81,815 research outputs found
How can SMEs benefit from big data? Challenges and a path forward
Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities.
The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft
How do top- and bottom-performing companies differ in using business analytics?
Purpose
Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA.
Design/methodology/approach
Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies.
Findings
Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment.
Practical implications
Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities.
Originality/value
This study provides useful management insights into the effective use of BA for improving organizational performance
Measuring the Online Impact of Your Information Project: A Primer for Practitioners and Funders
Evaluates fifteen Web sites funded by the Knight Foundation's Community Information Challenge, including data on visits, unique visitors, page views, and bounce rates. Outlines how to set goals, align metrics, and implement analytics
Alexandria: Extensible Framework for Rapid Exploration of Social Media
The Alexandria system under development at IBM Research provides an
extensible framework and platform for supporting a variety of big-data
analytics and visualizations. The system is currently focused on enabling rapid
exploration of text-based social media data. The system provides tools to help
with constructing "domain models" (i.e., families of keywords and extractors to
enable focus on tweets and other social media documents relevant to a project),
to rapidly extract and segment the relevant social media and its authors, to
apply further analytics (such as finding trends and anomalous terms), and
visualizing the results. The system architecture is centered around a variety
of REST-based service APIs to enable flexible orchestration of the system
capabilities; these are especially useful to support knowledge-worker driven
iterative exploration of social phenomena. The architecture also enables rapid
integration of Alexandria capabilities with other social media analytics
system, as has been demonstrated through an integration with IBM Research's
SystemG. This paper describes a prototypical usage scenario for Alexandria,
along with the architecture and key underlying analytics.Comment: 8 page
Digital marketing actions that achieve a better attraction and loyalty of users: an analytical study
Currently, the digital economy contributes decisively to an increase in competitiveness, especially as a digital transformation involves migrating to new technological models where digital marketing is a key part of growth and user loyalty strategies. Internet and Digital Marketing have become important factors in campaigns, which attract and retain Internet users. This study aims to identify the main ways in which users can be gained and retained by using Digital Marketing. The Delphi method with in-depth interviews was the methodology used in this study. The results of the research show the most important actions for achieving user recruitment and loyalty with Digital Marketing from the opinions of consulted experts. The limitations of this study are those related to the number of experts included in the study, and the number of research papers consulted in the literature review. The literature review and the results of this research are used to propose new solid research with a consolidated critical methodology. This research deals with a new approach that will optimize web technologies for the evolution of user trends, and therefore, will be of academic and professional use for marketing managers and web solution developers. The conclusions of the investigation show the key factors, discarding others that do not affect the optimization of conversions in B2C businesses such as the duration of the session and the rebound percentage. Likewise, the results of the research identify the specific actions that must be carried out to attract and retain users in B2C companies that use the Digital Marketing ecosystem on the Internet. The requirements for companies that wish to implement a model to optimize conversions using the current digital economy are also shown.info:eu-repo/semantics/publishedVersio
Towards Evaluating the Quality of a Spreadsheet: The Case of the Analytical Spreadsheet Model
We consider the challenge of creating guidelines to evaluate the quality of a
spreadsheet model. We suggest four principles. First, state the domain-the
spreadsheets to which the guidelines apply. Second, distinguish between the
process by which a spreadsheet is constructed from the resulting spreadsheet
artifact. Third, guidelines should be written in terms of the artifact,
independent of the process. Fourth, the meaning of "quality" must be defined.
We illustrate these principles with an example. We define the domain of
"analytical spreadsheet models", which are used in business, finance,
engineering, and science. We propose for discussion a framework and terminology
for evaluating the quality of analytical spreadsheet models. This framework
categorizes and generalizes the findings of previous work on the more narrow
domain of financial spreadsheet models. We suggest that the ultimate goal is a
set of guidelines for an evaluator, and a checklist for a developer.Comment: Proc. European Spreadsheet Risks Int. Grp. (EuSpRIG) 2011 ISBN
978-0-9566256-9-
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 Transition to...Open Access
This report describes and draws conclusions from the transition of the Association for Learning Technology’s journal Research in Learning Technology from toll-access to Open Access, and from being published by one of the "big five" commercial publishers to being published by a specialist Open Access publisher. The focus of the report is on what happened in the run-up to and after the transition, rather than on the process of deciding to switch between publishing models, which is covered in in ALT's 2011 report "Journal tendering for societies: a brief guide" - http://repository.alt.ac.uk/887/
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