showed the highest correlation. This indicates that the dimension most affected by the pandemic has the least impact on overall satisfaction in supermarket e-commerce. Conclusions: The study emphasizes the significance of understanding customer satisfaction in supermarket e-commerce, not just during the COVID-19 pandemic but also its post-pandemic consequences. Retailers should prioritize improving product presentation in apps, as it greatly influences overall satisfaction. Addressing the pandemic’s impact on delivery logistics is also crucial to ensure competitiveness in the e-commerce market.

Tokopedia E-Commerce Retail Business Innovation in the Transformation Retail Industrial Era 4.0

Tokopedia's strategy in the world of retail 4.0 will be explained in this paper. The transformation of the retail world occurred from the 1.0 era to the 4.0 era and each has its characteristics. Tokopedia was born when it was in the retail 3.0 era and began to grow in the 4.0 era. A business model innovation strategy is very necessary for the business to survive in the online marketplace. Many innovations have been carried out, including (1) store management facilities (2) Shopping by type category of goods sold and business processes are very easy to learn (3) For easy access to online shops, sellers receive their accounts created during initial registration (4) Collaboration with various transportation services facilitates delivery of goods to consumers (5) simple payments and safe. The method used is to collect some literature to further identify whether it is by the topic to be discussed and make important points related to Tokopedia's strategy in the 4.0 era.

The role of commitment in online reputation systems: An empirical study of express delivery promise in an E-commerce platform

Express delivery, enabled by automated warehouse systems, offers a competitive advantage to e-commerce retailers by shortening order shipping time. It is a common strategy used to attract customers to maintain customer loyalty and improve customer satisfaction on an electronic commerce platform. Yet promising express delivery may heighten customer expectations, subsequently affecting their post-purchase satisfaction and potentially affecting their rating behavior for the logistics service performance. Using a dataset from an e-commerce platform, we show that orders with an express delivery promise are less likely to be reviewed and are reviewed with a lower rating score compared to orders without an express delivery promise but with the same delivery time. Interestingly, the negative effect persists even if the express order arrives earlier than the promised delivery date. We propose that customers have a preference referencing a higher expectation driven by the promise effect of the express delivery, resulting in a change in rating behavior. We recommend that firms, especially online retailers, should carefully consider anticipated customer expectations and post-purchase reactions when developing an express delivery commitment strategy.

Nykaa: retailing decisions in an emerging market

Learning outcomes By working through the case and assignment questions, students will be able to conduct a marketing environmental analysis to aid strategic decisions; analyse the first-mover advantages of a retail firm and how these can be sustained; comprehend online retail business models and the challenges therein; understand the trade-offs of online/offline retail experiences specific to an emerging market’s beauty and personal care sector; conceptualize and formulate actionable growth strategies that balance the individual and collective requirements of brick and mortar and retail e-commerce environments. Case overview/synopsis The case is set in 2022, right after Nykaa – the pioneer of beauty and wellness e-commerce platforms in India makes a blockbuster stock market debut in 2021. Starting in 2012 with a disruptive online model for selling beauty and wellness products online in India, Nykaa had come a long way with expansions in physical retail and other segments such as fashion. The firm, which initially aimed to be a virtual store, is now thinking of aggressively expanding in the brick-and-mortar retail space as it opens its 85th retail outlet. Falguni S. Nayar, founder and CEO of Nykaa, wanted to roll out 300 physical stores targeting 100 cities in India in the next couple of years. She aspired to establish Nykaa as a category leader as the “Indian Sephora” in the beauty and personal care market. Nykaa’s first-mover advantage in the online beauty and personal care marketplace worked well to establish it as a brand with positive endorsements by digital shoppers that enhanced the investment potential with potential financiers. However, the pandemic had brought every physical retailer to the online platform. Most e-commerce platforms dealing in grocery to lifestyle had added personal care products to their existing merchandise. Additionally, several start-ups had ventured into the online marketplace. Online was a cluttered marketplace with little to no differentiation. In this bloodbath, would the first-mover advantage for Nykaa in the online space still count as a competitive advantage? Nayar was all set to expand Nykaa’s physical presence aggressively. The concern was that the beauty and personal care segment had also moved online as a function of long stay-at-home periods. In the post-pandemic times, would the customer indeed revert to brick and mortar once again? Nykaa was also into product formulations, but so was every big and small player in the space. What was the differentiated winning formula for the consumer’s heart and mind? Complexity academic level The case can be effectively used in foundation courses in marketing and a wide range of specialized courses on marketing management (core/foundation course), retail marketing and e-commerce/digital marketing and e-commerce for B-school learners. The complex decision points faced by an innovative e-commerce start-up firm on its road to market expansion make the case suitable for niche courses such as Marketing for Start-ups. Moreover, learners in executive MBA programs with considerable experience can benefit from the case analysis that balances a growing retail company’s long- and short-run objectives. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing.

Lead-Time-Constrained Middle-Mile Consolidation Network Design with Fixed Origins and Destinations

Many large e-commerce retailers move sufficient freight volumes to operate private middle-mile consolidation networks for order fulfillment, transporting customer shipments from stocking locations to last-mile delivery partners in consolidated loads to reduce freight costs. We study a middle-mile network design optimization problem with fixed origins and destinations to build load consolidation plans that minimize cost and satisfy customer shipment lead-time constraints. We propose models that extend traditional flat network service network design problems to capture waiting delays between load dispatches and ensure that shipment lead-time requirements are satisfied with a desired probability. We approximate these chance constraints using hyperparameterized linear constraints, resulting in new mixed-integer programs (MIPs) for service network design. To find high-quality solutions to the proposed MIPs, we develop an effective integer-programming-based local search (IPBLS) heuristic that iteratively improves a solution by optimizing over a smartly selected subset of commodities. For the largest problem instances, we propose a two-phase IPBLS heuristic that first utilizes a simplified, restricted MIP that constrains leg waiting delays individually. Computational experiments using data from a large U.S.-based e-commerce partner demonstrate the significant impact of tight lead-time constraints on the structure of the consolidation network designs and their concomitant operating costs. Notably, tighter constraints lead to solutions with increased shipment consolidation and higher dispatch frequencies on selected key transportation lanes. Such solutions trade off higher shipment transit times with significantly reduced shipment waiting times to meet lead-time constraints at lower cost.

The double-edged sword of delivery guarantee in E-commerce

E-commerce retailers (e-tailers) are increasingly competing in order fulfillment to win over customers. Delivery guarantee emerges as a key competitive factor. Prior studies have examined the relationship between delivery performance and consumer satisfaction, but little attention was paid to the practice of delivery guarantee itself. This study examines delivery guarantee as a decision support tool provided by e-tailers. Particularly, we draw on the signaling theory, expectation disconfirmation theory, and attribution theory to investigate whether it is worth to offer delivery guarantee, who (what kind of e-tailers) can benefit more from it, and how e-tailers should manage the order fulfillment process when they offer delivery guarantee. Through analyzing data from Alibaba using the Heckman Ordered Probit Model, we show that delivery guarantee can be a double-edged sword. That is, offering and meeting a delivery guarantee leads to higher consumer satisfaction towards order fulfillment than not offering one, but failing to meet the guarantee lowers consumer satisfaction more than not providing it. We further show that large e-tailers are more negatively affected by failed guarantees than small ones, and that the “middle mile” (delivery en route) of order fulfillment significantly reduces consumer satisfaction if failed. Taken together, this study generates implications for e-tailers that consider providing delivery guarantee. Large e-tailers should be especially cautious and assess their capability and commitment. The middle mile should receive enough managerial attention in addition to the widely concerned last mile.

Impact of Sensory Perceptions on the Urge to Buy Impulsively

ABSTRACT E-commerce retailers use augmented reality (AR) based apps for product presentations. This study investigates the influence of sensory perceptions in an urge to buy impulsively (UBI). This study further examines the mediating role of emotional states in the relationship between sensory perceptions and UBI. It also examines whether involvement moderates the impact of emotional states on the UBI. Data were collected from users who have used augmented reality (AR) features in Lenskart App and purchased products at least once during the last two months. Overall, the sensory perceptions elicited by AR apps significantly influence customers’ emotional states, driving the UBI. The influence of emotional states on the UBI was also considerably moderated by product involvement. Several practical and theoretical implications for e-commerce retailers are also discussed.

Dynamic discount pricing in online retail systems: Effects of post-discount dynamic forces

Discount offer is one of the widely used strategies of e-commerce retailers to promote sales. However, the success of a discount offer depends on how well inventory levels and discount offers are dynamically managed in relation to each other. In addition, empirical researches have proven that discount offers alter customers' reference prices. However, such alterations are ignored in determining the percentages of discounts despite their importance. This paper proposes a dynamic model determining the optimal discount rates based on the states of inventory and customers’ reference prices in online retailing systems. The model also determines when is the optimal time to stop discounts to have inventory under control, by considering the time lag in which the impacts of traces left of discounts in the post-discount period will fade. The results show that choosing the time to stop the discount, by considering inventory dynamics, demand dynamics, and traces left of discounts, helps in maintaining the inventory availability and is more profitable for the retailer compared with another arbitrarily chosen timepoint. Besides, the model identifies the last-instant discount rate as an effective factor in controlling the rate of decrease in reference prices in the post-discount period. The model can help the online retailers to better optimize their discount offers by dynamically adjusting the discounts.

EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODEL

AbstractAspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design.

Financial consequences of adding bricks to clicks

Many e-commerce retailers are adding “bricks to clicks” – that is, opening an offline channel in addition to their digital sales channel(s). Taking the perspective of such an online pure player, this research assesses the effects of offline channel additions on the financial performance (e.g., sales, profits) and customer behavior (e.g., basket size, return rate) in the extended channel network as well as the initial online channel of the retailer. Across two studies, one at the zip code level and the other at the customer level, we find that the channel addition of a fashion and lifestyle retailer is synergistic in terms of increasing not only overall sales but also profits. At the same time, the new offline channel does not significantly cannibalize the existing online shop, as new customers are attracted through the channel addition. The effects of channel additions, however, are influenced by characteristics of customers gained before the channel addition and of the trade area around the newly opened stores: among existing customers, those who bought more in the online channel do not react as positively to the addition of an offline channel, and trade areas with socioeconomic characteristics that are often viewed as disadvantageous for digital retailing (e.g., an older population, lower average income) show a stronger positive sales effect of a brick-and-mortar addition. The attractiveness of the offline channel for these customer segments highlights that adding bricks to clicks might be most attractive for those customers who were previously unwilling to purchase from an online-only retailer.

Can users embed their user experience in user-generated images? Evidence from JD.com

Nowadays, massive user-generated images (UGIs) are posted online to convey users' experiences with specific brands or products. Thus, this visual information is precious, as it conveys users' actual and subjective feelings about brands and products. Because of the unprecedented quantity of images and the heterogeneity of their content, it is quite challenging for brand marketers and retailers to probe into subjective user experience in large-scale UGIs. To address this gap, this study aims to identify the connection between user experience and different image semantic features (i.e. centrality and richness) by using deep learning models. By employing objective data (8963 images) from JD.com and using deep learning algorithms (faster R-CNN), we found that users with positive user experience prefer to generate high-centrality and high-richness pictures. Our study enriches the relevant literature and provides valuable practical implications for brand marketers and e-commerce retailers. Based on findings of this work, relevant stakeholders can understand their users’ experience better from objective UGIs and devise corresponding recommendation and service strategies.

TRENDS IN THE DEVELOPMENT OF E-COMMERCE MARKET IN THE COUNTRIES OF EAST AND SOUTHEAST ASIA

The article explores the trends in the development of retail e-commerce in East and Southeast Asia. The dynamics of sales volumes of individual product groups in the retail electronic market, both in the whole world and in individual countries, is analyzed. As the study showed, the most sold goods through e-commerce are the products of the "fashion" group. In each country studied, the most significant online stores and delivery service providers were identified, and the degree of internet penetration was assessed. The factors that had the strongest influence on the development of retail e-commerce in the countries of East and Southeast Asia were identified.

The double side of flow in regret and product returns: Maximizers versus satisficers

While the positive aspects of the flow state have been widely researched, the effects of being aware of this state and the negative consequences still need to be explored. The loss of awareness that flow state may bring can lead to the purchase of products that consumers afterward regret and want to return them. This research aims to understand flow consciousness's effect on consumer post-purchase regret from a dual perspective (process and outcome regret). The relevance of these relationships is also examined by analyzing the differences between 2 groups: maximizers and satisficers consumers. Through a structural equation model based on covariances tested with EQS 6.4, the results show that flow consciousness generates process regret but not outcome regret. However, the differences found between the two groups are worth noting. The research sheds light on the effect of flow consciousness on regret and provides insight into the mechanism of product return through post-purchase regret. The theoretical and managerial implications for e-commerce retailers are discussed.

Winning customer satisfaction toward omnichannel logistics service quality based on an integrated importance-performance analysis and three-factor theory: Insight from Thailand

Electronic commerce (e-commerce) is one of the fastest-growing businesses in the retail sector, especially in Asia. Owing to intense competition, retailers seek to consolidate all customer touchpoints into a single channel to enhance customer shopping experience. The omnichannel has emerged as a key strategic weapon in e-commerce retail business because its adoption hinges on customer perceptions of omnichannel logistics service quality. By drawing on an integrated importance-performance analysis, and a three-factor theory framework, this study examines how customers perceive and assess this quality. A leading e-commerce retailer in Thailand was used in the empirical study. Through a comprehensive literature review, 19 omnichannel logistics service quality attributes were determined based on customer shopping journeys. The attributes were categorized into four quadrants using importance-performance analysis and three-factor theory. As a result, attributes in each quadrant required different management schemes. Furthermore, performance benchmarking in customer satisfaction with omnichannel logistics service quality attributes between the case study and its best competitor was conducted using a performance ratio analysis. This study contributes to the omnichannel logistics literature in terms of assessing customer satisfaction; for e-commerce retail businesses, it provides insights into how managers can win customer satisfaction in the omnichannel logistics competition. Other business sectors that aim to improve customer satisfaction can also apply the framework proposed herein.

A Novel Scattered Storage Policy Considering Commodity Classification and Correlation in Robotic Mobile Fulfillment Systems

The commodity storage assignment problem (CSAP), which assigns stock-keeping units (SKUs) to a suitable location for matching the customer demand patterns, is crucial for improving the order picking efficiency. In this study, we jointly consider the SKUs classification and correlation, and propose a new scattered storage policy named scattered-correlation storage policy based on the commodity classification (SCSPCC) for mitigating CSAP in the robotic mobile fulfillment systems (RMFS). We call the new problem CSAP-SCSPCC. To address this problem, we construct a mixed-integer programming model, and propose a novel variable neighborhood search with self-adaption and simulated annealing acceptance mechanisms (SA-VNSSA). Besides, a heuristic algorithm is proposed to select the minimum number of shelves to evaluate the optimization effect of SA-VNSSA and SCSPCC in terms of the number of shelf transports. Extensive numerical experiments are conducted on small-, medium-, and large-scale instances, respectively. The results reveal that the proposed model and algorithms are reasonable and effective in solving CSAP-SCSPCC compared with the state-of-the-art methods. Specifically, SA-VNSSA outperforms the three state-of-the-art comparison algorithms [i.e., SA-1 (Muppani and Adil, 2008), SA-Pop (Assadi and Bagheri, 2016), and SA-2 (Zhang et al., 2019)] by more than 4.19% and 3.23% on average in medium- and larger-scale instances, respectively. The comparisons between SCSPCC and CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019) show that the order picking efficiency is improved by our SCSPCC more than 6.31%. It is can be concluded that SCSPCC is efficient and robust to match the SKU storage pattern and customer demand patterns in e-commerce retail. Note to Practitioners—Robotic mobile fulfillment systems (RMFS) have been widely used in the warehouses of Amazon, Jingdong, Cainiao, and so on. Considering practical situations and requirements in commodity storage assignment problems (CSAP) is necessary for improving RMFS order picking efficiency. We proposed a new problem named CSAP-SCSPCC for RMFS. Particularly, SCSPCC is a novel scattered storage policy that can assign best-selling SKUs and general-selling SKUs to a suitable location based on the SKU correlation, respectively. Computational results with small-, medium-, and large-scale instances show that our proposed SA-VNSSA and SCSPCC are effective, robust, and practically applicable compared with two state-of-the-art approaches [i.e., CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019)]. Compared with CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019), SCSPCC can improve order picking efficiency by more than 6.31%. In summary, the methods proposed in our work can match the SKU storage mode and the customer demand patterns in a giant e-commerce retail warehouse. This research work can contribute to the improvement of RMFS picking efficiency. In the future, it is necessary to study multiple problems in RMFS jointly, including CSAP-SCSPCC, shelves storage assignment problems, and order batching, etc.

A Systematic Requirement Analysis for Dynamic Pricing in Retail E-Commerce

Abstract: Retail e-commerce has been exploding recently, with billions of people using the internet to buy goods and services. Due to the vast number of vendors selling similar products to consumers and the high degree of price transparency enjoyed by purchasers, the e-commerce sector is fiercely competitive. Vendors frequently use dynamic pricing, also known as discount pricing, to ensure they generate a healthy profit. In this paper, we present a systematic analysis of the requirements for a modern e-commerce dynamic pricing system. We cover the business, stakeholder, functional, and non-functional requirements of ecommerce dynamic pricing. We additionally offer a generic architecture blueprint based on what we have learned about the requirements for dynamic pricing

Imitation or Development? Physical Retailers in China

E-Commerce is becoming increasingly important as a retailing channel especially during the COVID-19 pandemic, and large retailing companies in various industries has decided to embrace e-commerce. Nevertheless, some of the physical retailers not only managed to compete with e-commerce retailers but also gained exceptional growth even during the most stressful times of COVID-19 and yet has not received much scholarly attention. This research utilizes the concept of imitation strategy and development strategy as a theoretical lens to analyze how different attributes of the physical retailers influenced consumers’ decision-making process in the early post-pandemic era. The findings show that developing physical retailers’ unique attributes can be a more effective strategy when comparing to imitating e-commerce attributes. This study also discovered new consumer behavior patterns that can influence the effectiveness of the existing marketing channels and techniques. This research contributes to both academic and practical fields by developing cumulative theoretical frameworks related to physical retailing and e-commerce, and by offering corresponding strategic suggestions for retailers to obtain competitive advantage in the fast-changing retailing market.

Factors Influencing Consumers' Intentions to Switch to Live Commerce From Push-Pull-Mooring Perspective

Conventional e-commerce retailers are less advantageous in attracting online consumers than streamers in live commerce. In China, live commerce has gradually become the mainstream sales channel. Based on the push-pull-mooring model of migration theory, this study aims to identify the reasons that urge online consumers to switch from shopping on conventional e-commerce to live commerce, as well as the potential obstacles of such a switch. About 306 Chinese consumers with conventional e-commerce and live-stream shopping experience participated in this study. The results indicate that live commerce's attractiveness has the greatest impact on consumers' willingness to switch to live commerce, followed by dissatisfaction with conventional e-commerce, while switching costs has no significant effect. Low interactivity most greatly impacts dissatisfaction with conventional e-commerce, while streamers' charisma greatly impacts on live commerce's attractiveness, and low familiarity impacts switching costs. The authors analyze the data by gender and occupation to yield additional findings.

Fewer Clicks, More Purchases

E-commerce retailers are increasingly faced with challenges of finding ways to provide a seamless shopping experience to customers. The checkout process and its related touch points are especially critical in shaping the customer experience. We study the impact of adopting one-click buying, a feature that reduces the number of steps required to place a purchase order to a single click, on subsequent customer behavior. Using quasi-experimental data over a period of 35 months from an online retailer before and after the launch of one-click buying, we find adopting one-click buying is effective in lifting customer purchases and does so by making treated customers purchase more often and more items. The impact of adopting one-click buying on customer purchases after adoption is economically significant, persistent over time, and heterogeneous across customers. Analyzing clickstream data of customer activity online and purchases across product categories, we provide evidence that the increase in purchases is driven by richer engagement through both more visits to the website and more page views on visit and the expansion of purchases across categories. We discuss the implications of our findings for customer experience and targeting. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4716 .