Examining the presence of customer service-based strategic clusters in online retailing and the impact of service features on conversion rates

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.

Purpose – This study aims to theorize and empirically examine the relationship between “purchase intention and conversion rate”, “website satisfaction and conversion rate” and “purchase intention and conversion rate”. E-Commerce conversion rate represents the percentage of visits to an e-tailer’s website that includes a purchase transaction. Despite the importance of conversion rates for e-tailers, prior research predominantly used purchase intention and website satisfaction as main dependent variables and implicitly assumed that these variables will influence the actual purchase. Design/methodology/approach – Data on 85 US retail websites were used to test the hypotheses. The unit of the analysis is the online retail website. Regression analysis was used to perform the data analysis. Findings – The results indicate that both purchase intention and website satisfaction positively influence conversion rates. It was also found that website satisfaction positively influences purchase intention. Research limitations/implications – Only data from 85 US e-tailers from the top-100 US online retailers are used to test the hypotheses. Also, conversion rate is only one of the several important success metrics used by e-tailers. Originality/value – This study not only examines antecedents of e-commerce conversion rates, but also theorizes and tests if there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”. This is because, although previous studies used purchase intention and website satisfaction as main dependent variables and proxies for actual purchase behavior, they did not validate this relationship. This study shows that: there is a statistically significant relationship between “purchase intention and conversion rate” and “website satisfaction and conversion rate”, there is also a statistically significant relationship between “website satisfaction and purchase intention” and this study used firm-level data to theorize, measure and analyze the data, whereas prior literature used only individual-level data.

Do online shops support customers’ decision strategies by interactive information management tools? Results of an empirical analysis

Online retail has become more prominent around the world in the last decade. As a result, online retailers' website performance is increasingly important. Previous literature has extensively studied customer sensitivity to service speed and wait times in offline services. In “Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail,” Gallino, Karacaoglu, and Moreno extend this literature to online retail. They study the impact of delays in online retail on customer behavior. They estimate sizable negative effects of website slowdowns on online sales and conversion rates. Moreover, they explore how customer sensitivity to online delays varies throughout customers' shopping journeys. They find that the impact of waiting times varies along the different stages of the shopping journey, with customers becoming more sensitive to slowdowns at the checkout stage. Their findings have implications for website design decisions. This research is especially relevant in the current regulatory environment with ongoing policy debates about net neutrality.

Der Umsatz durch E-Commerce und auch die Anzahl der Online-Shopper in Deutschland steigt stetig. Die Online-Shops stehen jedoch vor dem Problem geringer Konversionsraten. Ein Ansatz zur Steigerung der Konversionsraten ist der Einsatz personlicher Kommunikation in Form von Live-Chats. Es stellt sich nun die Frage, wie der Einsatz von Live-Chats in Online-Shops auf die Kaufentscheidung der Kunden wirkt. In einer Online-Erhebung wurden 385 Personen zu ihren Erfahrungen mit und ihren Erwartungen an Live-Chats befragt. 76 Personen haben bereits eine Beratung via Live-Chat in Anspruch genommen und 105 Personen haben ein Angebot fur eine Beratung via Live-Chat erhalten, dies jedoch nicht genutzt. Insgesamt 204 Personen haben noch keine Erfahrungen mit Live-Chats gemacht, 98 dieser Personen konnten sich jedoch eine zukunftige Nutzung vorstellen. Fur die empirische Untersuchung wurde ein Modell zur Wirkung von Live-Chats in Online-Shops auf die Kaufentscheidung entwickelt, um daraus Hypothesen abzuleiten, auf deren Grundlage die Untersuchung durchgefuhrt wurde. Die Hypothesen wurden anhand der Befragungsergebnisse der 76 Personen gepruft, die bereits Erfahrungen mit Live-Chats gemacht haben. Die Prufung der Hypothesen hat gezeigt, dass qualitativ hochwertige Live-Chats mit einer hohen System-und Informationsqualitat, in der Lage sind das wahrgenommene Risiko der Kaufentscheidung zu senken, Vertrauen zu dem Online-Shop aufzubauen und positive Kaufentscheidungen zu begunstigen. Live-Chats stellen daher eine Service-Form dar, die von den bisherigen Nutzern positiv bewertet wird und bieten weiteres Potenzial, da auch viele der bisherigen Nicht-Nutzer eine positive Einstellung zu Live-Chats zeigen und sich eine Beratung in Zukunft vorstellen konnen. Da ein Teil der Probanden jedoch kein Interesse an einer Beratung via Live-Chat gezeigt hat, sollten Live-Chats die etablierten Serviceformen zumindest in naher Zukunft lediglich erganzen und nicht ersetzen.

PurposeThe study examines how young working women are motivated by online shopping. The study tests the relationship between Internet self-efficacy (ISE), website aesthetics, and purchase intention through perceived benefit. An investigation of the impact of perceived risk on purchase intention is also carried out.Design/methodology/approachThe paper carried out a quantitative study based on a purposive sample of 180 working women from the Delhi-NCR region of India and used Structural Equation Modelling (SEM) to test hypotheses based on the extended TAM model.FindingsPerceived benefit, website aesthetics, and ISE positively and significantly impact working women's purchase intention. The study also finds an indirect relationship between ISE and purchase intention through perceived benefit. Perceived risk has a negative and insignificant influence on working women's purchase intention for online shopping.Practical implicationsThe study finding reflects that perceived website aesthetics fill the gap between offline and online environments. ISE makes shopping easy and increases the shopper's confidence. A mobile-optimized website with ease of navigation would increase women shoppers' conversion rates on mobile devices, leading to a favourable impact on revenue generation for online retailers.Originality/valueDespite the vast literature on constructs derived from the TAM model, very few studies have researched young women consumers from an emerging economy perspective. The novelty of this research lies in identifying the factors that influence young working women's online shopping intention using smartphone through the glance of ISE and perceived aesthetics in the Indian context.

Online sales have been growing rapidly in recent years. With the growing competition, online retailers have been keen to increase the effectiveness of their e-commerce platforms by providing a more personalised experience and increasing the ”conversion rate” (i.e. the proportion of visits ending in sales). The early identification of those customers who are likely to buy items could significantly improve the ”conversion rate”. In this paper, we present a novel framework of early purchase prediction in online sessions for registered and unregistered consumers as soon as they land on an e-commerce platform. Also, the paper provides extensive analysis of the performance of different data mining models using the proposed framework. Computational experiments on real-world datasets show that the proposed framework produces good results when appropriate session features are selected in the data mining model training stage, even when no products are browsed during the session. Contextual features without navigational data in the sessions can be used for early detection. When users arrive at the e-commerce platform, before any item interaction, we are able to predict which sessions will result in purchases early, with a high accuracy of 90.2 %. When we combine users’ past number of visits and purchase data, the performance has an even a higher accuracy of 95.6 %. The findings in this paper provide an understanding of context features and users’ loyalty related features that can help online shops’ marketing strategies as well as delivering a better user experience through personalised offers and discounts based on users’ early purchase predictions.

This article introduces the concept motivating operation (MO) to the field of online consumer research. A conjoint analysis was conducted to assess the motivating impact of antecedent stimuli on online purchasing. Stimuli tested were in-stock status, price, other customers' reviews, order confirmation procedures, and donation to charity. The results indicate that the concept of MO is applicable to the analysis of the motivating impact of antecedent stimuli on consumer purchase behavior. The advantage of the concept of MO is, first, that it leads to greater understanding of the complex world of contingencies operating within the consumer behavior setting online. Second, the MO account is designed specifically to facilitate intervention as it is formulated in terms of environmental stimuli that can be manipulated directly. This is important for online companies that strive to increase economic earnings from their Web shops by means of increasing customers' conversion rates.

Due to the fast-growing e-commerce, the consumer behavior has changed, and business opportunities have been offered to personalize the experience and streamline operations. Recommendation systems, chatbots, predictive analytics, and computer vision are all examples of Artificial Intelligence (AI) technologies that are transforming online retail in a very critical way. This paper explores how AI can be applied to online shopping using actual online shopping data to conduct experimental research on how AI-based recommendation systems can change consumer behavior and online sales performance. An international e-commerce store consisting of more than 500,000 transactions in a Kaggle dataset were analyzed with the help of collaborative filtering and content-based AI recommendation algorithms. There were comparative experiments with a baseline of a non-personalized system of recommendations and an AI-enhanced one. The most important performance indicators were measured including the click-through rate (CTR), conversion rate (CR), and average order value (AOV). The results obtained showed that AI-based suggestions enhanced CTR (38), CR (24), and AOV (17) in comparison to baseline procedures. These findings show the real effects of AI technologies in enhancing user experience and contributing revenue to an online retail setup. In the end of the study, the methodological implications, limitations and future discussions of incorporating advanced AI methods in the e-commerce ecosystems are discussed

A Web site's conversion rate (the proportion of visitors who complete a desired action) is an important competitive metric. Web retailers invest significant effort in managing functionalities that can attract and convert visitors. Retailers' decisions are often based on tradition or simply follow competitors' efforts. The absence of an informed decision-making process usually leads to significant overlap in marketing efforts and investment in functionalities. This paper uses the two-step clustering algorithm to profile Web retailers in terms of Web site functionalities and Web performance metrics using data on the top 500 U. S. Web retailers ranked by their 2006 annual sales. The study finds an essential set of functionalities and indicates the presence of complementarities among sets of functionalities associated with significantly different rates of conversion and monthly visitation. It also finds different profiles for Web-only retailers versus those that have traditional channels in addition to the Web. These results may be useful for retailers in their decisions on providing Web site functionalities and in managing their conversion rates and other related metrics.

PurposeThis study conducts a systematic literature review to synthesize the extant literature primarily on “online shopping consumer behavior” and to gain insight into “What drives consumers toward online shopping”.Design/methodology/approachThe authors followed guidelines for systematic literature reviews with stringent inclusion and exclusion criteria. The review is based on 79 research papers published from 2000 to 2020 in 21 reputed peer-reviewed international journals. The papers were analyzed and synthesized based on their defining characteristics, methodologies, major constructs and themes addressed.FindingsThe literature synthesis indicated that consumers have to make a trade-off between 11 perceived benefits and six perceived sacrifices to improve their net perceived value before making the final decision to adopt online shopping. It is important to decode these factors as they could improve both the functional and recreational value of the shopping experience for online consumers, resulting in an improvement in conversion rates from a prospect to the final purchase at e-stores. This could improve turnover as well as profits for the e-tailers.Originality/value This study pioneers to consolidate these factors through the lens of the value adoption model. This study also suggests insightful directions for further research perspectives in the online context from both consumers' and retailers' perspectives.

<em>The present study explores the link between customer service features and conversion rates for the 500 largest online retailers in the U.S. market. Twelve distinct customer service features were examined, including </em><em>auto-replenishment, co-branded credit cards, currency conversion tools, free shipping, free return shipping, in-home services such as product installation or in-person consultations, live chats, providing website content in multiple languages, next-day delivery, online return processing, paid memberships with enhanced customer services, and same day delivery. Consistent with previous studies that indicate typical conversion rates in the range of 2-4 percent, the mean conversion rate for the firms in our sample was 3.194 percent. In addition, the findings indicate that customer conversion rates were significantly higher for firms offering auto-replenishment, free return shipping, home services, paid memberships, and same day delivery. As such, managers may want to emphasize these features when developing e-commerce websites. </em>

Online retailers have taken recourse to many smart marketing strategies to sell digital music. This paper investigates the strategic decisions of online vendors for offering different mechanisms such as sampling and online reviews of digital music to increase their online sales. In this research we seek answers to the following research questions (1) should online retailers offer sampling for experience goods such as music CDs; (2) under what circumstances is offering sampling more important than offering reviews. Our empirical study shows that online markets behave as communication markets, and consumers learn about product quality information both passively (by reading online reviews) and actively but subjectively (by listening to music sampling). Using data from Amazon.com , we empirically show that sampling is a strong product quality signal that reduces product uncertainty and attracts interested shoppers. Products with the sampling option enjoy a higher conversion rate (which leads to better sales) than those without it. Second, the impact of online reviews on conversion rate is lower for experience goods with a sampling option than those without. Third, when the uncertainty of the online reviews is higher, sampling plays a more important role because it mitigates the uncertainty introduced by online reviews. We believe this paper makes an important contribution by comparing and studying the interactions between two commonly adopted online marketing strategies (i.e., sampling versus online reviews) and provides important insights on which strategy is beneficial for vendors in the context of online selling of digital music.

In a recent paper we have shown how Internet retailers could optimize their price levels according to their strategy. The discussed method optimizes short-term profitability by determining the exact demand curve. The method involves the application of empirical price tests. For this purpose visitors of an Internet retailer are divided in statistically identical subgroups. Using the A-B testing method different prices are shown to each subgroup and the conversion rate as a function of price is calculated. We describe the organizational requirements, the technical approach, and the statistical analysis applied to determine the price optimizing the per-order profit and the average customer lifetime value. In this paper we review the results of a field study carried out with a large Internet retailer and shows that the company was able to optimize a specific price component and thus increase the contribution margin per order by about 7%. In addition we argue that at the same time the customer lifetime value could be enhanced by 13%. We conclude that the discussed method could be applied to answer further research questions such as the temporal variation of demand curves.

Price dispersion in the Internet has attracted attention from practitioners and academics alike, since it enables companies to adjust prices to a level appropriate to their strategy. This paper demonstrates how Internet retailers can optimize short-term profitability by determining the price elasticity of demand based on empirical price tests. For this purpose visitors of an Internet retailer are divided into subgroups of approximately same size and identical characteristics. Using A-B tests different prices are shown to each subgroup and the conversion rate as a function of price is calculated. We describe the organizational requirements, the technical approach, and the statistical analysis applied to determine the price optimizing the per-order profit. A field study carried out with a large Internet retailer is presented and shows that the company was able to optimize the analyzed price component and thus increase the contribution margin per visitor by about 7%. We conclude that the discussed method could be applied to answer further research questions such as the temporal behavior of demand curves.