The combination between Big Data and Marketing Strategies to gain valuable Business Insights for better Production Success

Saidali, Jihad; Rahich, Hassane; Tabaa, Yassine; Medouri, Abdellatif

doi:10.1016/j.promfg.2019.02.316

Cited by 32 publications

(19 citation statements)

References 15 publications

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“…According to Walls & Barnard (2020), insights provided by BDA can improve the efficiency of the whole organization operations, as well as the strategy. From a marketing point of view, Saidali et al (2019) argue that classical data analytics are unable to acquire valuable business insights. They propose combining BDA and classical marketing analytics in order to gain valuable and real time insights, thus improve the marketing decision-making process (Saidali, Rahich, Tabaa, & Medouri, 2019).…”

Section: Big Datamentioning

confidence: 99%

“…These can be turned into strategic advantages (Şen, Körük, Serper, & Çalış Uslu, 2019). Moreover, Saidali et al (2019) suggest combining BDA and classical marketing analytics in order to gain insights that are more valuable, and improve the marketing decision-making process. Furthermore, Suoniemi et al (2017) argue that BD resources improve a firm's ability to better innovate and optimize any marketing elements in the mix with BD predictive capability.…”

Section: Big Data Insights Stagementioning

confidence: 99%

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A framework for big data integration within the strategic management process based on a balanced scorecard methodology

Alnoukari¹

2021

JISIB

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The purpose of this research is to study the impact of big data initiatives onstrategic management processes. While the majority of strategic management disciplines havehad research dedicated to the use of strategic management theories to understand how big dataaffect organizational performance, the body of research on big data lacks academic work capableof examining how to integrate big data into the strategic management process. The maincontributions of this work are: (1) it highlights the strategic use of big data; (2) it analyses themain frameworks/models proposed by scholars that support the use of big data as a strategicmanagement tool, and outlines this research gap; and (3) it proposes a new framework thatintegrates big data within the strategic management process based on a balanced scorecardmethodology.

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Section: Big Datamentioning

confidence: 99%

Section: Big Data Insights Stagementioning

confidence: 99%

A framework for big data integration within the strategic management process based on a balanced scorecard methodology

Alnoukari¹

2021

JISIB

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“…), ads placed on YouTube and TV, companies' websites, E-Markets, Text messages, E-mails, etc. Big data analytics offers several solutions for helping enterprises in analyzing available tremendous datasets [123]. One example investigates at what kinds of advertisements compel viewers to continue watching and what turns viewers off.…”

Section: Marketing and E-commercementioning

confidence: 99%

Big Data: Analytical Solutions, Research Challenges and Trends

Ali¹,

Novikov²

2020

Proceedings of ISP RAS

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The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multidisciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, we review a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, we present a review of the recent main applications to obtain a broad perspective of big data analytics.

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“…Both are currently in use by online advertising platforms, yet it remains difficult to compare the performance of individual channels across customer segments and industry sectors. Therefore, scholars and advertisers have looked for new approaches to interpret online marketing data in the business context of the company [25,26,27].…”

Section: Mapping the Online Customer Journeymentioning

confidence: 99%

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semerádová

Weinlich

2019

IEEE Access

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The advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called microtargeting) is the use of detailed customer data and marketing automation to deliver highly targeted and personalized messages across a large number of channels. These campaigns are designed to appeal to specific people or small groups of customers. By using the ability to process large amounts of data through innovations, such as predictive analytics, marketers can gain a deeper understanding of their audiences, focusing on specific accounts and not on the entire segments. This reportedly allows B2B brands to target customers directly and provide unique personal and highly relevant experiences. However, the scientific evidence to support this claim is missing. Some previous studies even suggest a negative impact of highly personalized advertising content on user reactiveness and purchase behavior. In this article, we test the effects of different levels of personalized advertisements using the advanced campaign targeting tool called Facebook Lookalike Audiences. Facebook Lookalike Audiences works on the basis of the estimation of customer similarity based on the characteristics of a custom audience, as defined by the advertiser. We examine the performance of various targeting settings using data from 840 Facebook ads with different personalization levels. These advertisements are compared in terms of reach, number of reactions, frequency of impressions, number of clicks, average time spent on a website, number of viewed pages, number of conversions, and profitability. We believe that the findings presented in this article help clarify the factors that influence user reactiveness toward personalized online advertising using evidence from actual Facebook ad sets.

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The combination between Big Data and Marketing Strategies to gain valuable Business Insights for better Production Success

Cited by 32 publications

References 15 publications

A framework for big data integration within the strategic management process based on a balanced scorecard methodology

A framework for big data integration within the strategic management process based on a balanced scorecard methodology

Big Data: Analytical Solutions, Research Challenges and Trends

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

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