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Interdisciplinary Perspectives in Data-Driven Marketing
Abstract
The chapter explores the crucial role of interdisciplinary collaboration in modern data-driven marketing. It highlights the significance of bridging the gap between data scientists and marketing professionals, emphasizing effective communication and mutual understanding of each other's expertise. Exploring cross-disciplinary research opportunities, the chapter discusses how insights from psychology, sociology, anthropology, computer science, and information technology can inform targeted marketing strategies. Integration with business intelligence and analytics is also explored, showcasing how organizations can leverage data-driven insights to align marketing efforts with broader business objectives. By maximizing the value of interdisciplinary collaboration, organizations can unlock new insights, drive innovation, and stay ahead in a rapidly evolving market landscape.
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Enterprises nowadays are generating massive amounts of data at an alarming rate. Mastering data pipelines, analytics, and sharing of data insights is a key competitive advantage to grow business and ensure reaching the organization strategic goals. Due to the recent analytical and technological advances, data-driven decision-making has raised a particular attention as one of the best solutions for decision-making, combining both data insights and business expertise. Being a data-driven organization implies relying mainly on data to make decisions rather than intuitions which can mislead decision-making. Becoming a data-driven organization, however, is a long process that involves deep cultural, organizational, and technical transformations. In fact, despite the potential benefits of a data-driven migration, only few companies have successfully completed the transformation to becoming data-driven. This is due to the numerous challenges and barriers an organization must cope with to become data-driven. This paper aims to study the gap between data-drivenness barriers’ research, and practical implementation challenges based on our data expertise and the literature review.
Purpose: An omnichannel strategy creates a consistent brand image and customer experience across all channels, making it easier for customers to interact with a business and share information. This research aimed to investigated the relationship between consumers' information-sharing intention and their omnichannel experiences. Research design, data, and methodology: Through an online survey conducted in Vietnam, the study obtained 915 responses. The study used Partial Least Square Structural Equation Modeling (PLS-SEM) to analyze research data and confirm proposed research hypotheses. Results: Research results indicated that information-sharing intention is affected by both online and offline customer experience, and at the same time, the study also confirmed that omnichannel’s three characteristics (integration, individualization, interaction) positively impact on customer experience. Conclusions: From the research result, businesses may boost consumer trust and loyalty with the help of an omnichannel approach, which in turn increases customers' propensity to provide personally identifying information to the firm. One way to do this is to facilitate information exchange by delivering customized and relevant offers. Furthermore, companies show consumers the benefit of providing their data by utilizing it to enhance the customer experience
Data science and machine learning are subjects largely debated in practice and in mainstream research. Very often, they are overlapping due to their common purpose: prediction. Therefore, data science techniques mix with machine earning techniques in their mutual attempt to gain insights from data. Data contains multiple possible predictors, not necessarily structured, and it becomes difficult to extract insights. Identifying important or relevant features that can help improve the prediction power or to better characterize clusters of data is still debated in the scientific literature. This article uses diverse data science and machine learning techniques to identify the most relevant aspects which differentiate data science and machine learning. We used a publicly available dataset that describes multiple users who work in the field of data engineering. Among them, we selected data scientists and machine learning engineers and analyzed the resulting dataset. We designed the feature engineering process and identified the specific differences in terms of features that best describe data scientists and machine learning engineers by using the SelectKBest algorithm, neural networks, random forest classifier, support vector classifier, cluster analysis, and self-organizing maps. We validated our model through different statistics. Better insights lead to better classification. Classifying between data scientists and machine learning engineers proved to be more accurate after features engineering.
Zero Party Data (ZPD) is a hot topic in the context of privacy-aware personalization, as the exponential growth of consumer data collected by retailers has made safeguarding data privacy a key priority. Articles arguing for the value of ZPD to improve personalization and engender consumer trust have appeared in the popular press, in business magazines as well as in academic journals. Advocates of ZDP argue that instead of inferring what customers want, retailers can simply ask them. Provided that the value exchange is clear, customers will willingly share data such as purchase intentions and preferences to improve personalization and help retailers create a picture of who they are. While the rise of ZPD is a welcome development, this paper takes issue with the claim that ZPD is necessarily accurate as it comes directly from the customer. This view is at odds with established conclusions from decades of research in the social and cognitive sciences, showing that self reports can be influenced by the instrument and that people have limited insight into the factors underlying their behavior. This paper argues that while ZDP disclosures are an important tool for retailers, it is critical to carefully understand their limitations as well. The paper also provides a catalog of biases for identifying potential problems in survey design to help practitioners collect more accurate data.
In the last decade, a transition in research design and methodology is identified in social research methodology; however, the high entry threshold (i.e., technical knowledge) to utilize computational methods and the ethical concerns seem to slow down the process. A possible way out is that social sciences collaborate with computational or data scientists in interdisciplinary research projects to rely on each other’s skills and to develop jointly accepted ethical principles. In this exploratory study, we collected data from researchers with a variety of academic backgrounds to find out their views of interdisciplinary projects and related methodological or ethical issues. Our findings derived from one-on-one interviews (n = 22) reinforce the importance of interdisciplinary collaboration and highlight the significance of “interpreters,” i.e., individuals able to communicate with and connect various areas of science, education, and academic institutions’ role in enhancing interdisciplinary collaborations of sciences. Additional concerns of participants emerged in terms of research methodology applied in the digital world (i.e., data validity, credibility and research ethics). Finally, participants identified open science and the transparency of research as the key to the future development of social sciences.
Consumers perform their activities through digital channels more often as a result of technological advancements where those advancements also allow marketers to reach excessive information about consumers, store them, and use them whenever and however they consider necessary. These big data provide businesses to understand the unmet demands and expectations of consumers and achieve a sustainable business success. Despite the importance of big data analytics for marketing of businesses, research on this issue is scarce. In order to contribute the literature, the purpose of this chapter is to reveal the importance of big data in the digital marketing environment. In line with this purpose, a comprehensive literature review including the definition, components, sources of big data, and the role of big data in digital environments and the examples of businesses using big data is undertaken.
Aim/Purpose: The complexity of scientific problems has spurred the development of transdisciplinary science, in which experts are brought together to collaborate across disciplinary and practice boundaries. These knowledge diverse teams can produce novel solutions, but they often fail to achieve their potential. Background: Leaders have a crucial role to play in enabling effective collaboration among these diverse experts. We propose that a critical predictor of whether a newly formed interdisciplinary team will perform well is the leader’s multidisciplinary breadth of experience, which we define as a leader’s possession of significant experience in multiple areas of research and practice. We suggest that these leaders will have the capability to skillfully manage the interactions within the team. Methodology: We test our prediction in a sample of 52 newly formed interdisciplinary medical research teams. We also observe and examine the communication patterns in a subset of these teams. Contribution: There is a lack of systematic study of the impact leaders have on newly formed interdisciplinary science teams whose members have little or no prior collaborative experience with each other, possess specialized knowledge, and have limited overlapping expertise. This study combines quantitative and qualitative methods to examine the effect of leader multidisciplinary experience on team communication patterns and innovation. Findings: Our study finds that teams are more innovative when their leader has a moderate breadth of multidisciplinary expertise. Exploration of team communication patterns suggests that leaders with moderate multidisciplinary breadth of experience actively stimulated information sharing across expert domains by choosing cross-cutting topics and drew individuals’ attention to the knowledge and approaches of others in the team. Recommendations for Practitioners: Insights from this work can have practical implications regarding how to best select and train leaders to facilitate cross-boundary collaboration in transdisciplinary science. This study elucidates a variety of communication strategies that leaders can to enhance the team innovativeness. Recommendation for Researchers: Further investigation into the underlying psychological states that these communication strategies elicit is needed. Future research should investigate psychological mediators such as knowledge consideration, perspective taking, and cognitive flexibility. Impact on Society: Transdisciplinary science is needed to solve society’s most complex problems. The more insight we gather about factors that can help these knowledge diverse teams to be successful, but more society will benefit. Future Research: More research is needed on team formation, leader experience, and team outcomes in transdisciplinary science teams in a variety of contexts.
In the last decade, the use of Data Sciences, which facilitate decision-making and extraction of actionable insights and knowledge from large datasets in the digital marketing environment, has remarkably increased. However, despite these advances, relevant evidence on the measures to improve the management of Data Sciences in digital marketing remains scarce. To bridge this gap in the literature, the present study aims to review (i) methods of analysis, (ii) uses, and (iii) performance metrics based on Data Sciences as used in digital marketing techniques and strategies. To this end, a comprehensive literature review of major scientific contributions made so far in this research area is undertaken. The results present a holistic overview of the main applications of Data Sciences to digital marketing and generate insights related to the creation of innovative Data Mining and knowledge discovery techniques. Important theoretical implications are discussed, and a list of topics is offered for further research in this field. The review concludes with formulating recommendations on the development of digital marketing strategies for businesses, marketers, and non-technical researchers and with an outline of directions of further research on innovative Data Mining and knowledge discovery applications.
The motivation for this article is to understand future job requirements in the field of computer science and related fields. It is obvious that emerging technologies will necessitate new skills and roles. Technology powers the economy, allows businesses to prosper and creates jobs. In earlier years, the focus of the IT industry was primarily on programming. Strong programming skills are still needed but industries wish to select students skilled in technologies when they recruit them from institutions imparting engineering education. It is necessary to understand which disruptive technologies in fields related to computer science and engineering are going to shape future jobs.
The assumption that consumers voluntarily accept or decline marketing offerings provides the ethical justification that gives marketing as a social system its license to operate. Consumer autonomy is, therefore, the key ethical principle of marketing in capitalistic economies. However, even in domains with extensive regulatory frameworks and advanced market conditions, consumers are often ill-informed or underinformed. The resultant lack of epistemic confidence diminishes consumers’ ability to make informed choices. At the same time, consumers are by default exposed to promotional content designed to persuade them to accept marketing offerings. This threatens personal autonomy. We develop a concept of consumer autonomy which marketing regulations should protect and promote to enhance informed decision-making. We design autonomy to be robust in situations where individuals are exposed to persuasive attempts to influence them to choose a specific course of action. As such, our concept of autonomy is applicable to a range of contexts beyond marketing where it is necessary to balance external influences and individual autonomy.
What good is the most scientifically valuable analysis if it piles up in marketers’ inboxes but does not give them the necessary foundation for their decisions? Such a situation is no use to data scientists and certainly no use to the marketing team. The root of the issue is that two worlds meet here that speak completely different languages. Only if data scientists can ‘translate’ their results into marketing language will their work be successful. Marketing teams do not need as much information as possible; rather, they require just the right information, preferably with recommendations for action that can guide their decisions. To select the information that is truly useful for marketing and communicate it in an understandable way, data scientists must have more than expertise in analytics methods and tools (which is assumed and therefore not discussed in detail here); they also need to know about marketing objectives and have a comprehensive contextual understanding of their company’s industry and sector, including competitors. Knowledge of the general situation in the world as well as the legal, political and religious particularities of the countries in which the company operates is also required. In short, analytics results that truly drive marketing can only be delivered by data scientists with domain knowledge. Using a case study from the field, this paper shows how data scientists can gain the domain knowledge they need to be successful in marketing and in which aspects of their work it helps them perform more effectively.
Across the globe, the COVID-19 pandemic has had a massive — and likely long-lasting — impact on commercial and consumer trends. For companies specialising in consumer packaged goods and traditionally reliant on physical stores for the majority of their sales, success is no longer about staying relevant, but rather on preparing for a world where significant portions of their business will emanate from e-commerce. With brickand- mortar sales declining and consumers becoming more tech-savvy, this paper argues that beauty companies (both large and small) must look to advanced ways for customers to discover, consider and purchase beauty products. By way of illustration, the paper describes how L’Oréal embarked on a programme of digital transformation that would prepare it to outlast the current context and continue to advance in spite of a challenging market.
Marketers realise significant benefits such as greater sales, reduced marketing costs, higher profits, more lucrative salaries and enhanced influence within their organisation when they understand customers. To reap these benefits, marketers need customer insights: how customers think, how they feel, how they behave and how they are changing in response to evolving marketplaces. Marketing’s role in research is imperative, since marketing represents the voice of the consumer within the organisation. The best research-derived insights are realised when marketers have thought through the following questions before data is collected: 1) is there a clear and specific research question?; 2) which customers are most relevant to the research question?; 3) has this question been answered before (in the academic literature, industry/consulting reports, within my own company)?; if not, 4) which research methods and measures should be used; 5) where should one start in the research process?; and 6) what is optimal? This paper addresses each question.
This chapter delves into the captivating intersection of AI and wishlists, exploring how e-commerce undergoes a transformative shift with innovative strategies and enhanced consumer experiences. A critical examination of existing literature unveils a multifaceted relationship between AI and wishlists, presenting a myriad of opportunities that redefine their function and shape consumer behavior. From personalized recommendations to predictive analytics, this chapter illuminates the profound impact AI integration has on consumer satisfaction and engagement. It also addresses challenges, emphasizing issues like data privacy and security. Serving as a comprehensive guide, this chapter navigates the intricate terrain of AI-infused wishlists, providing insights to revolutionize the e-commerce industry. By ensuring a robust, personalized, and secure shopping experience, the integration of AI in wishlists emerges as a pivotal force in reshaping consumer interactions.
Fairness is threatened by algorithm bias, systematic and unfair disparities in machine learning results. Amazon's AI-driven hiring tool favoured men. AI promised data-driven, impartial decision-making, but it has revealed sector-wide prejudice, perpetuating systematic imbalances. The algorithm's bias is data and design. Biassed historical data and feature selection and pre-processing can bias algorithms. Development is harmed by human biases. Algorithm prejudice impacts money, education, employment, and crime. Diverse and representative data collection, understanding complicated “black box” algorithms, and legal and ethical considerations are needed to address this bias. Despite these issues, algorithm bias elimination techniques are emerging. This chapter uses secondary data to study algorithm bias. Algorithm bias is defined, its origins, its prevalence in data, examples, and issues are discussed. The chapter also tackles bias reduction and elimination to make AI a more reliable and impartial decision-maker.
AI will personalize marketing. Analysis of client behavior and preferences customizes product and service suggestions. AI-powered CRM solutions can automate customer service, help customers, and boost satisfaction. AI improves marketing targeting. Technology can improve client behavior targeting. AI will also impact digital marketing. Personalization boosts client engagement and sales. Virtual assistants and chat bots will increase marketing. Apps can swiftly answer customer questions, improve service, boost satisfaction, and develop brand loyalty. AI can enhance price by studying market trends, competition, and customer behaviour. Machine learning algorithms help organizations set rates, increasing sales and profit. Marketers may create more engaging content with AI. AI can analyze client data and behavior to determine which content performs best for target demographics, improving content marketing. AI marketing will develop in the future. Companies will benefit from AI-powered, tailored, and data-driven marketing that boosts customer engagement, loyalty, and revenue.
This chapter explores the importance of personalization and recommendation algorithms in OTT era, emphasizing their role in enhancing content discovery and customizing user experiences. Crucial techniques like collaborative filtering and content-based filtering underpin these algorithms, which ensues personalized user experiences. Recommendation algorithms shape media consumption patterns, content discovery, influencing user behavior, cross-platform consumption and binge-watching habits. This chapter also paid attention to acknowledging ethical considerations like privacy concerns and algorithmic bias. Additionally, it also explores the challenges and opportunities for content creators in catering to algorithmic preferences, along with significance of balancing effective ad targeting and user privacy in personalized advertising. Improving and assessing recommendation algorithms using different metrics and feedback loops is important, however future trends concentrate on contextual personalization and adaptive experiences, enhancing user's entertainment journey.
This research paper analysis Spotify data using Python to investigate the characteristics contributing to song popularity. The objectives are to assess the popularity index, identify key attributes of popular songs, and develop a model for predicting song popularity based on current characteristics. The analysis involves data cleaning, exploratory data analysis, and visualization using Python libraries. With over 381 million monthly active users, Spotify provides a rich dataset for understanding music listening habits. Previous studies have explored Spotify's technologies and popularity, enhancing understanding of its protocols and user behavior. This research paper aims to uncover patterns and relationships within the data by applying statistical and machine-learning techniques. The findings will inform actionable recommendations and contribute to a better understanding of music consumption patterns and preferences.
Nowadays, Artificial Intelligence (AI) is powering the data-driven advances that are transforming industries around the planet. AI has become one of the most popular technologies of computer science utilizing to build and develop smart machines. These are cognitive computing systems created to perform actions which can be executed by human intelligence. Major online retailers use AI in analyzing customers’ data to forecast the purchase behavior of consumers. Amazon, Walmart.com and Alibaba employ AI along with forecasting techniques to predict sales and plan inventory accordingly. Data collected from these online retailers indicate they have heavily invested in pricing algorithms that update prices at high speed. According to Vantage Market Research study, global AI in retail market will grow from 17.08 billion by 2028. This paper aims to analyze the impact of AI on the retail industry and how it enhances customer experience which leads to boost companies’ profit.
It has become increasingly difficult for individuals to exercise meaningful control over the personal data they disclose to companies or to understand and track the ways in which that data is exchanged and used. These developments have led to an emerging consensus that existing privacy and data protection laws offer individuals insufficient protections against harms stemming from current data practices. However, an effective and ethically justified way forward remains elusive. To inform policy in this area, we propose the Ethical Data Practices framework. The framework outlines six principles relevant to the collection and use of personal data-minimizing harm, fairly distributing benefits and burdens, respecting autonomy, transparency, accountability, and inclusion-and translates these principles into action-guiding practical imperatives for companies that process personal data. In addition to informing policy, the practical imperatives can be voluntarily adopted by companies to promote ethical data practices.
Data science in marketing has become critical in gaining sustained competitive advantage in a rapidly changing business environment. It involves using advanced analytics and scientific principles to extract valuable information from large volumes of data gathered from multiple sources, such as social media platforms. There are multiple benefits to using data science in marketing, including proper data-based planning, enhanced customization, enhanced forecasting through predictive analytics, effective ROI measuring, and improved pricing models. The research explains how companies can turn the potential and opportunities of these advanced analytics techniques into real company performance in a competitive marketing environment. This research aims to explore how firms can use marketing analytics and big data to improve capabilities and performance. Specifically, the study argues that big data and marketing analytics can be used to extract valuable and meaningful marketing information and insights that can be integrated to improve marketing effectiveness and performance.
To realize value from their wealth of digital data, organizations are investing in data-driven organizational initiatives—efforts in which they must draw expertise in data, algorithms, and visualization together with knowledge and skills in business domains such as marketing and human resources. However, they face the challenge of crossing the knowledge divide between analytics groups and business groups. Exploring relationships between the two groups in 37 data-driven organizational initiatives, we develop a configuration-based model that explains analytics and businessdomain knowledge integration through the lens of synergy. Our configurational analyses revealed five configurations of relationships between the two, which bring about two distinct change outcomes: “dedicated data groups” and “multidisciplinary teams” lead to the emergence of new datadriven ways to work, and “analytics institutionalization,” “analytics resource optimization,” and “networked communities” produce convergence, through the sharing of data-driven ways to work. Each configuration displays a distinct element of the core processes identified (“developing group connectedness,” “exchanging analytics and business domain knowledge,” and “incentivizing organizational data use”) and yields either an emergence or convergence of data-driven ways of working. The findings demonstrate how data-driven organizational initiatives can benefit from a pervasive form of organizing that entwines analytics groups and business groups such that their members’ tools, mindsets, and behaviors are merged to profoundly change ways of working. Together, these findings and the configurational methodology used provide a nuanced picture of how organizations integrate the requisite specialist knowledge across domains to realize value from data.
Data science is a multidisciplinary area that concerns managing, manipulating, analyzing, extracting, and interpreting knowledge from a tremendous amount of data (known as a big data field). Data science is a vast body of expertise that uses methods, approaches, algorithms, tools, and concepts belonging to other interrelated areas, such as information science, probability and statistics, mathematics, big data analytics, and computer science and engineering. As such, mechanisms employed in data science include machine/deep learning techniques, data engineering, data visualization techniques, pattern recognition, data mining, probability modeling, signal processing, and computer vision. Data science is being actively adopted by several vital divisions that take advantage of data science, such as organizations, governments, and academia. This chapter will provide an overview of the top 10 tools and applications that must interest any data scientist. Chapter objectives include (but are not limited to): realize the use of Python in developing solutions of data science tasks, recognize the use of R Language can be used as an open‐source data science provider, travel around the SQL to provide structured models for data science projects, navigate through data analytics and statistics using Excel, using D3.js scripting tools for data visualization. Also, short emphasis and practical examples/case studies are provided on data visualization, data analytics, regression, forecasting, and outlier detection.
Several organizations started to adopt the strategy to have data scientist professionals to help them identify the true value of the cost vs benefits and since data science is on fire for a quite long time, adopting the same comes in handy for several factors. i.e., investment, net profit, etc. At the same time, the clarity is missing around what data science is and this tends to introduce the concept of unambiguous theory or assumptions.In this research, we are aiming to pin down the fact of what data science is and how it can lead to crucial decision-making using a data-driven approach. We firmly believe that the accurate limitations of data science scope cannot be defined due to its vast possibilities. If the business wants to align themselves using the data science by understanding the true potential, the following needs to be underlined: 1) understand the importance of related concepts using the relationship approach, 2) identify how fundamental principles of data science can be useful for the different business use case, 3) Identify what the data science can offer depending on our requirement. In this research, we offer a decision-making solution for businesses using the underlying data science principles.
Consumer-facing health applications are increasingly requiring flexible approaches for expressing consumer consent preferences for the use of their health data across multiple providers, and across cloud and on-premises systems. This and the recognition of the need for clear governance and legislative rules that specify enforceable policies over how consumer data is used by the nominated and other providers, including AI vendors, increasingly require machine readable, i.e. computable consent expressions. These expressions can be regarded as additional constraints over security policies, applicable to all stakeholders, while accommodating rules from regulatory and legislative policies. Support for both kind of policies contribute to improving consumer trust in the use of their data. This is applicable to both care delivery processes but also research projects, such as clinical trials. This paper proposes a computable consent framework and positions it in the context of the new developments within Health Level Seven (HL7®) Fast Health Interoperability Resources (FHIR®) standard. The proposal is based on the use of precise policy concepts from the ISO/ITU-T RM-ODP (Reference Model for Open Distributed Processing) standard. The aim is to provide general standards-based policy semantics guidance to interoperability/solution architects and implementers involved in digital health applications. The framework is driven by consent requirements, while leveraging broader policy input from medico-legal community.KeywordsConsentPolicyInteroperabilityHealth Level Seven (HL7®)Fast Health Interoperability Framework (FHIR®)RM-ODPDigital health
The new technology helps collect and store a considerable amount of information about consumers’ preferences and decisions in real-time. Thus, the unprecedented volume, speed and variety of primary data, Big Data, are available from all sources and individual consumers. The term “Big Data” is now widespread and accepted globally. The term Big Data has become increasingly known and used in many industries. Big Data is a new source of product development ideas, customer service, shelf location, distribution, dynamic pricing, etc. Big Data will have an impact on almost every area of marketing. Firms that do not develop the resources and capabilities to use Big Data capabilities effectively will be challenged to develop a sustainable competitive advantage to survive the Big Data revolution. Consumer analysis is at the heart of a Big Data revolution. Marketing specialists are beginning to recognize the importance of Big Data as the new capital and that access to Big Data offers a company new ways to differentiate its products. In this article, exploratory research has been conducted to highlight areas where Big Data allows organizations to fundamentally know about their business and translate it into better decision-making and improved performance in marketing and consumer behavior. Moreover, a global analysis (based on public data) was performed using large volumes of data in marketing and the main reasons they were stored and used.KeywordsBig DataMarketingBig Data’s growth
Interdisciplinary teaching by teams of diverse faculty is highly effective in providing students with intellectual tools for creating innovative solutions to 21st century problems. This article examines the effective practices for interdisciplinary teaching used by a five-membered, disciplinarily diverse faculty team with expertise in chemistry, design, education, and business/psychology. The team found convergence, reaching a common understanding of information through discussion, to be more important, and difficult, than conveyance, sharing new information. The influence of convergence and conveyance on the faculty team impacted their team dynamic and their content delivery, encouraging similar characteristics from their students. A collaborative case study approach, augmented with interviews and meeting notes, provide the qualitative data from which best practices for fostering an effective interdisciplinary faculty team in higher education are identified. The findings reveal the importance of, and processes for, balancing individual and team priorities within the broad areas of convergence and conveyance: recognizing intrinsic rewards, maintaining a shared focus, developing a team mindset, translating ideas across disciplines, and proactively working on good team practices. The article addresses the dearth of research on the effective practices of interdisciplinary teams in higher education concluding with practical examples and strategies for high performing faculty teams.
Keywords: market system dynamics, consumer culture theory, field theory, network theory, actor network theory, performativity, institutional theory, political economy, population ecology.
Drawing on insights from the sociology of markets, we offer an analytical review of market system dynamics (MSD) in consumer culture theory (CCT) research. We surface important theoretical gaps in current understanding and suggest future research areas. To extend the breadth and depth of MSD’s explanatory power in CCT research, we invite researchers to place greater emphasis on exploring four key issues: (1) differing institutional contexts and their influences on market dynamics; (2) the place and impact of the institutions of the family and religion in market shaping; (3) the role of race, ethnicity and nationality in market dynamics, within and across national borders; (4) conceptualizations of marketplaces as ambiguous environments wherein multiple ideologies, power regimes, and politics interact. In so doing, as a community of researchers, we can advance knowledge on not only what markets do in society but also on what markets do to society.
Leveraging data science can enable businesses to exploit data for competitive advantage by generating valuable insights. However, many industries cannot effectively incorporate data science into their business processes, as there is no comprehensive approach that allows strategic planning for organization-wide data science efforts and data assets. Accordingly, this study explores the Data Science Roadmapping (DSR) to guide organizations in aligning their business strategies with data-related, technological, and organizational resources. The proposed approach is built on the widely adopted technology roadmapping framework and customizes its context, architecture, and process by synthesizing data science, big data, and data-driven organization literature. Based on industry collaborations, the framework provides a hybrid and agile methodology comprising the recommended steps. We applied DSR with a research group with sector experience to create a comprehensive data science roadmap to validate and refine the framework. The results indicate that the framework facilitates DSR initiatives by creating a comprehensive roadmap capturing strategy, data, technology, and organizational perspectives. The contemporary literature illustrates prebuilt roadmaps to help businesses become data-driven. However, becoming data-driven also necessitates significant social change toward openness and trust. The DSR initiative can facilitate this social change by opening communication channels, aligning perspectives, and generating consensus among stakeholders.
A practical data science, machine learning, or artificial intelligence project benefits from various organizational and managerial prerequisites. The effective collaboration between various data scientists and domain experts is perhaps the most important, which is discussed here. Based on practical experience, the principal theses put forward here are that (1) data science projects require domain expertise, (2) domain expertise and data science expertise generally cannot be provided by the same individual, (3) effective communication between the various experts is essential for which everyone requires some limited understanding of the others’ expertise and real-world experience, and (4) management must acknowledge these aspects by reserving sufficient project time and budget for communication and change management.
Big Data is the soul of today’s market. The idea of scaling and enhancing any market to its greater potential without considering Big Data is hooey. A prodigious number of data is generating today. Around 1.7 mb of data by each person is being spawned per second today. This data helps to get a greater insight into some sort of pattern. Predictive analytics is performed on this prodigious history data. Predictive Analytics predict the future outcomes or events, previous or history data with the help of various techniques and models including Machine Learning Models, Forecasting Models, Statistical Modeling, Pattern Prediction, Visualization, etc. Big Data has embedded a gigantic potential concealed within it. The analytics of several hidden aspects of Big Data helps companies to predict all the dimensions of the market in the future. A gigantic number of data is spawned on social media. Billions of accounts are activated on these platforms, which results out in producing the flood of data. Predicting this Big Data has leveraged in several dimensions. The Big data can be utilized for predicting the future sales results, finding the potential customers, impact of advertisements or campaigns upon some strategy. With the help of big data, segmentation of non-resembling customers, targeting the most profitable group of customers and positioning of related advertisements, and campaigns to captivate that particular segment of customers can be performed. In marketing, the behavior being manifested by the individual customers at different time spans is predicted for listing out all the potential customers for the market. As the market depends upon the response of the customers and their satisfaction with the product, the surveys such as their satisfaction with the salesperson, satisfaction with the product, will they recommend the product to someone else are used as the data for finding the future possibility of the customer to come back. Similarly, activities of the users on the social media platforms help the marketers to know a lot about the likes, dislikes, and cognitive process of theirs, which ultimately helps them to segment the clients and target the important group of clients. In this chapter, we will elaborate the concept of Predictive Analytics, Market Prediction, Prediction of customer behavior using Big Data Analytics, Big Data Analytics for Cognitive science and social media, and the process of Big Data Analytics for market prediction.
ABSTARACT
Business Intelligence (BI) is known to make smart decisions in various fields. BI proves beneficial for better-visualising of data through reports, charts, ad-hoc queries, dashboards, and benchmarks. Choosing the appropriate BI tools for an organisation may result in higher profit margins. BI tools are usually self-efficient and have a wide scope for data analysis. Furthermore, BI tools can also be used to aid results and monitor business aspects over a long period. This paper reviews fifteen open-source BI tools and analyzes comparisons of these tools based on user reviews while keeping track of the features offered specifically to each BI Tool. The results give an empirical study of BI tools in the hope to better assist users when faced with having to make decisions on which tools are superior as well as giving reasons behind such choices.
One of the earliest and most influential applications of personality psychology to consumer behavior was an approach based on Freud's psychodynamic principles referred to as “motivation research”. The lingering impact of motivation research is the influence of classic Freudian dream analysis on contemporary marketing research through the use of manifest and latent motives to understand the decisions of consumers. Beyond motivational factors, the emphasis on personality dynamics (e.g. needs and desires) continues to be a major force in contemporary marketing research. Psychographic segmentation is a technique in which people's attitudes, interests, values, behaviors, and lifestyle patterns are used to help marketing and advertise products in a manner that fits with the social and psychological profile of a particular segment of the buying public for which the products are being targeted. A recent development in the study of evolutionary psychology is the application of its principles to consumer behavior.
Organizations are looking for ways to harness the power of big data and to incorporate the shift that big data brings into their competitive strategies in order to seek competitive advantage and to improve their decision making by becoming data-driven organizations. Despite the potential benefits to be gained from becoming data-driven, the number of organizations that efficiently use it and successfully transform into data-driven organizations stays low. The emphasis in the literature has mostly been technology oriented with limited attention paid to the organizational challenges it entails. This paper presents an empirical study that investigates the challenges and benefits faced by organizations when moving toward becoming a data-driven organization. Data were collected through semi-structured interviews with 15 practitioners from nine software developing companies. The study identifies 49 challenges an organization may face when implementing a data-driven organization in practice, and it identifies 23 potential benefits of a data-driven organization compared to a non-data-driven organization.
Resumo — As organizações continuam a esforçar-se em reforçar as relações com os seus clientes e em captar novos clientes para os seus produtos ou serviços. A implementação de campanhas de marketing continua a ser extremamente importante neste contexto. No entanto, num ambiente cada vez mais globalizado, complexo e mais dependente de novas tecnologias de informação, as organizações necessitam de encontrar formas de serem mais eficientes nas suas iniciativas de marketing. O artigo apresenta um método para desenvolver uma campanha de marketing que tem por base os princípios do marketing de base de dados. O método tem dez etapas e inicia-se caraterizando adequadamente a organização, passando por detetar oportunidades que possam ser concretizadas através da exploração de dados internos ou externos da organização e que possam suportar uma base de dados adequada para campanhas de marketing a lançar.
Abstract — Organizations continue to strive to strengthen relationships with their customers and to attract new customers to their products or services. The implementation of marketing campaigns remains extremely important in this context. However, in an increasingly globalized, complex and more reliant on new information technology, organizations need to find ways to be more efficient in their marketing initiatives. The article presents a method for developing a marketing campaign that is based on the principles of database marketing. The method has ten steps and begins by characterizing the organization appropriately, identifying opportunities that can be realized through the exploitation of internal or external data of the organization and that can support a database suitable for marketing campaigns to launch.
