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A Systematic Literature Review and Research Agenda of Data-Driven Marketing
Abstract
The boom in the flow of information originated from customer activities. The Internet enhanced it, and information and communication technologies have drastically transformed the marketing domain, leading to innovative strategies such as data-driven marketing. Due to the rapid evolution of DDM this research analyzes the evolution of DDM in the scientific literature. For this purpose, a systematic review of the literature using bibliometric techniques has been carried out from 1980 to 2023. The results show that only some studies directly describe its development. However, through the analysis of related technologies such as big data, customer relationship management, artificial intelligence, and machine learning in marketing, this chapter provides insight into the evolution of DDM and potential future opportunities. The study's findings emphasize the need to focus on DDM to understand its evolution and impact on the business landscape. On a practical level, it provides strategic guidance for marketers who want to adapt to the changing dynamics of technology.
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Market segmentation is a crucial marketing strategy that involves identifying and defining distinct groups of buyers to target a company’s marketing efforts effectively. To achieve this, the use of data to estimate consumer preferences and behavior is both appropriate and adequate. Visual elements, such as color and shape, in advertising can effectively communicate the product or service being promoted and influence consumer perceptions of its quality. Similarly, a person’s outward appearance plays a pivotal role in nonverbal communication, significantly impacting human social interactions and providing insights into individuals’ emotional states. In this study, we introduce an innovative deep learning model capable of predicting one of the styles in the seven universal styles model. By employing various advanced deep learning techniques, our models automatically extract features from full-body images, enabling the identification of style-defining traits in clothing subjects. Among the models proposed, the XCEPTION-based approach achieved an impressive top accuracy of 98.27%, highlighting its efficacy in accurately predicting styles. Furthermore, we developed a personalized ad generator that enjoyed a high acceptance rate of 80.56% among surveyed users, demonstrating the power of data-driven approaches in generating engaging and relevant content. Overall, the utilization of data to estimate consumer preferences and style traits is appropriate and effective in enhancing marketing strategies, as evidenced by the success of our deep learning models and personalized ad generator. By leveraging data-driven insights, businesses can create targeted and compelling marketing campaigns, thereby increasing their overall success in reaching and resonating with their desired audience.
Objective: The purpose of this study is to investigate the relationship of Digital Marketing, Word of Mouth, Perceived Value and Perceived Quality on Indonesian Online Transportation (Go-Jek) Customers Satisfaction and Loyalty. Theoretical framework: According to Konuk,(2019) Digital marketing is a marketing of products through digital media that is connected by the internet. According to Lin et al. (2015) Word of mouth is a marketing activity in which two or more individuals exchange information through direct communication, media, or electronic devices based on experience of using a product or service. According to Kuo et al. (2013) Perceived value is a consumer's perception of the understanding of the benefits of a product. According to Juwaini et al. (2022) Customer satisfaction is where someone compares the results of what is felt by a product or service with the person's expectations for the product. According to Daud et al. (2022) Customer loyalty is a commitment that is firmly held by customers to buy or use the product or service again or ensure that a product or service will be chosen consistently in the future Method: The method for collecting data in this study is the Questionnaire Method, which is an activity to collect data from respondents in which the form of the questionnaire in this study is structured or a closed questionnaire with answers to statements that have been prepared in the form of choices. Respondents do not need to provide additional answers, respondents only need to answer a statement with 5 available answers, which will make it easier for researchers to manage and analyze data. The scale used in the preparation of this questionnaire is the Likert scale, in which the scale has 5 levels of answers and is structured into a statement followed by 5 response responses. The sample was selected using a technique that is purposive sampling, in which the technique determines the sample with special considerations or certain criteria so that the respondent is eligible to be sampled. The criteria are: Consumers who have used Gojek services at least 2 times. Because respondents already know Gojek's services and from various kinds of consumers who become respondents, they must be 16-60 years old, because respondents are considered adults so they will be able to answer questions asked by the author. In this study, the number of respondents used is 675. The data obtained from the questionnaire will then be analyzed using a method called multiple linear regression where data processing is assisted by SmartPLS for Windows program to facilitate research data processing Results and Conclusions: Digital marketing on customer satisfaction and loyalty have p value 16.443 <0.50 was obtained and t value 15.194 > 1.96, so it was concluded that digital marketing has a significant and positive effect on customer satisfaction and loyalty.Digital marketing on Perceived Value and Perceived Quality have p value <0.50 was obtained and t value 21.732 > 1.96, Perceived quality on the decision to use Gojek services have p value 18.285 <0.50 was obtained and a t value > 1.96. Based on the results of the smartPLS analysis concluded that digital marketing has a significant and positive effect on customer satisfaction and loyalty,digital marketing has a significant and positive effect on Word of Mouth, digital marketing has a significant and positive effect on Perceived Value and Perceived Quality, perceived quality has a significant positive role in the decision to use Gojek services. Implications of the research: For Gojek, it is expected that the company will pay more attention to and predict word of mouth that is spread in the community because word of mouth is a means of promotion that does not cost money so that positive things that are spread in the community about Gojek services will stimulate potential consumers to make purchases or use the services of Gojek. In the process of increasing brand awareness, the Go-Jek company uses several strategies, the strategy carried out by Go-Jek has been successful by getting positive results from the community. Where by creating a positive image will lead to trust, from that trust they will try to get to know more then they will feel comfortable and loyal to the company Originality/value: The novelty of this research is the creation of a variable relationship model of satisfaction, loyalty, digital marketing, word of mouth, perceived value and perceived quality in online transportation companies (Gojek).
In this paper, we propose an Intelligent Decision Support System (IDSS) for the design of new textile fabrics. The IDSS uses predictive analytics to estimate fabric properties (e.g., elasticity) and composition values (% cotton) and then prescriptive techniques to optimize the fabric design inputs that feed the predictive models (e.g., types of yarns used). Using thousands of data records from a Portuguese textile company, we compared two distinct Machine Learning (ML) predictive approaches: Single-Target Regression (STR), via an Automated ML (AutoML) tool, and Multi-target Regression, via a deep learning Artificial Neural Network. For the prescriptive analytics, we compared two Evolutionary Multi-objective Optimization (EMO) methods (NSGA-II and R-NSGA-II) when optimizing 100 new fabrics, aiming to simultaneously minimize the physical property predictive error and the distance of the optimized values when compared with the learned input space. The two EMO methods were applied to design of 100 new fabrics. Overall, the STR approach provided the best results for both prediction tasks, with Normalized Mean Absolute Error values that range from 4% (weft elasticity) to 11% (pilling) in terms of the fabric properties and a textile composition classification accuracy of 87% when adopting a small tolerance of 0.01 for predicting the percentages of six types of fibers (e.g., cotton). As for the prescriptive results, they favored the R-NSGA-II EMO method, which tends to select Pareto curves that are associated with an average 11% predictive error and 16% distance.
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.
Purpose
This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.
Design/methodology/approach
A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.
Findings
The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.
Research limitations/implications
Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.
Practical implications
The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.
Originality/value
The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.
Virtual try-on (VTO) apps are now used by many fashion consumers, but VTOs for the apparel category have met with resistance. This study examines privacy concern, body image and social value as antecedents to adoption intention towards an apparel VTO with two types of photorealistic avatars. Twenty users first tried out the app in lab sessions, then 301 completed an online survey with a video of the VTO. A majority of participants were concerned about potential misuse of their uploaded picture and preferred to use a pre-loaded avatar of a model with a similar body. This option explains why privacy concern had a weak negative impact on adoption intention in our model, albeit at the expense of self-presentation benefits. The trait of privacy disposition best predicted consumer responses overall, yet other motives were also revealed. Discussed are the implications of this study’s results and limitations to privacy calculus research.
Artificial Intelligence (AI) is increasingly adopted by organizations to innovate, and this is ever more reflected in scholarly work. To illustrate, assess and map research at the intersection of AI and innovation, we performed a Systematic Literature Review (SLR) of published work indexed in the Clarivate Web of Science (WOS) and Elsevier Scopus databases (the final sample includes 1448 articles). A bibliometric analysis was deployed to map the focal field in terms of dominant topics and their evolution over time. By deploying keyword co-occurrences, and bibliographic coupling techniques, we generate insights on the literature at the intersection of AI and innovation research. We leverage the SLR findings to provide an updated synopsis of extant scientific work on the focal research area and to develop an interpretive framework which sheds light on the drivers and outcomes of AI adoption for innovation. We identify economic, technological, and social factors of AI adoption in firms willing to innovate. We also uncover firms' economic, competitive and organizational, and innovation factors as key outcomes of AI deployment. We conclude this paper by developing an agenda for future research.
This paper proposes value addition to the classical Influence Maximization problem by introducing a quality measure to the participating nodes. The quality measure signifies the ‘propensity to buy’ of a customer (node) in a promotional marketing campaign context. Two metrics, Individual Net Worth (INW) and Neighborhood Net Worth (NNW) are proposed to measure the potential of a customer(s) in buying a given product. The proposed solution, through a heuristic approach, is capable of spreading the influence to the customers with a higher propensity to buy the product. The solution is scalable and adaptable to address user requirements. All these claims are substantiated through experimental results on public datasets. We performed a comparative study with notable algorithms in this domain. The result shows that the proposed approach selects seeds of higher quality as well as maximizes the overall quality (worth) of the influenced nodes in comparison to the notable algorithms, without any adverse impact on time complexity.
Many governments actively subsidize the green activities of manufacturers and consumers to effectively realize the achievement of carbon emissions peak and carbon-neutral goals, while the development of a platform economy can effectively contribute to sustainable development. Therefore, we have modeled a platform supply chain using game theory, in which the manufacturer conducts green research and development (R&D) activities, the third-party platform conducts data-driven marketing (DDM) activities to promote green products, and all consumers have green preferences. The numerical example and empirical analysis methods are used to mine management insights. The government subsidizes the manufacturer’s green R&D, the third-party platform’s DDM, and the consumers’ green consumption. The third-party platform provides an agency selling or reselling strategy to sell products. Our results show that: (1) the sensitivity coefficient of consumers to green R&D and DDM activities has positive impacts on all members’ profits and on the green R&D level of products in the platform supply chain, with three kinds of government subsidy policies. (2) The levels of the three kinds of government subsidies mainly have an impact on all members’ profits and on the green R&D level of products in the platform supply chain with an agency selling or reselling strategy; government subsidies to the manufacturer are more conducive to improving the green R&D level of products. (3) The levels of the three government subsidies and the unit service commissioning fee for selling products are the main factors affecting the preferred selling strategy of each member and the equilibrium of the selling strategy.
Purpose
Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.
Design/methodology/approach
Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.
Findings
AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.
Research limitations/implications
New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.
Practical implications
This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.
Originality/value
This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.
Usability is an emerging subject for smartphone design and service, which results in the overall quality and achievement of a product and allows users to perform various tasks. In this context, this study aims to propose an integrated smartphone usability framework for higher service level and user experience with a causal analytic approach. Involving tested relationships with theoretical concerns a conceptual usability assessment model is proposed including design, customer focus, quality, innovation, usability, and user perception variables. The provided model is developed using the Bayesian neural networks based universal structure modeling (USM) method. The reliability and validity are empirically tested for the questionnaire data collected from 1068 smartphone users. The results and findings showed that design, customer focus, quality, and innovation explain usability, and user perception as an ultimate variable is interpreted by usability. Also, strategic, and valuable information for smartphone designers and marketing people to understand user perceptions for smartphone usability is provided.
Global retail industry players have witnessed a grave scenario due to the impact of the COVID-19 pandemic. The pandemic has changed the way shoppers think, manifested in the decelerating footfall and increasing threat for traditional brick and mortar stores. The strategy of switching over to omnichannel seemed to have provided the needed relief to traditional retailers and manufacturers in the consumer goods industry. However, a robust omnichannel product assortment model requires integrating channels and remodeling managers’ roles to provide consumer experience and satisfaction and maximize profitability across all touchpoints with minor disruptions. The paper formulates and simulates an omnichannel data-driven fulfillment analytical model to analyze customers’ product mix and manage assortment accordingly. Further, an optimization model that maximizes revenue and profitability is formulated as a suggestive framework with strategies for the current scenario. The paper is helpful for marketing researchers and retail planners for omnichannel assortment management.
This study is the first to provide an integrated view on the body of knowledge on artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review (SLR) using a data-driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied.
The new business challenges in the B2B sector are determined by connected ecosystems, where data-driven decision making is crucial for successful strategies. At the same time, the use of digital marketing as a communication and sales channel has led to the need and use of Customer Relationship Management (CRM) systems to correctly manage company information. The understanding of B2B traditional Marketing strategies that use CRMs that work with Artificial Intelligence (AI) has been studied, however, research focused on the understanding and application of these technologies in B2B digital marketing is scarce. To cover this gap in the literature, this study develops a literature review on the main academic contributions in this area. To visualize the outcomes of the literature review, the results are then analyzed using a statistical approach known as Multiple Correspondence Analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. The research results classify the types of CRMs and their typologies and explore the main techniques and uses of AI-based CRMs in B2B digital marketing. In addition, a discussion, directions and propositions for future research are presented.
The development of the Internet and the implementation of traditional marketing strategies have given rise to the emergence of digital marketing strategies exploited both by SMEs and large companies. These companies combine data sciences with digital marketing strategies to sell products, generate brand awareness, or access new markets. The present study aims to understand the role and use of data science by SMEs in their online marketing performance. The research method used in this study is a systematic literature review. The data were analyzed using multiple correspondence analysis (MCA) in the programming language R. Based on the results, we identify a total of seven state-of-the-art uses of data science in digital marketing used by SMEs in their online marketing strategies that are graphically represented and analyzed. In addition, four future lines of research are proposed and discussed to understand the direction of the next steps that SMEs should take to successfully develop their digital strategies. Finally, the review concludes with a discussion of the theoretical and practical implications of our findings for further research on the influence and use of data sciences in SMEs' online marketing performance.
In recent times, there has been a significant decline in hotel occupancy rates, and this is primarily due to marketing performance. Hoteliers and the decision-makers are thus seeking new strategies to increase occupancy rates by enhancing marketing performance. The present work examined the relationship between customer relationship management performance and marketing performance by considering the moderating role of social customer relationship management on this relationship. In this work, both the "Resource-Based View Theory" and "Social Exchange Theory" were employed. Data from hotel managers in Jordan were collected, with 139 responses being collected and analyzed altogether. "Smart Partial Least Squares" were used for the analysis process, which showed that customer relationship management performance positively impacted marketing performance , and that Social customer relationship management also had a positive effect on marketing performance. Moreover, the relationship between customer relationship management performance and marketing performance is enhanced through social customer relationship management. These findings can be used by hoteliers to develop effective marketing strategies using new technology and communication tools.
Purpose
The purpose of this paper is to investigate the perception of marketing managers in a transition country Montenegro with regards to marketing metrics. The paper examines the degree in which managers are familiar with the way marketing metrics are applied and how important they are in the process of making business decisions in a company operating in a Montenegro.
Design/methodology/approach
Data was collected during 2020 through a survey of 171 randomly selected companies and was analyzed using structural equation model and the statistical method of analysis of variance tests.
Findings
The obtained results show that managers are quite familiar with financial and non-financial metrics. Both groups are applied to a significant degree, as managers believe that these indicators provide valuable information needed during the decision-making process. Still, more emphasis is placed on the knowledge, implementation and importance of non-financial metrics compared to financial metrics. This is probably due to the specificities of the economic activities of the companies operating in Montenegro, as most of them are service companies, which is why non-financial metrics (such as consumer metrics) are the most important indicators when it comes to ascertaining the market position of the company. Additionally, in recent years the primary focus in Montenegro, as country that is still in the process of transformation from planned economy to a free-market form, has been placed on strengthening of competitiveness and advancing the market orientation of companies. This led to an increase in the importance that managers in transition countries attach to non-financial metrics.
Research limitations/implications
The fact that the survey only covers companies from one country is its limitation.
Practical implications
The obtained results will have a significant empirical contribution, which is reflected in providing guidelines for managers on how to improve the system of measuring and controlling marketing performance, all that to strengthen the competitiveness of the company, and can serve managers of hierarchy levels in a company as guidelines for making decisions on the implementation of marketing strategy and marketing metrics, to improve business performance, multi-context customer interaction, cost-saving and strengthen competitiveness.
Social implications
Obtaining necessary knowledge management and implementing marketing metrics are important conditions for consideration when it comes to the continuous monitoring and improvement of business results, increasing competitiveness and advancing the market position of the company.
Originality/value
The originality stems from the analysis of the interconnection that exists between marketing metrics and strategic decision-making, which is expected to be positively reflected in the development of society, i.e. strengthening the competitiveness of companies based on knowledge management achieved through the assessment of the degree of knowledge, the implementation and the significance of each of the metrics covered within this research in business decision-making processes. The paper provides insights into the extent to which managers understand the meaning of these indicators and are able to combine different marketing metrics to obtain more complex indicators, serving as necessary inputs when making strategic business decisions.
The competitiveness of the business ecosystem is exponentially increasing nowadays. Cutthroat competition is being observed by the business leaders to retain the customers and to optimize the customer lifetime value (CLV). The marketers used to target and engage their customers based on the "gut-feeling" and "assumptions" of what is the in-sync audience for their offerings. However, data-driven marketing has heightened the campaign's efficiency and pull the business, voluminous rungs closer to the audience's behavior & expectations. Improving the information attribute is another crucial emphasis of the business leader to derive multi-dimensional success metrics and determine' long-term success.' The omnichannel experience (e.g., walk-in customers, social media, mobile app users, point of sale data, etc.) and cross channel customer experience is vital in elevating the customer lifetime value. The marketing strategies are crafted based on the different inputs of the big data-driven solution's information and its recommendation. This research model institutes an integrated relationship between big data-driven solutions, information quality, customer lifetime value, strategic decision, and business performance. The assertive usage of information with media and creatives is also discussed in the research. The moderating relationship of user behavior-based recommendations to marketing leaders is also established in the proposed model. The conceptual framework contributes to the business performance model through strategic marketing decisions.
In the new era of marketing, being at the top results of search engines constitutes one of the most competitive advantages to the organizations’ overall online advertising strategy. In search engines, users type their search terms to cover their informational or purchasing needs and subsequently, search engines rank websites to the relevance of users’ search terms. The higher are the rankings of the websites, the more is the percentage of visitors who explicitly come from search engines. Nevertheless this obvious one marketing advantage, there is no prior research evidence as regards the level of engagement between users and content, after they visit the websites from search engines’ results. That is, users probably visit a website that comes at the top of search engines’ results, however, they do not spend an amount of time, or they do not browse in several webpages inside of it and vice versa. Against this backdrop, the authors proceed into the construction of a methodology composed of the retrieval of web analytics datasets and the development of computational models with the purpose to evaluate users’ engagement and content use within the websites. At the first stage, the authors proceed into the retrieval of web behavioral analytics at certain metrics for 125 sequential days as regards the time users are spending, the number of pageviews they are browsing, the percentage of immediate abandonments, and the percentage of traffic that explicitly comes from search engines. Following a data-driven methodological approach for the development of computational models, the fuzzy cognitive mapping at the descriptive modeling stage is adopted with the purpose to indicate the possible correlations between web analytics metrics. One step further, a corroborative and predictive model is proposed through the agent-based modeling method in order to compute the date ranges that resulted in the highest and the lowest engagements of users as regards the content of seven examined courseware websites. The proposed methodology and the results of this study work as a practical toolbox for decision makers while computing and evaluating through a data-driven way the level of engagement between visitors and the content they receive for online presence optimization on the web.
Optimized paid search advertising campaigns composed of multiple data analytics insights and prior experiences of search engine marketing performances. However, when marketers compete in the battle of paid search ads’ rankings, complexity in optimization is increased. The higher the search ads’ ranking position, the greater the chance that users of search engines will click the ads. Despite the existing knowledge of the factors that contribute to the higher ranking position in search ads, such as proper relevancy among users’ search terms and text ads, or landing pages content, little is known about search engine users’ behavior after ads clicking. Low interaction or immediate abandonments from the landing pages potentially lead to a waste of budget spent on each paid advertising campaign. In this regard, marketers should pay much more attention to the interaction of paid traffic visitors after clicking on search ads, and not only to search engine rankings and user impression share rates. In this paper, the authors develop a computational data-driven methodology with a purpose to estimate and predict paid traffic visitors’ engagement in seven courseware websites after clicking on the search ads. The higher the engagement with the landing page, the higher will be the probability for conversions. At the first stage, web behavioral analytics are retrieved for 120 consecutive days in certain web metrics. These are the volume of paid traffic visitors, the average pages per session, the average session duration, and the bounce rate. Statistical analysis of the extracted web behavioral datasets takes place for understanding the cohesion, validity, and intercorrelations between the web metrics. KMO and Bartlett’s test of sphericity and Pearson coefficient of correlation are adopted. One step further, agent-based modeling and simulation is adopted as a methodology for abstracting and calibrating paid traffic visitors’ behavior inside the examined websites. Poisson distributions are implemented for predicting the potential engagement of paid traffic visitors in specific date ranges. Through this, the paper highlights its practical contribution to marketers with the purpose to develop search engine marketing campaigns composed of search ads relevant to the users and sufficient content engagement after ads clicking.
This exploratory research examines how the COVID-19 pandemic led to increases in consumers’ social media marketing behaviors in the United States (U.S.). Previous research on the impact of a pandemic has focused on behavior for preventive health, however, little attention has been given to the impact of a pandemic on consumer behaviors. To bridge this gap, the Consumer Decision-Making Model was used as a framework to investigate changes in consumers’ social media behaviors as they preform various consumer decision-making processes. More specifically, a questionnaire was used to collect survey data from 327 U.S. consumers. Analysis of Variance tests were performed to examine mean differences in consumers’ use of social media as a consumer decision-making tool. The findings showed that consumers have increased their utilization of social media as a tool for identifying products, collecting information on products, evaluating products, and making product purchases. Thus, the findings demonstrate the growing importance of social media marketing since the COVID-19 pandemic began. Given that the COVID-19 pandemic is a global phenomenon, the findings likely can be extrapolated across many nations. Suggestions are provided to help businesses adopt to changes in consumers’ social media behaviors as they relate to the consumer decision-making processes.
This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity.
Big data technologies and analytics enable new digital services and are often associated with superior performance. However, firms investing in big data often fail to attain those advantages. To answer the questions of how and when big data pay off, marketing scholars need new theoretical approaches and empirical tools that account for the digitized world. Building on affordance theory, the authors develop a novel, conceptually rigorous, and practice-oriented framework of the impact of big data investments on service innovation and performance. Affordances represent action possibilities, namely what individuals or organizations with certain goals and capabilities can do with a technology. The authors conceptualize and operationalize three important big data marketing affordances: customer behavior pattern spotting, real-time market responsiveness, and data-driven market ambidexterity. The empirical analysis establishes construct validity and offers a preliminary nomological test of direct, indirect, and conditional effects of big data marketing affordances on perceived big data performance.
Purpose
Considering the transition of communicational channels from physical to digital spaces, this study aims to provide a theoretical foundation for understanding engagement in electronic word of mouth (eWoM) among managers and customers in the hospitality and tourism industry.
Design/methodology/approach
This study uses the four aggregate dimensions, namely, performance expectancy, efforts expectancy, social influence and facilitations condition. Further, this paper uses the 14 second-order themes of the Unified Theory of Acceptance and Use of Technology with a data set that represents elements that can trigger eWoM, both from managers’ and customers’ perspectives. The process of data structuration follows thematic analysis and axial coding techniques.
Findings
The results of this study show that performance expectancy, facilitation conditions, social influence and effort expectancy all trigger positive eWoM generation in the hospitality and tourism industry indicating customers’ and managers’ perspectives.
Originality/value
This novel study provides a theoretical foundation and novel propositions for future research work on the role of novel antecedents that can trigger eWoM in the hospitality and tourism industry. This paper also provides a benchmark for practitioners and policymakers in their strategic decisions-making towards improving business performance through positive eWoM.
The COVID-19 issue has, albeit at varied rates, pushed digital transformation in industry and services across the majority of nations. Achieving the objectives on the path to excellence depends on the delivery of customized goods or services and changing consumer needs. Digital marketing can aid in expanding customer reach internationally and strengthening client relationships. While data-driven decision-making reduces risk, technology can help marketers deliver customers more of what they want and need. Artificial intelligence (AI), the internet of things (IoT), remote collaboration, cloud computing, blockchain, and data analytics are among the technologies that are rapidly transforming the way that digital marketers do business and develop strategies leading to global economic growth. This book chapter explores the potential of AI and IoT in digital marketing. It also provides information on the impact of new technologies on digital marketing and the top trends in this field.
Data-driven algorithms are becoming more prevalent in our everyday lives, automating various decisions that can impact access to opportunities and resources. For this reason, much research concerns the ethical and social consequences of algorithms. Algorithmic digital marketing represents a key means by which people encounter and/or are affected by algorithms in daily life. While previous work has considered the perceptions of consumers in this realm, little work to date has considered those of marketing professionals. The current work presents an exploratory study of marketers, aiming to uncover their general views on the use of algorithmic marketing, as well as their own practices and if/how they address the ethical concerns surrounding algorithmic marketing. The findings underscore the need for ethical regulations and guidelines to be defined for fair practices in the context of algorithmic digital marketing.Keywordsdigital marketingmarketers’ perceptionsfairness in algorithmic processesmicro-targetingethics in digital marketing
Marketing students often experience quantitative anxiety (QA) despite the industry needs and trend for data-driven decision-making. In the past marketing students may have selected their major to avoid extensive engagement with numbers, however, due to the large opportunity and market demands for graduates possessing greater comfort and ability with numbers and demands on marketing practitioners to financially justify their decision-making, this aversion to numbers is no longer a recommended approach.It is, therefore, more important than ever to understand what alleviates quantitative/statistical anxiety in students taking marketing courses that emphasize quantitative analysis. Quantitative reasoning and mathematical understanding will play a major role in the marketing curriculum for the foreseeable future. The role of QA in marketing education, other than anecdotal observations of students, has not been studied extensively. Based on Self Determination Theory (SDT), our goal is to investigate the drivers/inhibitors of both student intrinsic and extrinsic motivation to be successful in quantitative topics within the field of marketing.Of all the predictors of QA, only peer support was significant such that the increased level of peer support reduced QA amongst students. Self-efficacy, perceived usefulness, and desire to succeed increase the level of effort in the classroom. Our study suggests that, even though faculty support did not directly reduce quantitative anxiety, the instructors may still encourage students to self-select into more quantitative courses and also to put in extra effort by highlighting extrinsic rewards for marketing analytics (and other quantitative) careers. Recommendations for the instructors including peer-teaching methods and encourage team work to reduce QA that can inhibit their future success, as peer support appears to critical. Upper-level, strong, analytically-inclined students could serve as tutors encouraging that peer to peer support which underclassmen may be more receptive to. Additionally, as more and more courses move to online delivery, online study groups could be organized by instructors or guidance provided to students on how to self-organize these online groups or learning communities.KeywordsQuantitative anxietyAnalyticsSelf-determination theorySelf-efficacy
Business decision-making increasingly requires that the information technology (IT) and marketing functions work together. Despite their distinct objectives, marketing managers need improved access to data to drive strategy. Based on an absorptive-capacity perspective, this study examines how marketing and IT business knowledge at the executive level affect decision-making. Results indicate that IT business knowledge has a direct, positive effect on data-driven decision-making, and marketing business knowledge does not. In addition, information quality and marketing/IT cooperation have differential moderating effects.
Data quality has become an area of increasing concern in marketing research. Methods of collecting data, types of data analyzed, and data analytics techniques have changed substantially in recent years. It is important, therefore, to examine the current state of marketing research, and particularly self-administered questionnaires. This paper provides researchers important advice and rules of thumb for crafting high quality research in light of the contemporary changes occuring in modern marketing data collection practices. This is accomplished by a proposed six-step research design process that ensures data quality, and ultimately research integrity, are established and maintained throughout the research process—from the earliest conceptualization and design phases, through data collection, and ultimately the reporting of results. This paper provides a framework, which if followed, will result in reduced headaches for researchers and more robust results for decision makers.
Implementation of the GDPR changed the way how personal data of EU customers are processed. The purpose of this chapter is to explore the links between the rights of customers as a data subject and related aspects of customer satisfaction. Entities in modern economy (encompassing not only goods and services but also intellectual property) generate and process huge quantities of customer data. Information and communication technology (ICT) infrastructure became a basis for the digital economy and society in the EU (settled by Eurostat as ISOC) that definitely replaced the previous era of the information economy that was based on the effective acquisition, dissemination, and use of information. Data-driven marketing puts data at the center of additional value creation and brings new insights and perspectives, included in the results of this research. The impact of GDPR on customer-centric ICT, stronger consumer awareness of data protection rights, creates new pathways to customer centricity and the legal and technical aspects of data processing within the digital economy ecosystem.
Advancing value creation has gained momentum in recent years due to the dramatic growth of data-driven marketing analytics. As such, there is an ever-growing surge both in academia and industry to understand data-driven value creation. In particular, customers’ data exhibit a new pathway of value creation by deploying customer analytics. Despite the strategic importance, there is limited attention to develop and examine the customer analytics-driven value creation capability (CAVCC). Drawing on the resource-based view capability, knowledge-based view, and market orientation, this current research develops and validates a hierarchical model on CAVCC in the big data analytics spectrum. The findings identify the direct and indirect impact on sustained competitive advantage in which customer linking plays a mediating role. The paper discusses key theoretical and practical contributions, along with future research directions.
Purpose
Few well-documented studies have explained the importance of researching firms' marketing analytics capability (FMAC). In spite of its significance, there is scant attention to conceptualising and empirically investigating FMAC and its consequences in a data-driven business context. Thus, this study aims to develop and test a conceptual model that relates FMAC and its repercussions in the data-rich business environment.
Design/methodology/approach
This study analysed the data from 250 managers amongst large and medium-sized manufacturing and service-intensive firms. Furthermore, this research performed an empirical study by using operationalised questionnaire survey method to verify the hypotheses and reach its theoretical and managerial implications. Structural equation modelling with maximum-likelihood estimation method was applied to verify the validity of the proposed research model.
Findings
Multivariate analysis results show that FMAC significantly influences firms' competitive marketing performance (FCMP) with the presence of holistic marketing decision-making (HMDM) as a mediator. Moreover, adoption of artificial intelligence (AAI) enhances the relationship of FMAC-HMDM and FMAC-FCMP linkages.
Practical implications
This study analyses how FMAC can enhance FCMP and contributes to resource-based views and technological capability theories. From a managerial perspective, guidelines are provided for marketers to adopt advance technologies, such as AI, to optimise FMAC and HMDM to achieve competitive marketing performance.
Originality/value
Believing that “how to be competitive in marketing performance under data-rich-environment”, this research is the first to use the data of a firm manager to facilitate the understanding of FMAC, which provides a new direction for improving marketing performance. In addition, HMDM and AAI are also proposed for firms to optimise FCMP.
Bibliometric analysis is a popular and rigorous method for exploring and analyzing large volumes of scientific data. It enables us to unpack the evolutionary nuances of a specific field, while shedding light on the emerging areas in that field. Yet, its application in business research is relatively new, and in many instances, underdeveloped. Accordingly, we endeavor to present an overview of the bibliometric methodology, with a particular focus on its different techniques, while offering step-by-step guidelines that can be relied upon to rigorously perform bibliometric analysis with confidence. To this end, we also shed light on when and how bibliometric analysis should be used vis-à-vis other similar techniques such as meta-analysis and systematic literature reviews. As a whole, this paper should be a useful resource for gaining insights on the available techniques and procedures for carrying out studies using bibliometric analysis.
Keywords: Bibliometric analysis; Performance analysis; Science mapping; Citation analysis; Co-citation analysis; Bibliographic coupling; Co-word analysis; Network analysis; Guidelines.
Digital transformation has deeply influenced how innovation can rise in the production, distribution and consumption of cultural products. Although data-driven innovation has been proved effective in creating value across many business functions, the pace of adoption of strong data ecosystems seems slower for Arts and Cultural Organizations (ACOs). The paper theoretically explores how data analytics can affect different areas of innovation in the core cultural sectors. By integrating marketing intelligence, arts management and policy literature with illustrative evidence from secondary sources, we discuss the potential impact of data analytics for enhancing ACOs innovation. First, digitalization and connectivity have increased opportunities for customer engagement and empowerment, shifting cultural consumption from a transaction to a relationship with cultural organizations. Second, data-driven metrics allow ACOs and policy makers to match more effectively patterns of consumption and eventually to create value from harvesting and processing information. Finally, although ACOs are encouraged to review their traditional business models through this new trajectory, significant conceptual and organizational barriers question the benefits of data analytics and slow down its adoption. The paper contributes to the academic and policy debate on the role of data-driven innovation in arts management and marketing strategies for cultural organizations.
Predicting customer repurchase propensity/frequency has received broad research interests from marketing, operations research, statistics, and computer science. In the field of marketing, Buy till You Die (BTYD) models are perhaps the most representative techniques for customer repurchase prediction. Those probabilistic models are parsimonious and typically involve only recency and frequency of customer activities. Contrary to BTYD models, a distinctly different class of predictive models for customer repurchase is machine learning. This class of models include a wide variety of computational and statistical learning algorithms. Unlike BTYD models built on low-dimensional inputs and behavioral assumptions, machine learning is more data-driven and excels at fitting predictive models to a large array of features from customer transactions. Using a large online retailing data, we empirically assess the prediction performance of BTYD modeling and machine learning. More importantly, we investigate how the two approaches can complement each other for repurchase prediction. We use the BG/BB model given the discrete and non-contractual problem setting and incorporate BG/BB estimates into high-dimensional Lasso regression. In addition to showing significant improvement over BG/BB and Lasso without BG/BB, the integrated Lasso-BG/BB provides interpretability and identifies BG/BB predictions as the most influential feature among ∼100 predictors. The lately developed CART-artificial neural networks exhibit similar patterns. Robustness checks further show the proposed Lasso-BG/BB outperforms two sophisticated recurrent neural networks, validating the complementarity of machine learning and BTYD modeling. We conclude by articulating how our interdisciplinary integration of the two modeling paradigms contributes to the theory and practice of predictive analytics.
This study merged the technology acceptance model (TAM) with the theory of planned behavior (TPB) to examine how to form behavioral intentions in the context of drone food delivery services. The results of data analysis showed that all six hypotheses within the model that merged the TAM and the TPB were statistically supported. In addition, product innovativeness moderated the relationship between the subjective norm and behavioral intentions. In the latter part of this study, implications for the food service industry as well as implications for the tourism industry were presented.
Journal impact factor (JIF) quartiles are often used as a convenient means of conducting research evaluation, abstracting the underlying JIF values. We highlight and investigate an intrinsic problem associated with this approach: the differences between quartile boundary JIF values are usually very small and often so small that journals in different quartiles cannot be considered meaningfully different with respect to impact. By systematically investigating JIF values in recent editions of the Journal Citation Reports (JCR) we determine it is typical to see between 10 and 30% poorly differentiated journals in the JCR categories. Social sciences are more affected than science categories. However, this global result conceals important variation and we also provide a detailed account of poor quartile boundary differentiation by constructing in-depth local quartile similarity profiles for each JCR category. Further systematic analyses show that poor quartile boundary differentiation tends to follow poor overall differentiation which naturally varies by field. In addition, in most categories the journals that experience a quartile shift are the same journals that are poorly differentiated. Our work provides sui generis documentation of the continuing phenomenon of impact factor inflation and also explains and reinforces some recent findings on the ranking stability of journals and on the JIF-based comparison of papers. Conceptually there is a fundamental problem in the fact that JIF quartile classes artificially magnify underlying differences that can be insignificant. We in fact argue that the singular use of JIF quartiles is a second order ecological fallacy. We recommend the abandonment of the quartiles reification as an independent method for the research assessment of individual scholars.
This study investigates the driving forces of a firm's assimilation of big data analytical intelligence (BDAI) and how the assimilation of BDAI improve customer relationship management (CRM) performance. Drawing on the resource-based view, this study argues that a firm's data-driven culture and the competitive pressure it faces in the industry motivate a firm's assimilation of BDAI. As a firm resource, BDAI enables an organization to develop superior mass-customization capability, which in turn positively influences its CRM performance. In addition, this study proposes that a firm's marketing capability can moderate the impact of BDAI assimilation on its mass-customization capability. Using survey data collected from 147 business-to-business companies, this study finds support for most of the hypotheses. The findings of this study uncover compelling insights about the dynamics involved in the process of using BDAI to improve CRM performance.
Purpose
In the current business environment, more uncertain than ever before, understanding consumer behavior is an integral part of an organization's strategic planning and execution process. It is the key driver for becoming a market leader. Therefore, it is important that all processes in business are customer centric. Marketers need to harness big data by engaging in data driven-marketing (DDM) to help organizations choose the “right” customers, to “keep” and “grow” them and to sustain “growth” and “profitability”. This research examines DDM adoption practices and how companies can aim to enhance shareholder value by bringing about “customer centricity”.
Design/methodology/approach
An online survey conducted in 2016 received 180 responses from junior, middle and senior executives. Of the total responses, 26% were from senior management, 39% from middle management and the remaining 35% from junior management. Industries represented in the survey included retail, BFSI, healthcare and government, automobile, telecommunication, transport and logistics and IT. Other industries represented were aviation, marketing research and consulting, hospitality, advertising and media and human resource.
Findings
Success of DDM depends upon how well an organization embraces the practice. The first and foremost indicator of an organization's commitment is the extent of resources invested for DDM. Respondents were divided into four categories; Laggards, Dabblers, Contenders and Leaders based on their “current level of investments” and “willingness to enhance investments” soon.
Research limitations/implications
With storming digital age and the development of analytics, the process of decision-making has gained significant importance. Judgment and intuition too are critical to the process. Choosing an appropriate action cannot be done strictly on a rational basis.
Practical implications
The results of the study offer interesting implications for managing the growing sea of data. An iterative and incremental approach is the need of the hour, even if it has to start with baby steps, to invest in and reap the fruits of DDM. The intention to use any system is always dependent on two primary belief factors: perceived usefulness and perceived ease of use; however, attitudes and social factors are equally important.
Originality/value
There is a dearth of knowledge with regards to who is and is not adopting DDM, and how best big data can be harnessed for enhancing effectiveness and efficiency of marketing budget. It is, therefore, imperative to build a knowledge base on DDM practices, challenges and opportunities. Better use of data can help companies enhance shareholder value by bringing about “customer centricity”.
Abstract
Purpose
The marketing information system (MkIS) in the data-rich business environment receives all the attention these days, but as essential and perhaps even more essential is marketing information system management capability (MkISMC). Although many service firms apprehend the return from MkIS, others clearly struggle. It seems that MkIS management capability dynamics and their direct/indirect holistic influence on service firm's competitive performance are unsolved in the current data-driven service economy. This study aims to conceptualize a model and test the antecedents on service firms' competitive performance.
Design/methodology/approach
This study utilises a cross-sectional survey of a sizeable sample of service firms managers at the firm level. A total of 250 usable responses were obtained and analyzed through the structural equation modeling.
Findings
Results reveal that variables under their respective direct influences are positively and significantly related. Interestingly, MkISMC has a relatively large magnitude of positive and direct effects on service firms' competitive performance. The other variables, such as use of
marketing analytics (UMAN) and service innovation management (SINM), partially mediate MkISMC on the competitive performance of service firms.
Practical implications
The findings inform practitioners that MkISMC, UMAN, and SINM play a vital role in attaining service firms' competitive performance in the data-rich environment. Overall, it deepens the understanding of the contingency effect of UMAN and SINM of service firms on competitive performance.
Originality/value
The study advances theoretical understanding of resource-based view, market orientation, and dynamic capability that formulates the relationship of MkISMC, UMAN, and SINM in attaining service firm's competitive performance in the ever-changing data-driven business economy.
Keywords: Marketing information system; Marketing analytics; Service innovation management; Competitive performance, data-driven innovation
Artificial intelligence (AI) agents driven by machine learning algorithms are rapidly transforming the business world, generating heightened interest from researchers. In this paper, we review and call for marketing research to leverage machine learning methods. We provide an overview of common machine learning tasks and methods, and compare them with statistical and econometric methods that marketing researchers traditionally use. We argue that machine learning methods can process large-scale and unstructured data, and have flexible model structures that yield strong predictive performance. Meanwhile, such methods may lack model transparency and interpretability. We discuss salient AI-driven industry trends and practices, and review the still nascent academic marketing literature which uses machine learning methods. More importantly, we present a unified conceptual framework and a multi-faceted research agenda. From five key aspects of empirical marketing research: method, data, usage, issue, and theory, we propose a number of research priorities, including extending machine learning methods and using them as core components in marketing research, using the methods to extract insights from large-scale unstructured, tracking, and network data, using them in transparent fashions for descriptive, causal, and prescriptive analyses, using them to map out customer purchase journeys and develop decision-support capabilities, and connecting the methods to human insights and marketing theories. Opportunities abound for machine learning methods in marketing, and we hope our multi-faceted research agenda will inspire more work in this exciting area.
We are living in a world of data abundance and rapid technological advances in the digital realm. The consequences for marketing practices have been transformative. The Marketing Edge and Journal of Business Research sponsored this special issue to address the need for research in this domain. We draw upon past literature to trace how data-driven marketing practices and adoption of digital technologies have helped transform and expand the scope of marketing from a function that was primarily related to analyzing advertisements, to crafting analytics-driven customer-centric marketing, to a function that is fiscally responsible and increasingly technology enabled. The collection of nine studies in this special issue richly describes the challenges that marketing practitioners face and highlights research issues that need to be addressed.