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Personalised Digital Marketing Recommender Engine
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%).
... Today, the most used e-commerce system is the B2C (Business-to-Consumer)-a system characterized by the sale of goods or services at retail directly to the consumer. This includes any retail agreement between legal entities and individuals, e.g., transactions between an online store and a customer, purchase of training courses from registered experts, and software rental [1,2]. ...
... The proposed concept and method of assessing consumer demand in the target market is aimed at the prospective management of trading platforms using cloud technologies. Behera et al. [1] described a model for providing realtime personalized marketing information on recommended products to online and offline shoppers using a combination of sales strategies: up-selling, cross-selling, best-in-class 3 of 21 up-selling, and meeting needs. Authors [1,13,14] analyzed Internet marketing strategies that make it possible to increase the profit from sales. ...
... Behera et al. [1] described a model for providing realtime personalized marketing information on recommended products to online and offline shoppers using a combination of sales strategies: up-selling, cross-selling, best-in-class 3 of 21 up-selling, and meeting needs. Authors [1,13,14] analyzed Internet marketing strategies that make it possible to increase the profit from sales. However, to process them, we need to have high marketing skills, and these processes also take a lot of time. ...
The influence of Internet marketing has grown so much that producers must now reconfigure their businesses from offline operation to online presence simply to meet user expectations. Thus, the development of an intelligent information system for product promotion online is quite relevant. It may lead to automatized selection of competing products and advertising content, a subsequent increase in the effectiveness of advertisements, and a decrease in costs for Internet ad placements. The paper presents the approach for creating an intelligent information system for product promotion in online spaces that makes it possible to reduce advertising costs. A methodology is based on outcomes of own previous studies as well as the flow nature and semantics of data streams. The framework of the proposed intelligent system includes the four key procedures and functions: intelligent formation of keywords for advertising content based on feedback, intelligent formation of product catalogs of online stores, generation of advertising content, and generation of improved advertising content and its targeting generation of text based on keywords. An experimental study confirmed that the effectiveness of posts on social media increased by at least 125%, while the price decreased by 87%.
... Companies seek to make the experience of browsing a digital channel an emotional experience rather than just a traditional sales process. More than just selling, digital channels seek to interact with the customer throughout the buying process, evoke emotions and states of mind common to both businesses and consumers to establish a long-term relationship [67,68], and seek for customers to become loyal to brands [68]. ...
... A personalized marketing campaign generates traffic on digital channels [67,68], helps in selling products and developing long-term relationships with customers. Having personalized recommendations based on the consumer's characteristics and purchase history generates more positive results on parameters such as sales, revenue, and the average order price. ...
... Having personalized recommendations based on the consumer's characteristics and purchase history generates more positive results on parameters such as sales, revenue, and the average order price. Consumers feel that their minds have been read, which generates positive feelings associated with their experiences and enhances the relationship between the company and the consumer [67]. Online product ratings and reviews are crucial for brand image and company reputation. ...
This research establishes the relationship between the digital channels that organizations use to communicate with their audience and the stages of the consumer buying decision process in online stores. Researchers have not treated this relationship in much detail and little-known empirical research has focused on exploring relationships between the two subjects. Establishing this relationship is of crucial importance for organizations and consumers, as it ensures organizations use the digital channels that consumers want. A literature review of digital channels and consumer behavior models was performed, which allowed us to define which are the digital channels and to identify the different models of consumer behavior appropriate for the digital age. A quantitative methodology was used, supported on a questionnaire that allowed us to find out which digital channels are the most appropriate for each stage of the buying decision process. The results show that consumers use more than one digital channel at each stage of the buying decision process and for each stage, a set of digital channels is identifiable that is most preferred. In light of the above, those who are responsible for defining the digital marketing strategy know what types of content they should produce for each digital channel, allowing them to guarantee efficiency in the use of resources while ensuring that consumers get what they want.
... Estamos en un mundo dual con componentes físicos y digítales que se concreta en la aparición de sistemas ciberfísicos (Gubert, 2019). En el mundo digital, las plataformas en línea han facilitado la introducción del marketing electrónico y promete proporcionar nuevas formas de atender a los clientes (Sharma & Sheth, 2004), a partir de la tecnología como motor del marketing en línea (Cummins, Peltier, & Schibrowsky, 2018) que persigue como objetivo principal la comunicación de valor de los productos o servicios a clientes a partir de canales digitales (Behera, Gunasekaran, Gupta, Kamboj, & Bala, 2020). ...
... Así pues, Aswani, Ghrera, Satish, & Arpan (2017) ubica el marketing influencer, la creación de contenido, y el análisis de métricas como KPI para detectar sitios web de influencia. Behera, Gunasekaran, Gupta, Kamboj, & Bala (2020) plantean un modelo que delimita el mercado electrónico, recopilando, almacenando y procesando los datos transaccionales, los motores de búsqueda y la personalización de contenido en tiempo real en las plataformas de comercio electrónico (Kaptein & Parvinen, 2015). Adicional, se intensifica la aplicación de metodologías de investigación en internet como la netnografía, análisis de redes sociales (R. Sharma, Ahuja, & Alavi, 2018), inteligencia de negocios para estimar y potenciar la probabilidad de consideración de un producto o servicio en la búsqueda en línea de los consumidores (Wang, Wei, & Chen, 2013). ...
The purpose of this article is to publish the results of a bibliometric analysis of digital marketing and its relationship with e-commerce, its main trends, its evolution in scientific production, main authors and journals of publication. A bibliometric review methodology was used in an observation window between 1997 and 2020. The results recognised 480 research article-type documents from a search equation formulated in Elsevier's Scopus and 132 documents in Thompson Reuters' Web of Science (WoS). Finally, with the VOSviewer software, six bibliographic clusters (BC) are presented, where new lines of documentary and field research are possibly identified.
... To determine which type of content companies want to develop for a content marketing strategy, they should first perform audience mapping to understand better their target audience (Bruce et al., 2017). In this respect, Behera et al. (2020) showed that critical concepts could be linked to buyer personality (Lehnert et al., 2020) or customer journeys (Tueanrat et al., 2021) that define the main user profiles. Then, based on these profiles, creative ideas can be proposed for developing profitable content that makes the company spend less resources and increase the sustainability of its digital strategies. ...
... As indicated above, and following Stone (2015), the ideation of the content and its planning should have a relevant meaning for the audience and thus prevent the content from being discarded when users access a digital platform or social media. In addition, this generated content should create a connection between the brand's stories, on the one hand, and customers' anxieties, desires, and needs, on the other hand (Behera et al., 2020). As noted by Taiminen and Karjaluoto (2015), the most critical points of content marketing are related to its distribution, as products and services in operation strategies. ...
Content marketing involves producing and distributing content effectively and initially through digital channels. However, digital marketing strategies and business models can succeed only if content marketing is developed correctly. This study aims to develop a relevant theoretical framework linked to content marketing and identify the leading techniques and uses linked to its development. In this context, we developed an innovative data-driven methodology consisting of three steps. In the first phase, sentiment analysis that works with machine learning was conducted with Textblob, and four experiments were performed using support vector classifier, multinomial naïve bayes, logistic regression, and random forest classifier. First, we aimed to increase the accuracy of sentiment analysis (negative, neutral and positive) of a sample of user-generated content collected from the social network Twitter. Second, a mathematical topic-modelling algorithm known as latent dirichlet allocation was used to divide the database into topics. Finally, a textual analysis was developed using the Python programming language. Based on the results, we identified 11 topics, of which four were positive (Smart Content, Video Marketing, Podcast, and Influencer Marketing). Six of them were neutral (Content Personalization, Social Media Posts, Blogging, search engine optimization, Advergames, and NFTs), and one was negative (Email Marketing). Our results suggest that companies should use content personalisation ethically, mainly when AI-based techniques are used to predict user behaviors. While content marketing strategies are a fundamental part of digital marketing tactics, they can elicit changes in user online behavior when Big Data or AI algorithms are used. This fact raises concerns about the non-ethical design of online strategies in digital environments and the imperative that content marketing strategies should not be developed with purely economic and profitability interests.
... examine the problems of applying digital marketing tools to target audiences of premium brands and luxury products. Behera et al. (2020) examine the personalization of digital marketing. Dwivedi et al. (2021) analyze the impact of information quality in the digital space on consumer behavior. ...
... This study highlights a number of trends in the development of digital marketing tools in the context of digitalization and changes in the socio-economic environment. This is supported by the study of Behera et al. (2020), emphasizing the trend of personalization of digital marketing. Chang et al. (2019) notes the trends of real-time analytics development in the plane of digital marketing, in particular, on the example of the hotel and recreation industry. ...
Under modern conditions, the problem of achieving marketing and business goals is actualized for companies in all sectors of the economy. Digital marketing plays a key role in effectively solving this problem. The purpose of the paper is to highlight the features of the development of digital marketing tools in the context of product promotion on social media platforms. The article highlights the features of planning and implementation of digital marketing in the context of the tasks of promoting goods and services in the digital space. The key role of digital marketing in the implementation of the company's marketing and business tasks is emphasized. The paper highlights a number of trends in the development of digital marketing tools in the context of digitization and changes in the socio-economic environment. The importance of considering the evolution of digital marketing tools in the context of the development of the exogenous and endogenous environment is emphasized. The directions of changes in the digital marketing paradigm are outlined in historical retrospect. The stages of the evolution of digital marketing in the context of marketing and market management are formulated. It is proposed to consider digital marketing as a new paradigm of company management in the conditions of comprehensive digitalization. The peculiarities of digital marketing in terms of individual social networks have been revealed. It is proposed to profile the main social media according to close criteria, which made it possible to place them in priority places in the configuration of the digital marketing company's complex. The most advanced toolkit of digital marketing is considered. Emphasis is placed on the importance of the efficiency principle in the development of the company's digital marketing complex within the framework of the tasks of promoting goods and services in social networks. The results of this study may be useful to practicing marketers, marketing directors, top managers and owners of small and large businesses, and researchers in the field of digital marketing. Prospects for further research are the analysis of the features of the use of digital marketing tools to strengthen the integration of digital channels, as well as the identification of the features of digital marketing tools during a period of significant socio-economic turbulence in Ukraine.
... Ayrıca, öneriler yalnızca ürünler ve öğeler için değil, öğe kategorileri, ilgili sorgu önerileri ve markalar için de geçerli olduğu belirtilmiştir [24]. [25] numaralı referansta yapılan çalışmada, daha önceki hiçbir araştırmanın, çeşitli satış stratejileri bağlamında tavsiyenin sağlandığı perspektife ve stratejilerin müşteriler, ürünler ve USP ile nasıl bağlantı kurduğuna dikkat etmemiş olması bakımından önemli olduğu vurgulanmıştır. Daha doğru önerilerde bulunmak için kontrollü değişkenleri ve satış stratejilerini içermektedir. ...
... Önerilen model e-pazarın tasarımı, kişiselleştirilmiş tavsiye modeli ve tavsiye süreci olmak üzere üç katmanlıdır. Modelin gücü, öğeleri e-pazaryeri tasarımına uygun olarak önermek ve önerilen kişiselleştirilmiş öneri modelinin benimsenmesiyle önerilen öneri sürecini takip etmek olarak belirtilmiştir [25]. [26] numaralı referansta yapılan çalışmada, ilk olarak geri bildirim puanı olarak kullanıcının satın alma sayısına dayalı bir kullanıcı-öğe matrisi oluşturarak bu sorunu ele almaktadır. ...
... It has been observed that customer relationship matrices, such as customer satisfaction and loyalty, are the core predictors of firms' performance measures (e.g., higher future sales growth) (Otto et al., 2020;Trainor et al., 2011). High levels of customer satisfaction lead to customer retention, which generates high revenues and a desirable market position through cross-selling, up-selling, and positive word-of-mouth (Behera et al., 2020). Satisfied customers generally stay with a particular firm; thus, lower costs are incurred due to the benefit of repeat purchases, that ensures the firm's effectiveness in the competitive market environment (Gupta and Ramachandran, 2021). ...
... Algunas de las desventajas de los anuncios en línea para las empresas pueden incluir: Competencia en línea: A pesar de haber anunciado una gran variedad de empresas en línea, la posibilidad de competir por la atención de los consumidores puede ser difícil, especialmente si el mercado está saturado de ofertas similares (Behera et al., 2020). Fraude en línea: Las compañías pueden ser víctimas de fraudes en línea, como clics fraudulentos o impresiones de anuncios falsos, lo que podría derivar en un desperdicio de recursos (Rutz y Watson, 2019). ...
Las empresas emplean los anuncios en línea como una herramienta de marketing digital importante para llegar a una audiencia más amplia y mejorar la visibilidad y la presencia en línea de su marca. En base a ello, el presente articuló tuvo como objetivo general Identificar el impacto del marketing digital en las empresas, para el cual se empleó una metodología de enfoque cualitativo, siendo de revisión bibliográfica de 50 artículos de revistas indexadas, en las que se encontró que el marketing digital es una estrategia que utiliza canales digitales, como el correo electrónico, los motores de búsqueda, las redes sociales y los sitios web, para comercializar productos o servicios. Además, ofrece muchas ventajas, como la capacidad de llegar a una audiencia más amplia, aumentar la interacción del cliente y mejorar la conversión de ventas
... However, even with this information available, seemingly simple tasks such as predicting the customer's purchase intent presents a non-trivial challenge [10,11]. A key reason for this is that the total number of customers, and thus the number of interactions, who visit a website just to browse overshadows the comparatively few customers who actually have the intention to purchase [12,13]. Nevertheless, the potentials of real-time customer purchase prediction are manifold. ...
Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer’s past purchases, his or her search queries, the time spent on a product page, the customer’s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.
... Algorithm is the umbrella word for a finite, rigorous set of mathematical and logical tests for automated decision-making [1]. With the rapid expansion of network users and the rapid emergence of user-generated content (UGC), Internet service providers (ISPs) have increasingly adopted information distribution algorithms (IDA) for customised and personalised content distribution in various sectors, such as audio-visual entertainment [2][3][4], electronic shopping malls [5][6][7], information communities [8][9][10], search engines [11][12][13], to name a few. IDA collects and analyses vast amounts of user usage data to recommend content that users may enjoy, hence boosting user retention or business conversion efficiency. ...
... The exploitation of these big data is in great demand by different actors in order to make more informed decisions in various fields. For example, on the one hand, the analysis of these data would allow to a particular user to customize his activities over time according to his interests and the possibilities that arise (Beladev et al., 2016) (Behera et al., 2020) (Zhou et al., 2022). On the other hand, for a company (through its sales or marketing services), these data would allow to adapt its service offers by anticipating the needs of potential customers according to their interests (Fayyaz et al., 2020) (Esmaeili et al., 2020). ...
... Researchers effectively used users' interests and location to provide recommendations to users [23]. They recommended items that might be interesting for users through both online and offline methods based on multiple sales strategies [24], modeled potential user relationships based on user social information from multiple sources [25], and leveraged information from multiple sources to build users' multiperspective preferences and provided recommendations to them [26]. ...
Personalized recommendation is an important part of e-commerce platforms. In recommendation systems, a neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focus on the results of a single user behavior, ignoring the modeling of multiple interaction behaviors of users, such as click, add to cart and purchase. Although many studies have also focused on multibehavior modeling, two important challenges remain: (1) Since the multiple behaviors of the time-evolving trends of context information are ignored, it is still a challenge to identify the multimodal relationships of behaviors; (2) surveillance signals are still sparse. In order to solve these problem, this paper proposes a two-path multibehavior model of user interaction (TP_MB). First, a two-path learning strategy is introduced to maximize the multiple-interaction information of users and items learned by the two paths, which effectively enhances the robustness of the model. Second, a multibehavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different users. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multibehavior recommendation methods.
... The Covid-19 pandemic has transformed the business dynamics of organizations worldwide. Recent technological advancements have led to the application of digital marketing strategies and practices in residential marketing (Behera et al., 2020). According to Papageorgiou et al. (2020), digital marketing is a strategy in which businesses utilize data analysis to deliver personalized marketing messages to existing and potential customers. ...
IntroductionThe housing sector, including sharia housing, has been affected by Covid-19. Many studies have analyzed the impact of Covid-19 on the sharia housing industry, but research focusing on non-bank financing is still limited. Objectives
This study aims to analyze how consumer purchasing decisions for housing during Covid-19 are influenced by price and digital marketing. Method
This study can be categorized as quantitative study by collecting data from 500 consumers selected through purposive sampling in the Bandar Lampung City. The obtained data was then analyzed using SmartPLS software. ResultsThe result shows that price and digital marketing have significant impact on consumers’ purchase decision of non-bank Sharia housing during Covid-19. ImplicationsSharia housing developers should effectively utilize digital marketing content and pricing strategy to attract potential consumers in Bandar Lampung City.Originality/NoveltyThis study highlights unique pricing and digital marketing strategies by Sharia housing developers during Covid-19. This study is also unique for its approach to non-bank Sharia housing as mode of financing.
... In today's digital age, marketers and market researchers conduct intensive research on digital marketing (Kotler et al., 2017;Fierro et al., 2017;Ritz et al., 2019, Behera et al., 2020. However, up to now, few studies have quantitatively analyzed the evolution of Digital Marketing. ...
Digital marketing is a very important position in line with the ever-advancing technology and the changing customer demands and needs. Due to the increase in studies on digital marketing in recent years, this study has been carried out in order to present the studies in the relevant literature to researchers together. In this study, the literature in the field of digital marketing is examined. This study, which examines the literature in the field of digital marketing, offers a quantitative-based approach to current trends in digital marketing. The paper used Vosviewer to analyze 858 digital marketing records from the Web of Science database between 1975 and 2021. In this article explores the whole picture of digital marketing research and shows a visual information structure and evolution of digital marketing. It is thought that this study, in which the dynamism of scientific publication activity is investigated, will shed light on other studies in the field of digital marketing. In this direction, it provides an important reference for academics to show the current situation and effective trends in the digital marketing field. Öz Dijital pazarlama, sürekli gelişen teknoloji ve değişen müşteri talep ve ihtiyaçları doğrultusunda oldukça önemli bir konumdadır. Dijital pazarlama ile ilgili çalışmaların son yıllarda artması nedeniyle, ilgili literatürde yer alan çalışmaları araştırmacılara bir arada sunmak amacıyla bu çalışma gerçekleştirilmiştir. Dijital pazarlama alanındaki literatürün incelendiği bu çalışma, dijital pazarlamadaki mevcut eğilimlere nicel tabanlı bir yaklaşım sunmaktadır. 1975 ve 2021 yılları arasında Web of Science veritabanındaki 858 dijital pazarlama verisini analiz etmek için Vosviewer kullanılmıştır. Çalışma, dijital pazarlama çalışmalarının tüm resmini ortaya koyarak, dijital pazarlamanın görsel bilgi yapısını ve evrimini göstermektedir. Bilimsel yayın * Emine ŞENBABAOĞLU DANACI faaliyet dinamizminin araştırıldığı bu çalışmayla dijital pazarlama alanında yapılacak diğer çalışmalara ışık tutacağı düşünülmektedir. Bu doğrultuda, dijital pazarlama alanındaki mevcut durumu ve etkili trendleri ortaya koymak adına araştırmacılara önemli bir referans sağlamaktadır.
... Digital marketing is a type of marketing that is done through online and offline digital channels. In this method, all the facilities and channels available in the digital space are used to convey information to the customer or consumer (Behera, et al., 2020). Digital marketing is like an umbrella of technology and digital that can be used to market products or services, with or without the Internet. ...
The issue of growth is one of the fundamental issues in economics, which is very important both from a micro and macro perspective. It is no secret that in order to achieve sustainable growth, exports are one of the most fundamental variables, which is the main channel of monetization. For optimal export, you should have a good market share. Digital marketing is one of the most widely used tools to expand the market, so the present study has studied the effectiveness of digital marketing on job growth through export performance. The statistical population of the present study consists of 500 commercial companies in East Azerbaijan province, of which 271 companies have been selected as a statistical sample according to Morgan table and using Cochran's formula. Also, the data collection tool in this study is a questionnaire with Likert scale. Structural equation modeling and SPSS software version 25 and Smart PLS version 3 were used to analyze the data. The results indicate that digital marketing and export performance have a positive effect on job growth and micro and macro development.
... Today, the use of personal data has become a critical aspect of digital marketing campaigns to both businesses and consumers [15,26,52]. For businesses, it enables them to create targeted campaigns that are more likely to result in conversions, by delivering the right message to the right people [9]. Personal data can also be used to gain valuable insights into consumer behavior, preferences, and trends, which can be used to inform business decisions and improve products and services [16,30]. ...
This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.
... This is another advantage of permission marketing over mass advertising communication. In addition, as the digital age continues to take hold, personalization is a powerful and influential tool for companies to determine what customers want to buy (Behera et al., 2020). Personalisation is one of the prerequisites for a successful marketing policy in general. ...
In today's cluttered world, consumers often try to avoid receiving advertising messages through various communication channels. Interruptive advertising is losing its effectiveness. As a result, marketers must change their strategies to retain existing customers and attract new audiences. The purpose of this paper is to review various aspects of permission marketing and to outline some of the ways in which it can be used in practice. The object of the study is the concept of permission marketing and its influence on consumer behaviour. Similar to other marketing concepts, it is constantly changing and evolving. Observation is used as the primary methodology of the study. In addition, the literature review contributes to the analysis of various aspects of the issue. It is one of the concepts that can drastically change the way of communication and create innovative approaches to influence consumer behaviour. Today, online communication channels are the most favourable tools for implementing permission marketing policies. However, due to the fluctuating and unpredictable online environment, they can turn from opportunities into reasons for serious complications. Therefore, the concept introduced by Seth Godin in 1999 requires further theoretical research amidst the development of online communication technologies. Properly planned and executed, permission marketing campaigns can be effective in influencing consumer behaviour. However, it is a strategy designed for longer periods of time. Results. The paper provides a theoretical review of the main aspects of the permission marketing concept: the role of online communication channels, the peculiarities of consumer behaviour and the implementation of permission marketing strategies. In addition, the paper provides a multilateral analysis of the current characteristics of the concept. Finally, the paper proposes some recommendations on how to influence consumer behaviour through different online communication channels within the framework of permission marketing policies.
... Por lo que, indudablemente la publicidad no vende los productos o servicios, sino crea beneficios y promesas emocionales (Vaccari y Chadwick, 2020). A los anuncios se suman otros factores como los colores el rojo y naranja que estimulantes y enérgicos para la audiencia, el 31,9% de los anuncios agresivos, utilizan el color rojo para los consumidores (Li y Xie, 2020) y parten desde los más profundo y sensible en la publicidad con hechos que al público logra identificar mediante un video, y que este puede provocar emociones de tristeza y, podría generar una conexión con niveles de atención altos para que puedan actuar mente del consumidor (Behera et al., 2019). ...
Los mensajes publicitarios provocan reacciones emocionales en los consumidores y se ha intentado explicar cómo funciona la publicidad dentro de los procesos afectivos emocionales y la posición central encargada de seleccionar la información en la consciencia. Estas brechas surgen cuando se examina las emociones en la publicidad, especialmente, dentro de los entornos y medios digitales publicitarios. Por tal razón, el objetivo de investigación fue indagar cuáles son las escenas de la publicidad digital de "Origen Ambateño 2022” sobre la base de las expresiones faciales de las emociones básicas que más llamaron la atención en la audiencia. Se utilizó un enfoque cuantitativo, con un nivel descriptivo y contexto Neuromarketing, mediante la herramienta AffdexMe, a estudiantes universitarios, del sexo femenino y masculino. Como principales resultados, se observa que las emociones alegría y sorpresa fueron la más significativas en las escenas, por otro lado, el estado emocional negativo más significativo fue el asco. Sin embargo, el disgusto, la ira, el miedo y la tristeza presentaron activaciones bajas entre ascensos y descensos en cada una de las escenas. La campaña de “Origen Ambateño 2022” generó más emociones positivas en comparación a las emociones negativas, y sus beneficios se reflejan en que la publicidad digital sea más recordada por la audiencia, impulse e incentive al consumo de los productos locales y visibilizar toda la capacidad productiva y riqueza territorial.
... [9].Understand user buying style and create custom preferences. The process is illustrated in Figure.1 [10]. ...
Human nature tends to follow certain patterns in comparing the available options, the recommendation system is one of the secrets of the success of many companies, and this system is a magical marketer for services and products, observes customers and understands their behavior to help them make their decisions. In the era of the Internet and electronic stores, we still need Opinion and advice, the recommendation system that provides us with opinion and advice as e-mails or advertisements has become exclusive messages for us. The recommendation system mainly suggests a list of recommendations in a field, using one of two methods: Collaborative filtering or Content-based filtering or by combining the two methods (hybrid). The research aims to conduct a comprehensive survey of the methods of recommendation systems for the specialized local market, by identifying the most important challenges related to the accuracy of recommendations, problems of cold start, the problem of (NCTR) and data sparsity. By studying many concepts related to the patterns of recommendation systems, and the most important techniques used to measure the accuracy of recommendations and the application of learning methods. Deep Neural Network (DNN) for recommendation segmentation, the main goal of this work is to understand an idea describing niche marketing strategies and challenges. Through the use of various methods and techniques, including the text mining method.
... Furthermore, the high level of personalization coupled with interactivity can lead to positively related consumers' responses (Batra, 2019;Song and Zinkhan, 2008). Although it is a very important factor, there is a lack of studies explaining how personalization can influence consumers' online experience (Behera et al., 2020;Kim and Han, 2014). ...
Purpose
The main aim of this research is to portray how augmented reality (AR) characteristics (augmentation, interactivity, personalization, spatial presence, novelty, entertainment and informativeness) can enhance online customer experience (OCE).
Design/methodology/approach
This study conceptualizes a new framework that proposes various relationships between AR characteristics and OCE.
Findings
This study is extending the relationships between AR and OCE by including various AR characteristics that have not been tackled by the previous research.
Originality/value
This research provides an original framework on the relationship between AR characteristics and OCE through highlighting the role of media richness theory. The study is considered the first of its kind to combine these AR characteristics and customer experience in a comprehensive framework.
... Personalizing recommendations is one such method of enabling AI systems to curate suggestions that are specific to a user (Huang et al., 2022). Personalization has already proven to be effective in increasing consumer satisfaction (Xiao et al., 2019) and business revenues (Behera et al., 2020). Previously, content personalization for Recommendation Systems based on user preferences has been done through collaborative filtering, content-based filtering and hybrid approaches (Kumar and Thakur, 2018;He et al., 2017;Schafer et al., 2007;Lops et al., 2011). ...
Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system (p < 0.01). Finally, we report an interesting relationship between usability, alignment and perceived intelligence wherein greater perceived alignment of recommendations with one's own preferences leads to higher perceived intelligence (p < 0.01) and higher usability (p < 0.01).
... Jadi pada dasarnya digital marketing merupakan pemasaran yang menggunaan platform digital yang berada di internet yang mana menggunakan alat seperti web, social media, email, database, mobile / wirelees dan digital tv dalam meningkatkan target konsumen dan serta mengetahaui profile, perilaku, nilai produk, serta loyalitas para pelanggan atau target konsumen (Sharma et al., 2020). Sebelum melakukan kegiatan digital marketing ada beberapa hal yang mesti di ketehaui, Basis utama pemasar dalam digital marketing menurut zaki dan smitdev (2008) adalah dengan menggunakan dan memanfaaatkan Web, dengan tetap berorientasi pada prinsip pemasaan (marketing) konvensional yang harus menerapkan 3 hal yakni tujuan pemasaran, pasar sasaran, dan produk atau jasa yang ditawarkan (Behera et al., 2020). Karena fenomena inilah Nadiem Makarim mendapatkan ide cerdas tentang adanya peluang menghubungkan tukang ojek dengan penumpang. ...
Tujuan penelitian ini adalah Untuk mengetahui dan menganalisis pengaruh Layanan Go- Food sebagai media promosi terhadap keputusan pembelian makanan dikota Makassar. Untuk mengetahui dan menganalisis pengaruh Kualitas Layanan Go-Food terhadap keputusan pembelian Makanan dikota Makassar. Data dalam penelitian ini diuji menggunakan uji validitas dan uji reliabilitas, analisis regresi berganda, koefisien determinasi, uji f, dan uji t untuk menguji hipotesis yang diajukan peneliti. Berdasarkan hasil penelitian secara simultan promosi digital dan kualitas pelayanan go-food berpengaruh positif dan signifikan terhadap keputusan pembelian dengan nilai signifikan sebesar 0,015 < 0,05, pengujian ini menunjukkan bahwa hipotesis diterima. Variabel promosi digital berpengaruh positif dan signifikan terhadap variabel keputusan pembelia. Selanjutnya, variabel kualitas layanan berpengaruh positif dan signifikan terhadap variabel keputusan pembelian. Keputusan pembelian pada outlet makanan dipengaruhi oleh promosi digital dan kualitas layanan go-food, hal ini dibuktikan oleh nilai R square sebesar 37,9% sedangkan sisanya dipengaruhi oleh faktor-faktor lain.
... Peneliti menemukan bahwa sinar matahari, suhu, dan hujan memiliki dampak yang signifikan terhadap penjualan harian, terutama di musim panas, pada akhir pekan, dan pada hari-hari dengan cuaca ekstrem. Pada gilirannya, kecenderungan untuk membeli dan jumlah yang dibelanjakan untuk pembelian meningkat dan memilih jumlah produk di setiap keranjang belanja dan harga rata-rata setiap produk di setiap keranjang belanja sebagai indikator perilaku konsumen, dan penjualan harian toko sebagai indikator kinerja toko (Behera et al., 2020). ...
Prediksi perilaku konsumen pada perusahaan ritel bertujuan untuk menghadapi tantangan perubahan perilaku belanja, sehingga industri ritel perlu mengetahui faktor-faktor yang mempengaruhi perilaku konsumen. Perusahaan ritel menghadapi banyak faktor yang tidak pasti, salah satunya adalah cuaca. Tujuan penelitian ini adalah: untuk menguji pengaruh cuaca cerah, hujan, berawan, suhu, kualitas udara terhadap keputusan pembelian. Penelitian ini dilakukan di Toko Minimarket Indomaret yang berlokasi di Kota Jakarta. Cuaca cerah, hujan, berawan, suhu dan kualitas udara sebagai variabel bebas dan keputusan pembelian sebagai variabel terikat. Penelitian ini merupakan penelitian kuantitatif menggunakan jenis data primer. Kuesioner berskala likert digunakan untuk mengumpulkan data. Teknik pengambilan sampel adalah convenience sampling. Sampel penelitian ini berupa konsumen minimarket Indomaret berjumlah 150 orang. Uji regresi linier berganda digunakan untuk menguji hipotesis. Hasil penelitian menunjukkan bahwa cuaca cerah berpengaruh positif dan signifikan terhadap keputusan pembelian. Cuaca hujan, berawan, suhu dan kualitas udara berpengaruh negatif dan signifikan terhadap keputusan pembelian.
... The Journal of Retailing and Consumer Services is the sixth journal with eight articles on Digital Marketing, some of which are presented in Table 2. The Journal of Retailing and Consumer Services examines the relationship between digital marketing and customers (Ahani et al., 2019;Augusto et al., 2019), as well as the impact of personal characteristics (Behera et al., 2020;Jacobson et al., 2020;Niu et al., 2021), and service (Akram et al., 2020;Di Fatta et al., 2018;Kaatz, 2020 ...
Purpose: Digital marketing has evolved from the marketing of products and services to digital channels for activities, institutions, and processes facilitated by digital technology. This study provides a disciplinary broad overview of the academic contribution to the marketing revolution by exploring changes in digital marketing research in SMEs cited by scientific researchers over 19 years (2001-2020). Thus, this paper presents a systematic literature review using bibliometric analysis with the aim of examining development trends, identifying gaps, understanding the advantages and benefits of digital marketing on SMEs and mapping out new hypotheses for future research.
Design/methodology/approach: The method used is a bibliometric study to search for articles related to the research theme
Findings: The results show that social media is the keyword that is often employed. Related research has been achieved in many countries, and the most predominant are England and America.
Research limitations/implications: This study is limited to a single database, Scopus, from 2001 to 2020.
Practical implications: The findings of this study are likely to serve as a starting point for future research in digital marketing.Besides, it becomes a resource to make the most of social media marketing to grow SMEs.
Originality/value: This paper is original.
Paper type: a Research Paper
... Nevertheless, such information is central for companies when planning resources and inventories, among other things [11,21,35]. Typically, the number of customers with mere browsing intentions is far greater than customers with purchase intentions and it has been shown that it is more effective to target customers with a purchase intention [3,24]. Additionally, Esmeli et al. [6] state that it is more useful to target customers with purchase intention in real-time to not miss the chance to target them with appropriated marketing strategies. ...
Customers are increasingly using online channels to buy products. For e-commerce companies, this offers new opportunities to tailor the shopping experience to customers' needs. Therefore, it is of great importance for a company to know their customers' intentions while browsing their webpage. A major challenge is the real-time analysis of a customer's intention during browsing sessions. To this end, a representation of the customer's browsing behavior must be retrieved from their live interactions on the webpage. Typically, characteristic behavioral features are extracted manually based on the knowledge of marketing experts. In this paper, we propose a customer embedding representation that is based on the customer's click-events recorded during browsing sessions. Thus, our approach does not use manually extracted features and is not based on marketing expert domain knowledge, which makes it transferable to different webpages and different online markets. We demonstrate our approach using three different e-commerce datasets to successfully predict whether a customer is going to purchase a specific product. For the prediction, we utilize the customer embedding representations as input for different machine learning models. We compare our approach with existing state-of-the-art approaches for real-time purchase prediction and show that our proposed customer representation with an LSTM predictor outperforms the state-of-the-art approach on all three datasets. Additionally, the creation process of our customers' representation is on average 235 times faster than the creation process of the baseline.
... While digitization entails promising features, such as new communication instruments (Kannan & Li, 2017;Mulhern, 2009), a potential challenge may be the change in customer contact habits and, consequently, required adaptation to customers' needs (Killian & McManus, 2015;van Bruggen et al., 2010), with higher levels of distance and anonymity (Steinhoff et al., 2019). In order to reduce this distance, the concrete struggle lies in tailoring the right information at the right time with high levels of transparency to individual customers (Behera et al., 2020;Krishnaprabha & Tarunika, 2020;Makrides et al., 2020;Sundaram et al., 2020) through coherent interfaces . These customers potentially sacrifice only a small span of attention to a company's digital performance (Brown, 2016) and have increasing power about the choice of service providers (Sundaram et al., 2020). ...
SMR has attracted considerable attention in social media and advertising practices, but it lacks empirical investigation within marketing science. This study suggests applying ASMR in the design of interactions in relationships between organizations and customers in order to shorten spatial distance evoked by digitization. This experimental study comprises the first empirical investigation of ASMR in the context of relationship marketing with a focus on perceived customer intimacy. The findings demonstrate that the level of perceived customer intimacy depends on individuals’ extraversion and agreeableness, and ASMR may entail the ability to positively influence this intimacy, but is influenced by further variables.
... For example, when potential customers visit the company website, each visit is called a session. The number of sessions that results from the completed purchase is significantly smaller than the total number of sessions [23], which causes a class imbalance. Consequently, the class imbalance problem leads to biased results of the predictive model since the model is trained using a small number of positive examples. ...
Customer response models have gained popularity due to their ability to significantly improve the likelihood of targeting the customers most likely to buy a product or a service. These models are built using databases of previous customers’ buying decisions. However, a smaller number of customers in these databases often bought the product or service than those who did not do so, resulting in unbalanced datasets. This problem is especially significant for online marketing campaigns when the class imbalance emerges due to many website sessions. Unbalanced datasets pose a specific challenge in data-mining modelling due to the inability of most of the algorithms to capture the characteristics of the classes that are unrepresented in the dataset. This paper proposes an approach based on a combination of random undersampling and Support Vector Machine (SVM) classification applied to the unbalanced dataset to create a Balanced SVM (B-SVM) data pre-processor resulting in a dataset that is analysed with several classifiers. The experiments indicate that using the B-SVM strategy combined with classification methods increases the base models’ predictive performance, indicating that the B-SVM approach efficiently pre-processes the data, correcting noise and class imbalance. Hence, companies may use the B-SVM approach to more efficiently select customers more likely to respond to a campaign.
... Table 2 presents the research gap from reviewed literatures. Research concept development [3], [7], [8], [26], [27], [28], [29], [30], [31]. ...
The development of internet influences business, operationally and strategically. People has reached wider connection towards information. This leads to several marketing transformations, from conventional to digital technology conducted by businessmen. Yet, several marketing managers still unsure in such communications through digital channel and its impacts. This research is purposed to analyse the strategy and the content of the implementation of digital marketing, and to provide opportunity for future research. This research applies systematic literature review method towards 45 literatures. Later, it could be resumed the impacts that influence the company internally or externally in terms of global perspective, the frequent-used elements or contents in digital marketing strategies as well as the opportunity for future research to obtain comprehensive and accurate result for businessperson.
Cutting-edge technological advancements in artificial intelligence (AI) and its associated technologies such as machine learning (ML) and deep learning (DL) have garnered immense attention from academia and the industry due to their ability to automate tasks previously executed by human beings in various sectors of the economy. In the business sector, there's an increased interest to explore how these emerging technologies can be deployed to enhance engagements between businesses and clients by automating their interactions. Fueled by the prevalence of a plethora of digital marketing data, these data-driven techniques can help businesses to provide faster and more efficient customer support, while also freeing up human staff to focus on more complex business-related issues. Accordingly, this chapter seeks to provide a compact review of the different AI-powered techniques and their applications in scaling up customer support, highlight existing challenges in their usage in the marketing domain along with proposing future research directions.
To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.
The role of digital marketing is to assist us with getting new traffic, leads, and deals for our business by contacting individuals who are searching for our items and administrations. Without help from anyone else, digital marketing is the most common way of marketing our internet based business to possibilities and high-esteem purchasers. Information was together from the various individuals who were straightforwardly drawn in with the execution of digital marketing, in Pakistan. Respondents of the assessment were those individuals who have been buying things on the web/on the web. The investigation concentrate on gives an exhaustive framework to re-plan factors that lead to purchasers' buy goals in overseeing digital presence. The review centers on the material of online data transmission that has extended significantly. Especially as a result of online media organizations, clients have been given basic proportions of disinformation, deception, and genuine data. Electronic Word-of-Mouth (eWOM) by means of online media networks has been working with speedy data spread.
This paper focuses on services with unique information expressions that differ from existing content services against the background of distributing a large amount of video content and differentiating them from similar services in the market, as well as the impact of differences in information expressions on user recognition and user load. In this study, research was conducted on mobile applications of existing over-the-top (OTT) services. First, we investigated the user’s operational process in OTT services and found that visual and cognitive aspects of thumbnails were responsible for actions taken. Next, to clarify the influence of thumbnails on cognition and load in information representation, we conducted an experiment using a sample of thumbnails broken down into their constituent elements. The results showed that thumbnails could alter the impression made on users by focusing on the combination and arrangement of the elements. We also confirmed the influences of user gender, preferences, and differences in experience on genre judgment. In OTT services, changing thumbnail components according to user characteristics may induce interest and attention, leading to continued use of the service.KeywordsOver-the-TopThumbnailUser Interface
The internet of things (IoT) and digital marketing are two rapidly growing fields that have the potential to revolutionize the way businesses operate. However, for businesses to truly take advantage of the opportunities these technologies provide, it is important to understand the critical success factors that must be in place. This paper explores the critical success factors of IoT and digital marketing, including the importance of data analytics, security, and integration with existing systems. For this purpose, biblio-metric analysis has been utilized from the Scopus database. It was identified that not many studies have been conducted in this field, and hence a huge gap was explored and addressed. Through the inferences of literature and the practices in the arena of IoT and digital marketing, the critical success factors have been proposed. This chapter will provide valuable insights for organizations looking to implement IoT and digital marketing strategies, as well as for researchers studying the field.
In this paper, an approach is presented to improve the online direct marketing process regarding predictive customer response modeling. Namely, customer response models are usually faced with a class imbalance problem, due to a low conversion rate, in relation to the entire number of targeted offers. To avoid the bias towards the negative class in machine learning process, this paper proposes a combination of random undersampling and Support Vector Machine method for data pre-processing and increasing the predictive performances of subsequently used classifiers – Decision Tree and Random Forest. In addition, different attribute groups are tested in terms of their influence to the model performance, which enables marketing decisions makers to better understand which attributes have the highest impact in determining the customer’s response to a direct marketing campaign. The results showed that the proposed method successfully solved the class imbalance and significantly increased the accuracy of the response prediction, as well as that web behavior is the most significant when predicting the customer's response.KeywordsCustomer Response ModelClass ImbalanceWeb MetricsCustomer Behaviour MetricsPredictive analyticsSupport Vector Machine
Digital marketing is a very important position in line with the ever-advancing technology and the changing customer demands and needs. Due to the increase in studies on digital marketing in recent years, this study has been carried out in order to present the studies in the relevant literature to researchers together. In this study, the literature in the field of digital marketing is examined. This study, which examines the literature in the field of digital marketing, offers a quantitative-based approach to current trends in digital marketing. The paper used Vosviewer to analyze 858 digital marketing records from the Web of Science database between 1975 and 2021. This paper explores the whole picture of digital marketing research and shows a visual information structure and evolution of digital marketing. It is thought that this study, in which the dynamism of scientific publication activity is investigated, will shed light on other studies in the field of digital marketing. In this direction, it provides an important reference for academics in order to show the current situation and effective trends in the digital marketing field.
This paper aims to go deeper into how a firm executes digital transformation in marketing. The experiences discussed in this article will give insight from a business practice standpoint. The difficulties encountered and the solutions proposed will broaden knowledge and inspire additional research. This paper employs a qualitative technique, with one of Indonesia's significant telecommunications companies serving as the primary case study. Semi-structured interviews were used in this study, with 25 interviews conducted at the senior management level. As a result, this organization is developing digital transformation through digital and traditional marketing. A big challenge is focusing on existing consumers through conventional marketing while reaching out to additional customers through digital marketing. One of the aspects to consider while designing digital technology is the digital divide in some nations.
Food Recommender Systems (RecSys) are innovative knowledge systems that inform consumers of food choices according to criteria, including nutritional content, health concerns, production method, carbon footprint or other social and ethical considerations. They raise important questions at the intersection of technology accuracy and today evolving consumers’ knowledge seeking behaviours, which implies to unpack the epistemic value of food RecSys. This study investigates the drivers of the perceived value of food RSs consumption by proposing a model that establishes via PLS-SEM (n = 253) a positive relationship between the Yuka company’s food RecSys’ epistemic value and its perceived value. The model demonstrates that Yuka RecSys’ epistemic value relies on the disciplinary drivers of compatibility, self-confidence, and consumer innovativeness, and the problematising drivers of memory and learning, which come from using the application. The perceived value of food RecSys is found to relate to RecSys epistemic value beyond the functional accuracy aspects of recommendation algorithms. Results highlight the importance of developing a refined understanding of epistemic value considering the consumption of RecSys. RecSys’ developers, retailers, food manufacturers and policy makers must work on better mapping and adjusting information through consumers socialised RecSys’ usage to shape the design of the next generation RecSys.
Küreselleşme ile birlikte müşteri yapısı da değişmiş, ürün ya da hizmetlerin
daha geniş bir alanda, daha yüksek bir arz ve talep içerisinde pazarlanmasına
olanak vermektedir. Bu süreçte hizmetlerin hem iletişim olanaklarının
artması sayesinde daha etkili şekilde kitlelere pazarlanması, hem de ulaşım
olanaklarının artmasıyla birlikte hizmetlerin farklı pazarlarda, ülke ya da
bölgelerde pazarlanması mümkün hale gelmiştir. Bu süreçte, mevcut hizmet
kalitesi ölçüm yöntemleri ve buna bağlı olarak hizmet kalitesinin üretiminde
ve sunumunda bazı değişikliklerin yapılması gerekir. Bu bölümde, yerel ve
küresel boyutta hizmet kalitesinin ölçülmesi ve alternatif bir model önerisi
geliştirilmesine yer verilmiştir.
Bu kitapta pazarlama alanında yaşanan dijital dönüşüm ve dijital pazarlama stratejileri ağırlıklı olmak üzere, butik pastacılık, sigorta, tarım ve sağlık hizmetlerinde dijital pazarlama, kuşakların dijital pazarlamaya bakış açıları, oyunlaştırma, yerel ve küresel boyutları ile hizmet kalitesi, bütünleşik pazarlama iletişimi ve marka bilinirliği ilişkisi, kompulsif satın alma, paylaşım ekonomisi, pazarlama sosyolojisi ve pazarlamada tüketim ritüelleri ve organik ürünlerin pazarlanması konuları ele alınmış ve tartışılmıştır. Pazarlama alanında birçok konunun farklı yaklaşımlar ile ele alındığı bu kitap alanında yetkin 27 yazarın katkıları ile hazırlanmıştır.
Bu kitapta pazarlama alanında yaşanan dijital dönüşüm ve dijital pazarlama stratejileri ağırlıklı olmak üzere, butik pastacılık, sigorta, tarım ve sağlık hizmetlerinde dijital pazarlama, kuşakların dijital pazarlamaya bakış açıları, oyunlaştırma, yerel ve küresel boyutları ile hizmet kalitesi, bütünleşik pazarlama iletişimi ve marka bilinirliği ilişkisi, kompulsif satın alma, paylaşım ekonomisi, pazarlama sosyolojisi ve pazarlamada tüketim ritüelleri ve organik ürünlerin pazarlanması konuları ele alınmış ve tartışılmıştır. Pazarlama alanında birçok konunun farklı yaklaşımlar ile ele alındığı bu kitap alanında yetkin 27 yazarın katkıları ile hazırlanmıştır.
Gaming disorder was officially recognized as a disorder of addictive behaviour in the International Classification of Diseases 11th revision in 2019. Since then, other types of potentially problematic online behaviour have been discussed as possible candidates for inclusion in the psychiatric nosography of addictive disorders. Understanding these problematic online behaviours requires further study of the specific psychological mechanisms involved in their formation and maintenance. An important but underdeveloped line of research has examined the ways in which technology design features might influence users’ capacity to exert control over how they engage with and use websites and applications, thereby amplifying uncontrolled, and perhaps addictive, use. In this Review, we critically examine the available research on the relationships between technology design features and the loss of control and harms experienced by those who engage in online video gaming, online gambling, cybersexual activities, online shopping, social networking and on-demand TV streaming. We then propose a theory-driven general taxonomy of the design features of online applications that might promote uncontrolled and problematic online behaviours.
This paper aims to go deeper into how a firm executes digital transformation in marketing. The experiences discussed in this article will give insight from a business practice standpoint. The difficulties encountered and the solutions proposed will broaden knowledge and inspire additional research. This paper employs a qualitative technique, with one of Indonesia's significant telecommunications companies serving as the primary case study. Semi-structured interviews were used in this study, with 25 interviews conducted at the senior management level. As a result, this organization is developing digital transformation through digital and traditional marketing. A big challenge is focusing on existing consumers through conventional marketing while reaching out to additional customers through digital marketing. One of the aspects to consider while designing digital technology is the digital divide in some nations.
A significant proportion of smartphone notifications are indicative of human behaviour (e.g. delivery updates for purchased items, physical activity summaries, and notification of updates to subscribed content). However, present attempts to understand human behaviour from smartphone traces typically focus on sensors such as location, accelerometer and proximity, overlooking the potential for notifications as a valuable data source. In this paper, we propose a general framework that provides end-to-end processing of notifications to understand behavioural aspects. We realise the framework with an implementation that tackles the specific use case of establishing prior buying behaviour from associated notifications. To evaluate the framework and implementation, we conduct a longitudinal user study in which we collect more than 250, 000 notifications, from twelve users, over an average of three months. We apply knowledge-based and machine learning techniques to those notifications to assess the tasks of the proposed framework. The results show a substantial difference in the performance between the methods used to extract behavioural features from the collected notifications.
Exploring the impact of digitalisation on leadership actions and behaviours is an important
endeavour. This research took the form of a systematic literature review that contextualises the
continuing discussion on the impacts of digitalisation on leadership style and behaviour. It aimed to
establish how employees should be led in an increasingly digitalised business environment and to
ascertain what skills and competencies are needed to succeed. This study suggests that the next
generation of leaders will need to develop strategic and conceptual thinking, will have to
demonstrate high intellectual integrity and openness, but also find new ways to influence and
generate loyalty among employees. Given the dominance of digital technologies, leadership has
become a process of social influence, which is enhanced through the use of advanced information
technology and managerial innovation and leadership transformation. Nevertheless, the new
capabilities of digital leaders do not displace traditional leadership traits and skills.
Shopping convenience can be turned into a competitive advantage for online grocery retailers. Consequently, we study how personalized product recommendations (recommendation agents) and price promotions (algorithmic pricing) compensate for the negative impact that consumer's perceived cognitive effort causes on loyalty. By default, the relationship from perceived cognitive efforts to attitudinal and behavioral loyalty is negative, yet these results demonstrate that personalized price promotions lessen the negative impact, while personalized product recommendations do not have such an influence. The findings contribute to a better understanding of personalized marketing activities in today's data-driven online grocery retailing.
In this paper we have proposed a customer response model for extremely imbalanced data. In this case, the class imbalance occurs when it comes to online shoppers, as there is a huge number of website sessions, of which only a small number end up with an order. The influence of web metrics on the model performance was examined, as some previous papers have confirmed the importance of web metrics for various types of analysis in web marketing. However, in this paper, their impact on the predictive performance of customer response models based on undersampling in combination with ensemble techniques was tested for the first time. The obtained results indicate good model performance-AUC of 0.839, sensitivity (positive class of respondents) of 75% and fallout of 21.26%, especially having in mind that the test data set had a response rate of only 0.41%. Additionally, web metrics were found to be more significant predictors than classical predictors of purchasing behavior such as transaction recency and frequency, as well as monetary value, or data on purchased products.
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.
In current time of web and mobile applications, classic email is still one of the most popular means of communication over the Internet. Beset by many problems such as spam or information overload, yet it yields significant benefits especially to enterprise users when communicating, collaborating or solving business tasks. In addition, the current groupware frameworks still does not support well the collaboration between large and small & medium-sized enterprises. In this paper, we present an approach that addresses the challenges of inter-enterprise collaboration, putting together email as a tool for information sharing and lightweight semantics to automate the processing of the knowledge extracted from the emails and their attachments. The building blocks of our approach are: connectors to legacy applications retaining current working styles, use of email for collaboration especially on the SME’s side, information extraction enabling semantic search and recommendation in inter-enterprise collaborations, automatic tagging of documents, and integration of existing business process models. To evaluate our approach we use three different scenarios: new product development, software development supply chain, and supply chain collaboration between SMEs and LEs. Evaluation results suggests that our solution is a benefit for the collaborating enterprises.
This paper offers views on some current and future trends in marketing. The content is based on recent literature and on what is happening in the business world. The paper is based on secondary data. The paper is based on extant literature and internet sources. The various articles, researches, reports, newspapers, magazines, various websites and the information on internet have been studied. We experience a radical change in India towards the digitalization. The consumer are looking and searching more on internet to find the best deal form the sellers around India as compared to traditional or conventional methods. In this study, we acknowledged that businesses can really benefit from Digital Marketing such as search engine optimization (SEO), search engine marketing (SEM), content marketing, influencer marketing, content automation, e-commerce marketing, campaign marketing, and social media marketing, social media optimization, e-mail direct marketing, display advertising, e-books, optical disks and games and are becoming more and more common in our advancing technology. It is demonstrated that we all are connected through whatsapp and facebook and the increasing use of social media is creating new opportunities for digital marketers to attract the customers through digital platform. Awareness of consumer's motives is important because it provides a deeper understanding of what influences users to create content about a brand or store. Digital marketing is cost effective and having a great commercial impact on the business. Based on this study, it can further be argued that knowing which social media sites a company's target market utilizes is another key factor in guaranteeing that online marketing will be successful. The effectiveness of Internet marketing with respect to different business can be analyzed. The study can further be extended to compare the internet marketing techniques with specific to various businesses.
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.
Personalization of products and services is promising not only to enhance customer satisfaction but also to realize sustainable consumption and production. An obstacle in personalizing more products and services is the lack of instruction for designers working on personalization. Our research objective is to develop design methodology to realize personalization for supporting providers of personalization. First, this paper starts with the analysis of the process of personalization and suggest that there are various procedures for creating personalized products and services and to design procedure is the key for personalization design. Then, this paper proposes two conceptual levels and design process of personalization. This paper finally examines this methodology through a case study.
Web retailers invest significant resources to improve the proportion of website visitors that make a purchase, also known as the conversion rate. Improving this rate is particularly important to SMEs that have traditionally
lagged behind larger firms as they have found it difficult to justify the significant investment involved in website
development against the historical low returns associated with an online sales channel. Identifying methods that increase conversion rates reduces these perceived barriers and increases effective SME participation in the
growing e-commerce sector. This paper uses 1184 observations from 6 SME websites to identify and analyse the factors, or combination of factors, that improve conversion rates. This is achieved through a process of exploratory regression analysis to select the most relevant determinants and Qualitative Comparative Analysis
(QCA) to offer more ‘fine-grained’ detail on the conditions where conversion rates improve. Our findings suggest that a key factor that improves the conversion rate is a strategy that focuses upon either quality or promotion and avoids mixing such attributes within the web site offer.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Item orientated recommendation has recently been proposed to address the issue of finding the most suitable users of certain item so as to maximize the revenue of its manufacturer with limited advertising budget, which cannot be settled very well by the traditional recommendation algorithms. Existing multi-view models are instructive to solve this problem but either need extra information or suffer from scalability issue due to the high complexity when dealing with large number of records. This paper presents a solution to meet the need of improving both the quality and the scalability of item orientated recommendation by designing a new multi-view model. The basic idea is to visualize the rating records of each user as feature representations from an individual view to partially characterize the property of items. From the multiple but insufficient view representations, the latent feature representation of each item in the intact space, the view generation function of each user, and the overlapping of preferences among different users can be discovered, which are used to predict the unknown ratings from users to a certain item. Extensive experiments have been conducted on three types of datasets, and the results have confirmed the effectiveness of the proposed algorithm.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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-k