Marketing innovations in the face of the digital revolution and the push of emerging technologies

This article aims to analyze the evolutionary process of marketing from the intense changes in social and consumption patterns and the use of new advanced technologies. Therefore, a bibliographic research was carried out, with the intent to understand not only the progressions in marketing strategies and activities throughout history, but also their current stage, intrinsically linked to issues of humanization, digitization, technological innovations and building new consumer experiences based on a new hybrid world. From the analysis of the new context in which companies at a global level are making the transition from traditional marketing actions to digital marketing, it was possible to conclude that new emerging technologies have great potential for a more accurate understanding of consumer needs, obtaining insights and strategic guidelines on the market that optimize the decision-making process, segmentation and selection of target markets, building new consumer experiences and strengthening brand building and growth results

ENERGY DATA ANALYTICS FOR IMPROVED RESIDENTIAL SERVICE QUALITY AND ENERGY EFFICIENCY

Utility companies generally have an extensive customer base, yet their knowledge about individual households is small. This adversely affects both the development of innovative, household specific services and the utilities’ key performance indicators such as customer loyalty and profitability. With the goal to overcome this knowledge deficit, persuasive systems in the form of customer self-service applications and efficiency coaching portals are becoming the getaway of data exchange between utility and user. While improved customer interaction and the collection of customer data within respective information systems is an important step towards a service-oriented company, the immediate value generated from the collected data is still limited, mostly due to the small fraction of customers actually using such systems. We show how to utilize the knowledge gained from the sparse number of active web users in order to provide low-cost and large-scale insights to potentially all residential utility customers. We do so using machine-learning-based Green IT artifacts that allow for improving decision-making, effectiveness of energy audits, and conservation campaigns, thus ultimately increasing the customer value and adoption of related services. Moreover, we show that data from the publically available geographic information systems can considerably improve the decision quality

Information Management Capability as Competitive Imperfection in the Strategic Factor Market of Big Data

The interest of the organizations in developing Big Data strategies is increasing significantly. However, the expectation of the value of these benefits and of the costs involved in acquiring or developing these solutions are not homogeneous for all of the firms, generating competitive imperfections in the market of strategic resources. Using Information Management Capability (IMC) as a premise to provide the required unique insight for Big Data strategies to be successful, this article proposes to analyze IMC as an imperfection agent in the market of strategic resources of Big Data. The formulated hypotheses were tested from a survey of 101 valid participants and analyzed with SEM-PLS. The results indicate a positive IMC influence on value expectation and a negative one on cost expectation. Cost expectation inversely affects the intent to purchase or develop the resources to implant Big Data strategies. Value expectation has a positive effect in both intents

Big Data Evaluation Scorecard

This study seeks to examine the evolution of issues that have been espoused by both junior and senior scholars to aggregate out of literature, a criterion that can guide firms in evaluating their Big data analytic (BDA) projects. The systematic review approach took stock of varied socio-technical understanding, requirements, and capabilities used in addressing Big data issues and synthesized these issues for value accruals. The study strongly argues that Big data benefits accrue to firms whose economic activities require distributed collaborative effort, operational visibilities, cost, and time-sensitive decisions who adopt and implement the concept in their strategic, tactical, and operational levels. Though the trend shows steady growth in scholars’ interests and expectations in BDA, a significant percentage of the reviewed studies were not informed by any theory. The study contributes to BDA literature by affording scholars issue gaps and for practitioners, an analytical competency and evaluation scorecard that links strategic business goals to operational outcomes

THE IMPACT OF SUSTAINABLE BRANDING USING BIG DATA AND BUSINESS ANALYTICS IN THE MARKET RESEARCH INDUSTRY

Abstract Aim: The research aimed to explore how sustainable branding and big data analytics could enhance brand equity and sustainability in the market research industry. It reviewed existing literature, analysed branding strategies of data-driven companies, identified key attributes for sustainable positioning, used qualitative research methods to investigate competitive advantage, and created a theoretical framework to demonstrate how sustainable branding could improve performance in data-driven companies using big data and analytics. Methodology: This research used qualitative methods for a systematic review of sustainability, branding, and business analytics in the market research industry. It involved semi-structured interviews with 38 senior managers and directors from 24 companies across 8 countries. Despite the impact of COVID-19 on data collection due to changes in working patterns, this study showcased the potential of modern qualitative methods such as the 'inductive a priori' model. It utilized advanced technologies and multi-disciplinary research to tackle complex industry concepts. The research sought to bring about sustainable change in the market research industry. Results: The results of the study indicated that sustainable branding was positively related to consumer behaviour, corporate reputation, and financial performance. Big data and business analytics offered valuable insights into consumer preferences, attitudes, and behaviour which helped companies to develop and manage successful sustainable branding strategies. The study provided a comprehensive framework for understanding the role of sustainable branding, big data, and business analytics in the market research industry. Contribution to knowledge: The contribution of the study lies in identifying the importance of sustainable branding and its relationship with big data and business analytics. The study highlighted the potential benefits of integrating sustainability practices into branding strategies and suggested practical implications for companies to adopt sustainable branding approaches. The findings of the study offered insights into the value of big data and business analytics in the market research industry and provided a basis for future research in this field

Improving the Impact of Big Data Analytics Projects with Benefits Dependency Networks

Big data analytics is regarded as the next frontier in creating digital opportunities for businesses. Analytics projects rarely deliver the intended benefits for the organisation that invest in these data analytics, and currently, no widely accepted design method for analytics projects exists. To address this, we report from an action research project in an organisation highly involved with big data analytics and how benefits materialize from these projects through the practices of tailored and focused benefits management. We argue for using the benefits dependency network for orchestrating commitment to benefits. Benefits dependency networks create linkages between analytics technology, organisational change activities, stakeholders’ interests and to-be benefits of a project. With this study, we contribute with: (1) a tailored technique for benefits dependency networks, (2) focus on benefits into established project development practices for big data analytics (3) facilitation as a key capability in developing a benefits dependency network

Modelling quality dynamics on business value and firm performance in big data analytics environment

Big data analytics have become an increasingly important component for firms across advanced economies. This paper examines the quality dynamics in big data environment that are linked with enhancing business value and firm performance. The study identifies that system quality (i.e., system reliability, accessibility, adaptability, integration, response time and privacy) and information quality (i.e., completeness, accuracy, format and currency) are key to enhance business value and firm performance in a big data environment. The study also proposes that the relationship between quality and firm performance is mediated by business value of big data. Drawing on the resource based theory and the information systems success literature, this study extends knowledge in this domain by linking system quality, information quality, business value and firm performance

Unlocking the drivers of big data analytics value in firms

Côrte-Real, N., Ruivo, P., Oliveira, T., & Popovič, A. (2019). Unlocking the drivers of big data analytics value in firms. Journal of Business Research, 97(April), 160-173. DOI: 10.1016/j.jbusres.2018.12.072Although big data analytics (BDA) is considered the next “frontier” in data science by creating potential business opportunities, the way to extract those opportunities is unclear. This paper aims to understand the antecedents of BDA value at a firm level. The authors performed a study using a mixed methodology approach. First, by carrying out a Delphi study to explore and rank the antecedents affecting the creation of BDA value. Based on the Delphi results, we propose an empirically validated model supported by a survey conducted on 175 European firms to explain the antecedents of BDA sustained value. The results show that the proposed model explains 62% of BDA sustained value at the firm level, where the most critical contributor is BDA use. We provide directions for managers to support their decisions on BDA strategy definition and refinement. For academics, we extend BDA value literature and outline some potential research opportunities.authorsversionpublishe

Achieving useful data analytics for marketing: Discrepancies in information quality for producers and users of information

[EN] This study proposes as a key cause of the high failure rates in the implementation of analytical projects for marketing decisions, the discrepancy in the information quality (DIQ) perceived between producers (information technology [IT]) and users (marketing) of knowledge. Given that the DIQ between agents is a determining factor in the success of the ability to data analytics, this study focuses on examining this concept and its causes, specifically the resources related to data analytics that influence DIQ. The results of the surveys carried out with the IT and marketing managers of 95 companies in Spain, analyzed with a comparative methodological approach (dyadic), reveal the sources of the discrepancy, namely, the quality of the data, the technological capabilities, the talent, Chief Executive Officer (CEO) support, and alignment of the data plan with the marketing plan

A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach

E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature