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Article
Publication date: 24 March 2020

Guangming Cao and Na Tian

Evidence in the literature has indicated that customer-linking marketing capabilities such as customer relationship management (CRM) and brand management are important drivers of…

2148

Abstract

Purpose

Evidence in the literature has indicated that customer-linking marketing capabilities such as customer relationship management (CRM) and brand management are important drivers of marketing performance and that marketing analytics use (MAU) enables firms to gain valuable knowledge and insights for improving firm performance. However, there has been little focus on how firms improve their CRM and brand management via MAU. This study aims to draw on the absorptive capacity theory, research on marketing capabilities and marketing analytics to examine the capability-developing mechanisms that enable a firm to use marketing analytics to enhance its CRM and brand management capabilities, thereby improving its marketing performance.

Design/methodology/approach

A research model is developed and tested based on an analysis of 289 responses collected using an online survey from middle and senior managers of Chinese firms with sufficient knowledge and experience in using marketing analytics for survey participation.

Findings

The findings demonstrate that MAU is positively related to both CRM and brand management capabilities, which in turn are positively associated with marketing performance; and that both CRM and brand management capabilities mediate the relationship between MAU and marketing performance.

Research limitations/implications

The study’s outcomes were based on data collected from a survey, which was distributed using mass e-mails. Thus, the study is unable to provide a meaningful response rate. The research results are based on and limited to Chinese firms.

Practical implications

MAU is essential for enhancing customer-linking marketing capabilities such as CRM and brand management, but it alone is not sufficient to improve marketing performance. Firms wishing to improve marketing performance should leverage the knowledge and insights gained from MAU to enhance their critical customer-linking marketing capabilities.

Originality/value

This study explicates the capability-developing mechanisms through which a firm can use its market-sensing capability as manifested by MAU to enhance customer-linking marketing capabilities and to improve its marketing performance. In so doing, this study extends our understanding of the critical role of absorptive capacity in helping firms identify, assimilate, transform and apply valuable external knowledge.

Details

Journal of Business & Industrial Marketing, vol. 35 no. 7
Type: Research Article
ISSN: 0885-8624

Keywords

Article
Publication date: 26 January 2021

Marwa Rabe Mohamed Elkmash, Magdy Gamal Abdel-Kader and Bassant Badr El Din

This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish…

Abstract

Purpose

This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study.

Design/methodology/approach

Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data.

Findings

The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E).

Research limitations/implications

This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses.

Practical implications

This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies.

Originality/value

This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

Details

Accounting Research Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1030-9616

Keywords

Article
Publication date: 23 November 2022

Wu He, Jui-Long Hung and Lixin Liu

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate…

1509

Abstract

Purpose

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Design/methodology/approach

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

Findings

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

Originality/value

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

Details

Journal of Enterprise Information Management, vol. 36 no. 2
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 6 January 2023

Şenay Yavuz and Engin Tire

The present research aimed to identify the motivations, needs, wants, preferences and limitations of corporate professionals with regard to business social analytics.

Abstract

Purpose

The present research aimed to identify the motivations, needs, wants, preferences and limitations of corporate professionals with regard to business social analytics.

Design/methodology/approach

Online interviews were conducted with 26 professionals the majority of whom work at the management level at 20 reputable corporations in Turkey. Both qualitative and quantitative data was collected during these interviews, which lasted an average of one hour.

Findings

The findings shed light on the motivations of corporate professionals for monitoring social media and other digital media, their perceived capability and limitations in doing so, the media that they monitor and wanted to monitor if possible, their criteria and processes for working with service providers in the field of business social analytics, their needs which are not fully met by service providers, their suggestions on service improvement and their reflections on how internal and external customer data can be analyzed with an integrated approach.

Originality/value

This research is an attempt to bridge the gap between the priorities of engineers who generate artificial intelligence for the purposes of social listening and analytics and the end users, e.g. corporate communication professionals. Only by doing so, this field, which is getting more and more important as people spend more time online, will reach its full potential and benefit corporations by providing fruitful insight upon which strategic steps can be taken.

Details

Corporate Communications: An International Journal, vol. 28 no. 4
Type: Research Article
ISSN: 1356-3289

Keywords

Article
Publication date: 4 September 2020

Jing Lu, Lisa Cairns and Lucy Smith

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The…

3212

Abstract

Purpose

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Design/methodology/approach

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Findings

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

Research limitations/implications

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Originality/value

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Details

Journal of Modelling in Management, vol. 16 no. 2
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 29 November 2018

Yudi Fernando, Ramanathan R.M. Chidambaram and Ika Sari Wahyuni-TD

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

4462

Abstract

Purpose

The purpose of this paper is to investigate the effects of Big Data analytics, data security and service supply chain innovation capabilities on services supply chain performance.

Design/methodology/approach

The paper draws on the relational view of resource-based theory to propose a theoretical model. The data were collected through survey of 145 service firms.

Findings

The results of this study found that the Big Data analytics has a positive and significant relationship with a firm’s ability to manage data security and a positive impact on service supply chain innovation capabilities and service supply chain performance. This study also found that most service firms participating in this study used Big Data analytics to execute existing algorithms faster with larger data sets.

Practical implications

A main recommendation of this study is that service firms empower a chief data officer to establish the data needed and design the governance of data in the company to eliminate any security issues. Data security was a concern if a firm did not have ample data governance and protection as the information was shared among members of service supply chain networks.

Originality/value

Big Data analytics are a useful technology tool to forecast market preference based on open source, structured and unstructured data.

Details

Benchmarking: An International Journal, vol. 25 no. 9
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 18 September 2019

Samir Yerpude and Tarun Kumar Singhal

The purpose of this paper is to build a customer engagement strategy for an emerging market using the Internet of Things (IoT) origin real-time data analytics for a classical…

1763

Abstract

Purpose

The purpose of this paper is to build a customer engagement strategy for an emerging market using the Internet of Things (IoT) origin real-time data analytics for a classical retail business to customer domain.

Design/methodology/approach

The study presented is twofold. First, it empirically tests a theoretical model where the impact of different parameters influencing customer engagement are validated, and its influence on the resultant parameters, i.e. brand loyalty and brand ambassador, is analyzed. Second, it emphasizes on the use of real-time IoT origin data in customer analytics to determine a customer engagement strategy.

Findings

Results indicate that the four parameters, i.e., value propositions basis the buying patterns, loyalty programs, personalized communication and involving the customer in the new development process are influencing customer engagement positively, whereas the parameter loyalty program scores the maximum regression weight. IoT plays a crucial role in generating the real-time data used for generating customer analytics that proves to be vital for the longevity of the organization.

Practical implications

The organizations need judicious blend of four parameters such as value proposition based on buying patterns, participation in new product development, personalized communication and loyalty program while designing the customer engagement strategy. Results drawn from the focused group interview highlight the power of IoT origin real-time data in the customer analytics further strengthening the need of customer centricity in an organization.

Originality/value

Identified need of building a customer engagement strategy for an emerging market with the help of IoT data is addressed in this paper that is identified as an unexplored area and a research gap.

Details

International Journal of Emerging Markets, vol. 16 no. 1
Type: Research Article
ISSN: 1746-8809

Keywords

Article
Publication date: 13 February 2019

Michael DiClaudio

Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace…

6342

Abstract

Purpose

Employee and workforce insights are the greatest competitive advantage for organizations dealing with the disruption and uncertainty driving dramatic changes in today’s workplace. Embedded in this is the growing expectation of the human resource (HR) function to understand how workforce analytics informs the business and fuels success. This paper aims to explore how the HR function can achieve this.

Design/methodology/approach

The evolution of the “Future of HR” and how it is moving from “descriptive and diagnostic” to “prescriptive and predictive.”

Findings

According to KPMG’s 2019 Future of HR survey: 37 per cent of respondents feel “very confident” about HR’s actual ability to transform and move them forward via key capabilities such as analytics and artificial intelligence (AI). Over the next year or two, 60 per cent say they plan to invest in predictive analytics. Among those who have invested in AI to date, 88 per cent call the investment worthwhile, with analytics listed as a main priority (33 per cent). Despite data’s remarkable ability to deliver news insights and enhance decision-making, 20 per cent of HR believe analytics will be a primary HR initiative for them over the next one to two years, and only 12 per cent cite analytics as a top management concern.

Research limitations/implications

Taking a page from meeting customer needs, innovative technologies such as AI and the cloud, data analytics can give an organization the potential to gather infinitely greater amounts of information about customers.

Practical implications

Today’s workforce analytics focuses mostly on what happened and why. For instance, you might have tools for identifying areas of high turnover and diagnosing the reasons. But thanks to advancements in technology and data analytics capabilities, HR is better-positioned to be the predictive engine required for the organization’s success.

Social implications

There has never been a better time for HR to create greater strategic value, as the potential for meaningful workforce insights and analytics comes within reach. Even advancements in cloud-based systems for human capital management are coming packaged with analytics and visualization capabilities, enabling HR leaders to integrate people data with other data sources, such as customer relationship management, for a full view of the business.

Originality/value

This paper will be of value to HR leaders and practitioners who wish to use predictive analytics and emerging technology to drive performance improvement and gain the insights about their workforces.

Details

Strategic HR Review, vol. 18 no. 2
Type: Research Article
ISSN: 1475-4398

Keywords

Article
Publication date: 15 May 2019

Devon S. Johnson, Laurent Muzellec, Debika Sihi and Debra Zahay

This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a…

3812

Abstract

Purpose

This paper aims to improve understanding of data-driven marketing by examining the experiences of managers implementing big data analytics in the marketing function. Through a series of research questions, this exploratory study seeks to define what big data analytics means in marketing practice. It also seeks to uncover the challenges and identifiable stages of big data analytics implementation.

Design/methodology/approach

A total of 15 open-ended in-depth interviews were conducted with marketing and analytics executives in a variety of industries in Ireland and the USA. Interview transcripts were subjected to open coding and axial coding to address the research questions.

Findings

The study reveals that managers consider marketing big data analytics to be a series of tools and capabilities used to inform product innovation and marketing strategy-making processes and to defend the brand against emerging risks. Additionally, the study reveals that big data analytics implementation is championed at different organizational levels using different types of dynamic learning capabilities, contingent on the champion’s stature within the organization.

Originality/value

From the qualitative analysis, it is proposed that marketing departments undergo five stages of big data analytics implementation: sprouting, recognition, commitment, culture shift and data-driven marketing. Each stage identifies the key characteristics and potential pitfalls to be avoided and provides advice to marketing managers on how to implement big data analytics.

Details

Journal of Research in Interactive Marketing, vol. 13 no. 2
Type: Research Article
ISSN: 2040-7122

Keywords

Article
Publication date: 17 June 2020

Yichuan Wang, Minhao Zhang, Ying Kei Tse and Hing Kai Chan

Underpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer…

1883

Abstract

Purpose

Underpinned by the lens of Contingency Theory (CT), the purpose of this paper is to empirically evaluate whether the impact of social media analytics (SMA) on customer satisfaction (CS) is contingent on the characteristics of different external stakeholders, including business partners (i.e. partner diversity), competitors (i.e. localised competition) and customers (i.e. customer engagement).

Design/methodology/approach

Using both subjective and objective measures from multiple sources, we collected primary data from 141 hotels operating in Greece and their archival data from TripAdvisor and the Hellenic Chamber of Hotels (HCH) database to test the hypothesised relationships. Data were analysed through structural equation modelling.

Findings

This study confirms the positive association between SMA and CS, but it remains subject to the varied characteristics of external stakeholders. We find that an increase in CS due to the implementation of SMA is more pronounced for firms that (1) adopt a selective distribution strategy where a limited number of business partners are chosen for collaboration or (2) operate in a highly competitive local environment. The results further indicate that high level of customer engagement amplifies the moderating effect of partner diversity (when it is low) and localised competition (when it is high) on the SMA–CS relationship.

Originality/value

The study provides novel insights for managers on the need to consider external stakeholder characteristics when implementing SMA to enhance firms' CS, and for researchers on the value of studying SMA implementation from the CT perspective.

Details

International Journal of Operations & Production Management, vol. 40 no. 5
Type: Research Article
ISSN: 0144-3577

Keywords

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