Predictive Analysis for Digital Marketing Using Big Data

Big data analytics in recent years had developed lightning fast applications that deal with predictive analysis of huge volumes of data in domains of finance, health, weather, travel, marketing and more. Business analysts take their decisions using the statistical analysis of the available data pulled in from social media, user surveys, blogs and internet resources. Customer sentiment has to be taken into account for designing, launching and pricing a product to be inducted into the market and the emotions of the consumers changes and is influenced by several tangible and intangible factors. The possibility of using Big data analytics to present data in a quickly viewable format giving different perspectives of the same data is appreciated in the field of finance and health, where the advent of decision support system is possible in all aspects of their working. Cognitive computing and artificial intelligence are making big data analytical algorithms to think more on their own, leading to come out with Big data agents with their own functionalities.

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Business Open Big Data Analytics to Support Innovative Leadership and Management Decision in Canada

Business Ethics and Leadership ◽

10.21272/bel.4(2).56-74.2020 ◽

2020 ◽

Vol 4 (2) ◽

pp. 56-74

Author(s):

Nadia Delanoy ◽

arina Kasztelnik

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Big Data ◽

Data Analytics ◽

Customer Relations ◽

Big Data Analytics ◽

Innovative Leadership ◽

Innovative Management ◽

The World ◽

Leadership Decisions

This paper summarizes how social media and other technologies continue to proliferate; the shifting economic landscape will precipitate more adaptive approaches for managers attempting to understand the multi-dimensional virtual aspects of communication with the artificial intelligence aspect. Also, we discover the different existing support of big data analytics to make a rational business decision. The methodology is the systematization literature sources within this context and approaches for the underlining approach to open big data analytics and support innovative leadership decisions in Canada. The paper is carried out in the following logical sequence to gain an understanding of how customer relations managers could utilize social media within a data analytics frame from scholar and practitioner perspectives. This literature research review original paper outlines the main themes including the role of social media, the experiences of using data analytics for customer relations management, and the notion that customer-centric technologies could change the dynamic of understanding customer intentions, leadership decisions and introduce the innovative management with using the big data analytics in place. The results of the critical thinking with analysis both authors can be useful for any business around the World that would like to start using Artificial Intelligence to support innovative management decisions. The emergent themes that were highlighted based on the realities of customer relations management may be significant to how the integration of social media feedback resulting from crowdsourcing in addition to existing data analytics could better position organizations in this evolving world. The implications of linking innovative management processes such as demographic analysis, platform understanding, and communication methods together are crucial for any public business with a global impact. Finally, the understanding of innovation management in a social media era and understanding how customers utilized open big data analytics sources could help leadership practices across industries around the World. Keywords: Big Data Analytics, Innovative Leadership, Management of Social Media, Open Sources.

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The Role of Artificial Intelligence in Social Media Big data Analytics for Disaster Management -Initial Results of a Systematic Literature Review

2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM) ◽

10.1109/ict-dm.2018.8636388 ◽

2018 ◽

Cited By ~ 1

Author(s):

Vimala Nunavath ◽

Morten Goodwin

Keyword(s):

Artificial Intelligence ◽

Social Media ◽

Big Data ◽

Literature Review ◽

Disaster Management ◽

Systematic Literature Review ◽

Data Analytics ◽

Big Data Analytics ◽

Initial Results

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EXPRESS: “Get a £10 Free Bet Every Week!” – Gambling Advertising on Twitter: Volume, Content, Followers, Engagement and Regulatory Compliance

Journal of Public Policy & Marketing ◽

10.1177/0743915621999674 ◽

2021 ◽

pp. 074391562199967

Author(s):

Raffaello Rossi ◽

Agnes Nairn ◽

Josh Smith ◽

Christopher Inskip

Keyword(s):

Social Media ◽

Big Data ◽

Content Analysis ◽

Volume Content ◽

Data Analytics ◽

Big Data Analytics ◽

Regulatory Compliance ◽

The Internet ◽

Policy Recommendations ◽

Policy Makers

The internet raises substantial challenges for policy makers in regulating gambling harm. The proliferation of gambling advertising on Twitter is one such challenge. However, the sheer scale renders it extremely hard to investigate using conventional techniques. In this paper the authors present three UK Twitter gambling advertising studies using both Big Data analytics and manual content analysis to explore the volume and content of gambling adverts, the age and engagement of followers, and compliance with UK advertising regulations. They analyse 890k organic adverts from 417 accounts along with data on 620k followers and 457k engagements (replies and retweets). They find that around 41,000 UK children follow Twitter gambling accounts, and that two-thirds of gambling advertising Tweets fail to fully comply with regulations. Adverts for eSports gambling are markedly different from those for traditional gambling (e.g. on soccer, casinos and lotteries) and appear to have strong appeal for children, with 28% of engagements with eSports gambling ads from under 16s. The authors make six policy recommendations: spotlight eSports gambling advertising; create new social-media-specific regulations; revise regulation on content appealing to children; use technology to block under-18s from seeing gambling ads; require ad-labelling of organic gambling Tweets; and deploy better enforcement.

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Combination of a Big Data Analytics Resource System With an Artificial Intelligence Algorithm to Identify Clinically Actionable Radiation Dose Thresholds for Dysphagia in Head and Neck Patients

Advances in Radiation Oncology ◽

10.1016/j.adro.2019.12.007 ◽

2020 ◽

Vol 5 (6) ◽

pp. 1296-1304 ◽

Cited By ~ 1

Author(s):

Charles S. Mayo ◽

Michelle Mierzwa ◽

Jean M. Moran ◽

Martha M. Matuszak ◽

Joel Wilkie ◽

...

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Radiation Dose ◽

Head And Neck ◽

Data Analytics ◽

Big Data Analytics ◽

Intelligence Algorithm ◽

Artificial Intelligence Algorithm ◽

Resource System

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Artificial intelligence and big data analytics in mining geomechanics

Journal of the Southern African Institute of Mining and Metallurgy ◽

10.17159/2411-9717/847/2020 ◽

2020 ◽

Vol 120 (1) ◽

Author(s):

J. McGaughey

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics

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An assessment of factors that affect the implementation of big data analytics in the Zambian health sector for strategic planning and predictive analysis: a case of Copperbelt province

International Journal of Electronic Healthcare ◽

10.1504/ijeh.2020.113196 ◽

2020 ◽

Vol 11 (2) ◽

pp. 101

Author(s):

Richard C. Chellah ◽

Douglas Kunda

Keyword(s):

Big Data ◽

Strategic Planning ◽

Data Analytics ◽

Health Sector ◽

Big Data Analytics ◽

Predictive Analysis

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A Hybrid-Algorithm for E-project Selection on Social Media

Journal of Information Technology and Digital World - September 2019 ◽

10.36548/jitdw.2020.2.005 ◽

2020 ◽

Vol 2 (2) ◽

pp. 116-122

Author(s):

Dr. Pasumponpandian A.

Keyword(s):

Social Media ◽

Big Data ◽

Hybrid Algorithm ◽

Optimal Algorithm ◽

Big Data Analytics ◽

Minimal Risk ◽

Cognitive Computing ◽

Development Environment ◽

Nsga Ii ◽

The Right

The integration of two of the biggest giants in the computing world has resulted in the development and advancement of new methodologies in data processing. Cognitive computing and big data analytics are integrated to give rise to advanced technologically sound algorithms like MOIWO and NSGA. There is an important role played by the E-projects portfolio selection (EPPS) issue in the web development environment that is handled with the help of a decision making algorithm based on big data. The EPPS problem tackles choosing the right projects for investment on the social media in order to achieve maximum return at minimal risk conditions. In order to address this issue and further optimize EPPS probe on social media, the proposed work focuses on building a hybrid algorithm known as NSGA-II-MOIWO. This algorithms makes use of the positive aspects of MOIWO algorithm and NSGA-II algorithm in order to develop an efficient one. The experimental results are recorded and analyzed in order to determine the most optimal algorithm based on the return and risk of investment. Based on the results, it is found that NSGA-II-MOIWO outperforms both MOIWO and NSGA, proving to be a better hybrid alternative.

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