Jabbar, A, Akhtar, P and Dani, S ORCID: https://orcid.org/0000-0001-8547-0762 (2019) Real-time big data processing for instantaneous marketing decisions: A problematization approach. Industrial Marketing Management.

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Abstract

The collection of big data from different sources such as the internet of things, social media and search engines has created significant opportunities for business-to-business (B2B) industrial marketing organizations to take an analytical view in developing programmatic marketing approaches for online display advertising. Cleansing, processing and analyzing of such large datasets create challenges for marketing organizations — particularly for real-time decision making and comparative implications. Importantly, there is limited research for such interplays. By utilizing a problematization approach, this paper contributes through the exploration of links between big data, programmatic marketing and real-time processing and relevant decision making for B2B industrial marketing organizations that depend on big data-driven marketing or big data-savvy managers. This exploration subsequently encompasses appropriate big data sources and effective batch and real-time processing linked with structured and unstructured datasets that influence relative processing techniques. Consequently, along with directions for future research, the paper develops interdisciplinary dialogues that overlay computer-engineering frameworks such as Apache Storm and Hadoop within B2B marketing viewpoints and their implications for contemporary marketing practices.

Item Type: Article
Additional Information: The final version of this article along with all relevant information can be found at; https://www.sciencedirect.com/science/article/pii/S0019850118307454?via%3Dihub
Uncontrolled Keywords: Real-time processing; Batch processing; Internet of things; Social media; Programmatic marketing; Decision making; Big data; Problematization
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HF Commerce > HF5410 Marketing. Distribution of products
Divisions: Faculty of Humanities and Social Sciences > Keele Business School
Depositing User: Symplectic
Date Deposited: 19 Jun 2020 09:56
Last Modified: 19 Jun 2020 09:56
URI: https://eprints.keele.ac.uk/id/eprint/8141

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