39,194 research outputs found
Linking business analytics to decision making effectiveness: a path model analysis
While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DM
To boardrooms and sustainability: the changing nature of segmentation
Market segmentation is the process by which customers in markets with some heterogeneity
are grouped into smaller homogeneous segments of more ‘similar’ customers. A market
segment is a group of individuals, groups or organisations sharing similar characteristics and
buying behaviour that cause them to have relatively similar needs and purchasing behaviour.
Segmentation is not a new concept: for six decades marketers have, in various guises, sought to
break-down a market into sub-groups of users, each sharing common needs, buying behavior
and marketing requirements. However, this approach to target market strategy development
has been rejuvenated in the past few years. Various reasons account for this upsurge in the
usage of segmentation, examination of which forms the focus of this white paper.
Ready access to data enables faster creation of a segmentation and the testing of propositions to
take to market. ‘Big data’ has made the re-thinking of target market segments and value
propositions inevitable, desirable, faster and more flexible. The resulting information has
presented companies with more topical and consumer-generated insights than ever before.
However, many marketers, analytics directors and leadership teams feel over-whelmed by the
sheer quantity and immediacy of such data.
Analytical prowess in consultants and inside client organisations has benefited from a stepchange,
using new heuristics and faster computing power, more topical data and stronger
market insights. The approach to segmentation today is much smarter and has stretched well
away from the days of limited data explored only with cluster analysis. The coverage and wealth
of the solutions are unimaginable when compared to the practices of a few years ago. Then,
typically between only six to ten segments were forced into segmentation solutions, so that an
organisation could cater for these macro segments operationally as well as understand them
intellectually. Now there is the advent of what is commonly recognised as micro segmentation,
where the complexity of business operations and customer management requires highly
granular thinking. In support of this development, traditional agency/consultancy roles have
transitioned into in-house business teams led by data, campaign and business change planners.
The challenge has shifted from developing a granular segmentation solution that describes all
customers and prospects, into one of enabling an organisation to react to the granularity of the
solution, deploying its resources to permit controlled and consistent one-to-one interaction
within segments. So whilst the cost of delivering and maintaining the solution has reduced with
technology advances, a new set of systems, costs and skills in channel and execution
management is required to deliver on this promise. These new capabilities range from rich
feature creative and content management solutions, tailored copy design and deployment tools,
through to instant messaging middleware solutions that initiate multi-streams of activity in a
variety of analytical engines and operational systems.
Companies have recruited analytics and insight teams, often headed by senior personnel, such as
an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such
personnel out-weigh posts for brand and marketing managers. Far more companies possess the
in-house expertise necessary to help with segmentation analysis. Some organisations are also
seeking to monetise one of the most regularly under-used latent business assets… data.
Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a
Chief Data Officer. This emerging role has also driven the professionalism of data exploration,
using more varied and sophisticated statistical techniques.
CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users
of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of
value propositions and the need to look after core customers; CFOs because segmentation leads
to better and more prudent allocation of resources – especially NPD and marketing – around the
most important sub-sets of a market; COOs because they need to better look after key
customers and improve their satisfaction in service delivery. More and more it is recognised that
with a new segmentation comes organisational realignment and change, so most business
functions now have an interest in a segmentation project, not only the marketers.
Largely as a result of the digital era and the growth of analytics, directors and company
leadership teams are becoming used to receiving more extensive market intelligence and
quickly updated customer insight, so leading to faster responses to market changes, customer
issues, competitor moves and their own performance. This refreshing of insight and a leadership
team’s reaction to this intelligence often result in there being more frequent modification of a
target market strategy and segmentation decisions.
So many projects set up to consider multi-channel strategy and offerings; digital marketing;
customer relationship management; brand strategies; new product and service development;
the re-thinking of value propositions, and so forth, now routinely commence with a
segmentation piece in order to frame the ongoing work. Most organisations have deployed
CRM systems and harnessed associated customer data. CRM first requires clarity in segment
priorities. The insights from a CRM system help inform the segmentation agenda and steer how
they engage with their important customers or prospects. The growth of CRM and its ensuing
data have assisted the ongoing deployment of segmentation.
One of the biggest changes for segmentation is the extent to which it is now deployed by
practitioners in the public and not-for-profit sectors, who are harnessing what is termed social
marketing, in order to develop and to execute more shrewdly their targeting, campaigns and
messaging. For Marketing per se, the interest in the marketing toolkit from non-profit
organisations, has been big news in recent years. At the very heart of the concept of social
marketing is the market segmentation process.
The extreme rise in the threat to security from global unrest, terrorism and crime has focused
the minds of governments, security chiefs and their advisors. As a result, significant resources,
intellectual capability, computing and data management have been brought to bear on the
problem. The core of this work is the importance of identifying and profiling threats and so
mitigating risk. In practice, much of this security and surveillance work harnesses the tools
developed for market segmentation and the profiling of different consumer behaviours.
This white paper presents the findings from interviews with leading exponents of segmentation
and also the insights from a recent study of marketing practitioners relating to their current
imperatives and foci. More extensive views of some of these ‘leading lights’ have been sought
and are included here in order to showcase the latest developments and to help explain both
the ongoing surge of segmentation and the issues under-pinning its practice. The principal
trends and developments are thereby presented and discussed in this paper
The last five years of Big Data Research in Economics, Econometrics and Finance: Identification and conceptual analysis
Today, the Big Data term has a multidimensional approach where five main characteristics stand out: volume, velocity, veracity, value and variety. It has changed from being an emerging theme to a growing research area. In this respect, this study analyses the literature on Big Data in the Economics, Econometrics and Finance field. To do that, 1.034 publications from 2015 to 2019 were evaluated using SciMAT as a bibliometric and network analysis software. SciMAT offers a complete approach of the field and evaluates the most cited and productive authors, countries and subject areas related to Big Data. Lastly, a science map is performed to understand the intellectual structure and the main research lines (themes)
Can You Provide the Current Trends in HR on People Analytics?
[Excerpt] People analytics is an increasingly hot topic and many companies are working to gain insight through this emerging field. Business leaders are asking how analytics can help drive better decision-making in order to improve business results. Among these questions, turnover prediction and succession planning are two key areas that HR professionals identify as high value. Since there isn’t a one-size- fits-all solution to these questions, we compiled our most noteworthy insights and put forward several steps that an organization should follow in order to create its own internal models
Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics
Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.
Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.
Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.
Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli
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