Revisiting customer analytics capability for data-driven retailing

2020 ◽  
Vol 56 ◽  
pp. 102187 ◽  
Author(s):  
Md Afnan Hossain ◽  
Shahriar Akter ◽  
Venkata Yanamandram
Keyword(s):  
Data Driven ◽  
Customer Analytics ◽  
Analytics Capability

Customer Analytics Capabilities in the Big Data Spectrum

2020 ◽  
pp. 1-17 ◽  
Author(s):  
Md Afnan Hossain ◽  
Shahriar Akter ◽  
Venkata Yanamandram
Keyword(s):  
Big Data ◽  
Research Direction ◽  
Business Environment ◽  
Vital Role ◽  
Future Research ◽  
Managerial Decision ◽  
Sustainable Business ◽  
Innovation Product ◽  
Customer Analytics ◽  
Analytics Capability

Customer analytics plays a vital role in generating insights from big data to improve service innovation, product development, personalization, and managerial decision-making; yet, no academic study has investigated customer analytics capability through which it is possible to achieve sustainable business growth. To close this gap, this chapter explores the constructs of the customer analytics capability by drawing on a systematic review of the literature in the big data spectrum. The chapter's interpretive framework portrays a definitional aspect of customer analytics, the importance of customer analytics, and customer analytics capability constructs. The study proposes a customer analytics capability model, which consists of four principal constructs and some important sub-constructs. The chapter briefly discusses the challenges and future research direction for developing the customer analytics capability model in the data rich competitive business environment.

Data science in the business environment: customer analytics case studies in SMEs

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Lu ◽  
Lisa Cairns ◽  
Lucy Smith
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Process Model ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Complex Data ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Customer Analytics

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.

Fostering big data analytics capability through process innovation: Is management innovation the missing link?

2021 ◽  
pp. 026638212098471
Author(s):  
Edwin Henao-García ◽  
José Arias-Pérez ◽  
Nelson Lozada
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Process Innovation ◽  
Data Driven ◽  
Management Innovation ◽  
Competitive Advantages ◽  
Innovation Capability ◽  
Key Resources ◽  
Analytics Capability

Big data is heralded as the next big thing for organizations to gain competitive advantages. New data-driven firms need to control key resources in order to develop the new data-driven capabilities they need. The present paper analyzes the relationships between process innovation capability, management innovation and big data analytics capability, covering aspects related to a better understanding of how firms can obtain benefit from their investments in big data. PLS-SEM models with data from 195 firms are used. The main results suggest that management innovation and process innovation capabilities have an important role in the development of big data analytics capability. Big data analytics capability is much more than just investing in technology, collecting vast amounts of data, and allowing the technology department to experiment with analytics. The outcomes of this study present evidence on how innovative managers who promote innovations in process as well as innovations in different aspects of the organization favor the development of capabilities in big data analytics.

Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance

2021 ◽  
Vol 168 ◽  
pp. 120766
Author(s):  
Usama Awan ◽  
Saqib Shamim ◽  
Zaheer Khan ◽  
Najam Ul Zia ◽  
Syed Muhammad Shariq ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Circular Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Analytics Capability

The impact of business analytics capability on data-driven culture and exploration: achieving a competitive advantage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najah Almazmomi ◽  
Aboobucker Ilmudeen ◽  
Alaa A. Qaffas
Keyword(s):  
Product Development ◽  
Competitive Advantage ◽  
New Product Development ◽  
Structural Equation ◽  
New Product ◽  
Data Driven ◽  
Business Analytics ◽  
Content Type ◽  
The Impact ◽  
Analytics Capability

PurposeIn today's business setting, the business analytic capability, data-driven culture and product development features are highly pronounced in light of the firm's competitive advantage. Though widespread attention has been given to the above concepts, there hasn't been much research done on how it could support achieving competitive advantage.Design/methodology/approachThis research strongly lies on the theoretical background and empirically tests the hypothesized relationships. The primary survey of 272 responses was analysed by using the partial least squares structural equation modelling (PLS-SEM).FindingsThe findings of this study show a significant relationship for the constructs in the research model except for the third hypothesis. Accordingly, the firm's data-driven culture does not have a significant impact on new product newness.Originality/valueThis study empirically tests the business analytics capability, data-driven culture, and new product development features in the context of a firm's competitive advantage. The findings of this study contribute to the theoretical, practical and managerial aspects of this field.

scholarly journals Are personas done? Evaluating their usefulness in the age of digital analytics

2018 ◽  
Vol 4 (2) ◽  
pp. 47-65 ◽  
Author(s):  
Joni Salminen ◽  
Bernard J. Jansen ◽  
Jisun An ◽  
Haewoon Kwak ◽  
Soon-gyo Jung
Keyword(s):  
Large Scale ◽  
Digital Data ◽  
Data Driven ◽  
Future Research ◽  
Computational Techniques ◽  
Online Data ◽  
Aggregated Data ◽  
Individual Level ◽  
Customer Base ◽  
Customer Analytics

In this research, we conceptually examine the use of personas in an age of large-scale online analytics data. Based on the criticism and benefits outlined in prior work and by practitioners working with online data, we formulate the major arguments for and against the use of personas given real-time online analytics data about customers, analyze these arguments, and demonstrate areas for the productive employment of data-driven personas by leveraging online analytics data in their creation. Our key tenet is that data-driven personas are located between aggregated and individual customer statistics. At their best, digital data-driven personas capture the coverage of the customer base attributed to aggregated data representations while retaining the interpretability of individual-level analytics; they benefit from powerful computational techniques and novel data sources. We discuss how digital data-driven personas can draw from technological advancements to remedy the notable concerns voiced by scholars and practitioners, including persona validation, inconsistency problem, and long development times. Finally, we outline areas of future research of personas in the context of online analytics. We argue that to survive in the rapidly developing online customer analytics industry, personas must evolve by adopting new practices.

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