Article

Personalised Digital Marketing Recommender Engine

Authors:
  • KIIT, Deemed to be University
  • Pennsylvania State University Harrisburg
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Abstract

E-business leverages digital channels to scale its functions and services, and operates by connecting and retaining customers using marketing initiatives. To increase the likelihood of a sale, the business must recommend additional items that the customers may be unaware of or may find appealing. Recommender Engine (RE) is considered to be the preferred solution in these cases for reasons that include delivering relevant items, hence improving cart value, and boosting customer engagement. The paper describes a model for delivering real-time, personalised marketing information concerning the recommended items for online and offline customers, using a blend of selling strategies: up-selling, cross-selling, best-in-class-selling, needs-satisfaction-selling and consultative-selling. The model further defines the e-marketplace by clustering items, customers and unique selling proposition (USP), and then gathering, storing, and processing transactional data, and displaying personalised marketing information to support the customer in their decision-making process, even when purchasing from large item spaces. An experimental study using a quantitative research methodology was conducted in a mid-size healthcare retailer, based out of India, to determine the tangible benefits. The model was tested with 100 online customers and, with the adoption of the proposed methodology, the results indicated growth in average monthly revenue (33.49%), Average Order Value (AOV) (32.79%,) and Items per Order (IPO) (1.93%).

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... Theory Practise Survey Topic Theory Practise [16] Economics X ✓ [17] Stock market X ✓ [18] Digital marketing ✓ ✓ [19] Finance ✓ X [20] Multimedia content ✓ X [21] Travel X ✓ [22] Health ✓ ✓ [7] Health ✓ X [23] Health ✓ X [24] Health ✓ X [25] Health X ✓ [26] Health ✓ X [27] E-learning ✓ X [6] E-learning ✓ ✓ [28] E-Learning X ✓ [29] Machine learning ✓ X [30] Knowledge integration ✓ X [31] Explainability ✓ X [32] Context awareness ✓ X [33] Context awareness ✓ X [34] Collaborative filtering ✓ X [35] Collaborative filtering ✓ X [36] Hybrid methods ✓ X [37] Sequence awareness ✓ X [38] Session integration ✓ X [39] Session integration ✓ X [40] Conversation integration ✓ X [41] Music ✓ ✓ Continued on next page [42] Music ✓ X [43] Reinforcement learning ✓ X [44] Adversarial methods ✓ X [45] Review texts ✓ X [46] Graph neural network ✓ X [47] Graph Neural network ✓ X [48] Graph Neural network ✓ X [49] Deep learning ✓ X [8] Large Language Models ✓ X [50] Large Language Models ✓ X [51] Large Language Models ✓ X [52] Large Language Models ✓ X [53] Large Language Models ✓ X [54] Large Language Models ✓ X [55] Large Language Models ✓ X [56] Aspect integration ✓ X [57] General ✓ X [4] General ✓ X [58] News X ✓ [59] News ✓ X [60] News ✓ X [61] Privacy ✓ X [62] Tourism ✓ X [63] Evaluation ✓ X [3] General ✓ X [64] General ✓ X [65] Trustworthiness ✓ X [66] Cultural Heritage X ✓ Difference: Our survey covers the theory of RS and the application of its methods in practice. ...
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Recommender Systems (RS) play an integral role in enhancing user experiences by providing personalized item suggestions. This survey reviews the progress in RS inclusively from 2017 to 2024, effectively connecting theoretical advances with practical applications. We explore the development from traditional RS techniques like content-based and collaborative filtering to advanced methods involving deep learning, graph-based models, reinforcement learning, and large language models. We also discuss specialized systems such as context-aware, review-based, and fairness-aware RS. The primary goal of this survey is to bridge theory with practice. It addresses challenges across various sectors, including e-commerce, healthcare, and finance, emphasizing the need for scalable, real-time, and trustworthy solutions. Through this survey, we promote stronger partnerships between academic research and industry practices. The insights offered by this survey aim to guide industry professionals in optimizing RS deployment and to inspire future research directions, especially in addressing emerging technological and societal trends
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Artificial intelligence (AI)-powered tools such as recommendation engines are widely used in online marketing and e-commerce; however, online retailers often deploy these tools without understanding which human factors play a role in which products and at which stage of the customer journey. Understanding the interaction between AI-powered tools and humans can help practitioners create more effective online marketing platforms and improve human interaction with e-commerce tools. This paper examines customers' reliance on recommendation engines when purchasing fashion goods, electronics, and media content such as video and music. This paper also discusses the potential for improvement in recommendation engines in online marketing and e-commerce.
... The hypothesis is supported by a p-value of less than 0.05, which is considered statistically significant [123]. Table 6 summarises the direct effect and provides data supporting hypotheses H1a to H1c, H2a to H2C, and H3. ...
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While often overlooked, the snack industry is a quintessential aspect interweaved into almost all aspects of American society. Researchers have found that the snack industry is developing in digital media, but there are still research gaps in the industry's development prospects. As such, this essay analyzes Mondelez Food Company, exploring Mondelez Food Companys current promotional strategy and market position, arguing that Mondelz Food Companys highly successful visibility and influence is due to excellent digitalization marketing. Platforms and advertising, as well as the addition of new markets and expansion of existing markets, have helped the company's finances maintain a healthy growth rate. Mondelezs current strategy includes highlighting the companys brand image and outstanding digital media presence, both of which have now helped the company serve as a consistent leader in the snack industry. In particular, SWOT analysis shows that Mondelez's advantages lie in its good financial status, brand image and advertising. In contrast, some factors that may potentially cause harm to the companys future rising costs, fierce industry competition and health problems caused by products. However, this paper will demonstrate that product diversification, expansion into emerging markets and finding lower-cost alternatives could help the company grow further.
... For example, if a customer is buying a camera, the RE might suggest related accessories like a camera bag, lenses, or a tripod. Up-selling works by recommending higher-priced or premium versions of products, RE [210] aim to increase the average order value. For instance, if a customer is looking at a particular smartphone, the engine might suggest an upgraded model with additional features. ...
... Amidst the rapid development of financial technology, personalized financial product recommendation systems have emerged as a pivotal technology within the financial services sector (Behera et al., 2020). This system aims to offer users personalized recommendations of financial products by analyzing their financial behaviors, preferences, and needs, thereby enhancing user experience and the efficiency of financial services. ...
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With the rapid evolution of financial technology, the recommendation system for financial products, as a crucial technology to enhance user experience and reduce information search costs, is increasingly becoming the focus of the financial services sector. As market competition intensifies, the diversity of user demands, coupled with the continuous expansion of financial product types, has exposed limitations in traditional recommendation systems regarding accuracy and personalized services. Therefore, this study aims to explore the application of deep learning technology in the field of financial product recommendations, aiming to construct a more intelligent and precise financial product recommendation system. The metrics we focus on include precision, recall, and F1-score, comprehensively evaluating the effectiveness of the proposed methods. In terms of methodology, we first employ a Transformer model, leveraging its powerful self-attention mechanism to capture the complex relationships between user behavior sequences and financial product information.
... Scientists report this format to have immense potential and be a convenient channel of communicative interaction in the "brand-client" system, providing an opportunity to create personalised communication and offer exclusive content, etc. [9]. However, at the same time, the electronic newsletter is considered only as a tool of electronic marketing, and, therefore, we either encounter a slight mention of it [1], or researchers describe the specifics of the format in a few paragraphs in the review sections of their scientific works, unfortunately, without paying much attention to the detailed analysis, which newsletters, definitely deserve [11; 13; 21]. A different situation can be observed taking into consideration publications that focus on providing practical recommendations and advice to those who seek to increase the effectiveness of their own brand in the e-commerce system. ...
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The article substantiates the necessity to expand research methodologies into the electronic newsletter format. It was acknowledged that modern scientific discourse reluctantly chooses the electronic bulletin as an object of research. At the same time, scholars concur with the fact that this format demonstrates potential while being a user-friendly channel of communicative interaction as well as providing an opportunity to create personalised communication and offer exclusive content, etc. The article examines the examples of the most sought-after newsletters such as: editorial newsletters from The New York Times, aggregated newsletters from SmartBrief, Think with Google resource, The Menu newsletter from SparkToro technical startup, Confident Computing's latest technology newsletter from Ask Leo! website and the female-focused Daily Skimm newsletter from theSkimm media company. The specifics, characteristics, and advantages of this form of interaction with the audience are analysed. It is concluded that tailored personalised content that the user receives via e-mail allows the company to effectively communicate, interact with its target audiences, increasing the level of engagement, building trust and upholding reputation. In this case, the indicators of the website opening, clickability and conversion, make it possible to improve the strategy of email distributions, which will ensure better results. The electronic newsletter is a modern, up-to-date format which helps to perform effective multi-functional social communication, provided it contains interesting, high-quality, interactive, scheduled content aimed at the target audience. Keywords: electronic newsletter; e-mail; e-mail distribution; еmail-marketing; promotion; subscribers; interaction; engagement
... The system analyzes these attributes and creates a profile that captures the essence of the item [9]. To generate recommendations, the content-based system first creates a user profile [10] that represents the preferences or interests of the user. This profile is constructed by analyzing the items the user has interacted with or rated positively. ...
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Chapter
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Chapter
The retail sector is one of the essential players in both local and global economies. It has a considerable footprint on economic development by playing a critical role in distributing and consuming goods and services. This sector's economic contribution to country’s GDP is immense. Also, it facilitates economic growth by creating a platform for the consumption of any other sector’s goods and services.
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Conference Paper
In the rapidly evolving landscape of e-commerce, the need for advanced personalization strategies has become paramount. This research endeavors to develop a cutting-edge Convolutional Neural Network (CNN)-driven personalization engine explicitly tailored for real-time content marketing in ecommerce platforms. The proposed system goes beyond traditional methods by dynamically analyzing user behavior in real-time, utilizing CNN’s capabilities to interpret and respond to visual elements within the e-commerce platform. The methodology involves the integration of CNN algorithms into the personalization engine architecture, coupled with extensive data collection from various sources, including user interactions, preferences, and historical data. The CNN-driven engine aims to provide instantaneous and personalized content recommendations, enhancing user experience and engagement. Evaluation metrics encompass user interaction patterns, clickthrough rates, and conversion rates. This research contributes a novel perspective to the field of e-commerce personalization by leveraging CNN technology, promising not only enhanced accuracy in content recommendations but also the ability to adapt in real-time to the ever-changing preferences of users. With an accuracy rate of
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The research objectives are to assess the digital marketing capabilities of micro, small, and medium-sized enterprises (MSMEs) in Thailand’s service sector, as well as to develop persona profiles for each enterprise size. The survey of 400 digital marketing personnel was conducted alongside comprehensive interviews with 10 marketing executives. The result suggests that MSMEs have moderate digital marketing skills but need to improve their knowledge in creative thinking, online consumer interaction, affiliate marketing, and change management. An executive interview emphasized issues unique to micro-enterprises, like mismatched advertising and inadequate online marketing expertise. Medium-sized enterprises have difficulties achieving online target engagement due to their inadequate SEO capabilities, whereas small enterprises struggle to effectively manage customer inquiries. The study introduces persona cards that reveal distinct competency levels and areas for improvement for each enterprise size. The research provides actionable insights for MSMEs seeking to refine their digital marketing strategies and contributes to the academic literature with understanding. Persona cards serve as a novel tool for visualizing digital marketing competencies and needs within the MSME sector.
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Brand loyalty has become a vital need in today’s competitive market. It’s not only about repeat purchase intention that creates brand loyalty but also about the content of digital platforms.
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The integration of artificial intelligence (AI) into customer retention strategies is revolutionising service marketing by leveraging advanced technologies to enhance customer engagement and loyalty. This abstract explores how AI, including machine learning, natural language processing (NLP), and predictive analytics enables businesses to gain deep insights into customer behaviours, predict preferences, and deliver personalised experiences. By analysing vast datasets from diverse sources, AI facilitates effective customer segmentation and targeted marketing campaigns tailored to individual needs and preferences.
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In an era characterized by rapid technological advancements and digital proliferation, the landscape of marketing is undergoing a profound transformation. As businesses navigate the complexities of this digital realm, the dichotomy between the potential benefits and inherent risks of digital marketing has emerged as a pivotal concern. It is within this context that the conference "Exploring the Nuances of Digital Marketing: Boon or Bane?" convened on the 23rd of February, 2024, at the Kolkata Lake Club. Organized by Knowgen Education Services Pvt. Ltd., the conference served as a platform for scholars, industry experts, and practitioners to deliberate on the multifaceted dimensions of digital marketing in the Indian context. Against the backdrop of the chosen theme, the conference sought to unravel the intricacies of digital marketing, scrutinizing its potential as both a catalyst for growth and a harbinger of challenges. The conference volume encapsulates the culmination of these deliberations, featuring five compelling articles authored by esteemed professionals at the forefront of their respective fields. From insightful analyses of digital marketing's impact on Indian businesses to strategic approaches for fortifying client data against cyber threats, each article offers a unique perspective, enriching our understanding of the complexities inherent in digital marketing practices. The chosen theme, "Exploring the Nuances of Digital Marketing: Boon or Bane?" stands as a beacon guiding our collective inquiry into the intricate dynamics of digital marketing. It encapsulates the fundamental question that pervades the minds of industry practitioners, scholars, and policymakers alike: Does digital marketing herald a new era of unprecedented opportunities, or does it pose unforeseen risks and challenges to businesses and society at large? This theme serves as a catalyst for critical reflection, inviting participants to delve deeper into the complexities of digital marketing practices and their broader implications. Saurav Bhattacharjee, Senior Manager Marketing at Wipro Enterprises Pvt. Ltd., offers invaluable insights into the dichotomy of digital marketing in India, pondering whether it stands as a boon or a bane for businesses. Ferah Diba Izgi, a multifaceted researcher, author, psychotherapist, and software development engineer from Germany, navigates the opportunities, challenges, and future trends of digital transformation in business. Dr. Amit Chakladar, Principal at Techno India, Hooghly, along with Nilava Nandi, a seasoned member of various corporate committees, sheds light on the intersection of artificial intelligence and behavior sciences in combatting financial crime. Ajoy Kumar Mallick, an academic researcher and writer, unlocks the dynamics of email marketing in the digital era, offering pragmatic strategies for unlocking customer engagement. Lastly, Soham Roy, Partner of E and O Learnet and Chief Editor of Management Ind-Academia, in collaboration with Amit Chakladar, provides proven strategies for fortifying client data against cyber threats, drawing insights from contemporary literature. As we delve into the pages of this conference volume, we embark on a journey of exploration, introspection, and enlightenment. It is our sincere hope that the insights gleaned from these articles will not only inform but also inspire, fostering a deeper appreciation for the nuanced landscape of digital marketing and empowering stakeholders to navigate its intricacies with confidence and foresight.
Chapter
In this chapter, we will discuss the applications of various martechs in different activities and operations related to digital marketing, including value creation and capture; customer collaboration and co-creation; digital segmentation, targeting and positioning; integrated digital marketing communication; digital branding; digital consumer behavior; product management and development; price management; delivery and tracking; customer relationship, experience and journey; digital platforms and digital marketing channel management; digital selling and retailing; and digital evidence management.
Chapter
In this chapter, first, we discuss digital marketing conceptualization including its definitions, pillars, requirements, functions, implications and platforms for developing digital marketing. Then, we explain digital marketing management, including digital marketing analysis; digital marketing strategy; digital marketing implementation; digital marketing management models such as SOSTAC marketing planning model and RACE framework for digital marketing; digital marketing versus traditional marketing; and future of digital marketing.
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In this chapter, we will discuss different elements of digital marketing implementation, including tactics, actions, evaluation, correction and improvement.
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While marketing scholars have emphasized the importance of customer satisfaction, few studies have examined in detail consumers’ responses to dissatisfaction. This study examines correlates of one possible response—telling others about the dissatisfaction—and identifies variables that distinguish this response from others. Variables investigated include the nature of the dissatisfaction, perceptions of blame for the dissatisfaction, and perceptions of retailer responsiveness. Marketing management and consumer behavior research implications are discussed.
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In this study, we describe a recommendation system for electronic books. The approach is based on implicit feedback derived from user's interaction with electronic content. User's behavior is tracked through several indicators that are subsequently used to feed the recommendation engine. This component then provides an explicit rating for the material interacted with. The role of this engine could be modeled as a regression task where content is rated according to the mentioned indicators. In this context, we benchmark twelve popular machine learning algorithms to perform this final function and evaluate the quality of the output provided by the system.
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Marketing digital offre un contenu adapté au public francophone grâce à l'intégration de nombreux cas et exemples d'entreprises françaises.C'est aussi un outil pédagogique particulièrement riche qui comporte 150 tableaux et fi gures, 40 mini-cas illustrant des situations professionnelles réelles, 20 activités au fi l de la lecture ainsi qu'une étude de cas par chapitre (Dell, Amazon, Google, etc.).Par sa démarche unique d'embrasser l'ensemble du sujet de façon approfondie et pédagogique, ce livre s'impose de fait comme la référence incontournable pour tous ceux qui étudient le marketing digital et souhaitent se mettre à niveau dans ce domaine.Dans cette nouvelle édition :L’actualisation ou le renouvellement de toutes les études de cas.La présentation et l’intégration des dernières évolutions dans le domaine du marketing digital :- Chapitre 4 : le customer journey (parcours client, nouvelles méthodes de tracking, magasins connectés) et les modèles d’attribution ; - Chapitre 5 : la gestion des contenus numériques, le rôle central du mobile (géolocalisation, paiements, applications) ;- Chapitre 6 : le programmatique, son fonctionnement, ses évolutions et ses limites ;- Chapitre 7 : l’évolution des réseaux sociaux (usages, monétisation, marketing viral), les nouvelles plateformes sociales, les médias digitaux (télévision numérique, affi chage digital…) ;- Chapitre 8 : le marketing automation et l’intelligence artificielle.Et, tout au long de l’ouvrage, la réalité augmentée, les chatbots, les big data, etc.
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Nowadays, the exponential advancement of social networks is creating new application areas for recommender systems (RSs). People-to-people RSs aim to exploit user’s interests for suggesting relevant people to follow. However, traditional recommenders do not consider that people may share similar interests but might have different feelings or opinions about them. In this paper we propose a novel recommendation engine which relies on the identification of semantic attitudes, that is, sentiment, volume, and objectivity extracted from user-generated content. In order to do this at large-scale on traditional social networks, we devise a three-dimensional matrix factorization, one for each attitude. Potential temporal alterations of users’ attitudes are also taken into consideration in the factorization model. Extensive offline experiments on different real world datasets, reveal the benefits of the proposed approach compared with some state-of-the-art techniques.
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As far as new communication channels are concerned, there have been extensive developments in communications and marketing in digital era. Today, therefore, companies try to take advantage of digital marketing channels to provide suitable services to customers to improve their satisfaction level. However, this study aimed to identify and assess factors affecting skill gap in digital marketing. This was descriptive correlation study. The population consisted of experts in communications industry to identify most important skill gaps in digital marketing and factors affecting them; also, managers and specialists of these companies were investigated to determine the role of identified factors in reducing skills gaps. Using localized questionnaire and interviewing with ten experts who were selected by Delphi snowball method, the skill gaps in marketing and factors affecting them were identified. Also, a researcher made questionnaire with 32 questions was distributed among 226 employees to investigate the identified factors role in reducing skills gap in digital marketing. The results showed that from four identified factors, the components including operational strategic factors and environmental factors had direct and positive impact on creating skill gap in digital marketing of studied companies. The environmental factors such as social and cultural conditions, religion, technology, and economy had more proactive impact on skills gap in digital marketing. Also, the results showed that among skills gaps in digital marketing of studied companies, the skills (Principles of Communication) and (Predicting Future) had the highest and lowest gaps, respectively.
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Recommender systems use algorithms to provide users product recommendations. Recently, these systems started using machine learning algorithms because of the progress and popularity of the artificial intelligence research field. However, choosing the suitable machine learning algorithm is difficult because of the sheer number of algorithms available in the literature. Researchers and practitioners are left with little information about the best approaches or the trends in algorithms usage. Moreover, the development of a recommender system featuring a machine learning algorithm has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This work presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for the software engineering research field. The study concluded that Bayesian and decision tree algorithms are widely used in recommender systems because of their low complexity, and that requirements and design phases of recommender system development must be investigated for research opportunities.
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Purpose – The purpose of this paper is to propose that consumer choice be guided by price fairness judgements to increase consumer satisfaction and subsequently enhance market efficiency. Consumers en masse lack the information to judge price fairness, thereby causing their ability to influence the economy to be overlooked. Design/methodology/approach – This is an argumentative and conceptual work that aims to initiate a debate on this important yet unexplored issue. The arguments presented in the paper are based on economic and technological considerations. Findings – The measure for enabling a consumer price fairness judgement is unit cost information – the cost incurred by a firm to produce a product and/or service. The benefits and challenges stemming from the availability of unit cost information (i.e. cost transparency) to consumers and companies are presented and the likely impact of cost transparency on addressing information asymmetries between buyers and sellers are discussed. Originality/value – Although a significant body of knowledge exists on issues such as price transparency and how it is driven and enabled by the growth of the Internet, there is little or no evidence of research yet on issues related to cost transparency. The authors believe this work would create a new line of research for scholarly community leading to an impact on practice.
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On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems.
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Marketing information systems are those systems which make the gathering, processing, selection, storage, transmission and display of coordinated and continuous internal and external information. Includes systematic and formal methods used for managing all of an organization's information market. Recommendation systems are those systems that are widely used in online systems to suggest items that users might find interesting. These recommendations are generated using in particular two techniques: content-based and collaborative filtering. This paper aims to define a new system, namely Marketing Recommender System, a system that serves marketing and uses techniques and methods of the digital economy.
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Bringing communities of buyers and sellers together in the arena of electronic commerce stimulates three major potentials: the building of trust, the collection and effective use of community knowledge and the economic impacts of accumulated buying power. In this context, we introduce the concept of Virtual Communities of Transaction and review important personalization approaches which we may utilize in their design: collaborative filtering, data mining, and techniques to optimize the user interface and the underlying product offerings. The key contributions of this paper are the elaboration of Virtual Communities, the presentation of a categorization scheme for different types of communities, the identification of classes of member profiles, and the innovative concept of community products. We conclude with the case of the Amazon.com Recommendation Center to illustrate key design ideas and discuss an evolutionary application, the Participatory Product Catalogue.
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Alibaba has become the largest online and mobile commerce company in the world in just a few years and barely anyone expected it to be so successful. It has provided numerous business opportunities for small and medium enterprises to leverage the innovative technology to compete more efficiently domestically and globally. This study does not attempt to describe Alibaba as a perfect business; rather it discuss the marketing strategies, promotion, distribution channels and some important lessons that were carried out by Jack Ma to achieve success in this complex world of online trading. In this literature review, we examine a few key factors of Alibaba’s success such as its specific marketing strategies, various challenges, its strong branding image, superior customer value proposition, better shopping experience, huge sales volume and economies of scale.
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This study presents a real-world collaborative filtering recommendation system implemented in a large Korean fashion company that sells fashion products through both online and offline shopping malls. The company’s recommendation environment displays the following unique characteristics: First, the company’s online and offline stores sell the same products. Second, fashion products are usually seasonal, so customers’ general preference changes according to the time of year. Last, customers usually purchase items to replace previously preferred items or purchase items to complement those already bought. We propose a new system called K-RecSys, which extends the typical item-based collaborative filtering algorithm by reflecting the above domain characteristics. K-RecSys combines online product click data and offline product sale data weighted to reflect the online and offline preferences of customers. It also adopts a preference decay function to reflect changes in preferences over time, and finally recommends substitute and complementary products using product category information. We conducted an A/B test in the actual operating environment to compare K-RecSys with the existing collaborative filtering system implemented with only online data. Our experimental results show that the proposed system is superior in terms of product clicks and sales in the online shopping mall and its substitute recommendations are adopted more frequently than complementary recommendations.
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Considerable progress has been made in identifying market-driven businesses, understanding what they do, and measuring the bottom-line consequences of their orientation to their markets. The next challenge is to understand how this organizational orientation can be achieved and sustained. The emerging capabilities approach to strategic management, when coupled with total quality management, offers a rich array of ways to design change programs that will enhance a market orientation. The most distinctive features of market-driven organizations are their mastery of the market sensing and customer linking capabilities. A comprehensive change program aimed at enhancing these capabilities includes: (1) the diagnosis of current capabilities, (2) anticipation of future needs for capabilities, (3) bottom-up redesign of underlying processes, (4) top-down direction and commitment, (5) creative use of information technology, and (6) continuous monitoring of progress.
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Advancements in digital technology and devices enlarge dimensions of e-commerce, reforming the ways that consumers shop and purchase products and services. In particular, the mixed use of online, mobile, and offline channels and devices for shopping provides B2C firms with unprecedented challenges and opportunities to develop effective segmentation approaches that capture multitude of newly emerging consumers’ shopping patterns. This paper aims to classify consumers along with their shopping patterns and channel preferences by using rank order survey data from Korean and American consumers on their path-to-purchase behaviors. Cluster analysis and Association Rule Mining (ARM) are applied for segmentation and its characterization. Relative importance of path-to-purchase factors such as information search location, payment method, delivery option, and payment location are assessed to determine the differences in Korean and American consumers regarding their shopping patterns and preferences. Network visualization of rules shows the differences in shopping preference and patterns of Korean and US consumers both at micro and macro levels.
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In the modern retail environment, customers continuously rely on other consumers for assistance when interacting with retailers’ digital technologies. This study examined if technology acceptance drives affective commitment and ultimately existing users to assist potential users, as measured by direct customer citizenship behaviour (advocacy, help in using the technology) and indirect customer citizenship behaviour (tolerance and feedback to the retailer for improvement). The sample consisted of 533 electronic banking customers. All research hypotheses were supported. Practically, the research findings direct retailers on the strategies required to ensure customers engage in direct and indirect citizenship behaviour to assist fellow customers in interacting with the digital technologies, and to improve the customer retail experience. Theoretically, the study extends the extant research on technology acceptance by providing more insight into its connection with customer citizenship behaviour directed towards fellow customers and retailers in the post-consumption stage of digital technology and the extent to which affective commitment strengthens these relationships.
Article
Social media platforms can be a promising tool for retailers’ marketing campaigns. Especially for the purpose of new product introductions, social media may facilitate social interaction and online word-of-mouth and therefore, may broaden the reach and accelerate the diffusion of information about the new product. The impact of online word-of-mouth communication and social interaction on consumer behavior has been extensively analyzed in previous research. However, little knowledge exists so far on the influence of social media campaigns on new product introductions. Therefore, the goal of this study is to analyze the impact of a social media campaign on the success of a new product introduction by using survey as well as behavioral data. The data stems from an online community related to a social media tryvertising campaign implemented to promote the introduction of new high-end binoculars. The results of a mediation analysis show that campaign-related factors positively influence consumers’ attitude toward the new product, which in turn mediates the positive influence on purchase intention and recommendation behavior. Furthermore, a post-hoc analysis shows the importance of community members’ activity on the success of the new product introduction.
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An extensive study of 2348 individuals’ preferences for digital touchpoints sheds light on the perceived importance of websites, email, search engines, chat, social networks, photo and video content communities, discussion forums and blogs. Latent class analysis reveals four distinct segments: anti-digital, anti-social media, majority, and digital channel enthusiasts. A detailed look at the characteristics of the segments, including their technology readiness, internet use, and demographic factors, shows that the greatest difference across the segments lies in their overall technology readiness. We find that functional touchpoints (email, websites, and search engines) are the preferred digital touchpoints among all the segments.
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The purpose of this experimental study was to examine the effects of digital product presentation on consumer information processing and behavioral intent in apparel e-retailing contexts. The Stimulus-Organism-Response Model and Dual Coding Theory were used as theoretical frameworks. This research employed a 2 (visual: large vs. small) x 2 (verbal: concrete vs. abstract) between-subjects factorial design and included Need for Cognition as a moderator. Research findings showed that verbal stimuli which varied in concreteness of product descriptions were more effective in evoking both imagery and discursive processing than were visual stimuli which varied in sizes. Imagery processing was positively associated with behavioral intent. A significant moderating role of need for cognition was found.
Article
The aim of this paper is to explore how luxury brands use new technologies in the context of smart retailing. Building on qualitative data from multiple cases from the luxury industry, our analysis reveals that this sector is conscious of the benefits of using smart technologies as marketing tools, while the effective use of these innovative systems is still limited. However, studies on innovation forces affecting the retail industry are still limited in luxury sectors. The study provides an empirical contribution to the emerging topic of smart retailing with an emphasis on the luxury sector through its in-depth investigation of the usage of smart technologies by the firms studied.
Article
Return policy is a strategic tool widely used by firms to build long-term relationship with their consumers. We develop a novel O2O (online to offline) competition model to address how the competitive return policies can be employed to coordinate the O2O distributions under the manufacturer – traditional retailer supply chain where the manufacturer opens an online channel to compete with the traditional retailer. Our results show that utilizing the revenue sharing plus profit sharing mechanisms, the manufacturer and the traditional retailer can employ different return policies for their respective channels to coordinate the O2O distributions and achieve a Pareto solution for all parties in a manufacturer - traditional retailer supply chain. Particularly when the product is becoming increasingly compatible with online sales, the value of the differential of return policies would further increase for both the manufacturer and the retailer.
Article
Surprisingly, there exists a paucity of research examining the adoption of Internet-based technologies by luxury firms. This represents a major shortcoming in our understanding of how luxury firms maintain the image of their brand, sustain a personal link with customers, and retain an aura of exclusivity as they seek to provide their products and services to increasingly technologically-astute customers. Using content analysis, we present the findings of a qualitative investigation of 92 luxury firm websites across the categories of automobiles, fashion, jewelry, watches, and yachts. Study findings indicate that there are noticeable differences in website characteristics and functionality across sectors. Implications of the results are discussed, noting that decisions about using the Internet for branding and selling, one-way and two-way communications, as well as operational and innovative features, are driven by the characteristics of the products being offered. Avenues for future research are also offered.
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This inductive study elaborates on the value concept and unpacks the process of how value is created in the Swedish food industry by combining the theoretical tool of goal hierarchy with the methods of network analysis. Through real world insights, on how consumers and retail firms act and coordinate their activities to fulfill their roles in consumer value creation, this study proposes a differing perspective that extends the value-in-use concept by identifying four sequential consumer activities. They are (i) reflections about what is important (value) in the specific consumption situation (the focal goal), (ii) the starting point of value creation (selection and application of means), (iii) the process involved in providing and enjoying holistic solutions, and (iv) ultimately its contribution to life fulfillment in the sense that value creation is meaningful. Thus, consumers create value by setting clear differential hierarchical goals that include the identification of resources and consumers’ own capacity before choosing the products and using them. The network analyses also reveal an additional space for supporting value creation: firm to firm interactions which add more resources to providers’ support of consumer value creation. The paper provides new real world insights on how consumers and firms act to fulfill their roles in consumer value creation. This knowledge helps retailers to better identify and manage value creation tools to support their customers in value creation.
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The aim of this paper is to examine and conceptualize how the integration of smartphones is reconfiguring the retailscapes of stores and the implications that this has for retailers and consumers. Departing from an understanding of retailscapes as socio-material assemblages and drawing on an ethnographic study of mobile shopping, this paper shows how the integration of smartphones into the activities of in-store shopping is reconfiguring how store space is enacted. The integration of this digital device reorganizes the activities of shopping, and new informationscapes, socialscapes, and experiencescapes unfold as a result. In the process, stores are reconfigured, agencies change, and the relationship between consumers and retailers is remade.
Article
Evidently, the Internet has resulted in a fundamental shift in retailing practice, creating a shift in both consumer and business behavior, which has been compared to that of the Industrial Revolution. The purpose of this paper is to analyze customer satisfaction in e-commerce market. In particular, we determine the factors that affect customer e-satisfaction and the relationship between customer satisfaction and consumer spending in e-commerce retailing. We focus on how American based e-commerce firms are impacted by these developments and how marketing practices have reflected the developing e-commerce situation. The results show that customer satisfaction does have an impact on consumer spending in American based e-commerce retailers. Further, the relationship between customer satisfaction and consumer spending is positive, where higher e-satisfaction results in more spending in e-commerce. The results also show that there is a direct relationship among e-service quality, e- satisfaction and e-loyalty in terms of online spending by consumers. However, the analysis shows that e-commerce still faces challenges compared with traditional offline retailers since customers cannot feel and try the products, and may end up choosing the products that they do not want.
Article
Recent industry reports indicate that consumers own four digital devices on an average, and switching devices during shopping is the “new normal.” The addition of mobile Internet as a new channel of search and purchase has spurred the adoption of the digital medium, and easy accessibility of the Internet on multiple devices is influencing shopping patterns. A consumer may prefer some channels for search and others for purchase or use a combination of channels to search and purchase simultaneously. As a new channel, it is unclear 1) whether mobile Internet offers greater search or purchase benefits and 2) what type of products are more suitable for mobile Internet search and purchase. In this study, we develop a framework that describes the factors that drive the use of mobile Internet in a multi-channel environment. We test the framework using survey data from a sample of U.S consumers. The main findings from our study indicate that 1) the choice of channel combinations that include mobile relative to other channel combinations increases with an increase in perceived search convenience of mobile channel. 2) in the digital channel, mobile and desktop differ in their utility along search dimensions. The probability of choosing channel combinations that include mobile increases due to search convenience whereas desktop is attractive due to perceived gains of price comparison search; and 3) mobile Internet search increases for consumers searching for utilitarian products. The insights from this study deepen our understanding of how digital media is used in the search-purchase process and have important managerial implications.
Article
This study proposes a decision support framework to help e-commerce companies select the best collaborative filtering algorithms (CF) for generating recommendations on the basis of online binary purchase data. To create this framework, an experimental design applies several CF configurations, which are characterized by different data-reduction techniques, CF methods, and similarity measures, to binary purchase data sets with distinct input data characteristics, i.e., sparsity level, purchase distribution, and item-user ratio. The evaluations in terms of accuracy, diversity, computation time, and trade-offs among these metrics reveal that the best-performing algorithm in terms of accuracy remains consistent regardless of the input-data characteristics. However, for diversity and computation time, the best-performing model varies with the input characteristics. This framework allows e-commerce companies to decide on the optimal CF configuration as a function of their specific binary purchase data sets. They also gain insight into the impact of changes in the input data set on the preferred algorithm configuration.
Article
People worldwide are largely engaged and attached with the web 2.0 technology and Social media platforms. By the same token, businesses start looking at such technologies as effective mechanisms to interact more with their customers. Equally, the related issues of social media marketing have been also the focus of attention for academics and researchers to expand the current understanding about such phenomena over the marketing area. Accordingly, the main aim of this study is to systematically examine and review the current studies that have conducted over the related area of social media and marketing. By reviewing approximately 144 articles, the researchers were able to provide an overview of the main themes and trends covered by the relevant literature such as the role of social media on advertising, the electronic word of mouth, customers’ relationship management, and firms’ brands and performance. In this review, it has also studied the most common research approaches adopted to examine the related issues of social media marketing. Further discussion is also introduced followed by an explanation of the current review limitations and recommended directions to be examined by future studies.
Article
Over one billion people are currently using social media such as social websites (Facebook Newsroom, 2015); consequently, numerous academic scholars have developed interest in studying the use of social media and social networks. However, few studies have focused on examining the core factors of social networks. In this study, we collected studies on social-network-related topics that were published between January 1996 and December 2014, assembling a total of 2565 articles and 81,316 citations. Co-citation analysis and cluster analysis were applied to verify seven main factors regarding social networks: (a) the measure of complex social networks; (b) community structure; (c) strong and weak ties; (d) the evolution of social networks; (e) network structure and relationship; (f) value concept and measurement strategies; and (g) social capital. Finally, the results of this study were further discussed to elucidate the core topics relevant to social networks.
Article
Internet has highly transformed contemporary business practices and presented a new paradigm for business relationships and transactions. Mass market is dead and personalisation is the emerging trend, in fact it has become a necessity in e-commerce. Personalisation simply means individualising the shopping experiences for customers based on data collected about them by marketers. Over last few decades, personalisation has become key element in marketing strategy of e-commerce firms. While personalisation is a buzz word today but conceptually it still lacks clarity. Various academicians and practitioners have expressed different viewpoints on personalisation. In this paper, we try to synthesise various viewpoints on personalisation by analysing key themes, components and approaches in literature to describe the concept of personalisation. The paper also highlights customers' attitudes towards personalisation.
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This paper contributes to the service marketing literature with a focus on deal-of-the-day (DoD) website shopping. The work explores drivers of adoption of DoD shopping among young consumers. We show that value conscious consumers are less oriented towards DoD while deal-prone consumers are more likely to purchase DoD. In contrast to previous research, which found that price savings are the main reason for coupon use, our study finds that Enjoyment plays a major role in young consumers’ DoD shopping behaviour. DoD platforms could leverage Enjoyment to create a compelling value proposition for both consumer and merchant attraction and retention.
Article
Implicit feedback such as users’ examination behaviors have been recognized as a very important source of information in most recommendation scenarios. For recommendation with implicit feedback, a good similarity measurement and a proper preference assumption are critical for the quality of personalization services. So far, the similarities used in the state-of-the-art recommendation methods include the predefined similarity and the learned similarity; and the preference assumptions include the well-known pairwise assumption. In this paper, we exploit the complementarity of the predefined similarity and the learned similarity via a novel mixed similarity model. Furthermore, we develop a novel recommendation algorithm, i.e., pairwise factored mixed similarity model (P-FMSM), based on the mixed similarity and pairwise preference assumption. Our P-FMSM is able to (i) capture the locality of the user-item interactions via the symmetric predefined similarity, (ii) model the global correlations among items via the asymmetric learned similarity, and (iii) digest the uncertain implicit feedback via the pairwise preference assumption. Empirical studies on four public datasets show that our P-FMSM can recommend significantly more accurate than several state-of-the-art methods.
Article
We develop and describe a framework for research in digital marketing that highlights the touchpoints in the marketing process as well as in the marketing strategy process where digital technologies are having and will have a significant impact. Using the framework we organize the developments and extant research around the elements and touchpoints comprising the framework and review the research literature in the broadly defined digital marketing space. We outline the evolving issues in and around the touchpoints and associated questions for future research. Finally, we integrate these identified questions and set a research agenda for future research in digital marketing to examine the issues from the perspective of the firm.
Article
Over the past 15 years, digital media platforms have revolutionized marketing, offering new ways to reach, inform, engage, sell to, learn about, and provide service to customers. As a means of taking stock of academic work’s ability to contribute to this revolution, this article tracks the changes in scholarly researchers’ perspectives on three major digital, social media, and mobile (DSMM) marketing themes from 2000 to 2015. The authors first use keyword counts from the premier general marketing journals to gain a macro-level view of the shifting importance of various DSMM topics since 2000. They then identify key themes emerging in five-year time frames during this period: (1) DSMM as a facilitator of individual expression, (2) DSMM as decision support tool, and (3) DSMM as a market intelligence source. In both academic research to date and corresponding practitioner discussion, there is much to appreciate. However, there are also several shortcomings of extant research that have limited its rel...
Article
This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
Chapter
Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user. In this introductory chapter, we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook. Additionally, we aim to help the reader navigate the rich and detailed content that this handbook offers.