Real-time big data processing for instantaneous marketing decisions: A problematization approach

Abdul Jabbar, Pervaiz Akhtar, Samir Dani

Research output: Contribution to journalArticle

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.
LanguageEnglish
Number of pages12
JournalIndustrial Marketing Management
Early online date7 Sep 2019
DOIs
Publication statusE-pub ahead of print - 7 Sep 2019

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Marketing
Marketing organization
Industrial marketing
Decision making
Online advertising
Hadoop
Marketing practices
Batch
Search engine
Managers
Social search
Data sources
Internet of things
Business-to-business (B2B)
Social media

Cite this

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title = "Real-time big data processing for instantaneous marketing decisions: A problematization approach",
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.",
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